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presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana

STOCHASTIC SIMULATION OF POWER SYSTEMS WITH INTEGRATED RENEWABLE AND STORAGE RESOURCES. presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana November 26th , 2012. 1. KEY OBJECTIVES.

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presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana

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  1. STOCHASTIC SIMULATION OF POWER SYSTEMS WITH INTEGRATED RENEWABLE AND STORAGE RESOURCES presentation by YannickDegeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana November 26th, 2012 1

  2. KEY OBJECTIVES • Construct a comprehensive simulation approach to emulate the behavior of power systems with integrated storage and renewable energy resources in a competitive environment • Develop models for the resources and the loads that capture their salient characteristics –variability and uncertainty, with spatial and temporal dependencies – as well as their interactions over the grid and in the markets • Demonstrate the simulation approach capabilities with a number of case studiesthatassess the impacts of storage and renewableresourceintegration on the power system variable effects 2

  3. US RENEWABLE PORTFOLIO STANDARDS 29 states,+ Washington DC and 2 territories,have Renewable Portfolio Standards (8 states and 2 territories have renewable portfolio goals). Source: www.dsireusa.org, November 2012

  4. THE CHANGING ENVIRONMENT • There is a growing worldwide interest in integrating renewable resources, as well as storage resources, into the grid to displace costly and polluting fossil-fuel-fired generation • The objective of such integration is to push the creation of sustainable paths to meet the nation’s energy needs and to veer it towards energy independence • The context within which this progress unrolls is in the restructured, competitive electricity industry and the advances in the implementation of the Smart Grid 4

  5. SALIENT CHARACTERISTICS OF SOLAR AND WIND POWER OUTPUTS • Highly time-dependentnature: • variability characteristics • intermittency effects • seasonal dependence • Spatially correlated • Inherently uncertain and difficult to characterize analytically • Predictable with limited accuracy • Come with limited controllability/dispatchability 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. CAISO DAILY WIND POWER PATTERNS IN MARCH 2005 MW Source: CAISO 700 600 500 400 300 200 100 0 hour 8 Source: CAISO

  9. ONTARIO DAILY WIND POWER OUTPUT MW 700 600 500 400 300 200 100 0 hour Source: IESO 9

  10. 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 10

  11. ISSUES IN INTEGRATING WIND AND SOLAR RESOURCES • Misalignment of the wind power outputs with the load implies that the wind speeds are rarely ade-quate at times when needed to supply the loads • While morning and mid-day solar power outputs are aligned with the loads, their quick decline after sunset occurs when the loads are still high • The risk of spillingwinddue to inadequateloadsat night and the challenges of managing base-loadedunit shut down for short periodsduringlow-loadhoursis a major problem 11

  12. TAKING ADVANTAGE OF INTEGRATED STORAGE RESOURCES • In order to take advantage of the increased flexi-bility imparted by the grid-integrated storage devices, we developed appropriate models, methodologies and tools • The resulting approach has applications to: • planning and investment analysis; • policy analysis; • operations; and • market performance 12

  13. UTILITY - SCALE STORAGE CHARACTERISTICS • A storage unit may act as either • a generating unit; or • a load; or • is idle – neither as a load nor as a generator • Storage unit operations are driven by the other resources and so • storage is a highly time-dependent resource • storage operations are uncertain due to its dependence on other uncertain resources • Exploitation of arbitrage opportunities in the determination of storage operations is critical 13

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

  15. 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

  16. THE NEED FOR A NEW SIMULATION APPROACH • The detailed simulation of integrated storage, wind, solar and active demand response resources re-quires the representation of the time–dependent operational actions in line with the market results • Uncertainties in the loads, wind and solar outputs and conventional unit available capacities requires their explicit representation together with their time–varying nature

  17. 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)

  18. THRUST OF THE 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 • Wedeploy the storageschedulerto utilize 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

  19. THRUST OF THE APPROACH • We construct appropriate c.d.f. approximations 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

  20. 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

  21. STUDY 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

  22. THE TIME DIMENSION IN SIMULATION STUDIES . . . . . . study period time . . . period 1 period T period t • We decompose a multi-year study period into T non-overlapping simulation periods • We specify the simulation periods in such a way that no changes in the resource mix, unit commit-ment, the transmission grid and the policy environ-ment occur during each simulation period; such changes may occur in subsequent periods

  23. THE SUBPERIODS: THE SMALLEST INDECOMPOSABLE UNITS OF TIME . . . . . . sub-period 1 sub-period h sub-period H study period . . . . . . . . . period 1 period T period t time • We introduce sub-periods to explicitly represent the time dependence of the side-by-side power system and market operations • We use hourly sub-periods in the simulation and no phenomena of shorter duration are represented

  24. THE SIMULATION APPROACH: KEY ASSUMPTIONS • The system is in the “steady state” for each sub-period and we ignore shorter duration phenomena • The behavior of each market participant is independent of that of the other participants and no participant engages in strategic behavior • The gridislosslessand the DC power flow conditionsholdover the entirestudyperiod

  25. PROPOSED SIMULATION APPROACH: CONCEPTUAL STRUCTURE loads LMPs congestion rents conventional generator available capacities market clearing procedure (DCOPF) “outcome” stochastic processes “driver” stochastic processes renewable power outputs • CO 2emissions . . . storage schedule storage operations

  26. CONCEPTUAL REPRESENTATION OF A SINGLE SIMULATION RUN

  27. QUANTIFICATION OF SYSTEM VARIABLE EFFECTS • We use the approximations to the joint c.d.f.s of the market outcome stochastic processesto compute the expected values of the system variable effects in each sub-period • In this way, we evaluate all the figures of merit of interest, including expected electricity payments, expected energy supplied by each resource, reliability indices and expected emissions

  28. TO ENSURE NUMERICAL TRACTABILITY • We introduce various schemes that reduce the computing burden and provide tractability • Key implementational aspects: • selection of a group of representative weeks • variance reduction techniques: stratification, control variatesand common random numbers • warm-start of the linear program solver • parallelization of simulation runs

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

  30. CASE STUDIES 29

  31. 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

  32. 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: siting of 4energy storage units and the impacts on transmission usage • case study set III: substitution of conventional generation resources by a combination of storage and wind energy resources

  33. 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

  34. 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

  35. SENSITIVITY CASES IN STUDY SET I

  36. CASE D: AVERAGE HOURLY STORAGE UTILIZATION 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

  37. 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

  38. NODE 80AVERAGE HOURLY LMPDURATION CURVE 50 45 40 35 30 25 20 15 10 5 0 0 20 40 60 80 100 with storage without storage base case case A case B case C case D LMP in $/MWh % of hours in the week

  39. 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

  40. 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

  41. 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

  42. CASE STUDY SET II: 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

  43. 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

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

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

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

  47. 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

  48. 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

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

  50. 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

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