of electrification of heating in smart distribution networks

Transcription

1 Techno-economic economic analysis of electrification of heating in smart distribution networks Pierluigi Mancarella, Alejandro Navarro University it of Manchester EPRI Workshop on Impact of the Smart Grid on Bulk System Reliability Assessment Manchester, 8 th December 2011

2 Table of Contents Introduction Key areas under investigation Changes in load profiles Tool for LV network techno-economic economic analysis Case studies Smart options Final remarks and work in progress 2

3 Introduction Motivation Electric Heat Pumps (EHP) are likely l to spread across networks owing to potential energy and environmental benefits EHP might negatively impact on distribution networks, particularly in terms of voltage profiles and equipment thermal limits How to measure these impacts? Base load and EHP profiling -> impact on diversity Suitable test networks Unbalanced bl power flow model dland simulations i Techno-economic assessment tool to estimate reinforcement costs What are possible smart mitigation options? 3

4 Base load modelling and coincidence factors Load 1 Load 2 Load 3 Aggregated Demand (20 loads) W min 5 min 10 min 15 min 30 min 60 min :09 21:56 22:43 23:30 Coincidence Factor 00:000 00:47 01:34 02:21 03:08 03:55 04:42 05:29 06:166 07:03 07:500 08:37 09:24 10:11 10:58 11:45 12:32 13:19 14:06 14:53 15:40 16:27 17:14 18:01 18:488 19:35 20: Hours Numer of customers Coincidence Factor 4

5 EHP Load Profiling The EHP preliminary load model is based on available boiler data with five minutes resolution Since boilers are typically oversized, in order to generate more realistic EHP profiles (whereby a capacity limit is likely to happen), an hourly average is calculated House 1 House 2 House 3 House 4 House 5 House 6 House 7 EHP Base Profiles EHP Base Profiles House 1 House 2 House 3 House 4 House 5 House 6 House 7 kw 15 kw hours - 5 minutes resolution hours - 1 hour average 5

6 Network Power Flow Analysis Requirements to analyse EHP impact: Solution of unbalanced networks Inclusion of load and generation patterns (consideration of temporal dimension) OPENDSS: Open and free software developed by Electrical Power Research Institute (EPRI) It can be interfaced with other software (COM interface), i.e., Matlab and Excel It uses the injection current method and it can solve unbalanced networks It can solve problems with daily and yearly profiles with different resolutions 6

7 Network Impact Assessment Tool Specific tool for techno-economic analysis of LV networks VBA interface, OpenDSS as a power flow engine, and Matlab for techno-economic post-processing processing Studies performed: impact assessment of different levels of EHP penetration and different level l of imbalances introduced d by EHP Methodology: Check thermal and voltage problems through a matrix approach borrowed from the backward-forward sweep method Quantify the cost of reinforcement to satisfy the voltage and thermal limits OPENDSS File Creation Power Flow Solution Thermal Limit Reinforcement Power Flow Solution Voltage Regulation Rif Reinforcement 7

8 Study Cases First test network based on the urban network presented in the report System Integration of Additional Microgeneration (Mott Macdonald, 2004). The main features of the test network are: Base load density: 5 MW/km2 Main conductors: 185 mm2 Spur conductors: 95mm2 Service cables: 25 mm2 Load profile representation for each residential customer Study Cases: Balanced connection Unbalanced connection 8

9 EHP Balanced Connection Methodology: 3 phase connection of EHP loads Evaluation of different penetration levels (percentage of houses with EHP) V If the penetration number is X%, then X% of the loads are 222 randomly selected 220 One EHP is allocated to each of the selected loads The selection of EHP is randomly made among the feasible EHP Main distributor 0% Last Line 0% Main distributor 50% Last Line 50% Main distributor 100% Last Line 100% Voltage Profiles hours - 5 minutes resolutions Voltage Profiles 9

10 Unbalanced Connection Study cases Single phase load connection with different penetration levels, reasonably balanced network: Each load is connected between one phase and the neutral conductor Different penetration levels are analysed The results between 3 phase connection and 1 phase connection are compared Single phase load connection with different penetration and imbalance levels: Each load is connected between one phase and the neutral conductor Different levels of penetration and imbalance are analysed Assumption: loads before the EHP introduction are balanced 10

11 Single Phase EHP Connection 16% 14% Results: 12% Three Phases Loads Single Phase loads The three phase connection (simplified case) underestimates the impacts Number of Sim mulations Three Phase Connection Single Phase Connection 5% 8% 15% The single phase connection presents more variability across simulations i 8% 6% 4% 2% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration Levels % of components needing reinforcement 20% 18% 16% Three Phases Loads 14% Single Phase loads 12% 8% 6% 4% 2% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration Levels % of Initial Cost 11

12 Imbalance Effect A different number of EHP are connected to each phase to model 100% I% Phase B the imbalance effect Number of loads connected to 2 For example, if the imbalance index is equal to 60 %, then 60% of the 50% loads with EHP are connected to 45% 40% phase A, 20% to phase B and 20% 35% 30% to phase C. 25% 20% For different imbalance indices, the 15% maximum demand symmetrical 5% 0% component imbalance index is also calculated for different penetration and imbalance levels (I2/I1) metrical Component In ndex (%) Sym Phase A I% 100% I% Phase C 2 33% 40% 50% 60% 70% 80% 90% 100% Imbalance Level of penetration level 20% of penetration level 30%of penetration level 40% of penetration level 50% of penetration level 60% of penetration level 70% of penetration level 80% of penetration level 90% of penetration level 100% of penetration level 12

13 Imbalance Effect (ctd.) Results: % of components needing reinforcement 35% 30% 35% % of Grid with reinforcem ment 25% 20% 15% 5% 0% 33% 40% 50% 60% Imbalance Level 70% 80% 90% 100% 30% 50% 90% 70% Penetration Level % of Grid with reinforcem ment 30% 25% 20% 15% 5% 0% Imbalance Level 33% 50% 70% 90% 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration Level Reinforcements increase with the imbalance and the number of EHP The worst scenario occurs when the imbalance level is maximum An increase in the penetration level produces a decrease in imbalance level (assumption: the grid without EHP is balanced) 13

14 Imbalance Effect (ctd.) Results: Reinforcement as % of Initial Cost 40% 40% 35% 30% 25% 20% 15% % of Initial Cost ial Cost % of Initi 35% 30% 25% 20% 15% 5% 33% 0% 50% 5% 0% 33% 40% 50% 60% 70% 80% 90% Imbalance Level 100% Penetration Level 70% Imbalance 90% Level 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration Level The expected reinforcement cost could be minimised by minimising connection imbalances 14

15 Analysis for different types of networks Test networks models for typical load densities: 5 MW/km2 (urban) 2 MW/km2 (suburban) 0.2 MW/km2 (rural) All the laterals are modelled. There are not concentrated loads 15

16 Network Models Underground lines Overhead lines 16

17 Results for different penetration levels (balanced case) 60% reinforcement % of Grid with r 50% 40% 30% 20% 0% 5 MW/km2 2 MW/km2 0.5 MW/km2 0% 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration Level % of initial Cost 80% 70% 60% 50% 40% 30% 20% 5 MW/km2 2 MW/km2 0.5 MW/km2 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration Level 17

18 Mitigation options Smart Grid potential Asset based Network reinforcement (BaU expensive play it safe option) Technology mix? Smart Grid based Flexibility deployment and coordinated control Controlled storage to decouple electricity and heat Physical storage Building thermal inertia Measurements and ICT need to be in place Cost benefit analysis for deployment of intelligence Need for powerful and fast network analysis tool 18

19 Where do we store thermal energy? Commercial cooling example Energy De emand (kw) No Control +1 C C C off :30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 00:00 Variation in the thermostat temperature setting is a typical and straightforward control action that could be implemented while keeping track directly of the comfort level of the building s occupants For example, the bigger the thermostat setting change (or the longer the control duration), the greater is the energy/power reduction during the controlled period, but the higher is the payback at the end of the control period 19

20 Flexibility from building thermal inertia (intrinsic storage model) Users preferences and constraints Base temperature set-points, maximum temperature variations, etc Different types of flexibility: Change on-off control to price-driven, cost-driven, or direct load control Change temperature limits or set-points for limited time Normal operation Increased temperature limits High price period Low price period Source: Smart-A Project; equipment: refrigerators 20

21 What if we can store heat for longer? New peaks with 100% EHPs % 20% 30% 50% 100% kva Base case peak no EHP hour - Typical urban network, 2000 customer points, 95% terraces and 5% flat buildings - Electrical peak load density 5 MVA/km2, 25 transformers - Annual consumption 16,500 MWhe and 59,000 MWht 21

22 EHP/storage design: Best Practice terrace, 22 o C set point, extreme winter conditions (-13 o C) COP function of temperatures Electrical load Heat pump COP(t) Actual thermal profile seen by the heat pump Heat storage Storage modulation Heat load 16 kwht kwe Storage size available (% optimal storage size) Thermal peak demand (kwt) 4.6 Energy Consumption (kwht) 34 Energy Consumption (kwhe) 20 StorageEmax (kwht) 14 Ratio storage size to energy % 43 Storage Emax (kwhe) 8 Electrical dem mand (kw) ASHP, T out =55 o C 00:30 02:00 03:30 05:00 06:30 08:00 09:30 11:00 12:30 14:00 15:30 17:00 18:30 20:00 21:30 23:00 Storage capacity (relative to optimal value) Emax=0% Emax=50% Emax=100% 22

23 Can load-side storage provide network support? Impact analysis for different penetration levels of EHP with/without storage, in current and future houses forcements % of the grid with reinf 16% 14% 12% 8% 6% 4% 2% Current House Good Insulation without storage Current House Good Insulation with storage Future House Improved Insulation without storage Future House Improved Insulation with storage % of Initial Cost 20% 18% 16% 14% 12% 8% 6% 4% 2% Current House Good Insulation without storage Current House Good Insulation with storage Future House Improved Insulation without storage Future House Improved Insulation with storage 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 100% Penetration level Penetration level Grid reinforcements Reinforcement Costs - The insulation improvement (storage in buildings) and/or the physical storage (storage in hot water) decrease the EHP impacts on distribution networks - Reinforcements saturate t at 5% for penetration ti levels l above 50% because the feeder conductors need to be changed anyway 23

24 Final remarks A strategic assessment model has been developed that is capable to study distribution networks with different levels of imbalance and different levels of EHP penetration The model allows the analysis of voltage profiles, thermal problems, and the economic quantification of the reinforcements required Studies performed with different time resolutions indicate that the coincidence factor increases when the resolution decreases, so a suitable granularity is needed to capture the EHP changes in network loading 24

25 Final remarks (ctd.) Three phase load models underestimate the impacts of EHP on distribution networks Reinforcements costs increase when the imbalance level and penetration level increase There is room for thermal storage and flexible demand to reduce reinforcement requirements Work in progress is relevant to improving the EHP model and implement smart algorithms for demand response Reliability and risk analysis issues: How to model the correct impact? -> powerful tools How much power can we give to customers? What s the risk of running the network closer to its limit? Do we need to change our reliability metrics in the presence of flexible demand? 25

26 Many thanks for the attention!! Any questions? 26

27 Techno-economic economic analysis of electrification of heating in smart distribution networks Pierluigi Mancarella, Alejandro Navarro University it of Manchester EPRI Workshop on Impact of the Smart Grid on Bulk System Reliability Assessment Manchester, 8 th December 2011