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SIMULATION OF UTILITY-SCALE STORAGE RESOURCES IN POWER SYSTEMS WITH INTEGRATED RENEWABLE RESOURCES

SIMULATION OF UTILITY-SCALE STORAGE RESOURCES IN POWER SYSTEMS WITH INTEGRATED RENEWABLE RESOURCES. presentation by Yannick Degeilh Power Affiliates Program - 34 th Annual Review University of Illinois at Urbana-Champaign Friday May 3 , 2013. OUTLINE.

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SIMULATION OF UTILITY-SCALE STORAGE RESOURCES IN POWER SYSTEMS WITH INTEGRATED RENEWABLE RESOURCES

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  1. SIMULATION OF UTILITY-SCALE STORAGE RESOURCES IN POWER SYSTEMS WITH INTEGRATED RENEWABLE RESOURCES presentation by YannickDegeilh Power Affiliates Program - 34th Annual Review University of Illinois at Urbana-Champaign Friday May 3, 2013

  2. OUTLINE • Motivation – the challenges of integrating renewable resources and utility-scale storage resources, and the need for a comprehensive stochastic simulation approach • Key features of the simulation approach – key contributions • Illustration of the simulation approach capabilities with a number of case studies • Concludingremarks 2

  3. CAISO DAILY WIND POWER PATTERNS IN MARCH 2005 MW Source: CAISO 700 600 500 400 300 200 100 0 hour 3 Source: CAISO

  4. ONTARIO DAILY WIND POWER OUTPUT MW 700 600 500 400 300 200 100 0 hour Source: IESO 4

  5. MISALIGNMENT OF WIND POWER OUTPUT AND LOAD 8000 250 7000 load 200 6000 150 load (MW) wind power output (MW) 5000 100 4000 50 wind power 0 3000 48 72 96 120 144 168 24 hour 5

  6. PV POWER OUTPUT OF 1-MWCdTeARRAY IN GERMANY 1000 900 800 700 600 500 kW 400 300 200 100 0 5:00 6:00 8:00 9:00 7:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 samples collected on a 5 – minute basis

  7. PV OUTPUT AND LOAD 50 500 40 400 PV output (MW ) load (GW ) 300 30 200 20 100 0 10 24 48 72 96 120 144 168 time (h) 7 Sources: ERCOT and NREL

  8. UTILITY - SCALE STORAGE APPLICATION MW storageresource discharging during peak hours storage resource charging during low-load hours 0 12 24

  9. WIND/STORAGE SYMBIOTIC INTERACTIONS energy discharged during peak hours MW unit i + 3 units are loaded in order of increasing prices of unit production displaced units unit i + 2 daily load shape unit i + 1 unit i unit i - 1 unit i - 2 charging units … modified daily load shape by wind base-loaded units hours 0 12 24 energy charged during low-load hours

  10. MOTIVATION • The conventional probabilistic simulation approach cannot be used to capture the time-varying nature and the inter-temporal effects required in the simulation of the storage and renewable resources; the impacts of the transmission network and the market environment/policies cannot be represented either • Since the detailed representation of such phenomena/features is analytically intractable, we propose to address such problem by making use of Monte Carlo simulation techniques

  11. NEED TO EXPLICITLY REPRESENT • The loads and their associated uncertainty • The resources and their associated uncertainty: • conventional generators • utility-scale storage units • renewable resources • The spatial and temporal correlations among the resources at the various sites and the loads • The impacts of the grid constraints • The hourly day-ahead markets (DAMs)

  12. THRUST OF THE SIMULATION APPROACH • We develop a comprehensive, computationally efficient Monte Carlo simulationapproach to emulate the behavior of the power system with integrated storage and renewable energy resources • We model the system load and the resources by discrete-time stochasticprocesses • Wedevelop a storageschedulerto exploit arbitrage opportunities in the storageunit operations • We emulate the transmission-constrained hourly day-ahead markets(DAMs)to determine the power system operations in a competitive environment

  13. PROPOSED SIMULATION APPROACH: CONCEPTUAL STRUCTURE demands LMPs congestion rents conventional generator available capacities market clearing procedure (OPF) “output” stochastic processes “input” stochastic processes renewable power outputs • CO 2emissions . . . storage schedule storage operations

  14. THRUST OF THE APPROACH • We collect sample paths of the market outcome stochastic processes to evaluate the expected system variable effects • Metrics we evaluate include: • nodal electricity prices (LMPs) • generation by resource and revenues • congestion rents • CO2 emissions • LOLPand EUE system reliability indices

  15. KEY CONTRIBUTIONS • Development of a new simulation toolappropriate to addresstoday’s power industry challenges • Salientfeaturesinclude: • quantification of the power system expected variable effects – economics, reliabilityand environmental impacts – in eachsub-period • computationally tractable for practicalsystems

  16. KEY CONTRIBUTIONS • detailedstochasticmodels of the time–varying resources and loadsallow the representation of spatial and temporal correlations • storageschedulerfor optimizedstorageoperation to exploit arbitrage opportunities • representation of the transmission–constrainedmarketoutcomes • flexibility in the representation of the marketenvironment / policies

  17. TYPICAL APPLICATIONS • Resource planning studies • Production costing issues • Transmission utilization issues • Environmentalassessments • Reliability analysis • Investment analysis

  18. CASE STUDIES 29

  19. MOTIVATION • We present case studies aimed at illustrating the various capabilities of the simulation approach and relevant to the integration of storage and renewable resources • We perform sensitivity studies to investigate several aspects of storage integration into the grid, notably its impact in a system with deepening wind penetration, its siting, and to what extent storage and renewable resources may replace conventional generation

  20. CASE STUDIES We present 3sets of representative case studies: • case study set I: impacts of an integrated utility-scale storage unit under a deepening wind penetration scenario • case study set II: substitution of conventional generation resources by a combination of storage and wind energy resources • case study set III: siting of 4energy storage units and the impacts on transmission usage

  21. CASE STUDY SET I: DEEPENING WIND PENETRATION • The objective of this study is to perform a wind penetration sensitivity analysis and to quantify the enhanced ability to harness wind resources withthe addition of a storage energy resource • We evaluate the key metrics for variable effect assessment, including wholesale purchase payments, reliability indices and CO2 emissions

  22. THE STUDY TEST SYSTEM: A MODIFIED IEEE 118-BUS SYSTEM • Annual peak load: 8,090.3 MW • Conventional generation resource mix: 9,714 MW • 4 wind farms located in the Midwest with total nameplate capacity in multiples of 680 MW • A storage unit with 400 MW capacity, 5,000 MWhstorage capabilityand89 % round-trip efficiency • Unit commitment uses a 15 % reserves margin provi-ded by conventional units and the storage resources

  23. SENSITIVITY CASES IN STUDY SET I

  24. CASE D: AVERAGE HOURLY STORAGE UTILIZATION system load 8.5 400 8.0 300 7.5 200 7.0 100 hourly storage charge/dischargecapacity inMW load in GW 6.5 0 6.0 - 100 5.5 - 200 5.0 - 300 4.5 - 400 0 24 48 72 96 120 144 168 hour

  25. NODE 80 AVERAGE HOURLY LMPs with storage without storage 50 base case 45 case A 40 case B 35 case C 30 case D 25 LMP in $/MWh 20 15 10 5 0 0 24 48 72 96 120 144 168 hour

  26. EXPECTED WHOLESALE PURCHASE PAYMENTS 180 170 160 150 140 130 120 110 100 with storage without storage - 5.1 % - 9.1% - 15.4% - 17.9% - 24.4% thousand $ - 25.2% - 31.1% - 31.2% - 36.9% case A base case case B case C case D

  27. EXPECTED CO2 EMISSIONS 1.05 + 0.3 % with storage without storage 1.00 - 6.0% - 5.6% 0.95 - 10.5% - 11.8% - 14.5% 0.90 thousand metric tons - 16.4% - 17.5% 0.85 - 19.8% 0.80 0.75 0.70 case A case B base case case C case D

  28. ANNUAL RELIABILITY INDICES 6.24 5.20 4.16 3.12 2.08 1.04 0 EUE LOLP MWh x 10-4 without storage with storage without storage with storage 14 12 - 37.5% - 43.8 % - 50.0% -55.1 % - 42.5% -56.3 % 10 -73.5 % -67.1 % - 65.6% -71.9 % - 64.1% -82.4 % - 73.9% -90.0 % - 80.2% 8 - 80.1% -94.3 % -87.5 % 6 4 2 case A 0 base case base case case B case B case D case C case D case C case A

  29. STUDY SET II:SUBSTITUTION FOR THE CONVENTIONAL RESOURCES • The aim of this study is to quantify the extent, from a purely reliability perspective, wind resources can substitute for conventional generation capacity in a power system with integrated storage resources • We deem storage units to be firm capacity and use them to meet the desired reserves margin • As the wind resources are integrated, we decrease progressively the system reserves margin, retire conventional unit capacity and assess the impacts

  30. THE STUDY TEST SYSTEM: A MODIFIED IEEE 118-BUS SYSTEM • Annual peak load: 8,090.3 MW • Conventional generation resource mix: 9,714 MW • 4 wind farms located in the Midwest with total nameplate capacity of 2,720 MW • 4 units: each has a 100 MW capacity, 1,000 MWhstorage capability and 89 %round-trip efficiency • The unit commitment is performed to ensure the desired reserves margin is attained from the conventional and storage resources

  31. SET IV SENSITIVITY CASES

  32. WEEKLY RELIABILITY INDICES vs. RESERVES MARGINS 0.0040 0.0035 0.0030 0.0025 0.0020 0.0015 0.0010 0.0005 0 7 8 9 10 12 14 LOLP EUE 0.7 0.6 0.5 0.4 weekly EUE contribution in MWh weeklyLOLP contribution 0.3 base case base case 0.2 0.1 0 11 15 7 8 9 10 11 12 13 14 15 13 reserves margin in % reserves margin in %

  33. MEAN HOURLY WHOLESALE PURCHASE PAYMENT IMPACTS base case 400 15% 14% 350 13% 12% 11% 300 10% 9% 8% 250 7% 200 150 100 50 0 24 48 72 96 120 144 168 wholesale purchase payments in thousand $ hour

  34. CASE STUDY SET III: STORAGE UNIT SITING • The objective of this study is to perform a sensiti-vity analysis on the siting of 4 storage units in the system and assess its impacts on transmission usage and on the economics at the most heavily loaded bus in the network • We quantify the expected LMPs at the load center at node 59 and the total congestion rents

  35. TEST SYSTEM OF THE STUDY: A MODIFIED IEEE 118-BUS SYSTEM • Annual peak load: 8,090.3 MW • Conventional generation resource mix: 9,714 MW • 4 wind farms located in the Midwest with total nameplate capacity 2,720 MW • 4 identical utility-scale storage units, each having 200 MW capacity, 5,000 MWhstorage capabilityand89% round-trip efficiency • Reserves margin is set at 15 %and is provided by conventional and storage resources

  36. STORAGE SITING ON THE MODIFIEDIEEE 118 – BUS TEST SYSTEM W W most heavily loaded bus at node 59 W W storage siting region 43

  37. SENSITIVITY CASES IN STUDY SET II each case has 2,720MW nameplate wind capacity

  38. STORAGE SITING REGION S1 S1 S1 S2 S0 S0 S2 S0 S0 S3 S1 S3 S3 S2 S3 S2 45

  39. NODE 59 EXPECTED HOURLY LMPs 60 10 60 50 9 40 8 40 30 7 6 20 20 5 10 4 0 0 0 24 48 72 96 120 144 168 0 24 48 72 96 120 144 168 case S0 case S0 case S1 load in GW LMP in $/MWh LMP in $/MWh case S2 case S1 case S3 case S2 case S3 base case base case number of hours in the week hour

  40. EXPECTED HOURLY CONGESTION RENTS 10 case S0 60 60 9 case S0 50 50 8 40 case S1 40 congestion rents in thousand $ load in GW congestion rents in thousand $ 7 30 30 case S2 6 20 20 case S2 base case 10 case S1 5 10 case S3 0 0 4 0 24 48 72 96 120 144 168 0 24 48 72 96 120 144 168 hour number of hours in the week case S3 base case

  41. TRANSMISSION PATH CONGESTION AND ITS REENFORCEMENT most heavily loaded bus at node 59 S2 S0 S0 S2 S0 S0 2x400MW nuclear plants 48

  42. PRE – PATH – REENFORCEMENT NODE 59 AVERAGE HOURLY LMPs 60 60 10 50 9 40 40 8 30 7 20 20 6 10 5 4 0 0 0 24 48 72 96 120 144 168 0 24 48 72 96 120 144 168 case S0 case S0 case S1 load in GW LMP in $/MWh LMP in $/MWh case S2 case S1 case S3 base case case S3 base case case S2 number of hours in the week hour

  43. POST – PATH – REENFORCEMENT NODE 59 AVERAGE HOURLY LMPs 60 9 50 50 8 40 40 7 30 30 6 20 20 5 10 10 4 0 0 0 24 48 72 96 120 144 168 0 24 48 72 96 120 144 168 case S0 60 base case case S1 case S2 case S3 base case load in GW LMP in $/MWh LMP in $/MWh case S0 case S2 case S1 case S3 hour number of hours in the week

  44. PRE – PATH – REENFORCEMENT AVERAGE HOURLY CONGESTION RENTS 10 60 case S0 60 9 case S0 50 50 8 40 case S1 40 congestion rents in thousand $ load in GW congestion rents in thousand $ 30 7 30 case S2 20 6 case S2 20 case S1 base case 10 5 10 case S3 0 0 4 0 24 48 72 96 120 144 168 0 24 48 72 96 120 144 168 hour number of hours in the week case S3 base case

  45. POST – PATH – REENFORCEMENT AVERAGE HOURLY CONGESTION RENTS 3,500 3,000 2,500 2,000 1,500 1,000 500 0 24 48 72 96 120 144 168 case S2 case S0 case S0 3,500 case S1 case S2 8000 3,000 base case case S1 2,500 7000 congestion rents in $ load in GW 2,000 congestion rents in $ 1,500 6000 case S3 1,000 500 5000 base case 0 0 24 48 72 96 120 144 168 hour number of hours in the week case S3

  46. KEY FINDINGS OF THE ILLUSTRATIVE STUDIES • Deeper penetration of wind resources reduces DAM LMPs, wholesale purchase payments, CO2 emissions and improves system reliability • Storage works in synergy with wind to drive wholesale purchase payments further down and improve system reliability • Overall,CO2 emissions are not significantly affected by the integration of a storage unit

  47. KEY FINDINGS OF THE CASE STUDIES In a system whose storage resources are used to substitute for conventional generation to meet the desired reserves margin requirements, large amounts of wind capacity are required to replace the retired conventional generation capacity: in the case studies presented the 2,720 MW of wind can substitute for about 300MW of retired conventional generation capacity – about 3.7% of peak load

  48. KEY FINDINGS OF THE CASE STUDIES • Absent storage units, with all other conditions remaining unchanged, the 2,720 MWwind can replace only about 220MW of retired conventional generation capacity – about 2.7% of peak load • We attain significant reductions in wholesale purchase payments – about 25% – when storage and wind resources substitute for conventional resource capacity, at the same reliability level

  49. KEY FINDINGS OF THE CASE STUDIES • Storage siting significantly impacts the congestion rents and the LMPs at certain nodes • Studies must be made on a case by case basis to determine the best siting options for the utility-scale storage units

  50. SALIENT SIMULATION APPROACH CHARACTERISTICS A practically-oriented approach to simulate large-scale systems over longer-term periods Comprehensive and versatile approach to quantify the impacts of the integration of storage devices into power systems with deepening penetration of renewable resources Demonstration of the capabilities of the proposed approach to a broad range of planning, investment, transmission utilization and policy analysis studies

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