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IRIS Webinar: How can software support smart cities and energy projects?

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IRIS experts looked at 15+ software tools to help accelerate replication and uptake of smart city and energy initiatives. Discover their findings and practical applications in Alexandroupolis, Greece (electricity, heating & cooling) and Nice, France for Battery sizing.

Held in conjunction with fellow smart city project POCITYF (www.pocityf.eu) 7 December 2019

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IRIS Webinar: How can software support smart cities and energy projects?

  1. 1. These projects have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 774199 and 864400 Webinar: How numerical software tools support the creation of replication plans in smart cities energy projects December 17th, 2019
  2. 2. Agenda IRIS Webinar - How numerical software tools support the creation of replication plans in smart cities energy projects Software tools for evaluating replication strategies for near zero energy districts – Case study on the city of Alexandroupolis Nikos Nikolopoulos, CERTH Battery sizing – Case study on IMREDD building Christian Keim, EDF Q&A - Discussion
  3. 3. IRIS Webinar 17/12/2019 Panagiotis Tsarchopoulos, Thanasis Tryferidis, Komninos Angelakoglou, Paraskevi Giourka, Emmanouil Kakaras, Nikos Nikolopoulos, Konstantinos Lymperopoulos, Dimitrios Kourtidis Software tools for evaluating replication strategies for near zero energy districts – Case study on the city of Alexandroupolis
  4. 4. Table of contents ❑ Aim and objectives; ❑ Alexandroupolis replication activities for TT1; ❑ Software selection; ❑ Case Study Analysis; ❑ Replication measures evaluation; ❑ Summary and lessons learnt; ❑ The case of a Battery Sizing (from EDF by Christian Keim) 4
  5. 5. Aim and objectives Aim: Demonstrate capacities of potential software for evaluating replication strategies for TT1 Integrated Solutions 1.1 and 1.2 in the city of Alexandroupolis The objectives of this webinar are: ❑ Present a) available software, and b) CERTH’s and Alexandroupolis selection for feasibility studies on building level and district level analysis; ❑ Present the Linking and interoperability of selected software; ❑ Discuss information required and potential sources of information; ❑ Demonstrate Case Study Analysis; ❑ Evaluate the replication measures on a techno-economical and environmental basis; ❑ Discuss strengths and weaknesses of selected approach; ❑ Present the case of a Battery Sizing, by commercial oriented partners, with the use of Software 5
  6. 6. The city of Alexandroupolis • IRIS replication activities fit well with the city’s ambitions and targets IRIS Solutions 1.1 (Positive Energy Buildings) • Individual Buildings retrofitted • New-built neighbourhood IRIS Solutions 1.2 (Near Zero Energy District) • Retrofit at district level to reach near zero energy performance 6 • Administrative Centre of Regional Unit of Evros; • Member of Covenant of Mayors; • Founding member of Greek Green Cities Network; • Vision to become a sustainable city and an innovation hub • Sustainable Energy Action Plan target is to reduce emissions by 20% by 2020 through energy upgrade of buildings and increase of RES
  7. 7. Replication buildings for TT1 – IS1.1 existing buildings Alexandroupolis buildings • 1st Nursery school; • 2nd Nursery school; • 7th Nursery school; • 1st senior citizen community centre (KAPI); • 2nd senior citizen community centre (KAPI); • Office building (Polidinamo); • Urban setting; 7
  8. 8. Replication buildings for TT1 – IS1.1 New-built neighbourhood 8 Area: 42 acres Construction requirements Coverage ratio: 40%; Maximum height: 8m; Neighbourhood • 100 detached 2-storey dwellings; • 6 x 10m footprint; • E-W axis for maximizing solar gains; • Large south facing glazing;
  9. 9. Replication buildings for TT1 – IS1.2 Near Zero Energy District 9 • 95 2-storey dwellings; • Built in 1970s • No insulation was considered at design stage • Semi-detached and mid-terrace houses • Different orientations
  10. 10. IS 1.1 IS 1.2 Retrofitted buildings New-built neighbourhood Retrofitted district Replicated technology Increasing insulation levels ✓ ✓ ✓ Photovoltaic ✓ ✓ ✓ Electrical Storage ✓ ✓ ✓ District Heating and Cooling  ✓ ✓ Geothermal Heat Pump ✓ ✓ ✓ Other technologies ASHP ✓   Solar thermal ✓   Solar powered ORC unit ✓   Biomass ✓   10 Transition Track 1 – Technologies Replicated
  11. 11. Tool Selection General Requirements • Feasibility Analysis • Building level; • District level; • Performance Evaluation • Technical; • Financial; • Environmental; 11 Selection Criteria • Technical capacity to model as many technologies as possible (building retrofit, PV, heat pump, etc); • Cost effectiveness – if possible, no license fee; • Ease of use; • Ability to conduct preliminary analysis fast, allowing the fast comparison of alternative scenarios; • Ability to provide results with simplified input; • Ability to provide results in the right format; • Technical and financial capabilities; • Databases included that will assist the users in finding technical information;
  12. 12. Building System District /grid Heat Electricity Transport Storage Simulation level Access TRNSYS X X X X X X X Detailed generic simulations of transient systems Priced HOMER Pro - X X X X X Advanced simulation for assessing power plant and grid performance Priced PV SYST - X - - X - X Advanced simulation for assessing PV performance Priced T*sol - X - X - - X Advanced simulation for solar thermal systems performance Priced PV*sol - X - - X - X Advanced simulation for assessing PV performance Priced GeoT*sol - X X X X - X Advanced simulation for assessing heat pump and/or solar thermal system performance Priced IDA ICE X X - X X - X Detailed simulations Priced ESP-r X X - X X - X Detailed simulation of building thermal and electricity loads and HVAC systems. Capacity for façade integrated PV Free EDSL Tas X X - X X - Detailed simulation of building thermal and electricity loads and HVAC systems Priced Design Builder X X X X - Detailed simulation of building thermal and electricity loads and HVAC systems Priced Energy Plus X X - X X - X Detailed simulation of building thermal and electricity loads and HVAC systems Free RETScreen X X X X X X X Preliminary analysis of various renewable energy and energy efficiency measures Priced EnergyPLAN - - X X X X X Advanced simulation of complex energy systems at regional level Free Energy Pro - X X X X - X Advanced simulation of complex energy systems at system/regional level Priced 12
  13. 13. 13 Steady (state) simulations – representation of all three energy vectors (electricity, heating, cooling) on a common platform, to investigate the behavioral characteristics of integrated grids
  14. 14. Tools selection RETScreen for the building level analysis • Simplified technical modelling of a range of technologies (power, heating, cooling); • Simulation as well as performance analysis tool for existing projects; • Extensive databases for various technologies; • Fast results; • Extensive financial and risk analysis capabilities; • User friendly; • Tutorials available for training; • Cost effective - Free distribution for academic and research institutes at the moment; • Annual fee for 10 PCs ~ €620 14
  15. 15. Tools selection EnergyPLAN for the district level analysis • Developed to analyse the energy, environmental, and economic impact of various energy strategies on national and regional energy systems; • simulates the operation of energy systems on an hourly basis, including the electricity, heating, cooling, industry, and transport sectors; • User friendly; • Deterministic in nature; • Allows fast comparison of different scenarios; • Freeware distribution; • Training available; 15
  16. 16. Simulation Process 16 Climate Internal conditions Building fabric Building systems Renewable Energy Heating loads Cooling loads Electricity loads Energy production Emissions reduction InputOutput Aggregated Energy demands Aggregated Energy balances Output Step 1 Building Step 2 District
  17. 17. Analysis – Pre-processing • Prior to the analysis, a great deal of information was required to be gathered; • Central to this, information is collected onsite regarding the local conditions and context mapping; • Core information of the preliminary analysis is collected onsite (site, building, planning terms and restrictions, etc) • This is then supplemented with information obtained through • Relative literature and online sources and databases; • Software databases; • Feedback from partners adjusted to site localities; • Expert feedback from previous projects 17 Onsite / Local context mapping Literature/ online Feedback Software databases Previous experience
  18. 18. Onsite/Local context mapping • A two-stage approach has been followed including a broader and a specific local context mapping process Stage Α: • Broader approach includes • assessment of city needs & challenges, • policy context and stakeholder network analysis, *All in respect to the IS to be replicated 18 “Rs” selection SEAP, technical program οperational plan … Decision makers, Experts, Market, … Peer to peer Case studies selection
  19. 19. Onsite/Local context mapping Stage B - Specific local context (case studies) information collection • Collaboration with the technical department of Municipality • Collaboration with local technical experts (engineers) (Energy HIVE Cluster, technical chamber of Thrace) • Collaboration with local academic organization (Democritus University Thrace) 19 Input data for Retscreen & EnergyPlan
  20. 20. Information • Feedback from previous experience and partners • Cost of measures (insulation, PV, battery, GSHP, District Heating and Cooling network, conventional equipment); • Efficiency of GSHP with long term borehole storage in summer and winter; • DHC Network losses; • Literature/online/databases • Technical directives (indoor conditions, water consumption, electrical appliances consumption, equipment efficiencies etc); • DHC network losses; • Online data and information (hourly weather files, DHC network losses etc); • Software databases • RETScreen databases (weather data, equipment efficiencies, SHGC coefficients etc); 20
  21. 21. RETScreen Analysis Steps Building level analysis using RETScreen 21 Step 1: Defining base information (climate, occupancy and thermostat settings) Step 2: Defining equipment characteristics (HVAC) Step 3: Defining end-use and energy production Step 4: Results
  22. 22. Analysis – Building level 22 Climate Data • RETScreen includes extensive worldwide climate database; • Alternatively, the user may enter their own climate data manually; Schedule and thermostat settings • 24/7 use considered; • Thermostat settings for the heating and cooling season; • External temperature when system switches from heating to cooling; Step 1: Defining base information
  23. 23. Analysis – Building level 23 Heating and cooling system: • Input required is the fuel type and seasonal efficiency; • District heating; • District cooling; • In this case, this information is used in the analysis conducted in EnergyPlan Step 2: Equipment characteristics
  24. 24. Analysis – Building level 24 Input data • Building element areas and U-values (walls, windows, roof, floor etc); • Solar Heat Gain Coefficient – Shading; • Infiltration; • Heating/cooling system for base case and retrofit; • Intervention costs; Output • Fabric Heating/cooling Load; • Energy saved; Step 3: Defining end- use and energy production Information from national technical guidelines may be used in filling in information on this section (SHGC, thermophysical properties, infiltration etc). Alternatively, RETScreen databases can be very useful for quick analysis.
  25. 25. Analysis – Building level 25 Input data • Lighting characteristics for base case and proposed; • Impact on space cooling and heating (unwanted and wanted heat gains); • Electrical appliances (hours of usage, power rating) Output Energy consumption for • lighting Step 3: Defining end- use and energy production
  26. 26. Analysis – Building level 26 Input data • Electrical appliances (hours of usage, power rating); • Impact on space cooling and heating (unwanted and wanted heat gains); Output Energy consumption for • appliances Step 3: Defining end- use and energy production
  27. 27. Analysis – Building level 27 Input data • Typical usage; • Water temperature (supply, usable); • Equipment for hot water supply; Output • Energy consumption for hot water usage; Step 3: Defining end- use and energy production
  28. 28. Analysis – Building level 28 Renewable Energy production is considered for: • Electricity (PV, wind or generic green energy source); • hot water (solar water heater); • space heating requirements (solar air heater); • In this case, a PV system was considered Step 3: Defining end- use and energy production
  29. 29. Analysis – Building level 29 Results presented for: • Space Heating requirements; • Space cooling; • Lighting; • Appliances; • Hot water; • A simple payback period for each measure is provided; • Provides a fast estimation of the contribution of each measure and the change in the payback period of the investment; Step 4: Results
  30. 30. Analysis – District level From building to district level… 30 District level analysis using EnergyPLAN • EnergyPLAN was developed model various energy strategies on national and regional energy systems; • However, with minor data manipulation/ restructuring it was found useful for conducting the technical evaluation at the district scale; • Technical Analysis is conducted in three steps Step 1: Defining energy demands Step 2: Defining energy supply systems Step 3: Defining system balancing and storage
  31. 31. Analysis – District level 31 Building level (RETScreen output) kWh District level (EnergyPlan input) Heating demand 11,150 1,115,000 Cooling demand 8,017 801,700 Electricity demand 2,715 271,500 • Inputs are provided in the form of an annual value and an hourly distribution profile; • Annual values were obtained from RETScreen considering the district level; • Hourly profiles were required to be developed for: • Electricity demand; • Heating demand; • Cooling demand; • PV production;
  32. 32. Analysis – District level 32 • Electricity profile was developed assuming typical schedule (winter/summer) for the appliances and lighting considered in the analysis in RETScreen.
  33. 33. Analysis – District level • Heating and cooling demand profiles were determined with the use of Heating and Cooling Degree Days (HDDs and CDDs). Weather data in hourly format and choosing an appropriate base temperature were required; • Base temperature of 18oC for heating and 24oC for cooling were considered as suitable for the Greek climate (Papakostas et al., 2010). 33 • Weather data in an hourly format were obtained from climate.onebuilding.org. Typical Meteorological Year files (TMYs) for building simulations are provided for various locations worldwide • Another source for TMY weather data is the JRC TMY generator (available at: https://re.jrc.ec.europa.eu/tmy.html). TMY files are provided for a given longitude and latitude
  34. 34. Analysis – District level 34 • Due to the lack of available data from local DH networks, information was sought from literature; • Heating demand was increased by 7% to account for district heating network losses; • 7% losses was considered conservative value based on findings from literature1,2 and the size of the network. 1 Elmegaard, B., Schmidt O.T., Markussen, M. and Iversen, J. (2016) Integration of space heating and hot water supply in low temperature district heating, Energy and Buildings, vol. 124, pp. 255 – 264 2 Marguerite C., Geyer, R., Hangartner, D., Lindhal M. and Pedersen S.V. IEA Heat Pumping Technlogies Annex 47 - Heat Pumps in District Heating and Cooling Systems – Task 3: Review of concepts and solutions of heat pump integration IEA-HPT
  35. 35. Analysis – District level 35 • Due to the lack of available data from local DC networks, information was sought from literature; • 4% network losses was considered based on findings from literature3,4 and the size of the network (conservative value); • For this reason, cooling demand was increased by 4%; 3 Dominković, D., & Krajačić, G. (2019). District Cooling Versus Individual Cooling in Urban Energy Systems: The Impact of District Energy Share in Cities on the Optimal Storage Sizing. Energies, 12(3), 407 4 Calance M.A. (2014) Energy Losses Study on District Cooling Pipes – Steady State Modeling and Simulation (Master Thesis), University of Gävle, Gävle, Sweden
  36. 36. Analysis – District level 36 • Temporal electricity production from the PV system is determined through the PV capacity and the hourly distribution • An hourly PV production profile that takes into account the effect of irradiation levels and pane temperature was developed based on the formulas provided by the relevant technical directive on climatic conditions5 5 TOTEE 20701-3/2010 6 Available at: https://heatroadmap.eu/energy-models/ • A simple approximation method would be to assess the PV production in RETScreen and then creating the distribution profile using the hourly solar irradiance data from the TMY file; • Alternatively, PV production profiles, as well as other Renewable Energy and typical demand profiles, for 14 EU countries may be found from the Heat Roadmap Europe 4 project6. These are available at country-level, not site specific;
  37. 37. Analysis – District level 37 • Battery capacity was determined at 1500 kWh corresponded to one-day’s autonomy; • Parametric analysis was also conducted for: • 750 kWh (half-day’s autonomy); • 2,250 kWh (1.5 days autonomy); • 3,000 kWh (2 days) autonomy; • Geothermal Heat Pump providing thermal energy to the District Heating Network; • Capacity of the system to cover the needs of district and the COP required; • COP: 7 based on assumption on the performance of the borehole storage system
  38. 38. Post Processing - Results • Results are obtained manually on an hourly basis in excel • Heating and cooling demand (kWh); • Electricity demand (kWh); • PV production (kWh); • PV electricity direct use (kWh); • PV electricity stored and used later (kWh); • Electricity imported from grid; • Electricity exported to the grid; 38 Assessment of replication measures was done using project KPI’s and common financial criteria Some degree of manipulation required to obtain: • Degree of Energetic Self supply; • Emissions reduction; • Payback Period; • IRR; • NPV; Replication measures are assessed against a Business-as-Usual Scenario
  39. 39. Replication Strategy Assessment 39 BAU • Building regulations insulation; • No energy production; • Heating oil boiler; • A/C units; • Heating requirements: 1,225,895 kWh heating oil; • Cooling requirements: 810,000 kWh; • Electricity requirements: 433,500 kWh; • Electricity production: 0 kWh; • Electricity imports: 433,500 kWh; • CO2 emissions (primary): 1,438, 406 kg CO2; • Capital Cost: € 300,000; • Operating cost: € 174,270.80; Zero energy scenario • Improved insulation; • PV - 500 kW; • Battery 1500 kWh/day; • GSHP + DHC network; • Heating requirements: 1,115,000 kWh; • Cooling requirements: 801,700 kWh; • Electricity requirements: 611,484; • Electricity production: 789,090 kWh; • Electricity exports: 269,440 kWh; • Electricity imports: 91,874 kWh; • CO2 emissions (primary): -2,588 kg CO2 (savings); • Capital Cost: € 3,054,000; • Operating cost: € 5,614.66;
  40. 40. Replication Strategy Assessment • Degree of Energetic self-supply DET = 100% (all thermal energy is geothermal) DEE= PV production PV direct + PV stored + (electricity import x 2,9) = 789,090 189,833 + 329,769 + (91,874 x 2,9) = 100.4% (Electrical Energy consumption includes appliances, lighting and electrical power for GSHP) • Emissions reduction ER = Emissions of positive energy scenario - CO2 emissions of BAU scenario = -1,440,994 kg CO2 40
  41. 41. Replication Strategy Assessment Financial Evaluation – Assumptions • Cost of Geothermal Heat Pumps: €1,500/kW; • Cost of District Heating and Cooling Network: €1,000,000; • Cost of the PV system (PV panels, inverters etc): €1,000/kW; • Cost of the battery storage: €400/kW; • Cost of heating oil boilers: €1,500/house; • Cost of A/C units: €1,500/house; • Selling Price of electricity: €0.065/kWh; • Cost of electricity purchase: €0.10 /kWh; • Heating oil costs: €1.10/litre; • Additional insulation costs: € 3/m2 per 0.05W/m2K reduction in the U-value; 41 • Project lifetime: 25 years; • Energy price increase: 2% annually; • PV performance reduction: 0.5% annually; Financial criteria Simple payback period = 14,7 years Discounted payback period = 11,6 years Net Present Value = € 276,860 Internal Rate of Return = 6 % This strategy is feasible both technically and economically
  42. 42. Replication Strategy Assessment 42 Case A – Building Regulation insulation levels Case B – increased insulation Case C – further increased insulation and triple glazing
  43. 43. The case of dynamic simulations – representation of all three energy vectors (electricity, heating, cooling) on a common platform, to investigate dynamic behavioral characteristics of integrated grids - INTEMA 43
  44. 44. Near Zero-Energy Districts Energy analysis using INTEMA framework • Examined Case: Electrification of existing DH network • District Heating Network to be heated by Heat Pump (HP) power by RES electricity; • Use of Thermal Energy Storage to increase RES penetration and HP operation reliability 44 district CO2 coal biomass • Alternative software tools for advanced energy management analysis; • Operation optimization using appropriate EMS algorithms; • INTEMA framework is capable of performing multi-domain transient simulation runs Heat pump District RES Thermal energy storage
  45. 45. The INTEMA Framework In the context of INTEMA framework a number of tools have been developed in order to asses: • Electrical Grid Performance • Multidomain EMS (e.g heating/cooling/electricity) • Grid Ancillary Services • Economic Dispatch Strategies • Energy Storage Technologies The INTEMA framework contains: • The INTEMA Library: Energy (production, consumption and storage) and control components’ models, in Modelica as core Simulation Module • Energy Demand and Production Forecasting Module (Python) • Demand Response Module(Python) • Optimal Power Flow Module (Python) 45
  46. 46. INTEMA Applications INTEMA (in Modelica) can perform: • Short-Term Simulations, to evaluate and address specific technical challenges • Examine solutions that can offer ancillary services in electrical grid • Estimate the response of electrical grid, electrical / thermal systems and specific components • Assess efficiency and performance of specific components under various conditions • Long-Term Simulations, to evaluate and optimize seasonal, annual or life cycle systems’ performance • Perform power flow studies • Identify rules and scenarios in energy management systems • Propose economic dispatch strategies 46 Modelica Environment
  47. 47. INTEMA Example – Electrification of Heating • District with Electric & Heating Demand • Local Energy Production from PV & Wind Turbines • District Heating Network with Heat Pump, Solar Heater and Thermal Storage • Excess Electric energy will provide heat to network through heat pump • Heat pump electrical load will be added to total load • Storage and Heat Pump Controller (rules and set points) will define the operation (ON-OFF) of the Heat Pump • With INTEMA advance Energy Management Rules can be implemented 47 District DH System PV System Wind System Outer Grid Connection
  48. 48. INTEMA Example – Electrification of Heating 48 District (Detail) Loads / Houses Solar Heater Heat Pump DH System (Detail) HP Controller Storage RE Systems Lines Input Variables Design Parameters Output Variables Heat Demand TS HP Electric Power Electrical Demand TS Elect. Energy send to mainland signal Heat Tank Size Thermal Peak Power TS Solar Irradiation TS COP vs Temp Ambient Temp TS Solar System Size and efficiency Heat Pump (Detail)
  49. 49. Indicative Results Simulation Data (for approx. 100 houses) • Solar heater area: 500m2 • Heat pump electric power: 70kW • Storage Thermal: 8.3MWh • PV install power: 500kWp • Wind Turbine install power: 500kW • Mean Annual Electrical Demand: 30kW • Mean Annual Heat Demand: 127kW 49 Annual: Before Electrification (MWh) After Electrification (MWh) Elect2Grid (Net) 1827 1485 Elect2Heat 272 Wind Production 1546 PV Production 554 Elec Demand 273 273 • Before Electrification no need for energy imports • Elect2Grid + Elec2Heat After electrification is lower than the Elec2Grid Before (70MWh) • There are small periods that the district has to import energy from the grid to cover the combined needs from the Grid (heat + electricity)
  50. 50. INTEMA Example – Electrification of Heating System Operation – Weekly profile 50 DH Thermal Storage SOC SOC 1.0 0.5 0.05 Heat Pump Operation Charges from Solar Heater Power to Grid Power from Grid PV Production Wind Production Elec Demand Heat Demand Excess Energy and HP Starts 0 100 200 500 400 300 800 700 600 Power(kW)
  51. 51. Summary and Lessons Learnt 51 Approach: 1. The analysis followed a bottom ->> top approach (Level of Detail) From low (building) to higher level (district), input for was obtained from simulations at building level; 2. The tools involved were able to provide results with simplified top-level input; 3. Fast analysis allowed the conduction of parametric analysis examining several different variations of replication measures in short time; 4. Gathering of Input Data (Technical, Economic, at least estimations) through LHs, onsite collection, literature, online and software databases and assumptions 5. Calculation of basic expected KPIs (Technical, Economic) to answer key questions (is it worth investing?) 6. The output data can act as the basis for tenders; 7. Identification of Investors;
  52. 52. Summary and Lessons Learnt • RETScreen includes various databases that may significantly reduce simulation time and includes the majority of renewable energy technologies. However, in certain cases external software was required to model specific technologies (outside the scope of the replication measures) • RETScreen models a base case and a retrofit scenario simultaneously. Renewable energy technologies are considered for the retrofit case only. Care should be taken when examining a case study where renewable energy in included in the pre-retrofit stage. Energy requirements and production may be obtained but manual calculations are recommended for the financial evaluation. • EnergyPLAN is developed for simulating much larger systems (national, regional level) and has the capacity to conduct financial and emissions analysis. For the purpose of this study, it was found suitable for performing technical analysis only at the district level with minor adjustments. Financial and emissions analysis was conducted manually. 52 For ease of use, RETScreen may even be used for conducting generic financial and risk analysis
  53. 53. These projects have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 774199 and 864400 Battery sizing Case study on IMREDD building These project have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 774199
  54. 54. Battery sizing – Case study on IMREDD building NEXITY Common self consumption Tertiary building 90 KWp PV + 79 kW/88 kWh battery storage IMREDD Individual self consumption University building 175 KWp PV + 100kW/150kWh battery storage 36 kW 2nd life batteries compared to V2G with 41 kW
  55. 55. Background 1/3 Battery sizing – Case study on IMREDD building PV production vs building’s needs for a week of June 0 50 100 150 200 250 Building consumption PV production weekend
  56. 56. Background 2/3 Battery sizing – Case study on IMREDD building 1 32 Selection of representative pixelsSatellite image analysis PV production calculation KT Psat Isat reflectance r for each pixel Clear sky index = f(r) x Imax (t) projected on the panels Irradiance PV production Transfer function (production P, T°, …) 1.1. Estimation of PV production (2015) At a 15 min step
  57. 57. Background 3/3 Battery sizing – Case study on IMREDD building 1.2. Estimation of the building consumption 0 50 100 150 200 250 Building consumption
  58. 58. Battery sizing – Case study on IMREDD building 26/06 27/06 PV surplus injected to the grid PV production totally consumed by the building 0% extraction from the grid hours 100% 12 76% PV SURPLUS LOSSES SELF CONSUMPTION RATE * PROFITABILITY (years) 21% SELF PRODUCTION RATE ** PV surplus represents a substantial amount especially during weekends and holidays. * Self consumption rate = PV production consumed on site / total PV production ** Self production rate = PV production consumed on site / total site consumption Holiday PV production Building needs Extraction from the grid Injection to the grid 3.1. Hypothesis 1 : PV only Scenario 1 : PV only
  59. 59. Battery sizing – Case study on IMREDD building 59 59% 0% 21% 40% STORAGE CAPACITY(kWh) PROFITABILITY (years) 1505001400 250 45 24 18 15 100 14 72% PVSURPLUS LOSSES SELFCONSUMPTION RATE 85%88%91%95%100% PV production totally consumed by the building Scenario 2 : Maximisation of self consumption rate 3.2. Hypothesis 2 : Maximisation of the self consumption rate 26/06 27/06 Battery not used 150 kWh / 100 kW battery Battery charging Battery discharging
  60. 60. Battery sizing – Case study on IMREDD building 12/01 13/01 Battery discharging to lower the peak Power < 170 kW Battery charging with PV production Charging in off-peak hours Battery discharging in peak hours SELF CONSUMPTION RATE * SELF PRODUCTION RATE ** With both tariff optimisation and peak shaving services, less PV is injected to the grid. However, the losses are more important as the battery is more used and the tariff optimisation is less efficient. PV production Building needs Battery power Battery state of charge Extraction from the grid Injection to the grid 150 kWh / 50 kW battery 84% 13 MWh Losses + auxiliaries consumption Scenario 4 : Tariff optimisation + peak shaving 3.4. Hypothesis 4 : Tariff optimisation + peak shaving 23%
  61. 61. Sensibility analysis Battery sizing – Case study on IMREDD building Technical and economic optimum of battery power and capacity versus contract power Power (kW) Capacity (kWh) Exponential The decrease of contract power thanks to the battery is limited by the exponential growth of the capacity installed. Therefore a contract power below 160 kW doesn’t seem to appropriate. Contract power - kW kW/kWh Capacity (kWh) PMax : 180 kW PMax : 170 kW PMax : 160 kW NPV15years(k€) The peak shaving and tariff optimisation services lower the amount of PV sold. However the profit linked to the reduction of contract power is lower than the resale to the grid, hence the negative NPV. 1.1. Influence of the choice of contract power 1. Economic optimisation
  62. 62. Battery sizing – Case study on IMREDD building ➢ The decrease of PV resale price improves the profitability of the battery. NPV15years(k€) PV resale price becomes more important as the battery capacity increases Sensibility analysis Capacity (kWh) 70 €/MWh 42 €/MWh 0 €/MWh 26 kW / 124 kWh 50 kW / 150 kWh 75 kW / 175 kWh 100 kW / 200kWh 125 kW / 225 kWh The economic optimal sizing of the battery is 150 kWh – 50 kW 1.2. Influence of PV resale price – contract power 170 kW 1. Economic optimisation
  63. 63. Battery sizing – Case study on IMREDD building Battery sizing 2. Self consumption rate maximisation 0 100 200 300 400 500 600 700 800 1 6 11 15 20 25 30 35 39 44 49 54 59 63 68 73 78 83 88 92 97 Daily PV surplus in weekends (kWh) 39% of PV surplus in weekends can be stored with a 150 kWh battery 150 All the simulations presented lead to the choice of a 150 kWh / 100 kW battery. 50 kWh battery : 10% of PV surplus can be stored 100 kWh battery : 25% of PV surplus can be stored 200 kWh battery : 41% of PV surplus can be stored --> 150 kWh seems to be a good compromise 50
  64. 64. Battery sizing – Case study on IMREDD building Integration of grid services To provide the 2 hour block erasing service, the minimum dimensioning to be considered in capacity is 2 times the installed capacity. No assumption of inflation of the erasure service remuneration is taken. Tertiary reserves would, under current conditions, be cumulative with peak-shaving and rate optimization services. Thus, for a 50 kW - 150 kWh battery, an updated flow of gains of around 24 k € over 15 years could be obtained in addition without oversizing the battery. Evolution of the NPV (15years) depending on the capacity made available for the III service [50 - 500 kW] Battery power (kW) Scenario 1: 80% available. If calls < 60 days/an Scenario 2: 80% available. + no additional investment for a 50 kW / 150 kWh battery 7,2k€ NPV15ans(€)
  65. 65. Battery sizing – Case study on IMREDD building Simulations on a power range engaged on the Primary Reserve (RP - FCR) market from 50 kW to 500 kW over 15 years based on the previously exposed assumptions: > By pooling the battery investments for the 3 Peak-Shaving services, tariff optimization and Primary Reserve, the availability in RP is reduced to 28% but the model still seems profitable with a NPV15years of 7.1 k €, > By participating in the RP 80% of the time, an addition of battery sizing is required because this service cannot be combined with the previous services. Profitability is found for a minimum commitment of 250 kW with a battery of 300 kW - 185 kWh (optimal steering margins for the RP taken into account). Evolution of the NPV15years as a function of the power engaged for the FCR service 7,1 k€ NPV15ans(€) Battery power (kW) Integration of grid services Scenario 1: 80% available. Scenario 2: 28% available. + no additional investment for a 50 kW / 150 kWh battery
  66. 66. Thank you for your time
  67. 67. Thank you for your attention! Any Questions? @IRISsmartcities https://irissmartcities.eu @ pocityf https://pocityf.eu

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