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Energy Systems Optimization Of A Shopping Mall

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Energy Systems Optimization of a Shopping Mall: The present study focuses on the development of software (general mathematical optimization model) which has the following characteristics: …

Energy Systems Optimization of a Shopping Mall: The present study focuses on the development of software (general mathematical optimization model) which has the following characteristics:
• It will be able to find the optimal combination of installed equipment (power & heat generation etc) in a Shopping Mall (micro-grid)
• With multi-objective to maximize the cost at the same time as minimizing the environmental impacts (i.e. CO2 emissions).
• To date, this tool is scarce to the industry (similar to DER-CAM, Homer).

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  • 1. Energy systems optimization of a Shopping mall Aristotelis Giannopoulos 26/09/08 Supervised by: Prof. David Fisk (Civil and Environmental Engineering) Prof. Stratos Pistikopoulos (Chemical Engineering) A thesis submitted to Imperial College London in partial fulfilment of the requirements for the degree of Master of Science in Sustainable Energy Futures and for the Diploma of Imperial College Faculty of Engineering Imperial College London London SW7 2AZ, UK 1
  • 2. 2
  • 3. Table of Contents Table of Contents ...................................................................................................................... 3 List of Figures and Tables ......................................................................................................... 6 Glossary .................................................................................................................................. 11 Abstract ................................................................................................................................... 12 1. Introduction 1.1 Global Energy Consumption & Buildings contribution ........................................... 13 1.2 Decentralized energy systems ................................................................................... 14 1.3 Short plan and explanation of the model ................................................................... 16 2. Literature review 2.1 Energy Consumption in a Shopping Mall ................................................................. 19 2.2 Alternative Technologies and Energy sustainability in SM ...................................... 27 2.2.1 Description of the different technical alternatives ........................................... 27 2.2.2 Photovoltaic’s ................................................................................................. 28 2.2.3 Co-generation ................................................................................................... 28 2.2.4 Tri-generation model........................................................................................ 29 2.2.5 Gas boiler ......................................................................................................... 30 2.2.6 Grid Electricity and other parameters .............................................................. 30 2.2.7 Electric chiller .................................................................................................. 31 2.2.8 Absorption chiller ........................................................................................... 32 2.3 Distributed Energy Resources in SM and other Commercial Buildings ................... 32 3. Model inputs 3.1 Technology database ................................................................................................. 36 3.2 Shopping mall description ........................................................................................ 41 3.3 Tariffs inputs 3.3.1 Natural gas prices ....................................................................................... 46 3.3.2 Electricity prices (Grid) ............................................................................. 47 4. Mathematical Model 4.1 Introduction ............................................................................................................... 50 4.2 Mathematical Programming ...................................................................................... 50 3
  • 4. 4.3 General Algebraic Modeling System (GAMS) ........................................................ 51 4.4 Model Description .................................................................................................... 52 4.5 Mathematical Formulation ........................................................................................ 54 5. Results 5.1 Scenarios and Sensitivities .............................................................................................. 59 5.2 Outline of results .............................................................................................................. 61 5.3 Overview of spot market prices results scenario .............................................................. 62 5.4 Assessment of specific cases 5.4.1 Case 1: Grid plus boiler ................................................................................... 67 5.4.2 Case 2: Without CHP/CCHP ........................................................................... 69 5.4.3 Case 3: Without CCHP .................................................................................... 71 5.4.4 Case 4: Final case ............................................................................................. 73 5.4.5 Case 5: PV plus Grid plus Boiler ..................................................................... 78 5.4.6 Case 6: At least seven PV ................................................................................ 81 5.4.7 Case 7: High carbon price ................................................................................ 88 5.4.8 Case 8: High carbon price with a 20% PV capital reduction ........................... 88 5.4.8 Case 9: 50% PV capital reduction .................................................................... 89 5.4.9 Case 10, 11: 50 % cheaper electricity prices, 50% more expensive NG ......... 89 5.5 Fixed electricity price scenario ........................................................................................ 90 5.5.1 Electricity price up to 0.08 $/KWh .................................................................. 91 5.5.2 Electricity price from 0.09 to 0.12 $/KWh....................................................... 91 5.5.3 Electricity price 0.13 $/KWh ........................................................................... 92 5.5.4 Electricity price 0.14$/KWh ............................................................................ 93 5.5.5 Electricity price from 0.15 to 0.49 $/KWh ....................................................... 94 5.5.6 Electricity price from 0.5 to 0.57 $/KWh......................................................... 96 Electricity price from 0.58 $/KWh ........................................................ 98 5.5.7 Conclusions ................................................................................................................. 99 6. 4
  • 5. Bibliography ............................................................................................................... 104 Appendix .................................................................................................................... 106 5
  • 6. List of Figures Figure 1, World Population distribution in urban and rural place……………………………..….13 Figure 2, Total London Energy use breakdown……………………………………………….….13 Figure 3, Electricity generation by fuel in US (IEA, World Energy Outlook, 2004)……………..14 Figure 4, graphic representation of the DGT-SM…………………………………………………17 Figure 5, technical alternatives used in this model………………………………………………..18 Figure 6, monthly electricity consumption profiles for the four shopping malls (Joseph C.Lam D. H., 2003)……………………………………………………………………...21 Figure 7, breakdown of the major end uses in the four shopping malls (Joseph C.Lam D. H., 2003)…………………………………………………………………..….22 Figure 8, measured hourly electrical load profiles for Building A………………………….…....23 Figure 9, measured hourly electrical load profiles for Building B…………………………..…....23 Figure 10, measured hourly electrical load profiles for Building C………………………………23 Figure 11, measured hourly electrical load profiles for Building D………………………………23 Figure 12, January Peak Load for Mall……………………………………………………………25 Figure 13, August Peak Load for Mall……………………………………………………………..25 Figure 14, Mall Week Load Shape………………………………………………………………...25 Figure 15, Mall Peak Load Shape………………………………………………………………….25 Figure 16, Mall Weekend Load Shape……………………………………………………………..26 Figure 17, Superstructure with the most important technical alternatives meeting the electricity and heat demand in a SM………………………………………………………………………………..27 Figure 18, Average costs and productivity of PV’s………………………………………………...28 Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006)……………………………33 Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *. C., 2006)…………………………………………………………………………………………….34 Figure 21, Annual savings, (Nan Zhou a *. C., 2006)………………………………………………34 Figure 22, Technology database (Firestone, 2004)………………………………………………….40 Figure 23, SM Electrical load (F. Javier Rubio, 2001)………………………………………...……44 Figure 24, SM Electrical-only demand……………………………………………………………...44 Figure 25, SM cooling demand……………………………………………………………………..45 Figure 26, SM Heating demand…………………………………………………………………….45 Figure 27, monthly natural gas prices in $ per MMBTU for the calendar years 2007, 2008……...46 6
  • 7. Figure 28, graph representation for natural gas prices in $ per MMBTU for 2008……………..….47 Figure 29, Contribution of distribution costs to electricity bill (Williams P. a., 2001)……………..48 Figure 30, Spot market electricity prices……………………………………………………………49 Figure 31, Grid electricity price with the distribution company revenue…………………………..49 Figure 32 Bill savings over grid + boiler basic scenario……………………………………………63 Figure 33, Carbon savings over basis grid + boiler scenario………………………………………..64 Figure 34, Energy payments to the grid…………………………………………………………….65 Figure 35, Capital investment cost (includes installation and fixed costs) (section results overview).65 Figure 36, Energy sales back to the grid (section results overview)………………………………..66 Figure 37, Net present value (all included) (section results overview)……………………………..66 Figure 38, Carbon Taxes (all included) (section results overview)…………………………………67 Figure 39, Natural gas payments (all included) (section results overview)…………………………67 Figure 40, Energy balance and economic result for the grid plus boiler case……………………….68 Figure 41, NG purchases for meeting the SM heating load (Grid plus boiler case)……………...…69 Figure 42, total electricity purchases from grid, for all months and hours (grid plus boiler case)….69 Figure 43, Energy balance and economic results for without CHP/CCHP case…………………….70 Figure 44, Total electricity purchases from the grid (without CHP/CCHP case)……………….…71 Figure 45, Sales back to the grid (without CHP/CCHP case)………………………………………71 Figure 46, energy balance results (without CCHP case)……………………………………………73 Figure 47, economic results (without CCHP case)…………………………………………………73 Figure 48, energy balance results (final case)………………………………………………………75 Figure 49, economic results (final case)……………………………………………………………75 Figure 50, NG-1000CCHP power generation for electrical-only end use loads (final case)………76 Figure 51, NG-1000CCHP power generation for cooling end use loads (final case)………………76 Figure 52, NG-1000CCHP Recovered heat going to meet cooling demand (final case)…………..77 Figure 53, NG-1000CCHP Recovered heat going to meet heating demand (final case)…………..77 Figure 54, NG-1000CCHP Energy sales back to the grid (final case)………………………………78 Figure 55, energy balance results (PV plus grid plus boiler case)……………………………………79 Figure 56, economic results (PV plus grid plus boiler case)…………………………………………79 Figure 57, Total electricity purchases from grid (PV plus grid plus boiler case)……………………80 Figure 58, PV power generation for electrical-only end use loads (PV plus grid plus boiler case)…80 Figure 59, PV power generation for cooling end use loads (PV plus grid plus boiler case)……….81 7
  • 8. Figure 60, energy sales back to the grid (PV plus grid plus boiler case)………………………..…81 Figure 61, energy balance results (at least 7 PV case)…………………………………………..…82 Figure 62, economic results (at least 7 PV case)……………………………………………………83 Figure 63, NG-1000CCHP power generation for electrical-only end use load (at least 7 PV case)..83 Figure 64, NG-1000CCHP power generation for cooling end use loads (at least 7 PV case)………84 Figure 65, 7 PV-100 power generations for electrical-only end use loads (at least 7 PV case)…….84 Figure 66, 7 PV-100 power generations for cooling end use loads (at least 7 PV case)…………….85 Figure 67, NG-1000CCHP recovered heat going to meet heating demand (at least 7 PV case)…….86 Figure 68, NG-1000CCHP recovered heat going to meet cooling demand (at least 7 PV case)…...86 Figure 69, NG purchased for meeting heating demand by direct-fire burning (at least 7 PV case)...87 Figure 70, Energy sales back to the grid by power generated from PV’s (at least 7 PV case)…….87 Figure 71, Energy sales back to the grid by power generated from NG-1000CCHP (at least 7 PV case)…………………………………………………………………………………………………88 Figure 72 (appendix), energy balance and economic results (high carbon price scenario)……….106 Figure 73, energy balance results (High carbon price with a 20% PV capital reduction case)……107 Figure 74, economic results (High carbon price with a 20% PV capital reduction case)………….107 Figure 75, NG-1000CCHP power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case)………………………………………………………………………….108 Figure 76, NG-1000CCHP power generation for cooling end use (High carbon price with a 20% PV capital reduction case)……………………………………………………………………………..108 Figure 77, PV’s power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case)…………………………………………………………………………….109 Figure 78, PV’s power generation for cooling end use (High carbon price with a 20% PV capital reduction case)…………………………………………………………………………………….109 Figure 79, NG-1000CCHP recovered heat going to meet heating demand (High carbon price with a 20% PV capital reduction case)……………………………………………………………………110 Figure 80, NG-1000CCHP recovered heat going to meet cooling demand (High carbon price with a 20% PV capital reduction case)…………………………………………………………………….110 Figure 81, NG purchased to meet heating demand in a boiler (High carbon price with a 20% PV capital reduction case)……………………………………………………………………………………111 Figure 82, NG-1000CCHP power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case)……………………………………………………………………111 Figure 83, PV-100 power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case)………………………………………………………………………………112 Figure 84, energy balance results (50% PV capital reduction)……………………………………..112 Figure 85, economic results (50% PV capital reduction)……………………………………………113 Figure 86, NG-1000CCHP power generation for electrical-only end use loads (50% PV capital reduction)……………………………………………………………………………………………113 8
  • 9. Figure 87, NG-1000CCHP power generation for cooling end use loads (50% PV capital reduction)………………………………………………………………………………………….114 Figure 88, NG-1000CCHP power generation for selling back to the grid (50% PV capital reduction)………………………………………………………………………………………….114 Figure 89, PV power generation for electrical-only end use loads (50% PV capital reduction)…115 Figure 90, PV power generation for cooling end use loads (50% PV capital reduction)…………115 Figure 91, NG-1000CCHP recovered heat going to meet cooling demand (50% PV capital reduction)…………………………………………………………………………………………116 Figure 92, NG purchased to meet heating demand by direct burning in boiler (50% PV capital reduction)…………………………………………………………………………………………116 Figure 93, recovered heat going to meet heating demand (50% PV capital reduction)…………117 Figure 94, PV power generation for selling back to the grid (50% PV capital reduction)………117 Figure 95, energy balance results (50% cheaper electricity prices case)…………………………118 Figure 96, economic results (50% cheaper electricity prices case)………………………………118 Figure 97, energy balance results (50% more expensive NG price case) ………………………..119 Figure 98, economic results (50% more expensive NG price case)………………………………119 Figure 99, graph representation of the model results for different electricity prices……………..90 Figure 100, economic results for electricity price less than 9p/KWh (Fixed electricity price scenario)……………………………………………………………………………………………119 Figure 101, NG-100CHP total electrical production (Electricity price from 0.09 to 0.12 $/KWh case)………………………………………………………………………………………………92 Figure 102, Heating demand met by NG-100CHP (Electricity price from 0.09 to 0.12 $/KWh case)………………………………………………………………………………………………92 Figure 103, energy balance and economic results (Electricity price from 0.09 to 0.12 $/KWh case)………………………………………………………………………………………………120 Figure 103, energy balance and economic results (Electricity price 0.13 $/KWh)………………121 Figure 104, NG-60 CHP total electrical production (Electricity price 0.13 $/KWh)……………122 Figure 105, Heating demand met by NG-60CHP (Electricity price 0.13 $/KWh)………………122 Figure 106, Purchased NG to meet heating demand (Electricity price 0.13 $/KWh)……………93 Figure 107, total electricity purchases from grid (Electricity price 0.14$/KWh case)……………123 Figure 108, NG-300CCHP total electricity production (Electricity price 0.14$/KWh case)……94 Figure 109, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)………………………………………………………………………………………………94 Figure 110, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)………………………………………………………………………………………………123 Figure 111, energy balance and economic results (Electricity price 0.14$/KWh case)…………124 Figure 112, energy balance and economic results (Electricity price from 0.15 to 0.49 $/KWh)…125 9
  • 10. Picture 114, NG-1000CCHP power generation for electrical-only end use loads (Electricity price from 0.15 to 0.49 $/KWh)………………………………………………………………………………95 Picture 115, recovered heat going to meet cooling demand (Electricity price from 0.15 to 0.49 $/KWh)……………………………………………………………………………………………96 Figure 116, energy balance and economic results (Electricity price from 0.5 to 0.57 $/KWh)…126 Figure 117, recovered heat going to meet cooling demand (Electricity price from 0.5 to 0.57 $/KWh)…………………………………………………………………………………………97 Figure 118, energy sales back to the grid (Electricity price from 0.5 to 0.57 $/KWh)…………97 Figure 119, energy balance and economic results (Electricity price from 0.58 $/KWh)…………98 List of Tables Table 1, summary of the building envelops and HVAC designs, (Joseph C.Lam D. H., 2003)………21 Table 2, summary of annual electricity per unit floor area (Joseph C.Lam D. H., 2003)……………..22 Table 3, summary of the buildings envelops and HVAC designs, (Joseph C.Lam D. H., 2003)……...22 Table 4, Characteristics of cogeneration technologies available for use at the scale of individual large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks (simple- and combined-cycle turbines) (Lemar, 2001)…………………………………………………………………………………………………....29 Table 5, Costs (electricity, gas, and biomass) and also CO2 trading factor, (SEA/RENUE, 2006)……30 Table 6, Proportion of electricity supplied to the national grid from different sources, and associated CO2 emission factors, 2005……………………………………………………………………….……31 Table 7, CO2 factors (grid, boilers, natural gas, and renewables) and other parameters (inflation, discount factor etc), (SEA/RENUE, 2006)…………………………………………………………….31 Table 8, CO2 equivalents of electricity and fuels (1998 data), (F, 2005)……………………………..33 Table 9, Underlying Assumptions……………………………………………………………………..39 Table 10, β and γ values………………………………………………………………………………..39 Table 11, Scenarios examined………………………………………………………………………….59 Table 12, Examined sensitivities……………………………………………………………………….60 10
  • 11. Glossary DGT-SM: Distributed Generation Technologies Selection Model DGT: Distributed Generation Technologies SM: Shopping Mall DG: Distributed generation PV: Photovoltaic’s CHP: Combined Heat and Power CCHP: Combined Cooling Heat and Power BIPV: Building-integrated photovoltaic’s LCA: Life Cycle Analysis NG: Natural Gas GHG: Green House Gases HVAC: heating, ventilation and air-conditioning NPI: normalized performance indicators COP: Coefficient of performance OTTV: overall thermal transfer value GAMS: Generic Algebraic Modeling System FC: fuel cell MAISY: market analysis and information system O&M: operation and maintenance MMBTU: Million British thermal unit NPV: Net Present Value 11
  • 12. Abstract The usage of distributed generation technologies (DGT) for on-site electricity, heating and cooling production gives great opportunities to commercial consumers to evade all the transmission, distribution, supply and other non-energy delivery costs. Additionally, the usage of DGT technologies close to the thermal load gives the prospect to utilize the waste heat (for heating and cooling purposes) from the electricity production and finally reach higher efficiencies of burning the fuel from the conventional centralized power station. Despite the previous two very important facts, the usage of DGT and especially CHP/CCHP in commercial level is nearly not existed. This denial for installing these distributed technologies is the bad economic results of some bad installed systems. In order one system like CHP to meet the customer demand cheaper than the mature centralized power stations, a very careful planning of the system needed in order to be utilized most of the waste heat which will compensate for the lower electrical output compared to conventional power station. Until now very few tools existed, which are able to make a careful planning of these small scale generation systems. In this thesis, a mathematical model developed in GAMS, which is able to address these decisions and planning problems commercial consumers face to install DGT. The models name is Distributed Generation Technologies Selection Model (DGT-SM), and it is a mixed-integer linear program. Given the customer load (electricity, cooling, and heating), market information (natural gas prices, electricity prices), technologies database (capital cost, lifetime etc) DGT-SM is able to find the optimum combination of DGT that minimize the annual customer energy bill while at the same time the model decides their capacities and operation schedule throughout the year. The model was tested in a commercial shopping mall under many different scenarios and sensitivities and the results indicate substantial economic savings for all the cases (over the already existing grid and boiler case). Most of them (except one) had also enormous carbon savings. For the final scenario, where all the technologies was available for installation, the technology chosen by the model was one MW combined cooling, heating and power (CCHP) natural gas engine. The results for this scenario were 51% annual energy bill savings and 17% carbon savings over the grid plus boiler basic scenario. 12
  • 13. 1. Introduction 1.1 Global Energy Consumption & Buildings contribution According to world energy outlook the world’s primary energy needs in the Reference Scenario are projected to grow by 55% between 2005 and 2030, at an average annual rate of 1.8% per year (IEA, World Energy Outlook:China and India insights, 2007). The population will exceed the 9 billion (now 6 billion) until 2050 (IEA, World Energy Outlook, 2004), and more than 80% of global population will live in cities (Figure 1). As we can see and from the Figure 2 below, buildings (domestic & commercial) account for the biggest amount of energy consumed in a city, almost 60% of the total. As becomes obvious, in the future the energy consumption in the buildings will dramatically increase comparing with the present. Urbanization of China and India is a representative Figure 1, World Population distribution in example of the above fact. urban and rural places Between the buildings commercial sector seems to have a growing interest. Several numbers of existing cities going from industrialism to service oriented economies. The last gives clear signal for the upcoming big growth of the commercial sector. One existing example of this change is Hong-Kong which economy shifted from being manufacturing based to more service oriented financial Figure 2, Total London Energy use breakdown structure (Joseph C.Lam D. H., 2003). As a result, there has been rapid development in many large scale commercial building projects. It becomes obvious that the result of this transition is more energy consumption in commercial buildings. Out of all the buildings, Shopping Malls, is a rapidly growing sector which until now very little research has been done and it is very interesting area because of the high electricity consumption per 2 compared to the other commercial buildings (Joseph C.Lam D. H., 2003). 13
  • 14. Until now building demand met in a very inefficient way, both electricity supply (which consumed in buildings) and electricity demand. Electricity for the cities produced in power plants with a mean efficiency 30% (Tester W. Jefferson, 2005). More over this electricity consumed in the buildings in inefficient appliances such as light bulbs (3-5% efficiency), badly designed air-conditioned systems etc (Tester W. Jefferson, 2005). 1.2 Decentralized energy systems According to Lovins and Gumerman there is great potential for benefits from moving our economy from the centralized to a more distributed power generation model (Gumerman, 2003) (Lovins, 2002). Some concepts of decentralizing which are common and used systematically are micro-grids or distributed generation technologies (DGT) etc. All these concepts have differences between of them, but at the same time all of them agree that is a great need for our economies to decouple themselves as much as is possible from fossil fuels (e.g. renewable) or if this is not feasible for the near future at least to try to minimize the losses (e.g. unutilized heat). As we discussed in the previous section the greatest energy consumers of our economies in total are cities, where the greatest needs associated with the electricity consumption (e.g. cooling, lighting). The losses in electricity production (when fossil fuels are used) are mainly heat loses, heat losses which are growing if we consider the continuing increase of the electricity consumption worldwide. A characteristic example of this inefficiency is the case of USA, as can be easily noted in figure 3. Figure 3, (IEA, World Energy Outlook, 2004) 14
  • 15. As can become obvious from this graph the losses are huge and going hand in hand with the electricity needs. The common logic says that these inefficiencies are a very good starting point for our economies to start moving to a more ‘’Sustainable Energy Future’’. For a more sustainable and green future except the renewable energy technologies key role can and must play the Combined Heat and Power (CHP) technologies. The biggest advantage of CHP (commercial use) is that can utilize the waste heat due to the fact that is close to the customer load, compared with the common power stations which are far from the end-user and cannot use this heat. The last happen due to the fact that the low-grade heat cannot travel like the electricity without significant loses. These technologies are common in industrial places but in order to make a big difference worldwide this technology must be applied and penetrate successfully in a commercial level (shopping malls, hotels, houses etc). Successful penetration of CHP in commercial level needs the acceptance of the people which means that must have a better economic result (also take into account the environmental effect) compared to the current conventional way of power production. The greatest challenge a CHP faces in a commercial level is the need to utilize a high amount of waste heat in order to reach high efficiencies and be economically feasible compared to the state of the art centralized power stations (economies of scale). This power and heat match becomes even more difficult if we thing the high volatility in buildings requirements driven by the working hours, electricity tariffs, fuel cost and weather. The last great challenges for scheduling and control in the commercial use of CHP was the spark for this project. Self-generation big advantage except the advantage of utilization of heat (if exist) is the avoidance of transmission and distribution of the electricity which typical account almost for the 50% of the final energy bill (Williams P. a., 2001). For most of the commercial buildings the electricity cost is much higher than the heating cost and the potential energy bill savings will come from the provision of the electricity and not from the heat. Due to the fact as mentioned before that the centralized power stations have bigger efficiencies for electricity production (less waste heat in analogy) it becomes obvious that the high utilization of heat for heating or cooling purposes is a 15
  • 16. must. In order this to happen the commercial building must have except from electricity needs and high heating or cooling (use absorption chillers) loads. Bearing in mind the two previous facts shopping mall (SM) seems an ideal solution for many reasons. First of all SM are in particular consuming more energy than the other buildings and appear increasing across the world. Moreover, due to the variety of different stores and the nature of a SM (great cooling and lighting demand) there is a good ratio of electrical and heating loads, if we consider that the cooling demand can be covered with absorption chillers driven by heat. Another great advantage of a shopping mall is that during the working hours of year have an almost flat electrical load profile and a relatively high load profile all the off-working hours (e.g. high refrigeration demand during night). Until now very few methods are available for optimizing operation of commercial scale CHP, especially under variable fuel prices, and with the burden of small-scale diseconomies. Taken into account the grade importance of CHP (and generally the distributed generation technologies) in commercial scale in this report will developed a method for jointly optimizing heat and electricity production and use within a cost- minimizing framework while taking into account the carbon emissions. 1.3 Short plan and explanation of the model The present study focuses on the development of a general mathematical optimization model, with name Distributed Generation Technology Selection Model (DGT-SM), in GAMS (General Algebraic Modeling System) which will be able to minimize the energy payments of a shopping mall while minimize the environmental effect (CO2). In other words DGT-SM is able to make a Shopping Mall more ‘’Sustainable’’ while at the same time don’t compromise any comfort and meeting all the cooling, heating and electrical demand. In order DGT-SM to achieve this goal we must provide as data: Technologies (figure 2) information, market information and finally customer information. After the optimization the model will give as outputs: optimal technology combination, 16
  • 17. operating schedule as well as and some other outputs (e.g. energy bill cost, CO2 etc). Figure 4 gives a graphic representation of the DGT-SM and figure 5 gives the technical alternatives which will be used in this version of the model. In chapter 3 will be explained in more detail the inputs of the model, in chapter 4 will be given and explained thoroughly the mathematical model while in chapters 5 and 6 will discussed the results and some conclusions on them. Figure 4, graphic representation of the DGT-SM 17
  • 18. Demand Source Generation Conversion Technologies Technologies s GRID Electricity- Electricity only PV VC air cooled VC water cooled CHP Natural Gas Cooling Absorption Cooling Boiler Heating Waste Heat Figure 5, technical alternatives used in this model 18
  • 19. 2. Literature Review Previous works have been selected and reviewed based on the relevance to Energy Consumption in a Shopping Mall, Distributed Energy resources in Shopping Malls and big Commercial Buildings in General, Alternative Technologies and Energy sustainability in SM. The related journals have been summarized with the problem, method used, and how successful the work was. Also, some of the definitions and introduction of basic principles of urban energy Buildings modeling and Optimization are presented. 2.1 Energy Consumption in a Shopping Mall In order to be able to see and compare the different options for meeting and decreasing the demand in SM it is important to understand and become familiar with the actual needs of this type of building first. Despite the fact that SM penetrating the building market in a very fast pace, few studies have been done as regards the electricity characteristics in shopping malls. According to energy audits and surveys which have been made for commercial air-conditioned buildings by the University of Canberra (Lam JC, 1995), air-condition account for 40-60% of the total electricity consumption with lighting in the second place accounting for the 20-30%. For a shopping mall these two factors become even more important if we consider the population density and the larger lighting load and, hence, the higher air-conditioning needs compared with the common commercial buildings. Until now the needs of commercial buildings covered from grid as regards the electricity and from boilers (natural gas, diesel) as regards the heating. As becomes obvious from now on this scenario will be the common or base case. Below, will be exhibited, some previous works as regards the demand and the loads in shopping malls and other with similar needs commercial buildings. One good approach in analyzing the consumption characteristics in shopping malls in subtropical climates was made in China by the City University of Hong Kong (Joseph C.Lam D. H., 2003). The objective of this study was to investigate the electricity use characteristics in shopping centers in subtropical Hong Kong. The four buildings 19
  • 20. examined in this study are fully air-conditioned and was made during the 1990s. The table 1 below summarizes the main characteristics of the buildings envelope. Twelve months electricity consumption data were gathered for each of the four shopping centers. The monthly electricity consumption for the different shopping mall’s showed in Figure 6. As was presumable the electricity consumption peaks during the summer period due to the hot summer months and the air-conditioning needs. During the mid-season the electricity consumption is also high due to the high internal loads, such as people, office but mainly the thermal loads from the artificial light. The next information was takes was the breakdown of the four major electricity end uses in percentages (Figure 7). In order to breakdown this total electricity consumption the following method was followed. For lighting consumption, the number of light fixtures and their corresponding power ratings in both the landlord and tenants areas were surveyed and estimated wherever appropriate. Then taking into account the daily operating hours, the electricity consumption for lighting was determined. A similar approach was adopted for the electrical appliances consumption. For the escalators and the lifts were used energy analyzers (DRANETZ 8000-2) in order to measure the electricity consumption. The HVAC consumption was obtained by subtracting the total electricity consumption from the other three. The biggest consumer was the HVAC system, with percentages 47 to 54 of the total consumption. Lighting accounted for the 33-38% and with average lighting load densities for the landlord and tenants areas 15 and 55 2 , respectively. On average, HVAC and lighting accounted for about 85% of the total building electricity use. Finally in table 2 showed the normalized performance indicators (NPI), which defined as the electricity use per unit floor area. For the landlord and tenants the consumption were 485–795 2 (landlord area only) and 294–327 2 (tenants area only), respectively. The total annual electricity use per unit gross floor area was from 391–454 2 , with a mean NPI of 430 2 . In conclusion, we can say that this report gave a good indication of the electricity consumption characteristics of SM in subtropical climates. Of course the number of the shopping mall was limited; the sub-metering wasn’t 100% accurate due to the lack of all tenants’ data. The breakdown of major electricity end uses was estimated 20
  • 21. using only the non-weather sensitive loads (lighting, appliances etc) and finally, they didn’t give more specific data for the electrical loads (cooling, lighting etc) during the days of a normal week and for different seasons of a year (summer, winter etc). Table 1, summary of the building envelop and HVAC designs, (Joseph C.Lam D. H., 2003) Figure 6, monthly electricity consumption profiles for the four shopping malls, (Joseph C.Lam D. H., 2003) 21
  • 22. Figure 7, breakdown of the major end uses in the four shopping malls (Joseph C.Lam D. H., 2003). Table 2, summary of annual electricity per unit floor area (Joseph C.Lam D. H., 2003) Office building 1 Office building 2 Office building Office building 3 4 Number of storeys Multi-tenant 18 22 18 Total gross floor area ( ) 22.000 10.000 29.000 9.000 Curtain walling Building envelope Inserted windows Inserted RC structure Window-to-wall ratio windows 50% 50% 60% (WWR) 20% Glazing type Single tinted glass single reflective Single clear Single tinted Shading Coefficient 0.7 glass glass glass 0.3 0.9 0.6 HVAC plant/equipment Air side system PAU/Fan-coil unit PAU/Fan-coil unit Ceiling-mounted Fan-coil unit Chiller type Hermetic Variable air fan coil centrifugal volume (VAV) Constant air- VAV volume Heat rejection method Air-cooled Air-cooled Sea water- Air-cooled cooled Chiller COP (kWr 3 3 5 3 output/kWe input) Table 3, summary of the buildings envelop and HVAC designs, (Joseph C.Lam D. H., 2003) 22
  • 23. In a second study made in air- conditioned commercial/office buildings (Joseph C.Lam D. H., 2003), almost the same results were takes as before. The buildings characteristics are given in the above table 3. In this study the hourly load profiles was monitored Figure 8, measured hourly electrical load during the hot months of July and August profiles for Building A and the results for the four buildings (A, B, C, D) showed in the figures 8, 9, 10, 11. The results show that HVAC was the larger electricity end user, accounting for 30-60% of the total electrical demand during the office hours. Lighting came in the second place Figure 9, measured hourly electrical load profiles for Building B accounting for the 20-35% of the total electrical demand. Small power for a 15-25% with lifts in the last place with only few percentages mainly in peak hours. During the office hours (08:00 – 18:00) the variation was up to 10%, which occurred mainly between 12:00- 15:00 when peak demand was reached. The Figure 10, measured hourly electrical load profiles for Building C major consumer between the HVAC systems was the chiller which consumes the 70% of the HVAC consumption (or 40% of the total electric load). In this study was suggested a chiller load shifting in the night using thermal chilled store if it is Figure 11, measured hourly electrical load profiles for Building D 23
  • 24. economically feasible. Concluding, from the previous study we noticed that the electrical needs for big commercial office buildings don’t defer that much with the shopping mall demand. Both have the same marginal needs in HVAC and lighting (in summer) and of course they have almost the same electrical load profiles. This derives from the fact that both have many commons. They have same working hours, almost the same building envelop, and finally are in the same climate. Of course they have and some differences such as lighting loads and people densities. In a shopping mall the lighting loads are much higher (20-50 W/2 ) than in an office (12-25 W/2 ) which not only cause a higher electrical need but also cause and higher thermal loads, which means higher cooling loads. Moreover the higher occupancy density causes the need of higher cooling loads and in humid climates we have the humidity more easily in the building (also cooling problem). Another important difference is that in the night the shopping mall has bigger electrical loads, comparing with the peak demand, due to the refrigeration needs from the food stores. 24
  • 25. In the CERTS Customer Adoption Model paper (F. Javier Rubio, 2001) examine the use of distributed energy sources in a Mall and give the electrical loads of them. According to these data the ratio of minimum to maximum load is smaller in January than it is Figure 12, January Peak Load for Mall in August (0.31 in January and 0.53 in August). This implies that the difference between minimum load and the peak is more evident in January (Figure 12) than in August (Figure 13). The seasonal differences in the shape of the Figure 13, August Peak Load for Mall profiles are obvious in the two figures (12, 13). In January (Figure 12) there is a high level of load demand from approximately 9:00 to 22:00, and then the demand drops dramatically to the low level (these are the mall working hours). On the other hand, August (Figure 13) has a peak in the profile at around 15:00 (during the hottest part of the day). In all other hours, the load declines to or rises from the level that is maintained from around 22:00 to 10:00. The load factor for this customer is 0.36, pretty low, showing that the peaks are well above the average load demanded (686 kW). At the below figures 14, 15, 16 we can see the week, peak, and weekend loads for the different months of the year during the day. Figure 14, Mall Week Load Shape Figure 15, Mall Peak Load Shape 25
  • 26. Other papers attempts to analyze the Electricity consumption of Commercial buildings are the: Electricity use characteristics of purpose-built office buildings in subtropical (Joseph C. Lam *, 2004), a study of energy performance of hotel buildings in Hong Kong (Deng Shi-Ming, 2000) For a specific site, the source of end use energy Figure 16, Mall Weekend Load Shape load estimates is typically building energy simulation using a model based on the DOE-2 engine, such as eQUEST, or the more advanced but less user-friendly EnergyPlus. These tools can calculate the hourly energy loads and costs of several types of commercial buildings given information about: building location, construction, operation, utility rate schedule, heating, ventilating, air- conditioning (HVAC) equipment, and finally distributed generation unit performance parameters and operation strategy. Concluding, Shopping Malls are large energy consumers, with energy consumption per 2 larger than the majority of the commercial buildings. The main energy need in a SM is electricity for cooling and lighting. Especially the cooling requirements are large due to the high density of people during the working hours and the high thermal loads from the artificial lighting inside the building. Also and the light requirements are high due to the special needs of a SM. Until now very little work has been done in SM as regards the energy optimization and sustainability in adverse with the large amount of papers existing for other commercial buildings. In the next section will be introduced the different alternative technologies can be used in SM. What are the future challenges? As becomes obvious from the existing analysis buildings due to their increasing contribution in the global energy consumption and their inefficient way they meet their demand until now there is a lot of potential to both decrease and meet the demand in a different more efficient way. In other terms, the objective is the Sustainable Development of the Buildings and especially in this case Shopping Malls while the comfort level of these buildings remains constant. 26
  • 27. 2.2 Alternative Technologies and Energy sustainability in SM 2.2.1 Description of the different technical alternatives In the next figure 17, we can see some of the most important technical alternatives can be used in a SM to meet the electricity and heat demand. As we can see from the figure for electricity the alternatives are: Grid, Photovoltaic’s (PV), Combined Heat and Power (CHP, natural gas). For heat the alternatives are: CHP and boiler. For cooling we can use both electricity or/and heat in a VC cooled air condition and in an Absorption cooling system respectively. A short introduction and description of the above technologies are listed below. Sources Generation Conversion Demand Technologies Technologies GRID Electricity- Electricity only PV VC air cooled CHP VC water cooled Natural Gas Cooling Absorption Cooling Boile r Heating Waste Heat Figure 17, Superstructure with the most important technical alternatives meeting the electricity and heat demand in a SM 27
  • 28. 2.2.2 Photovoltaic’s Solar radiation can be converted directly into electricity using photovoltaic (PV) cells. The electrical efficiency of PV is between 5-15%, and the energy output of such a system depends from the solar radiation, for UK the radiation range between 800- 1000 kW h (Northern to Southern England). According to the common technologies the installed cost of a BIPV is about 500 pounds per for roof tile and 900 pounds per for the most expensive facades (F, 2005). One squared meter of mono-crystalline array will produce roughly 150 kW h per year, and also for each kW installed will produced about 700 kW h per year. The maintenance and operation cost of a PV system is too low that is not included, and the lifetime is in average about 30 years. Finally, PV is almost ‘emission-free’, because there is no need for fuel or cooling water; it operates silently and is believed to fit in urban development. One kW panel can save 0.1 to 1 tonne of emitted per year. However, the manufacture of PV requires a lot of energy and is embodied some (F, 2005). The above prices summerized in the 18 Figure. Capacity Energy output kWh kWh per year for Cost per in each kW installed pounds pa 1>kW 500 - 900 120 - 150 560 - 700 Figure 18, Average costs and productivity of PV’s 2.2.3 Co-generation Co-generation is also called combined heat and power (CHP). CHP in contrast with conventional power plants uses heat that is normally discarded to produce thermal energy, which can be provided to district heating systems, with result to reduce2 emissions and running costs. The efficiency depends from the type, scale and operation of the CHP with an average of 70-80% (25-35% electricity and 45-55% high grade or useful heat 71-82 c) (F, 2005). The different types of CHP are: Micro turbines, Fuel cells, Reciprocating engines, Gas turbines (simple-cycle cogeneration), Gas and steam turbines (combined-cycle cogeneration) and gas engines. Some data about those (capital cost, efficiency, power to-heat ratio, emissions etc) are represented in the table 4. Interesting issue is the operation of CHP’s, because in cogeneration it is important to optimize the balance of heat and electricity generation. This balance depends on the customer loads (electrical and thermal) and is possible 28
  • 29. the CHP to follow the thermal or the electrical load. Other option is to produce more electricity and/or heat and sell it back to the grid/customer in order to have some profit. One other option is the fuel, natural gas or biomass. All the above options and other must be taken into account and optimized in the CHP installation to meet the same demand with less cost and emissions. Table 4, Characteristics of cogeneration technologies available for use at the scale of individual large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks (simple- and combined-cycle turbines) (Lemar, 2001) 2.2.4 Tri-generation model Tri-generation is also known as combined heating, cooling and power generation or CHCP. CHCP uses the waste heat from CHP not only to meet the heat but also the cooling demand by applying the heat to absorption chillers. This chiller utilizes the heat to increase the pressure of refrigerant instead of using compressors which highly consume electricity. All the facts from co-generation also existing here, with more complexity because the optimization problem now extended further more. The advantage of the CHCP compared to co-generation becomes clear in buildings with high cooling demand like in this case in a Shopping Mall. 29
  • 30. 2.2.5 Gas boiler A boiler is a device for generating steam for power, processing, or heating purposes or for producing hot water for heating purposes or hot water supply (used until now for the majority of the buildings). It provides the building with heating and hot water with efficiencies between 80-90% (Tester W. Jefferson, 2005) and can burn natural gas or biomass. A great disadvantage of the boiler is the ``bad`` use or degradation of high quality fuels like natural gas for the production of low grade heat for heating needs comparing with the CHP which use the same fuel to produce some high quality energy source (electricity) and some low grade heat. 2.2.6 Grid Electricity and other parameters The cost of electricity, gas and biomass are given in the table 5 for domestic commercial and wholesale use and also the 2 trading factor. In table 6 depicted the average 2 emissions factor for the total UK grid mix (g/kWh) and in table 7 are given values about the kg 2 emitted per kWh produced for the natural gas, boilers, renewables, and grid. Also in the table 7 are given prices about the inflation, discount rate etc. Table 5, Costs (electricity, gas, and biomass) and also trading factor, (SEA/RENUE, 2006). 30
  • 31. Table 6, Proportion of electricity supplied to the national grid from different sources, and associated emission factors, 2005. Table 7, factors (grid, boilers, natural gas, and renewables) and other parameters (inflation, discount factor etc), (SEA/RENUE, 2006) 2.2.7 Electric chiller The majority of the shopping malls use Vapor compression (VC, base scenario) with air-cooled chiller for air conditioning. The electric chiller is defined by its efficiency which expressed by the coefficient of performance (COP). The bigger is the COP the more efficient is the electric chiller with result the decrease of the electricity used (for the same comfort) and consequently the reduction of the fuel used (to produce electricity) and the emissions going into the environment. Most of the HVAC systems used in the shopping malls until now have a COP 3, but there are already existing vapor compressions with water-cooled chiller systems in the market with COP 5. 31
  • 32. 2.2.8 Absorption chiller The alternative choice of the VC is the absorption cooling (AC) with absorption chiller (COP 1.2, heating) (Tester W. Jefferson, 2005). Absorption chillers use heat instead of mechanical energy to provide cooling. A thermal compressor consists of an absorber, a generator, a pump, and a throttling device, and replaces the mechanical vapor compressor. The basic cooling cycle is the same for the absorption and electric chillers, but the basic difference between the electric chillers and absorption chillers is that an electric chiller uses an electric motor for operating a compressor used for raising the pressure of refrigerant vapors and an absorption chiller uses heat for compressing refrigerant vapors to a high-pressure. The rejected heat from the power- generation equipment (e.g. turbines, micro turbines, and engines) may be used with an absorption chiller to provide the cooling in a CHP (Combined Heat and Power) system. The interesting part is to see through the optimization if it is more economic and environmentally feasible to operate a CHP with higher electric to thermal ratio in order to produce more electricity which will be used by an electric chiller in order to meet the cooling demand or is better to operate the CHP in a higher thermal to electric ratio in order to drive the heat through an absorption chiller and produce in this way the cooling demand. 2.3 Distributed Energy Resources in Shopping Malls and Commercial Buildings Many researchers have been conducted until now as regards the passive design of the building and the potential for reducing the demand (electricity, heating), but very few have been done as regards the different ways to meet this demand (e.g. renewable, CHP etc) in a Commercial building and especially for Shopping Mall less than five. As regards the Shopping Mall until now there is no paper which use a simulation or model optimization tool to integrate different distributed energy resources (more than one e.g. PV & CHP) in it. For other Commercial Building like hospital, big offices etc, there are studies with the majority of them examine only one energy source (e.g. 32
  • 33. PV) and not a combination of them, and in the case they examine more than one usually they do an exhaustive case by case simulation (no global optimum guarantee). One other fact is that most of the studies are not develop an energy optimization model but they use the existing commercial tools to examine different buildings. Furthermore from the energy optimization models existing, most of them focused only in some technologies (e.g. only in photovoltaic’s, or only in micro-turbine CHP, or only efficiency techniques etc) and in some aspects (e.g. only economic benefits or only environmental benefits examined but not both etc) of using decentralized energy resources in buildings. Until now no research has been done in which will examined different ways of meeting the demand and decreasing at the same time the demand of a building (without take into account the passive design of the building), with final objective not only the economic but also and the environmental benefit. One of the interesting studies was conducted in Japan by Nan Zhou (Nan Zhou a *. C., 2006). The objective was to find the best distributed energy resource system for different types of commercial buildings (hospital, big office, hotel sport facilities and retail) with constraint to meet the energy demands. In order this to be achieved was used an information base with different distributed technologies, Japanese energy tariffs and fuel prices, and the buildings needs which have been developed. Three scenarios were taken for each building type. The first scenario was to take no action in order to take the baseline (grid, NG boiler) costs, consumption and emissions. The second scenario made available to purchase a generation technology only for electricity production (without heat recovery and absorption cooling), and the third scenario was included everything (generation, recovery and with waste heat cooling). The results show a significant increase in the efficiency (Figure 19), decrease in carbon emissions (Figure 20) and finally decrease in annual energy cost (Figure 21). The results show a great potential and a very promising payoff (between 3 - 6.8 years). Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006). 33
  • 34. Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *. C., 2006) Figure 21, Annual savings, (Nan Zhou a *. C., 2006) In the next paper Medrano (M. Medrano, 2008) try to investigate the economic, energy-efficiency, and environmental impacts of the integration of distributed technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar panels) into four representative generic commercial buildings (office building, medium office building, hospital, and college/school), using as simulation tool the DOE-2.2- derived user-interface eQUEST program. This tool can calculate the hourly energy loads and costs of several types of commercial buildings given information about: building location, construction, operation, utility rate schedule, heating, ventilating, air-conditioning (HVAC) equipment, and finally distributed generation unit performance parameters and operation strategy. The methodology Medrano follow have four steps. First, is the base case where n DG are included and during this step the electric and gas hourly profiles for days corresponding to peak electric and gas consumption are analyzed. In the second step are introduced and implemented different cost effective energy efficiency measures 34
  • 35. day lighting, exterior shading, and improved HVAC performance) according to (e.g., energy use intensity with objective to reduce energy consumption and emissions. In the third case different DG technologies integrated in the buildings with the constraint that the waste heat utilized only for hot water and/or space heating. In the last approach, the traditional HVAC systems were replaced by heat driven absorption chillers alternatives, systems which works with hot water loops. In this way the thermal loads are utilized with result the increase of the overall efficiency of the DG system. Finally, the influences of utility gas and electric tariffs and weather conditions are illustrated, comparing the DG economic viability of the same office building in two U.S. locations. According to this paper the results gave a promising potential of the DG in these types of buildings. But I won’t stay in these results but in the methodology and the tools Medrano used in this report. Using this kind of simulation tools like eQUEST he investigates case by case combinations of DG in buildings, with the result not to find the optimum solution for cost reduction and environmental benefits and efficiency maximization. 35
  • 36. 3. Model Inputs In this section will be presented and explained all the different inputs to the model. First will be explained the technology database, then the shopping mall loads and finally the market inputs. 3.1 Technology database In this section will be presented and explained the technology database that was used as input to our model. These dada depicted in figure 22 was initially produced by the National Renewable Energy Laboratory (NREL) in the study ‘’Gas-Fired Distribution Energy Resource Technology Characterizations’’ (Goldstein, 2003), and then further developed by Ernest Orlando Lawrence Berkeley National Laboratory in 2004 report Distributed Energy Resources Customer Adoption Model Technology Data (Firestone, 2004). This technology database contain information for the technologies: fuel cells (FC), gas turbines (GT), micro-turbines (MC), natural gas engines (NG), and photovoltaic’s (PV). Each technology described by a number of parameters, parameters which are inputs to the model and are explained below:  Capacity (maxp): This represents the maximum electrical output of the machine in KW.  Lifetime (years): is the average life of the machine in years.  Capital cost (capcost): includes the machines cost, the system design and finally the installation cost. This parameter defined as the cost per KW electrical output capacity ($/KW). These machines can be purchased: a) Without heat recovery potential (no CHP) b) With heat recovery for heating purposes (CHP) c) With heat recovery for both heating and cooling (CCHP) 36
  • 37.  Operation and Maintenance Fixed Costs (OMFix): OMFix includes all the fixed annual operation and maintenance costs ($/KW per annum) (excludes fuel costs)  Operation and Maintenance Variable Costs (OMVar): OMVar includes all variable operation and maintenance costs ($/KWh) (excludes fuel costs)  Heat rate (HeatR): is the equipment heat rate (kJ fuel/KWh). Heat rate is linked to electrical efficiency, E by the equation: 3600 ℎ HeatR = HeatR in expressed with esteem to the higher heating value (HHV) of natural gas, due to the fact that the purchase of NG is with respect to the HHV  Heat to power Ratio (α): α is the ratio of recoverable heat per KWh electrical produced (maxp to maxp). According to Firestone, α value is based on the waste heat energy content prior to conversion via a heat exchanger, and here referred as recoverable heat (e.g. 1 KWh recoverable heat doesn’t cover 1 KWh heating demand but 1KWh x heat exchanger efficiency).  Conversion Efficiency for Recoverable Heat to Load Displacement (γ): γ value is an estimate of the portion of the recoverable heat that is useful and can displace real heating or/and cooling loads. γ value for heating is 0.8 and is actually the heat exchanger efficiency. Cooling loads according to Firestone are defined as the amount of electricity required to give the amount of cooling needed (assuming a specified value for electric chiller efficiency). γ for absorption cooling is consequently the ratio of electrical cooling load displacement to recoverable heat. This must take into account the heat exchanger efficiency in addition to the relative performance of electric and absorption chillers as described in the below 37
  • 38. equation (where the COPabs is the coefficient of performance of an absorption chiller and COPelectric is the coefficient of performance of an electric chiller). COP abs γabs = EfficiencyHeatExanger * COP electric COPabs has value 0.65 for single-stage hot-water fired absorption chillers and COPelectric has value 4 for electric compression driven chillers. Thus, γabs has a value of 0.13 for CCHP (Firestone, 2004). The γ values for different end-uses are shown in table 10.  Conversion Efficiency for Fuel to Load Displacement (β): β is an estimate of the portion of the fuel energy content that is useful for displacing heat loads with the use of heat exchanger or/and cooling by the use of absorption chillers. β value for heating is 0.8 (boiler efficiency) and for cooling 0.13 as before. The lower value for cooling is due to the fact that cooling loads are expressed as the amount of electricity requested to provide the wanted amount of cooling and cooling data is invariably expressed as electricity used by the air conditioner. Thus, β for absorption chillers must incorporate the ratio of fuel energy to useful heat as well as the relative performance of electric and absorption chillers as discussed before (Firestone, 2004). The β values are depicted in table 10, while the table 9 summarizes the assumptions used for the β and γ values. 38
  • 39. Table 9, Underlying Assumptions (Firestone, 2004) Table 10, β and γ values (Firestone, 2004) 39
  • 40. Figure 22. Technology database (Firestone, 2004) 40
  • 41. 3.2 Shopping mall description In this section, we are going to describe the shopping mall load profiles (electrical- only, cooling and heating). The most difficult part through this study was to find real 24 hour load profiles for SM’s due to the fact that these profiles either must calculated from a company (in response to a customer) or to produced by simulation tools like EnergyPlus or DOE-2, tools that wasn’t available in this MSc course boundaries. For that reason, ready electrical loads profiles were taken from the CERTS Customer Adoption Model paper (F. Javier Rubio, 2001). This shopping mall is located in southern California and the profiles were extracted from Maisy from the year 1998 data for the state of California. These data were reproduced and depicted in figure 23. Someone can claim that the SM in California has many differences with a SM in UK and thus the existed load profiles can’t be input to this report. But here this isn’t actually the case for two reasons: First, SM’s are a very specific consumer with especially large energy demand for cooling and lighting. From the previous two, only the cooling could have great differences between a building from California to London (due to climate differences), but actually in the SM this is not happening because the thermal loads that must be removed from a SM usually come not that much from the outside thermal mass transfer but mainly from the high density of people during the working hours and the high thermal loads from the artificial lighting inside the building. Second, in this report the most important is not actually the results as numbers but actually the model and the accuracy of the thermodynamic equations it uses in order to produce the results. This electrical load profile is described in a more detail in the literature review chapter in the section energy consumption in a SM. The problem with these data is that these load profiles are the total electrical load profiles (aren’t separated) and are 41
  • 42. not fitted to our model which takes as input for every month the 24 hour electrical- only, cooling and heating loads separately. For that reason these profiles were separated manually, without great detail but following a constant logic. From the research in energy consumption in shopping malls the energy breakdown was:  40-60% HVAC  20-30% LIGHTING  5-10% other appliances  3-4% lifts The heating demand in a SM due to the great thermal loads from the lights and the high people densities during the working hours is mainly in early morning or late afternoon hours with bigger needs during the winter months. On the other hand cooling demand for the same reasons is peaked during the hours 12:00 to 15:00 with greater effect on summer months, when and the outside temperature comes to be added in the high internal thermal loads. Finally the electrical-only loads are almost stable during the 24 hours and the 12 months. Mainly for the previous reasons the breakdown of the total electrical load to the electrical-only, cooling and heating follow the below separation rules (For each hour of a day, every month and season the sum of the electrical-only, cooling and heating percentages must have sum the 100% of the total electrical load ): Summer months: 1) Electrical-only loads (percentages to the total):  From the hours 22:00 to 6:00, 50%  All the rest hours of the day, 40% 2) Cooling loads:  From the hours 22:00 to 6:00, 50%  From the hours 6:00 to 10:00, 30% 42
  • 43.  From the hours 10:00 to 18:00, 45%  From the hours 18:00 to 22:00, 35% 3) Heating loads:  From the hours 22:00 to 6:00, 0%  From the hours 6:00 to 10:00, 30%  From the hours 10:00 to 18:00, 15%  From the hours 18:00 to 22:00, 25% Winter months: 4) Electrical-only loads (percentages to the total):  All the hours, 40% 5) Cooling loads:  From the hours 22:00 to 6:00, 30%  From the hours 6:00 to 10:00, 20%  From the hours 10:00 to 18:00, 30%  From the hours 18:00 to 22:00, 20% 6) Heating loads:  From the hours 22:00 to 6:00, 30%  From the hours 6:00 to 10:00, 40%  From the hours 10:00 to 18:00, 30%  From the hours 18:00 to 22:00, 40% By following the previous rules the SM detailed profiles are depicted in figures 24, 25, and 26. 43
  • 44. SM Electrical Load 1400 January 1200 February March 1000 End-use load (KW) April 800 May June 600 July 400 August 200 September October 0 November 0 5 10 15 20 25 30 December Hours Figure 23, SM Electrical load (F. Javier Rubio, 2001) SM Electrical-only demand 600 January 500 February March End-use load (KW) 400 April May 300 June 200 July August 100 September October 0 November 0 5 10 15 20 25 30 December Hours Figure 24, SM Electrical-only demand 44
  • 45. SM Cooling demand January 600 February March 500 April End-use load (KW) May 400 June 300 July August 200 September 100 October November 0 December 0 5 10 15 20 25 30 Hours Figure 25, SM Cooling demand SM Heating demand January 450 February 400 March 350 April End-use load (KW) 300 May 250 June 200 July 150 August 100 September October 50 November 0 December 0 5 10 15 20 25 30 Hours Figure 26, SM heating demand 45
  • 46. 3.3 Tariffs inputs Tariffs are a key input to our mathematical model. The two market inputs that will be explained in the next two sub-sections in detail are the natural gas prices and the grid electricity prices. 3.3.1 Natural gas prices Natural gas prices are a commodity that don’t change price so often during the month, has small volatility, and thus we take average monthly prices in contrast with electricity prices which change in a few minutes basis. The natural gas prices in $ per MMBTU were taken from the Energy Information Administration website (http://www.eia.doe.gov/) which is the official energy statistics from the U.S. government. For the case of the SM the commercial prices of 2008 were used (Release Date: 8/29/2008) and represented in the below figure 27, 28. Due to the fact that the NG prices for 2008 are not completed for this year (data are up to June 2008), but also it wasn’t wise to use the 2007 (this year prices are lower) for the rest of the year (July to December) an analogy was used in this way: we calculated the percentage that 2008 prices (up to June) are higher from 2007 prices and we added this to the 2007 prices for the rest of the year. NG price in $ per MMBTU 2008 2007 January 11.07 11.14 february 11.37 11.24 March 11.76 11.82 April 12.45 11.51 May 13.23 11.51 June 14.41 11.87 July 12.74 11.63 August 12.24 11.18 September 11.94 10.9 Octomber 11.83 10.8 November 12.09 11.04 December 12.07 11.02 Figure 27, monthly natural gas prices in $ per MMBTU for the calendar years 2007, 2008 46
  • 47. Natural gas Price in $ per MMBTU 16 14 12 $ per MMBTU 10 8 6 4 2 0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Figure 28, graph representation for natural gas prices in $ per MMBTU for 2008 3.3.2 Electricity prices (Grid) After the explanation of the NG prices the next important tariff input to the model is electricity price purchased from the grid to the customer. For this model the electricity price is calculated as shown in the figure 29. According to Peter Williams and Goran Strbac, in their book costing and pricing of Electricity distribution services the consumer final electricity price consisted by 51% from the electricity generation cost and the rest 49% from the transmission, distribution, and supply cost. The generation cost will be assumed to be the spot market electricity prices for this year (2008). These spot market prices will be taken from the Elexon BSC website (http://www.elexon.co.uk/), and more specifically in the section Pricing data the market index data for the year 2008 (http://www.elexon.co.uk/marketdata/PricingData/MarketIndexData/default.aspx). Like with the natural gas and here because of the fact that this year is not ended yet, the real 2008 data will be from January to June and the rest months July to December will be calculated as before: find the percentage that 2008 prices (up to June) are higher from 2007 prices, then add this to the 2007 prices for the rest of the year and finally keep these new prices as the rest 2008 values. These market spot prices are depicted in figure 30. 47
  • 48. As soon as the spot market prices calculated, then the annual average price is calculated and this value is multiplied by 49/51 in order to find the distribution, transmission and supply payment. This parameter is called DistrPay in the GAMS and its value is 0.13936531 $/KWh (for 2008). So each time the customer purchases one KWh, the price will be payed back to the grid consisted of the spot market price for the exact time of the purchase and the constant DistrPay. Using this method the final electricity prices for the whole year depicted in figure 31. Figure29. Contribution of distribution costs to electricity bill (Williams P. a., 2001) 48
  • 49. Spot market electricity price January 0.45 february 0.4 March 0.35 April 0.3 May $ per KWh 0.25 June 0.2 July 0.15 August 0.1 September 0.05 Octomber 0 November 0 5 10 15 20 25 30 December Hour Figure 30, Spot market electricity prices Grid electricity price with the distribution January company revenue february 0.6 March 0.5 April May 0.4 June $ per KWh 0.3 July August 0.2 September 0.1 Octomber November 0 December 0 5 10 15 20 25 30 Hour Figure 31, Grid electricity price with the distribution company revenue 49
  • 50. 4. Mathematical Model 4.1 Introduction In this part, the mathematical model will be presented and explained but also and the reasons behind this venture. The results which are presented are intended more to show: the great usage of GAMS in solving difficult optimization problems, the possible savings that can be achieved by the optimization of the energy systems (combination of DG and grid) in a shopping mall (extended in a microgrid) and not the actual numbers of the final energy cost of a shopping mall and the actual carbon savings. Improvements must be undertaken in the tariffs in order the model to use an accurate electricity tariff system, in the load profiles which are key input to the model (must be monitored a real SM for a year and calculated the accurate electricity, heating and cooling profiles), and also the technology information (more accurate costs and a more accurate thermodynamic model which will take into account the efficiency drops etc). Finally we can say, that given all the foresaid inputs (customer demand, electricity/NG tariffs and technology information) the model can give back some strategic results about the way DG technologies and Grid must be combined (which DG must installed) and work (when this capacity will operate during the year) in order to have some energy, money and carbon savings while we meet the constant customer demand. 4.2 Mathematical Programming We use mathematical programming in order to build the energy models. H. Paul Williams gives a definition for the mathematical programming (Williams H. , 1999): Mathematical programming has a sense of planning for the purpose of optimization, it is a mathematical problem regarding to maximizing or minimizing something which is known as objective function and it has to satisfy the conditions called constraints. The mathematical programming models are able to be classified as linear programming models, non-linear programming models and integer programming models.  Linear Programming Model (LP): Linear programming is the optimization problem in which the objective functions and the constraints are all linear. 50
  • 51.  Non-linear Programming (NLP): Non-linear programming is the optimization problem in which at least one of the objective functions or the constraints is a non-linear function.  Mixed-integer Programming (MIP): Mixed-integer programming is the optimization problem that has both continuous variables together with integer variables. It can be mixed-integer linear programming (MILP) or mixed- integer non-linear programming (MINLP).  Mixed-integer linear Programming (MILP): Mixed-Integer Programming (MIP) methods (L.T. Biegler, 1997) are suitable for modeling and analyzing buildings energy systems towards design, investment planning and optimization: this established algorithmic framework fulfills the requirements and captures the complexities of an investment planning procedure, by considering the superstructure of all alternatives, representing all possible choices for a system by binary (0–1) variables, while all the physical and economic quantities are expressed as continuous variables. All logical and physical relations are translated into equality or inequality constraints. The best plan is derived by conducting an optimization for a specific objective function (Liu Pei, 2007). 4.3 General Algebraic Modeling System (GAMS) General Algebraic Modeling System (GAMS) is multipurpose optimization software which is particularly designed for modeling linear, non-linear and mixed integer optimization (MIP) problems. Most of the researchers use GAMS for solving large and complex mixed integer linear programming (MILP) problems. Of course GAMS can do much more than these but is not in the current needs of this subject. The basic reasons GAMS selected for this optimization are: 51
  • 52.  Offer a high level language, for the illustration of large, difficult and complex models.  Provide easy and safe changes in the specification of the model.  Allows unambiguous statements of algebraic relationships  Allows comments in the model which are independent to the model solutions. 4.4 Model Description In this current model, there are two input fuels: natural gas and electricity from the grid. At the other end of the model there are three end uses that can be met: electrical- only, cooling and heating loads. The models objective function is to minimize the cost of meeting the Shopping malls energy demand for one year (while taking into account the carbon emissions) by optimizing the usage of different distributed generation technologies and the power from grid. In order to reach this objective the model must answer the following questions:  If it is economically feasible, which DG technologies must be adopted?  The chosen DG technologies in which capacity will be installed?  How this capacity must be operated during day/year in order to minimize the energy cost while meeting at all times the customer demand?  It is more economically for the customer to disconnect for the grid or there are profit opportunities by selling electricity back to the grid (especially the times of the high demand / high price)? The model inputs are:  The SM electricity-only, cooling and heating load profiles,  The hourly spot electricity prices for the year 2008/2007 2 (with the payment of distribution company) and the monthly spot natural gas prices for the same year, 52
  • 53.  DG technology information which includes: capital (include installation), operation and maintenance, and fuel costs (taken into account the lifetime and annualized for one year), basic technology physical characteristics, and finally some general thermodynamic parameters for the efficiency of the combined heat and power.  Rate of carbon emissions from the microgrid and from the on-site onsite generation by the usage of natural gas  Carbon taxes by using the price from ETS The model outputs will be:  The mixture of the DG technologies that will installed to the shopping mall  The capacity level in which these technologies will be installed  Hourly operating schedule of the installed capacity (not 15 minute basis that is common in spot markets due to the computational constraints but also and the difficulty to find the load profiles in this study)  The final energy cost to meet the shopping mall demand and also the carbon emissions through the usage of the DG equipment and/or grid Some important model’s assumptions:  The optimization based clearly on economic criteria. At the same time while there is a try to capture the externality of the carbon emissions by taxation there is no consideration of a detailed environmental model (e.g. maybe there is a reduction on carbon emission but not local pollution)  The customer can buy and sell electricity back to the grid at any time. Despite the fact that the spot market electricity prices are real, the distribution payment (is constant for the year) and the price the customer sells back to the grid (half of the spot market price and if we take into account the payment for the distributor is almost the ¼ of the price we buy from the grid) are not accurate but based more in an average base.  Are not taken into any deterioration in output efficiency of the equipment during its lifetime, and also there is no penalty in the efficiency for part load operation (start-up also). 53
  • 54.  In this economic report is not taken account the reliability of these equipment (e.g. if the CHP broke for one reason the power purchase from grid are not in the same tariffs as usual)  At the same way, CHP/CCHP benefits, power quality and reliability also are not taken into account.  Equipment price and performance are accepted without question. 2 Will be used the prices until July for the 2008, and the prices for the rest of the year from 2007. 4.5 Mathematical Formulation Customer Data Name Description Cdemand m,h,u Customer demand in kW for end-use u during hour h, and month m (end-use are electric-only, cooling, heating). Market Data Name Description ElectrPricem,h Spot market electricity price during month m and hour h ($/KWh) CarbonTax Taxes on Carbon emissions ($/Kg) MCRate Carbon emission rate from the grid (Kg/KWh) NGCRateu Carbon emission rate from burning onsite natural gas to meet the end use u (Kg/KWh) NGpricem Spot market natural gas price during month m ($/Kj) Technology Information Name Description Name Description DGmaxi Capacity rating of technology i ( kW) DGlifetimei Expected lifetime of technology i (a) DGcapcosti Capital and installation cost of technology i ( $/kW) DGOMfixi Fixed annual operation and maintenance costs of technology i ($/kW) DGOMvari Variable operation and maintenance costs of technology i ($/kWh) DGCostKWhi,m Generation cost of technology i during month m ($/kWh) 54
  • 55. CarbonRatei Carbon emissions rate from technology i (kg/kWh) DFCap Capacity of direct-fired absorption chiller (kW) DFPrice Capital and installation cost of direct-fired absorption chiller ($/KW) E(i) Set of end-uses that can be met by technology i ai The amount of heat (in kWh) that can be recovered from every kWh of electricity is produced by using DG technology i (a has value 0 for all technologies that are not CHP or CCHP) bu The amount of heat (in kWh) generated from unit kWh of natural gas obtained for end-use u (the corresponding value of bu for electricity-only load is zero due to the fact that never uses NG) gi,u The quantity of valuable heat (in kWh) that can be allocated to end-use u from unit kWh of recovered heat from technology i (given that the electricity-only loads never use recovered heat,the gi,u equals to zero) Aditional Parameters Name Description InterestRate Interest rate on DG investments (%) SolarInsm,h Average portion of maximum solar insolation received (%) during hour h and month m and used from photovoltaic cells Distribution company revenue1 DistRev NGHR Natural gas heat rate (kJ/kWh) 1 This price is added to the spot electricity market when the customer buys from the grid (wholesale market) and pay the cost of the distribution of the electricity from the producer to the customer. According to Williams and the paper Costing and pricing of electricity distribution services (Williams P. a., 2001) this value is almost the 50% of the total electricity cost. For the simplicity here the average annual electricity spot price multiplied by 49/51 and this is the value for the whole year. Variables Name Description InvDGi Integer variable which shows if one technology will be installed and in which quantity DF Binary variable which shoes if a direct-fired absorption chiller will be installed 55
  • 56. GenOni,m,u,h Generated electricity by technology i during month m, hour h to meet the onsite demand u (kWh) GenSeli,m,h Generated electricity by technology i during month m and hour h to sell back to the grid (kWh) PurNGm,u,h Obtained natural gas during month m hour h for end use u (kWh) (for direct burning) GridElectrm,u,h Purchased electricity from the grid by the customer during month m, hour h to meet the customer demand u (kWh) RecHeati,m,u,h Recovered heat from technology i that is used to meet the customer demand u during month m and hour h (kWh) Objective Function The mathematical formulation of the problem is: Min ( InvDGi GridElectrm,u,h) ∙ ( ElectrPricem,h+ DistRev) ℎ GenOni,m,u,h GenSeli,m,h ℎ (GridElectrm,u,h ∙ CarbonTax ∙ MCRate) + PurNGm,u,h RecHeati,m,u,h DF ℎ (GenOni,m,u,h ∙ DGCostKWhi,m ) + + DF ∙ DFPrice ∙ DFCap ∙ Annuity ℎ (GenSeli,m,h ∙ DGCostKWhi,m ) + ℎ (PurNGm,u,h ∙ NGHR ∙ NGpricem) + ℎ (GenOni,m,u,h ∙ DGOMvari) + ℎ (PurNGm,u,h ∙ CarbonTax ∙ NGCRateu) + ℎ (GenSeli,m,h ∙ DGOMvari) + ℎ (GenSeli,m,h ∙ (ElectrPricem,h /2)) - ℎ (GenOni,m,u,h ∙ CarbonTax ∙ CarbonRatei ) + ℎ (GenSeli,m,h ∙ CarbonTax ∙ CarbonRatei) + 333 InvDGi ∙ DGmaxi ∙ (DGcapcosti ∙ Annuityi + DGOMfixi) + (1) 56
  • 57. Constraints: GenOni,m,u,h + GridElectrm,u,h + bu ∙ PurNGm,u,h + (gi,u ∙ Cdemand m,h,u = RecHeati,m,u,h) ∀ m,h,u (2) ∀ i, m, h GenOni,m,u,h + GenSeli,m,h ≤ InvDGi ∙ DGmaxi (3) InterestRate ∀i Annuityi = (4) 1 (1− ) 1+InterestRate GenOnj,m,u,h + GenSelj,m,h ≤ InvDGj ∙ DGmaxj ∙ SolarInsm,h ∀ m, h if j ∈ {PV} (5) RecHeati,m,u,h ≤ ai ∙ (GenOni,m,u,h ) + ai ∙ GenSeli,m,h) ∀ i, m, h (6) ∀ i, m, u, h if u ∉ E(i) RecHeati,m,u,h = 0 (7) ∀ i, m, h if u ∈ {heating} GenOni,m,u,h = 0 (8) ∀ m, h if u ∈ {heating} GridElectrm,u,h = 0 (9) ∀ m, h if u ∈ {cooling} PurNGm,u,h ≤ DFCap ∙ DF (10) Explanation of the equations: (1) This is the objective function of the model which will try to minimize the total demand cost / maximize the net present value for the whole year while at the same time will take into account the carbon emission externality. This cost consisted from : the cost of buying electricity from the grid (included the payment to the distribution company), the carbon taxes we have to pay due to this purchase (internalize the externality), the fuel cost (natural gas) for producing electricity using the DG technologies for the purpose to meet our own demand or to sell electricity back to the grid. The variable operation and 57
  • 58. maintenance cost for producing electricity onsite (meet the demand or sell back to the grid), the carbon taxes we have to pay for the onsite production, the fixed operation and maintenance cost for producing electricity onsite, the annualized capital (included installation) costs for the technologies that will installed, the Natural gas cost for the direct burning application with also the equivalent carbon taxes. All these costs will be added and will subtracted from the revenue we make by selling back electricity to the grid. (2) The second equation/constraint will make sure that will covered all the customer demand during the year and also specify the means through which the load for energy end use u may be satisfied (the demand will be covered through on-site production and/or purchase from the grid and/or direct burning of natural gas and/or the recovered heat). (3) The third equation make sure that the on-site production is limited by the capacity we initially invest (this balance is for every hour for every day the whole year). (4) The fourth equation annualizes the cost of capital investment of DG technologies. (5) Fifth equation is responsible to enforce the constraint on how much power PV’s can produce during the day and this production is in proportion to the solar insolation. (6) This equation limits how much heat can be recovered from each type of DG equipment/technology. (7) This equation avert the usage of recovered heat by end uses that can not satisfied by specific DG technology. (8) This equation not allow the usage of electricity for meeting heating loads (electrical heaters are extremely un-efficient) (9) The same with the previous equation, now for the grid (10) Finally this equation is responsible to prevent the direct burning of natural gas for meeting cooling demands without the existence of absorption chiller. 58
  • 59. 5. Results In this part of the report will be represented the results of the model for different working scenarios, and different sensitivities analysis (change of the variables by alteration of the input parameters). The first target of using different scenarios and sensitivity analysis is the verification of the model. Secondly, by giving different input values we can see in the lump the great opportunities DG and renewables are giving for the future for money saving and carbon emissions reductions (regardless the absence of some details in the model, like extremely accurate tariff system). 5.1 Scenarios and Sensitivities In this report will be examined two different tariff scenarios (ways SM buy electricity from the grid). One of the two scenarios will be used furthermore for a detailed sensitivity analysis. The different scenarios depicted and explained in Table 11. Table 11, Scenarios examined Scenarios Description In this case the model will take as Constant electricity price electricity tariff input constant prices for the whole year. That means that the shopping mall will purchase electricity in different hours, different months at a fixed price. The goal behind this scenario is the verification of the model through the different selection of the technologies (grid, CHP etc) under different average electricity prices. In this case the shopping mall buys Spot market prices electricity from the grid in the real spot market prices (2008) plus the distribution revenue for each KWh purchased (for using the lines, for the operator etc). This scenario will be the bases for the sensitivity analysis due to the fact that is closer to the reality. The different sensitivities that will be examined are described in Table 12. 59
  • 60. Table 12, Examined sensitivities Sensitivities Description In this case the model cannot use any of Grid plus Boiler the technology to meet the shopping mall demand. This will be the base for the comparison with the other results (Net present value, carbon emissions). In this case the model will be constraint Without CHP/CCHP and won’t be able to invest in a CHP and/or a CCHP system, but will be able to invest in PV and on-site power generation only The shopping mall can invest in Without CCHP everything except to CCHP (not absorption cooling). Here the model will be able to take all the Final case inputs without any constraint and find the final solution (include all the technologies, detailed tariff system, detailed customer demand). In this case the model examine the Only PV, Grid and Boiler optimum solution if the only technology existed from DG is PV (Included boiler and grid). Here will be put in the model constraint to At least 7 PV invest at least 7 PV panels. Then the model will find the optimum solution starting from this point Here will be examined a friendly to PV High carbon price with a case with a capital cost reduction for PV 20% PV capital reduction 20% and 100$ per tone of CO2 emitted. The spot market electricity prices (and the 50 % cheaper electricity distribution payment) will be reduced by prices 50% (all the rest remain the same). The customer will buy natural gas 50% 50% more expensive NG more expensive prices (without change the grid electricity prices). High price per tone of CO2 emission will High carbon price be examined (100$ per tone compared with the 40$ existing now). 60
  • 61. 5.2 Outline of results For the bases scenario (spot market prices) and all sensitivities, the below data given as output:  Technology adopted (name and capacity in KW)  SM total power demand (KWh)  SM electricity-only demand (KWh)  SM cooling demand (KWh)  SM heating demand (KWh)  Electricity met by the grid (KWh)  Electricity me by on-site generation (KWh)  Electricity sales back to the grid (KWh)  Cooling demand met by the grid (electric chiller) (KWh)  Cooling demand met by on-site power generation (electric chiller) (KWh)  Cooling demand met by on-site recovered heat (if any CCHP) by driving absorption chiller (KWh)  Cooling demand met by on-site direct fire burning of NG (boiler plus absorption chiller, not CCHP) (KWh)  Heating demand met by recovered heat (CHP or/and CCHP) (KWh)  Heating demand me by direct burning of NG (boiler) (KWh)  NG purchases (KWh)  NG purchases (without those are targeted for selling back to grid) (KWh) And the annual economic results:  Energy payments to the grid ($)  Carbon taxes for grid purchases ($)  Fuel cost for on-site generation (include the fuel for sales back to the grid) ($)  Variable cost for on-site generation ($)  Carbon taxes for on-site generation ($)  Capital investment costs (included installation) ($) 61
  • 62.  NG purchases for direct burning (boiler, boiler plus absorption chiller) ($)  Carbon taxes for direct burning of NG ($)  Carbon emissions for meeting total SM demand (tones CO2)  Carbon savings over basic grid plus boiler scenario (%)  Energy sales back to the grid ($)  Net Present Value (NPV) ($)  Money savings over basic grid plus boiler scenario (%) For the constant electricity price scenario, will be given some of the above due to the fact that the goal of this scenario is the verification of the model and not the actual results. 5.3 Overview of spot market prices results scenario The usage of distributed generation technologies (DGT) in a shopping mall and generally in a microgrid can substantially reduce the annual energy costs. The figure 32 below represents the percentage of annual bill savings of some examined DGT scenarios over the basic grid + boiler scenario. It becomes clear that all the DGT cases (some are independent to the grid and some not) have considerable savings with the final case to have 51% savings over grid + boiler, the case without CCHP 48%, the case without CHP/CCHP 44%, the case at least 7 PV 38%, and finally the PV+ Grid + Boiler 16% (all these cases/sensitivities will examined analytically at the sub- section each case detailed results). The reduction of the final cost/NPV is greater in the final case, where all the technologies are accessible, and is available not only the CHP but also the CCHP (invest on CCHP and take the advantage of absorption cooling). The second cheapest solution (without CCHP) takes the advantage of using CHP (to meet the SM heating demand for ‘free’) but has a bit worse results than the final case due to the absence of the absorption cooling (lose the ability compared to the previous scenario to use some ‘spare/waste’ heat to drive the absorption cooling and meet some cooling demand for free). The third case in the row analogically can 62
  • 63. take the advantage of all the DGT technologies except the ones with CHP or CCHP. Also here existed very promising result, and the deference with the previous cases is that here we don’t utilize at all the waste heat. The next case allow to the model to use only PV, grid and boiler (PV + Grid + boiler). Also here the model invests on PV’s and gives considerable savings (16%). The final case was the one in which the model had the constraint that must invest at least 7 PV, and find the optimum combination with this constraint (note that in the model you can put any constraint and start the optimization from this point). In all the above cases, with exception the PV + Grid + Boiler, the power purchases from the grid were negligible and almost all the demand were met on-site by a DGT combination. The last shows that the model find cheaper to buy, install, and run DGT than buy the power from the grid. Note that the model ensures that every time is chosen the combination of technologies (including grid) that minimizes the cost to meet the customer demand (internalize also the carbon emissions externality). Figure 32 Bill savings over grid + boiler basic scenario At the figure 33 below we can observe for each of the above cases the carbon results compared to the basic case. All the cases have considerable carbon savings except the first case which is without CHP/CCHP. The last result was expected due to the fact that in this case the DG doesn’t take the advantage of the recovered heat, which is one substantial difference between DG and the big power stations (as regards the efficiency of burning the fuel). The big power stations due to the economies of scale work like in this first case (without utilizing in most cases the waste heat) but with 63
  • 64. higher efficiencies and for that reason emit less carbon per KWh produced (but big power station are substantially more expensive due to the need of transmission, distribution and supply). On the other hand, the other two cases (without CCHP, and final case) which are using natural gas for power, heat and cooling (CCHP) have less emissions because they utilize the waste heat and finally reach higher efficiencies of burning the natural gas. Finally the two last cases have even better carbon results (but worse financial results see above) because of the utilization of PV which are ‘carbon free technologies’ (not considered the carbon from manufacturing). Percentage of carbon savings over grid + boiler basic scenario (%) Percentage of carbon savings over grid 60 plus boiler basic case (%) 50 DG without CHP/CCHP 40 Without CCHP 30 Final Case 20 PV+Grid+Boiler 10 At least 7 PV 0 -10 case Figure 33, Carbon savings over basis grid + boiler scenario Finally at the figures 34 to 39, represented the net cost breakdown of the above five cases. At the figure 34, we can observe that only the cases PV + Grid + Boiler and the basic case have important grid purchases. The first two cases (without CHP/CCHP) have extremely small purchases and the final scenario and the remaining at least 7 PV are completely independent. 64
  • 65. Energy payments to the Grid (K$) 1600 1400 Without CHP/CCHP 1200 Thousands $ Without CCHP 1000 Final case 800 PV+Grid+Boiler 600 At least 7 PV 400 grid+boiler 200 0 case Figure 34, Energy payments to the grid The next figure 35 depicts the capital investment costs. The cases without PV’s have smaller investments (all annualized) compared to the ones with (the basic case have zero investment). Figure 36 represents the power sales back to the grid, sales which are small compared with the NPV because of the small prices that a microgrid can sell back to the grid and the high running costs of the DGT (not PV). Exception is only the case with at least 7 PV, and this because of the existence of the PV’s (once installed they use all the spare capacity after meeting the demand to sell back to the grid no matter what the price is). The same is not happened and in the case of PV+ Grid + Boiler due to the fact that all the PV’s capacity is used for the on-site demand (less economically to sell from PV’s and buys from the grid). Capital investment cost (include installation and fixed costs) (K$) 700 Thousands $ 600 Without CHP/CCHP 500 Without CCHP 400 Final case 300 PV+Grid+Boiler 200 At least 7 PV 100 Grid + Boiler 0 case Figure 35, Capital investment cost (includes installation and fixed costs) 65
  • 66. Energy sales back to the grid 80 70 Without CHP/CCHP 60 Thousands $ 50 Without CCHP 40 Final case 30 PV+Grid+Boiler 20 At least 7 PV 10 Grid + Boiler 0 case Figure 36, Energy sales back to the grid The next three figures 37, 38, 39 show the Net present value, the carbon taxes and the fuel costs respectively. It is worthwhile to notice in the figure 39 the different cost for natural gas purchases. For the first three cases these costs are high because the SM meets its demand by burning NG. For the PV + Grid + boiler and the Grid + Boiler that purchases are narrowed just to the fuel needed to be burned in a boiler for meeting the heating demand. Finally for the case at least 7 PV there is a reduction of fuel usage compared to the three first cases because of the power produced from PV’s. Net present Value (NPV) 1800 1600 1400 Without CHP/CCHP Thousands $ 1200 Without CCHP 1000 Final case 800 PV+Grid+Boiler 600 At least 7 PV 400 Grid + Boiler 200 0 case Figure 37, Net present value (all included) 66
  • 67. Carbon taxes 140 120 Without CHP/CCHP 100 Thousands $ Without CCHP 80 Final case 60 PV+Grid+Boiler 40 At least 7 PV 20 Grid + Boiler 0 case Figure 38, Carbon Taxes (all included) Fuel costs (NG) 800 700 Without CHP/CCHP 600 500 Without CCHP Thousands $ 400 Final case 300 PV+Grid+Boiler 200 At least 7 PV 100 Grid + Boiler 0 case Figure 39, Natural gas payments (all included) 5.4 Assessment of specific cases In this section will be examined all the different sensitivities for the spot market prices scenario in detail. Note that for some cases the graphs will be transferred to the Appendix. 5.4.1 Case 1: Grid plus boiler For the grid plus boiler case the model results breakdown are depicted in the Figure 40. This case and the results are simple due to the fact that there is no other technology and the whole electricity-only and cooling demand met by the grid while 67
  • 68. the heating demand is met by burning natural gas in boilers with efficiency 80%. The NG purchases during the different months and the different hours of the day are represented in figure 41 and the total electricity demand in figure 42 (coincide with the SM demand). Two factors that make this scenario extremely expensive are: Firstly the high 2008 electricity prices, and secondly the facts that the SM purchases electricity during periods when the spot market prices are extremely high like 12:00 August/July (0.45$), 18:00 November (0.55$) and 19:00-20:00 October (55$) (of course and the added costs for transmission and distribution). On the other hand when DGT existing in SM these peak hours are beneficial to sell back to the grid and make a profit. Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 81470 Cooling demand met by the grid (KWh) 68432 Heating demand met direct NG burning(KWh) 59298 NG purchases (KWh) 74123 Energy payments to the grid ($) 1360706 Carbon taxes for grid purchases ($) 96149 NG purchase for direct fire ($) 74331 carbon taxes for direct burning of NG ($) 17507 Carbon emissions for meeting on-site total demand (tonnes) 2841 NPV 1548694 Figure 40, Energy balance and economic result for the grid plus boiler case 68
  • 69. Figure 41, NG purchases for meeting the SM heating load (Grid plus boiler case). Figure 42, total electricity purchases from grid, for all months and hours (grid plus boiler case) 8.4.2 Case 2: Without CHP/CCHP At this case, the SM is able to meet its electrical demand (electrical-only, cooling) by purchasing electricity from the grid, invest in PV’s, invest in power production by burning NG (no CHP/CCHP) or a combination of the three above. The heating demand can only be met by burning NG in boiler. The results of this case are represented on figure 43. For this scenario the technology adopted was one MW Natural gas engine (electrical). It is obvious from the results all the electrical-only demand met by self-generation. The cooling demand also met by 69
  • 70. DG except only 20 KWh that met by the grid during the 15:00 of the August (figure 44) and that due to the fact that the DG capacity wasn’t enough to cover all the SM cooling needs . The heating demand met all by boilers (not recovered heat existed). Energy Balance results Column1 Technology Adopted (name,capacity) NG-1000E Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 1523 Cooling demand met by the grid (KWh) 20 Cooling demand met by on-site power generation (KWh) 68412 Cooling demand met by on-site recovered heat (KWh) 0 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat(KWh) 0 Heating demand met by direct burning of NG(KWh) 47439 NG purchases (KWh) 518074 NG purchases without those for selling to grid(KWh) 513608 Economic Results Column1 237 Energy payments to the grid ($) 13 Carbon taxes for grid purchases ($) 568719 Fuel cost for on-site generation (with selling to the grid)($) 41475 Variable cost for onsite genaration ($) 104859 Carbon taxes for onsite generation ($) 73384 DG annualized capital cost(include installation) 74331 NG purchase for direct fire ($) 17507 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 3033 (tonnes) 9323 Energy sales back to the grid ($) NPV 871203 44 Savings over basic grid plus boiles scenario (%) -7 Carbon savings over basic grid plus boiles scenario (%) Figure 43, Energy balance and economic results for without CHP/CCHP case As regards the economic results of this case, the savings over the basic grid plus boiler are significant with a 44% reduction in the final energy bills of the SM. We can also identify some sales back to the grid (figure 45). These sales have been made during the peak hours when the spot market price were too high and could cover the production cost and the reduced price that SM is able to sell back to the grid (also there was spare capacity after meeting the on-site demand). Finally, there is an 70
  • 71. increase of carbon emissions to the environment in this scenario with a negative carbon savings over the basic scenario. This result could be predictable if we take into account the bigger efficiency a power plant can reach due to the bigger capacity compared with the one MW installed in the SM. The same is not happening and with the electricity price because of the transmission, distribution and control needed for the electricity produced in the power station. For this case the overall efficiency of burning NG to meet the onsite demand is 40%. Figure 44, Total electricity purchases from the grid (without CHP/CCHP case) Figure 45, Sales back to the grid (without CHP/CCHP case) 8.4.3 Case 3: Without CCHP This case is very familiar with the previous one, with the only difference that now the customer is able to invest also in CHP technologies but still isn’t available the CCHP options. CHP technologies give the opportunity through the usage of heat exchangers 71
  • 72. to recover some ‘waste heat’ and utilize it in order to meet some or all the heating demand of the SM while producing power to meet the electrical-only and cooling demand of the SM. This feature of this technology is also the main advantage of the DG over the centralized power plant due to the fact that the power production is near the heating load and can be utilized, unlikely with a power station which usually is far from the heating loads (e.g. cities), and as it is widely known the heating is not economically travelling long distances (electricity does). In this specific SM’s case the model decide to invest in a one MW CHP Natural gas engine, and the energy balance results are represented in figure 46. Similarly with the case without CHP/CCHP almost all the SM’s electrical demand met by on-site generation (exception was 20KWh cooling load met by the grid), and the schedule of the self-generation output and the schedules (month, hour) are exactly the same as the SM’s demand profile (see in customer description section). As regards the heating, on the contrary with the previous case now the SM meet this entire load by the recovered heat with a 12% reduction of NG usage in this scenario compared to the previous one. At the same time this heat utilization improves not only the economics results (figure 47) of this case compared to the grid plus boiler scenario from 44% to 48% but also invert the negative carbon results from -7% to +9% and makes this case more sustainable. Finally we can see clearly from the results an overall burning efficiency of the NG for meeting onsite demand of 45% (much bigger compared to the previous case) 72
  • 73. Energy balance results Column1 Technology Adopted (name, capacity) NG-1000CHP Electricity-only demand (KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand (KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 1523 Cooling demand met by the grid (KWh) 20 Cooling demand met by on-site power generation (KWh) 68412 Cooling demand met by on-site recovered heat (KWh) 0 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 443951 NG purchases without those for selling to grid(KWh) 439485 Figure 46, energy balance results for without CCHP case Economic results Column1 237 Energy payments to the grid ($) 13 Carbon taxes for grid purchases ($) Fuel cost for on-site generation (with selling to the 568719 grid)($) 41475 Variable cost for onsite generation ($) 104859 Carbon taxes for onsite generation ($) 96316 DG annualized capital cost(include installation) 0 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 2595 (tonnes) 9323 Energy sales back to the grid ($) NPV 802297 48 Savings over basic grid plus boiler scenario (%) 9 Carbon savings over basic grid plus boiler scenario (%) Figure 47, economic results for without CCHP case 5.4.4 Case 4: Final case Final case is the most cost effective solution for meeting the SM’s demand by allowing the model to choose between all the technologies without constraint. Due to the fact that in this economic model we internalize the carbon externality, by turning the carbon emissions into cost, we could say that this case is the more sustainable 73
  • 74. ones. This case also don’t differ a lot from the previous two ones, because the technology choice remain the one MW natural gas engine with the difference that hear the model decide to invest in CCHP (combined cooling heating and power). Obviously the model find that the utilization of the recovered heat for not only heating (part or all of them) but also cooling (part or all of them) loads overcome all the other costs (capital, variable/fixed costs, carbon taxes etc) and makes this case the most attractive of all. Indeed the economic results (figure 49) of this case are stunning with an annual energy bill saving reaching 51% (compared to the basic grid plus boiler scenario) while at the same time the carbon savings to be near 20%. The energy sales back to the grid increased almost 300% (compared the two previous cases 2, 3) (figure 52) and simultaneously the microgrid is completely autonomous from the grid. As regards the self generation output and schedule for the electricity-only end use (figure 50) and the recovered heat which is going to meet the heating demand (figure 53) are coincide with the corresponded electricity-only SM demand and the SM heating demand (actually figure 53 give the recovered heat need to cover the SM heating demand, recovered is the 120% of the heating demand due to the fact that his heat passes from heat exchangers that have efficiency 80%). The interesting part in this final case is how the CCHP is going to meet SM’s cooling demand by utilizing the whole spare recovered heat (figure 52) (after meeting the heating demand) to drive absorption coolers and then meet the rest of the cooling demand driving electrical chillers (usual air-conditioning systems) and using by self- generated power (figure 51). Actually, if we notice in more detail the figures 52 and 53, becomes clear that these stunning results came naturally because of the different hours the heating demand and the cooling demand peaks and thus there is no antagonism of the two different demands (cooling, heating) for the recovered heat. For the numbers, the cooling that has been met from the recovered heat is 21% (out of the total cooling demand, due to low conversion of recovered heat by absorption cooling), and the overall efficiency for burning on-site NG reached the 50% (much higher from the usual conventional centralized power stations). Finally this case meets the same demand with 20 % and 10 % less NG compared to the two previous scenarios respectively. 74
  • 75. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 4733 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 53982 Cooling demand met by on-site recovered heat (KWh) 14449 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 411055 NG purchases without those for selling to grid(KWh) 397176 Figure 48, energy balance results (final case) Economic results Column1 0 Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) Fuel cost for on-site generation (with selling to the 525969 grid)($) 38402 Variable cost for onsite genaration ($) 97089 Carbon taxes for onsite generation ($) 120825 DG annualized capital cost(include installation) 0 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 2345 (tonnes) 24297 Energy sales back to the grid ($) NPV 757988 51 Savings over basic grid plus boiles scenario (%) 17 Carbon savings over basic grid plus boiles scenario (%) Figure 49, economic results (final case) 75
  • 76. Figure 50, NG-1000CCHP power generation for electrical-only end use loads (final case) Figure 51, NG-1000CCHP power generation for cooling end use loads (final case) 76
  • 77. Figure 52, NG-1000CCHP Recovered heat going to meet cooling demand (final case) Recovered heat going to meet heating demand 600 500 January February 400 March April May KW 300 June July 200 August September October 100 November December 0 0 5 10 15 20 25 30 Hour Figure 53, NG-1000CCHP Recovered heat going to meet heating demand (final case) 77
  • 78. Figure 54, NG-1000CCHP Energy sales back to the grid (final case) 5.4.5 Case 5: PV plus Grid plus Boiler PV plus grid and boiler is one interesting scenario to examine, especially for places where the NG is either unreachable or even extremely expensive. In this case the model is able to find the optimum solution by taking into account only the grid, PV’s and direct fired boiler. Due to the fact that the PV cannot produce heating loads (no solar thermal as option), and also can produce electricity only some hours of the day (there are no storage options), this scenario cannot be independent from the grid and the boiler. For the existing SM the combination that minimize the annual energy bills are seven 100 KW PV panels combined with the grid and the boiler. The energy balance results and the economic results for this scenario are shown in the figures 55, 56. As becomes clear from the results almost half of the electrical-only and cooling demands are met by self-generation and the rest purchased from the grid. Figures 57, 58 and 59 show how the Electrical-only and cooling loads are going to be met. These results was expected if we consider that the hours the PV produce electricity coincide with the hours when the electricity from the grid is expensive (look grid electricity price diagram). So, the 700 MW PV can not only cover almost the sunny 78
  • 79. hour’s electrical demand of the SM but at the same time can sell back to the grid and make a considerable profit (figure 60) (without any subsidies included). As regards the economic results this case has 16% savings over the basic grid plus boiler scenario, and finally 41% carbon savings which is and the highest amongst all the previous results. Energy balance results Column1 Technology Adopted (name,capacity) 7 PV-100 Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 41909 Electricity met by on-site generation (KWh) 39561 Electricity sales back to the grid (KWh) 8030 Cooling demand met by the grid (KWh) 34542 Cooling demand met by on-site power generation (KWh) 33890 Cooling demand met by on-site recovered heat (KWh) 0 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 0 Heating demand met direct NG burning(KWh) 59298 NG purchases (KWh) 313040 NG purchases without those for selling to grid(KWh) 289496 Figure 55, energy balance results (PV plus grid plus boiler case) Economic results Column1 674010 Energy payments to the grid ($) 49037 Carbon taxes for grid purchases ($) Fuel cost for on-site generation (with selling to the 0 grid)($) 0 Variable cost for onsite genaration ($) 0 Carbon taxes for onsite generation ($) 496229 DG annualized capital cost(include installation) 74331 NG purchase for direct fire ($) 17507 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 1664 (tonnes) 17033 Energy sales back to the grid ($) NPV 1294081 16 Savings over basic grid plus boiles scenario (%) 41 Carbon savings over basic grid plus boiles scenario (%) Figure 56, economic results (PV plus grid plus boiler case) 79
  • 80. Figure 57, Total electricity purchases from grid (PV plus grid plus boiler case) Figure 58, PV power generation for electrical-only end use loads (PV plus grid plus boiler case) 80
  • 81. Figure 59, PV power generation for cooling end use loads (PV plus grid plus boiler case) Figure 60, energy sales back to the grid (PV plus grid plus boiler case) 5.4.6 Case 6: At least seven PV This case is also a very interesting scenario for many reasons. First of all the economic results indicates an annual bill saving of 38% over the basic scenario and an amazing 53% of annual carbon savings, results that makes this scenario after the 81
  • 82. final scenario the most interesting because of the high green profile and the considerable money savings. Secondly, some customers maybe have some initial requirements (like in this case PV) and the model can take these requirements as initial constraints and find the optimum solution with them. This scenario wasn’t chosen accidentally but comes as continuity from the previous PV plus grid plus boiler scenario. Due to the fact that before the optimum scenario indices 7 100 KW PV panels, now in this scenario this was the constraint in order to see if the model finds cheaper to use the grid for the off peak hours or a DGT. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP , 7 PV-100 Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 19671 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 60779 Cooling demand met by on-site recovered heat (KWh) 7652 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 31865 Heating demand met direct NG burning(KWh) 19467 NG purchases (KWh) 499118 NG purchases without those for selling to grid(KWh) 441439 Figure 61, energy balance results (at least 7 PV case) 82
  • 83. Economic results Column1 0 Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) Fuel cost for on-site generation (with selling to the 302115 grid)($) 22036 Variable cost for onsite generation ($) 55711 Carbon taxes for onsite generation ($) 617055 DG annualized capital cost(include installation) 24190 NG purchase for direct fire ($) 5748 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 1349 (tonnes) 68715 Energy sales back to the grid ($) NPV 958137 38 Savings over basic grid plus boiler scenario (%) 53 Carbon savings over basic grid plus boiler scenario (%) Figure 62, economic results (at least 7 PV case) The results for this scenario shown in figures 61, 62 and indicates except the 7 PV panels the installation and one MW CCHP natural gas engine. The system is completely independent from the grid and the sunny hours the electrical-only and cooling demand met by PV’s (figure 65, 66) while the rest hours the electrical loads met by the natural gas engines (figures 63, 64). That indicates that even for the off peak hours the system find cheaper to self-generate the rest recommended power than purchasing from the grid. Figure 63, NG-1000CCHP power generation for electrical-only end use load (at least 7 PV case) 83
  • 84. Figure 64, NG-1000CCHP power generation for cooling end use loads (at least 7 PV case) Figure 65, 7 PV-100 power generations for electrical-only end use loads (at least 7 PV case) 84
  • 85. Figure 66, 7 PV-100 power generations for cooling end use loads (at least 7 PV case) At the same time CCHP with the power production, recover some useful heat that covers 12% of cooling demand (figure 68) and more than 60% of the total heating demand (figure 67). The rest of the heating demand is going to be met by the usage of the boiler (figure 69), hours that coincide with the PV working hours (these hours CCHP don’t produce power and there is a lack of recovered heat to meet heating demand and drive absorption coolers for cooling). Finally at the figures 70 and 71 represented the energy sales to the grid from the PV and the CCHP respectively. Concluding for this case we not only identify some very promising economic and environmental results but also diversity in the technology combination which makes the system more flexible in cases of one technology failure, high fuel/electricity prices and make the system more secure, in high volatility years. 85
  • 86. Figure 67, NG-1000CCHP recovered heat going to meet heating demand (at least 7 PV case) Figure 68, NG-1000CCHP recovered heat going to meet cooling demand (at least 7 PV case) 86
  • 87. Figure 69, NG purchased for meeting heating demand by direct-fire burning (at least 7 PV case) Figure 70, Energy sales back to the grid by power generated from PV’s (at least 7 PV case) 87
  • 88. Figure 71, Energy sales back to the grid by power generated from NG-1000CCHP (at least 7 PV case) 5.4.7 Case 7: High carbon price This is a hypothetical case in which the price per tone of CO2 emitted cost 100$ compared to the current 40$ which exist in ETS now. The energy balance and economics results are depicted at figure 72 (appendix), and the optimization still indicates that the optimum combination of technologies and the output schedules remaining the same as in the final scenario. 5.4.8 Case 8: High carbon price with a 20% PV capital reduction A more realistic future case than the previous examined could be a high carbon price (100$ per tone CO2 emitted) combined with a 20% PV capital reduction. The technologies adopted in this analysis was one MW CCHP natural gas engine and two 100 KW PV panels. The interesting thing in this scenario compared to the previous, which is exactly the same with the final case with the only difference the carbon price, is that here the model due to the PV capital reduction find cheaper to meet the SM peak electrical demand (coincide with the sunny hours) by using PV panels and not by using CCHP. The last is very important and have a hidden meaning, and this is 88
  • 89. that in the peak hour demands (10:00 to 16:00) the power loads don’t coincide with the heating loads which remain low (look the initial electrical loads compared to the heating loads). That means that the spare recovered heat (if we didn’t have the PV’s, like in the final case) would go to drive an absorption cooling with a very low efficiency which is almost equivalent to ‘dump the heat’ and thus running the CCHP in an extremely low efficiency which in this high carbon cost case means high energy bill. In contrast here, with the usage of PV’s for the peak demand the CCHP run with a better analogy of the electricity to heat ratio which means higher efficiency of burning the fuel (look the figures 44 to 48, and think that for this NG for every KWh e produced and 1.36 KWh th). The energy balance results, economic results, DG power generation for electrical-only/cooling (CCHP, PV), recovered heat for cooling/heating, NG purchased for heating, and finally energy sales back to the grid (PV, CCHP) are depicted in figures 73 to 83 in appendix. 5.4.8 Case 9: 50% PV capital reduction At this case we assume that the capital cost of PV’s reduced by 50%, by a future subsidy or by innovation in the PV construction or both two. Under this assumption the model invest in six PV-100 panels and in one MW natural gas engine CCHP. The results of this combination are very similar to the at least 7 PV case and the results and graphs are depicted in Appendix in figures 84 to 94. 5.4.9 Case 10, 11: 50 % cheaper electricity prices, 50% more expensive NG Here the natural gas price is 50% more expensive than the current market prices. Despite the fact that this increase in the NG prices is extremely high, the model still find more cost effective to meet all the SM demand by installing and running a natural gas engine (one MW CCHP). This fact indicates the great opportunities these technologies can give to large consumers with a balanced electrical, cooling and heat profile but also and the favorable circumstances with high electricity prices and the upcoming carbon emissions taxing (which are favorable for DG with high waste heat utilization). 89
  • 90. The same happen in the case of decreasing the current electricity prices by 50% on average. The energy balance and economic results for both cases are depicted in Figures 95 to 98 in Appendix. 5.5 Fixed electricity price scenario In this section will be described the fixed electricity scenario for different grid electricity prices. The scope of this unrealistic scenario (not existed fixed prices for all the hours of the day and the different months) is to verify once again that the model ‘’thinks’’ and respond in a reasonable way. For different electricity prices the model results are represented in figure 99 and explained in the below sub-sections. Just to make clear the grid fixed electricity price includes in this scenario the distribution, transmission and supply costs (also all the other inputs on the model remain exactly the same). Note that each gap in figure 99 represents and the below sub-sections. No sales No sales No sales No sales No sales Use the entire Turn SM to a Sales spare cap. For power station to grid sales back to grid CHP power CHP power CCHP power CCHP abs.cooling CCHP abs.cooling CCHP abs.cooling CCHP power Coolin g Grid CCHP power load Grid Grid Grid Grid Grid Grid Grid CCHP power Grid Grid CHP rec heat CHP recovered heat Boiler Boiler CCHP recovered CCHP recovered CCHP Heatin heat heat recovered heat g Boiler Boiler load CHP power CHP power CCHP power CCHP power CCHP power CCHP power Electri- Electri- Electri- Grid Electri- only Electri- only only only Grid load only load Grid load load load Grid Figure 99, graphic representation of the model results for different electricity prices Electricity price 90
  • 91. 5.5.1 Electricity price up to 0.08 $/KWh When the fixed electricity price is less than 9p/KWh the model decides that the ‘’sustainable’’ combination of technologies that minimize the energy bill while takes into account the carbon emissions is the basic scenario grid plus boiler. For this case the whole electrical load met by the grid while the heating demand met by direct burning of NG in the boiler. The economic results for fixed grid electricity price at 0.08 $/KWh represented in figure 100 (appendix). 5.5.2 Electricity price from 0.09 to 0.12 $/KWh For electricity price from 9p/KWh and up to 12p/KWh the model except from grid and boiler invest in one 100 KW CHP natural gas engine. In this case the model find economic feasible to use the NG-100 engine and follow the SM base load heating demand and cover at the same time some electric base load. In other words the model find cheaper to produce 1 KWh electrical plus the displaced heating demand (from the recovered heat, displaced heating demand = recovered heat (α) * EfficiencyheatExchanger) in CHP unit than meet the correspondent electricity and heating demand separately by using grid and boiler. At the same time the capacity which invest (100KW) is small (not follow all the heating demand), due to the fact that in order CHP to be more economical compared to the grid plus boiler combination must have high load factor (work almost 100% throughout the year in order to cover the investment and fixed annual costs). The above become much clearer if we look the CHP electrical and heating production in figures 101 and 102 respectively. Finally the energy balance and economic results are represented in figure 103 (appendix). 91
  • 92. Figure 101, NG-100CHP total electrical production (Electricity price from 0.09 to 0.12 $/KWh case) Figure 102, Heating demand met by NG-100CHP (Electricity price from 0.09 to 0.12 $/KWh case) 5.5.3 Electricity price 0.13 $/KWh For 13 p/KWh electricity price the model decides to further invest in one 60 KW CHP natural gas engine. The same things existing as discussed before with the difference that now the model almost covers all SM heating demand by CHP, and this can be clearly seen in figure 106 were the represented the NG purchases for direct burn in boiler. The electrical and heating outputs of the NG-60 CHP are depicted in 92
  • 93. figures 104 and 105 respectively (appendix). We can understand from that case that the one p/KWh difference in electricity price from the grid can compensate for the lower load factor of the small natural gas engine, which also follows strictly the heating loads. Finally the energy balance and economic results depicted in figure 103 (appendix). Figure 106, Purchased NG to meet heating demand (Electricity price 0.13 $/KWh) 5.5.4 Electricity price 0.14$/KWh In this case the model decides that the more sustainable combination is a CCHP NG- 300 engine combined with the grid. CCHP will cover the whole heating demand (figure 110 appendix) and the base electrical load (electrical-only and cooling) while grid will meet the rest electrical demand (figure 107, appendix) which usually is peak load demand. Figure 108 show the electrical output of the CCHP, while figure 109 depict the cooling load me by the recovered heat remained after meeting the heating demand. We can notice that this system works with a very high load factor (CCHP works 100% most of the time) and utilize all the ‘’waste heat’’ and thus we can say that this system has very high efficiency. All the energy balance and economic results are represented in figure 111 (appendix). 93
  • 94. Figure 108, NG-300CCHP total electricity production (Electricity price 0.14$/KWh case) Figure 109, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case) 5.5.5 Electricity price from 0.15 to 0.49 $/KWh This case is the same with the final case of the spot market electricity price scenario. Here the model decides that the sustainable technology solution is to invest on a NG- 1000CCHP engine and meet the whole demand on-site (not use boiler and grid). The energy balance and the economic results are represented in figure 112 (appendix). The electrical-only loads are met by on-site power generation while the heating loads 94
  • 95. are met only by the recovered heat from the power generation. Finally, cooling loads are going to be met both by self-generated power but also and from recovered heat which driven through absorption chiller. The graphs representing the electrical-only outputs and heating output are exactly the same as the final case explained in the previous sections and thus skipped. The only graphs that are different from the final case are the ones have to do with the cooling demand. The on-site power production for meeting cooling loads depicted in figure 114 and the recovered heat which going to meet cooling loads depicted in figure 115. DG power generation for cooling end use loads 500 450 400 January 350 February March 300 April May KW 250 June 200 July August 150 September October 100 November 50 December 0 0 5 10 15 20 25 30 Hour Picture 114, NG-1000CCHP power generation for electrical-only end use loads (Electricity price from 0.15 to 0.49 $/KWh) 95
  • 96. Recovered heat going to meet cooling demand 1200 1000 January February March 800 April May KW 600 June July 400 August September 200 October November December 0 0 5 10 15 20 25 30 Hour Picture 115, recovered heat going to meet cooling demand (Electricity price from 0.15 to 0.49 $/KWh) 5.5.6 Electricity price from 0.5 to 0.57 $/KWh For electricity prices 0.5 $/KWh to 0.57 $/KWh the model finds optimum not only to use the NG-1000CCHP for meeting the onsite demand but also to use and the spare capacity (capacity not used for the on-site demand) for selling electricity back to the grid (figure 118). What we can conclude from these results is that the fuel (plus variable) cost for producing one KWh e is lower than the price that we can sell back to the grid and thus is profitable (the investment and fixed costs are already in place). Here due to the high power generation production for selling back to the grid we have a great portion of recovered heat which is used to meet almost half of the cooling demand through the absorption chillers (figure 117, note that this is the recovered heat which will pass through absorption coolers to produce cooling and not the displaced cooling demand), unlike with the previous case where the biggest quantity of the cooling demand was met by on-site power generation. The economic and energy balance results depicted in figure 116 (appendix). 96
  • 97. Recovered heat going to meet cooling demand 1600 1400 January 1200 February March 1000 April May KW 800 June July 600 August 400 September October 200 November December 0 0 5 10 15 20 25 30 Hour Figure 117, recovered heat going to meet cooling demand (Electricity price from 0.5 to 0.57 $/KWh) Energy sales back to the grid 900 January February 800 March 700 April May 600 June 500 July KW August 400 September 300 October November 200 December 100 0 0 5 10 15 20 25 30 Hour Figure 118, energy sales back to the grid (Electricity price from 0.5 to 0.57 $/KWh) 97
  • 98. 5.5.7 Electricity price from 0.58 $/KWh From this electricity price and above the SM makes huge profits by turning into a power station (invest in large number gas turbines). Obviously the cost of just producing electricity (included the investment, OMVar, OMFic costs etc) without utilizing almost any of the recovered heat is lower than the selling price back to the grid and thus is economically profitable to transform the SM into a power station. The results for this case are depicted in figure 119 (appendix). Energy balance results Column1 Technology Adopted (name,capacity) 100 GT-40000E Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 959875302 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 68432 Cooling demand met by on-site recovered heat (KWh) 0 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 0 Heating demand met direct NG burning(KWh) 59298 NG purchases (KWh) 2815076266 NG purchases without those for selling to grid(KWh) 513666 Economic results Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) 0 Fuel cost for on-site generation (with selling to the grid)($) 517592 Variable cost for onsite genaration ($) 122727222 Carbon taxes for onsite generation ($) 610979477 DG annualized capital cost(include installation) 241351243 NG purchase for direct fire ($) 74331 carbon taxes for direct burning of NG ($) 17507 Carbon emissions for meeting on-site total demand (tonnes) 15274925 Energy sales back to the grid ($) 0 NPV -39357324 Figure 119, energy balance and economic results (Electricity price from 0.58 $/KWh) 98
  • 99. 6.Conclusions Buildings are a growing sector of energy consumption and shopping malls are in particular consuming more energy than the other buildings and appear increasingly across the world. The potential of DG and Energy efficiencies measures in the building sector and especially in commercial sector has been discussed as part of the energy agenda in the last decade. On-site generation offers to commercial customers the opportunities to increase the electricity production efficiency decrease the energy bills costs and finally decrease the carbon emissions compared to energy from the conventional centralized power stations. These above results achieved through the utilization of the ‘’waste heat’’ from CHP/CCHP schemes and the absence of transmission, distribution and control of electrical energy. Moreover self-generation gives advantages of autonomy and reliability in energy purchases. Many research projects led by academics and engineers have been steered towards decreasing and meeting the basic demand in buildings in different ways. Most of these projects are using long analytic tradition methods for getting an efficient selection and operation of distributed generation technologies in large centralized power stations or large scale community heating CHP’s. In contrast the methods for optimizing in commercial level are very rare and usually the ones existed are very limited. At this thesis was developed an optimization mathematical model, model which is able to minimize the annual energy bills (also takes into account the carbon emissions) of a shopping mall (a general building or Micro-grid) by finding the optimal combination of distributed generation technologies (included grid), the capacity of those technologies and finally the operating schedule of them during the year. The model takes as inputs the customer load (electrical, cooling and heating), the different technologies (their economics and characteristics), market prices (electricity and natural gas prices) and gives as outputs the combination of technologies, their capacities, their operating schedule, their energy balance and 99
  • 100. economic results. The given name of this mathematical model is Distributed Generation Technology Selection Model (DGT-SM) and was developed as a GAMS model. The model was tested under many different scenarios and sensitivities. The two different scenarios was diverged in the way customer buy’s electricity from the grid, and the one incorporates the actual spot market prices for the whole year (hourly and monthly changes), while the other scenario use fixed electricity prices throughout the year. As regards the different sensitivities, for the spot market price scenario was examined thoroughly a variety of different cases in order both to validate the model but also to scan some real life cases (different places, different needs, and different constraints). The results were really encouraging, and actually all the examined cases invested in distributed generation and gave astonishing annual energy bills and carbon savings. Firstly was examined the common now case of grid plus boiler in order to use these results as a guide to the other cases. Then the first case with DG availability was the one without CHP/CCHP. In this case the model was able to invest in every technology but without the ability to purchase CHP or CCHP systems (only electrical production), which means in other words that there is no heat utilization. The results of this case were the expected ones with annual energy bills saving (44% over grid plus boiler case) but with worse environmental impact (-9% over grid plus boiler case). We refer the outcome as expected because as regards the economic results the absence of the transmission costs makes the on-site power generation cheaper, but worse environmentally due to the lower efficiency (not utilization of heat) compared with the big centralized power stations. When the CHP option unlocked in the model (the model invest in a NG-1000CHP), case without CCHP, except the economic results (48%) and the environmental results (always compared to the grid plus boiler case) turned to be positive (9% annual savings). This outcome is logical due to the utilization of the waste heat, waste heat which usually dumped in the conventional power stations. Finally, in the final scenario the adoption of the combined cooling, heating and power (NG-1000CCHP) gave the opportunity to the model to utilize almost all the waste heat, reach very high 100
  • 101. efficiencies of burning the fuel on-site (50% burning on-site natural gas) and gave the astonishing annual economic savings of 51% and carbon savings 17%. Further, examined the case were the only DGT option except the basic grid and boiler was the PV’s. Also in this case the model found more economic to invest in 700 KW PV panels and meet almost half of the SM total electrical demand onsite. Concerning the results of this case, there was a 16% annual bill savings and 41% carbon savings. The reason for carbon savings is obvious, but the most interesting is the economic savings despite the high capital PV costs and these are existed due to the fact that the productive sunny hours for PV’s coincide with the Peak SM’s and spot market (during lunch time observed the higher electricity prices) electrical demand which means that the PV’s displace the higher and most expensive SM’s electrical loads. Astonishing results accomplished also in the case at least seven PV’s. The constraint in this case was that the model has to invest at least on seven PV panels. This case seems somehow unrealistic but it’s not if we consider that many customers are interested more for their ‘’Green profile’’ no matter what is the cost. The interesting in this case compared with the previous one is that the model finds optimum to also invest in a NG-1000CCHP engine in order to meet the electrical demand the hours that PV’s are not producing (and not purchase from the grid like before) and at the same time meet almost all the heating demand by recovered heat. This scenario has 38% energy bill savings and the highest carbon savings over all the cases (53%). Except the very good results of this case this combination gives and a good security (NG/electricity prices, CCHP out of work for sometime) over the volatile period of time. Model, also experience very high NG prices (50% more expensive) and extremely low grid electricity prices (50% less cost from grid), in two cases in order to see what will happen if a very sharp change happen to the market. The results didn’t change from the final scenario and the SM find under these sever circumstances that is more economic feasible to be independent from the grid and produce all the electricity and heat on-site by using the NG-1000CCHP engine. Finally, the last interesting case was the one where the PV panels prices reduced by 20% and the carbon price set to 100$ per tone CO2 emitted. This case is also very likely to happen in the near future due to the continuing innovation on PV panels, and the increasing environmental problem. 101
  • 102. At this scenario the model invests not only in a NG-1000CCHP but also and in two PV-100 KW panels. By this way the model cover the peak hour demand by PV’s and uses the CCHP in the base load in order to keep utilizing almost the whole waste heat and reach high burning efficiencies (reduce the CO2 emissions which now are computable). In the fixed price scenario (see figure 99) we examined the adequacy of the model for different electricity prices from the grid and the results indicates that the model decides logically. Up to 9p/KWh the model decides not to invest in DGT and meet the demand by using grid and boiler. For electricity price from 9p/KWh and up to 12p/KWh the model except from grid and boiler invest in one 100 KW CHP natural gas engine. In this case the model find economic feasible to use the NG-100 engine and follow the SM base load heating demand and cover at the same time some electric base load. It is clear in this case that in order the CHP to be cheaper from the grid must work almost 100% all the time. For 13p/KWh the model except from the previous 100KW invest in 60KW more and now meets the biggest part for heating by recovered heat (also meets a small portion of the electrical load). After this point and for price at 14p/KWh the model invest in 300KW CCHP and meet the entire heating load (recovered heat) but also a great part of the electrical load also. Here the model stops to invest in CHP and started to invest to CCHP because finds more economic feasible to pay more for the absorption coolers and use the rest recovered heat (after meeting heating load) to meet some cooling demand than just buy it from the grid. The next gap is from 15p/KWh to 49p/KWh. Here the case is the same with the final case from the spot market price scenario and the model invest in a NG-1000CCHP. Now the SM is in depended from the grid and meet the whole demand onsite but is not selling back to the grid. From 50p/KWh to 57p/KWh the model finds economically feasible to use the spare capacity and sell back to the grid and make some profit. The final case after this is when the electricity price is so high to be profitable to turn the SM into a power station. Besides the fact that the model seems to be able to find accurately results as regards the selection of technologies, capacities and operating schedule improvements must be undertaken in the inputs of the model as regards both the validity but also the detail and the more sophisticated structure of them. Moreover changes needed to the 102
  • 103. thermodynamic model for more accuracy and finally more options (e.g. technologies) and a graphic environment is under way for the next version of this model. 103
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  • 106. Appendix Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 1857 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 54357 Cooling demand met by on-site recovered heat (KWh) 14075 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 403719 NG purchases without those for selling to grid(KWh) 398273 Economic results Column1 0 Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) 516695 Fuel cost for on-site generation (with selling to the grid)($) 37717 Variable cost for onsite genaration ($) 238391 Carbon taxes for onsite generation ($) 120825 DG annualized capital cost(include installation) 0 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 5879 (tonnes) 11371 Energy sales back to the grid ($) NPV 902257 Figure 72, energy balance and economic results (high carbon price scenario) 106
  • 107. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP, 2 PV-100 Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 1995 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 57317 Cooling demand met by on-site recovered heat (KWh) 11114 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47012 Heating demand met direct NG burning(KWh) 534 NG purchases (KWh) 413470 NG purchases without those for selling to grid(KWh) 407622 Figure 73, energy balance results (High carbon price with a 20% PV capital reduction case) Economic results Column1 0 Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) Fuel cost for on-site generation (with selling to the 441068 grid)($) 32188 Variable cost for onsite genaration ($) 203447 Carbon taxes for onsite generation ($) 234730 DG annualized capital cost(include installation) 660 NG purchase for direct fire ($) 394 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 2005 (tonnes) 12106 Energy sales back to the grid ($) NPV 900381 Figure 74, economic results (High carbon price with a 20% PV capital reduction case) 107
  • 108. Figure 75, NG-1000CCHP power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case) Figure 76, NG-1000CCHP power generation for cooling end use (High carbon price with a 20% PV capital reduction case) 108
  • 109. Figure 77, PV’s power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case) Figure 78, PV’s power generation for cooling end use (High carbon price with a 20% PV capital reduction case) 109
  • 110. Figure 79, NG-1000CCHP recovered heat going to meet heating demand (High carbon price with a 20% PV capital reduction case) Figure 80, NG-1000CCHP recovered heat going to meet cooling demand (High carbon price with a 20% PV capital reduction case) 110
  • 111. Figure 81, NG purchased to meet heating demand in a boiler (High carbon price with a 20% PV capital reduction case) Figure 82, NG-1000CCHP power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case) 111
  • 112. Figure 83, PV-100 power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case) Technology Adopted (name,capacity) NG-1000CCHP, 6 PV-100 Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 13882 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 60369 Cooling demand met by on-site recovered heat (KWh) 8063 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 34801 Heating demand met direct NG burning(KWh) 15797 NG purchases (KWh) 476354 NG purchases without those for selling to grid(KWh) 435648 Final efficiency of burning the natural gas (%) 0 Figure 84, energy balance results (50% PV capital reduction) 112
  • 113. Column1 Column2 Economic results Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) 0 Fuel cost for on-site generation (with selling to the grid)($) 322496 Variable cost for onsite genaration ($) 23526 Carbon taxes for onsite generation ($) 59479 DG annualized capital cost(include installation) 337100 NG purchase for direct fire ($) 19625 carbon taxes for direct burning of NG ($) 4664 Carbon emissions for meeting on-site total demand (tonnes) 1604 Energy sales back to the grid ($) 53944 NPV 712945 Savings over basic grid plus boiles scenario (%) 54 Carbon savings over basic grid plus boiles scenario (%) 49 Figure 85, economic results (50% PV capital reduction) Figure 86, NG-1000CCHP power generation for electrical-only end use loads (50% PV capital reduction) 113
  • 114. Figure 87, NG-1000CCHP power generation for cooling end use loads (50% PV capital reduction) Figure 88, NG-1000CCHP power generation for selling back to the grid (50% PV capital reduction) 114
  • 115. Figure 89, PV power generation for electrical-only end use loads (50% PV capital reduction) Figure 90, PV power generation for cooling end use loads (50% PV capital reduction) 115
  • 116. Figure 91, NG-1000CCHP recovered heat going to meet cooling demand (50% PV capital reduction) Figure 92, NG purchased to meet heating demand by direct burning in boiler (50% PV capital reduction) 116
  • 117. Figure 93, recovered heat going to meet heating demand (50% PV capital reduction) Figure 94, PV power generation for selling back to the grid (50% PV capital reduction) 117
  • 118. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 3497 Electricity met by on-site generation (KWh) 77973 Electricity sales back to the grid (KWh) 0 Cooling demand met by the grid (KWh) 3414 Cooling demand met by on-site power generation (KWh) 52084 Cooling demand met by on-site recovered heat (KWh) 12934 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 381354 NG purchases without those for selling to grid(KWh) 381354 Figure 95, energy balance results (50% cheaper electricity prices case) Economic results Column2 Economic results Energy payments to the grid ($) 24124 Carbon taxes for grid purchases ($) 4433 Fuel cost for on-site generation (with selling to the grid)($) 486351 Variable cost for onsite genaration ($) 35627 Carbon taxes for onsite generation ($) 90074 DG annualized capital cost(include installation) 120825 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) 0 Carbon emissions for meeting on-site total demand (tonnes) 2363 Energy sales back to the grid ($) 0 NPV 761435 Figure 96, economic results (50% cheaper electricity prices case) 118
  • 119. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 1857 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 54357 Cooling demand met by on-site recovered heat (KWh) 14075 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 403719 NG purchases without those for selling to grid(KWh) 398273 Figure 97, energy balance results (50% more expensive NG price case) Economic results Column1 0 Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) 775043 Fuel cost for on-site generation (with selling to the grid)($) 37717 Variable cost for onsite genaration ($) 95356 Carbon taxes for onsite generation ($) 120825 DG annualized capital cost(include installation) 0 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) 2384 Carbon emissions for meeting on-site total demand (tonnes) 11371 Energy sales back to the grid ($) NPV 1017570 Figure 98, economic results (50% more expensive NG price case) Column1 Economic results 365010 Energy payments to the grid ($) 96149 Carbon taxes for grid purchases ($) 74331 NG purchase for direct fire ($) 17507 carbon taxes for direct burning of NG ($) Carbon emissions for meeting on-site total demand 2841 (tonnes) NPV 552998 Figure 100, economic results for electricity price less than ninep/KWh (Fixed electricity price scenario) 119
  • 120. Energy balance results Column1 Technology Adopted (name,capacity) NG-0100CHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 66076 Electricity met by on-site generation (KWh) 15394 Electricity sales back to the grid (KWh) 0 Cooling demand met by the grid (KWh) 61960 Cooling demand met by on-site power generation (KWh) 6472 Cooling demand met by on-site recovered heat (KWh) 0 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 35860 Heating demand met direct NG burning(KWh) 14473 NG purchases (KWh) 82207 NG purchases without those for selling to grid(KWh) 82207 Economic results Energy payments to the grid ($) 350738 Carbon taxes for grid purchases ($) 82124 Fuel cost for on-site generation (with selling to the grid)($) 91847 Variable cost for onsite genaration ($) 11980 Carbon taxes for onsite generation ($) 17163 DG annualized capital cost(include installation) 13760 NG purchase for direct fire ($) 17688 carbon taxes for direct burning of NG ($) 4273 Carbon emissions for meeting on-site total demand (tonnes) 2589 Energy sales back to the grid ($) 0 NPV 589572 Figure 103, energy balance and economic results (Electricity price from 0.09 to 0.12 $/KWh case) 120
  • 121. Energy balance results Column1 Technology Adopted (name,capacity) NG-0100CHP, NG-060CHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 62380 Electricity met by on-site generation (KWh) 19090 Electricity sales back to the grid (KWh) 0 Cooling demand met by the grid (KWh) 60248 Cooling demand met by on-site power generation (KWh) 8184 Cooling demand met by on-site recovered heat (KWh) 0 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 45206 Heating demand met direct NG burning(KWh) 2791 NG purchases (KWh) 83462 NG purchases without those for selling to grid(KWh) 83462 Economic results Energy payments to the grid ($) 485221 Carbon taxes for grid purchases ($) 78655 Fuel cost for on-site generation (with selling to the grid)($) 114869 Variable cost for onsite genaration ($) 14943 Carbon taxes for onsite generation ($) 21595 DG annualized capital cost(include installation) 22088 NG purchase for direct fire ($) 3359 carbon taxes for direct burning of NG ($) 824 Carbon emissions for meeting on-site total demand (tonnes) 2527 Energy sales back to the grid ($) 0 NPV 741554 Figure 104, energy balance and economic results (Electricity price 0.13 $/KWh) 121
  • 122. Figure 104, NG-60 CHP total electrical production (Electricity price 0.13 $/KWh) Figure 105, Heating demand met by NG-60CHP (Electricity price 0.13 $/KWh) 122
  • 123. Figure 107, total electricity purchases from grid (Electricity price 0.14$/KWh case) Figure 110, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case) 123
  • 124. Energy balance results Column1 Technology Adopted (name,capacity) NG-0300CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 22481 Electricity met by on-site generation (KWh) 58989 Electricity sales back to the grid (KWh) 0 Cooling demand met by the grid (KWh) 41331 Cooling demand met by on-site power generation (KWh) 17963 Cooling demand met by on-site recovered heat (KWh) 9137 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 225642 Economic results Energy payments to the grid ($) 271917 Carbon taxes for grid purchases ($) 40930 Fuel cost for on-site generation (with selling to the grid)($) 312814 Variable cost for onsite genaration ($) 30449 Carbon taxes for onsite generation ($) 58453 DG annualized capital cost(include installation) 48423 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) 0 Carbon emissions for meeting on-site total demand (tonnes) 2485 Energy sales back to the grid ($) 0 NPV 762986 Figure 111, energy balance and economic results (Electricity price 0.14$/KWh case) 124
  • 125. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 0 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 54598 Cooling demand met by on-site recovered heat (KWh) 13833 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 398982 NG purchases without those for selling to grid(KWh) 398982 Economic results Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) 0 Fuel cost for on-site generation (with selling to the grid)($) 510824 Variable cost for onsite genaration ($) 37274 Carbon taxes for onsite generation ($) 94238 DG annualized capital cost(include installation) 120825 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) 0 Carbon emissions for meeting on-site total demand (tonnes) 2356 Energy sales back to the grid ($) 0 NPV 763162 Figure 122, energy balance and economic results (Electricity price from 0.15 to 0.49 $/KWh) 125
  • 126. Energy balance results Column1 Technology Adopted (name,capacity) NG-1000CCHP Electricity-only demand(KWh) 81470 Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 0 Electricity met by on-site generation (KWh) 81470 Electricity sales back to the grid (KWh) 133042 Cooling demand met by the grid (KWh) 0 Cooling demand met by on-site power generation (KWh) 37286 Cooling demand met by on-site recovered heat (KWh) 31146 Cooling demand met by direct burning of NG(KWh) 0 Heating demand met by recovered heat (KWh) 47439 Heating demand met direct NG burning(KWh) 0 NG purchases (KWh) 738325 NG purchases without those for selling to grid(KWh) 348217 Economic results Energy payments to the grid ($) 0 Carbon taxes for grid purchases ($) 0 Fuel cost for on-site generation (with selling to the grid)($) 933157 Variable cost for onsite genaration ($) 68977 Carbon taxes for onsite generation ($) 174389 DG annualized capital cost(include installation) 120825 NG purchase for direct fire ($) 0 carbon taxes for direct burning of NG ($) 0 Carbon emissions for meeting on-site total demand (tonnes) 4360 Energy sales back to the grid ($) 566927 NPV 730421 Figure 116, energy balance and economic results (Electricity price from 0.5 to 0.57 $/KWh) 126