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).

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
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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
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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
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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
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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
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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
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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.
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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 reduce������������2
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