LOLE Seasonal Implementation: Software Review LOLEWG - Agenda Item 02. January 6, 2016

Transcription

1 LOLE Seasonal Implementation: Software Review LOLEWG - Agenda Item 02 January 6, 2016

2 LOLE High level overview and LOLE Software basics Loss of Load Expectation (LOLE) : Number of days per year for which the available generation capacity is insufficient to serve the demand at the daily peak hour. Low probability, high impact events Largely centered around Hierarchical Level 1 (Generation and load) Capture probabilistic uncertainty in load and generation Predominant method is Monte Carlo probabilistic analysis Software utilized within industry for LOLE analysis GE MARS Astrape SERVM ABB GridView Tie Line and Generation Reliability (TIGER) Etc. 2

3 GE Multi-Area Reliability Simulation (MARS) Program General Electric Company product Originally developed for New York State Uses a sequential Monte Carlo simulation Steps through time chronologically and randomly drawing unit availability Replicating simulation with different sets of random events until statistical convergence is obtained Ability to analyze reliability of interconnected generation systems Benefits of diversity Tie-line effectiveness Additional capabilities Utilizes a flat file input format and multiple text file outputs Most widely used LOLE software in the Power Systems Industry 3

4 Current MARS application use Primary software at MISO for: Annual LOLE study Wind ELCC Monte-Carlo modeling of generator outages Utilization of annual forced outage rates Deterministic Load modeling Single load shape with load uncertainty multiplier (LFU) 4

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6 Some challenges with MARS when moving to seasonal Difficulty in modeling seasonal outages Generator outages limited to annual variables (planned and forced) Seasonal LFU Concerns with LFU methodology when applied to seasons Difficulty modeling multiple weather years Energy limited issues in modeling planning year Energy limits based off of calendar year and not planning year 6

7 SERVM introduction and overview Strategic Energy & Risk Valuation Model (SERVM) Developed by Southern Company in the 1980 s Managed by Astrape Consulting SERVM resource adequacy metrics consider: Wide Variation of Load Shapes Growth Uncertainty Renewable Profiles Unit Performance 7

8 SERVM Features Probabilistic, Hourly Chronological Model Designed Specifically for Resource Adequacy Studies with production cost capabilities All in 1 Software Package (Reliability and Production Costing Analysis) Resource Adequacy Studies (1 in 10 LOLE, EUE, LOLH) Flexibility and Operational Requirements Due to Variable Energy Resources Intermittent Resource Evaluation (ELCC) Demand Response Evaluation (reliability contribution and economic value) Fuel Availability Studies (natural gas availability, fuel back up, pipeline availability) Transmission Interface Studies Production Cost Analysis (Short Term and Long Term) Zonal Market Price Forecasts Generator Energy Margins Expansion Planning (not currently automated) Retirement Analysis Environmental Retrofit Analysis 8

9 SERVM Proof Of Concept Analysis Phase 1: SERVM parallel proof of concept analysis to annual LOLE study SERVM/MARS PRM and LRR LOLE results are comparative Phase 2: Introduction of historical weather shapes to model load uncertainty 35 weather years modeled using neural net methodology Demonstration of seasonal outage rate modeling Rate based Event based 9

10 Phase 2: Load Hourly Load and Temperature from Trained data in Neural Net Load Temperature Time of Day Month Previous Hour Load 24 hour ago Temperature 48 hour ago Temperature Neural Net Relationship is then applied to hourly temperature Results in 35 synthetic load shapes Simulations: Simulate with all 35 weather years and up to 5 additional economic load forecast error multipliers 10

11 Phase 2: Loads Summer Peak Variance 11

12 Phase 2 Loads: Peak Load Monthly Variance Due to Weather 12

13 Potential advantages with SERVM LFU training from weather/load history 30+ weather shapes created to model weather related load uncertainty Economic Load uncertainty Seasonal Outage rates Ability to model seasonal forced and planned outage rates Improvements to LMR Modeling Potential future modeling Ability to input environmental variables Loss of load due to ramping capabilities Event data to determine forced outage probabilities vs. rate based methods 13

14 Plan moving forward Continued evaluation of SERVM and MARS for seasonal implementation Continual review within LOLEWG Contact Info Jordan Cole (651)