Forecasting & Scheduling Renewables in North American Power Systems

Transcription

1 Forecasting & Scheduling Renewables in North American Power Systems Current Status and Future Trends Mark Ahlstrom, CEO, WindLogics Inc. (a subsidiary of NextEra Energy, Inc.) IEEE PES General Meeting Panel Session - Statistical Resource Modeling for Renewables Integration Vancouver, BC, Canada July 24, 2013

2 Two Statements from Monday The future of wind power forecasting is probabilistic! Pierre Pinson Probabilistic forecasts are... difficult to appreciate for a significant share of forecast users. Pierre Pinson

3 Wind & Solar Integration - Perception & Evolution The success of wind & solar integration is influenced by the perception of integration costs & operating limitations Our current perception is largely based on current operating assumptions, tools and experience All power plants and fuel supplies have their own unique operational characteristics and integration costs Current rules were created with conventional power plants in mind A change in perspective often simplifies a complex problem

4 Implications on Forecasting Value Bad operating or market rules can make forecasts more valuable But we are after long-term solutions Artificial value propositions are inefficient and unsustainable Improving operating and market rules is often low hanging fruit Enabling more renewables creates long-term opportunities But how does this change the value of various types of forecasts? Is there enough remaining value to support forecasting R&D? What forecasting time horizons are most valuable? When and how are probabilistic forecasts valuable and usable? Is the value to system operators, market participants or traders?

5 Best Operating Practices - Integrating Variable Generation From easy to more difficult: 1. Larger balancing areas 2. Shorter scheduling intervals 3. Commitment closer to real time 4. Better use of existing flexibility from the entire power system 5. Better decision support tools that integrate wind and solar forecasts 6. Probabilistic and look-ahead simulation tools to optimize scenarios and risks

6 Midcontinent ISO (MISO) Market MISO runs a fairly standard Day Two market system: Submit day-ahead offer Some ability to adjust day-ahead offers until 4 hours ahead Submit real time offer Submitted 30 minutes in advance of each operating hour Follow five-minute dispatch signal from MISO (+/- 8% band) Dispatch & curtailment are based on offer prices (offers set price ) and reliability needs (security constrained economic dispatch)

7 MISO Dispatchable Intermittent Resource Tariff MISO runs a fairly standard Day Two market system: Submit day-ahead offer Some ability to adjust day-ahead offers until 4 hours ahead Submit real time offer Wind provides a rolling five-minute forecast for the next hour The 10-minute-ahead forecast value is used for each 5-minute dispatch Follow five-minute dispatch signal from MISO (+/- 8% band) Dispatch & curtailment are based on offer prices (offers set price ) and reliability needs (security constrained economic dispatch)

8 The Short Term Wind Forecast Error Curve From: Jacques Duchesne, AESO

9 The Short Term Wind Forecast Error Curve System-wide Error (% MAE) Time (Hours) Adapted from Jacques Duchesne, AESO

10 Dispatching Wind Changes the Perception of the Problem Variability is the change or error within the dispatch period Uses a small amount of regulation Uncertainty is mostly the error from the day-ahead forecast Largely handled through the real time dispatch stack Uses some non-spin reserve for extreme situations Is there a ramping or flexibility problem? With a deep and robust real time dispatch not really Wind ramping up - you have dispatch control of wind if needed Wind ramping down - units backed down and have room to move up Without a deep and robust real time dispatch stack, integrating wind is more challenging but the most effective action is to fix the market

11 Example - Wind Variability in ERCOT (Texas) ERCOT uses the output at the start of the dispatch period as the dispatch value for the wind plant (unless curtailed) Under this approach, wind variability is the change within the five- minute period Within the five- minute period, regulacon must cover the difference How much extra regulacon is needed and what does it cost? MW (Load) Load and Wind Power Output for 5 Minute Dispatch :00 System Load 5:05 Time 5:10 5:15 Actual WGR Output Assumed WGR Output MW (Wind) Adapted from a presentacon by David Maggio of ERCOT at the IEEE PES Annual MeeCng 2012

12 What does wind variability cost in ERCOT? For ~10,000 MW of wind (>9% of ERCOT energy): Typical months: RegulaCon up: $404k RegulaCon down: $98k Total: $500k/month Including August 2011 with record prices: RegulaCon up: $937k RegulaCon down: $92k Total: $1 million/month Adapted from a presentacon by David Maggio of ERCOT at the IEEE PES Annual MeeCng 2012

13 Example - Wind Uncertainty in ERCOT Forecast error creates addiconal uncertainty that may require addiconal non- spin reserves 1) Normal real Cme dispatch covers most of the forecast error 2) Non- spin reserves cover the risk of insufficient dispatch resources How much extra non- spin reserves are needed and what does it cost? MW (Wind) Day- Ahead Wind Forecast Error for 4/8/2012 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, OperaCng Hour Actual Wind Output Day- Ahead Wind Forecast Adapted from a presentacon by David Maggio of ERCOT at the IEEE PES Annual MeeCng 2012

14 What does wind uncertainty cost in ERCOT? For ~10,000 MW of wind (>9% of ERCOT energy): Typical month: $1 million/month Including August 2011 with record prices: $2 million/month Adapted from a presentacon by David Maggio of ERCOT at the IEEE PES Annual MeeCng 2012

15 What does this mean to us? ERCOT annualized numbers: Variability cost: $6 million $0.22/MWh Uncertainty cost: $12 million $0.44/MWh Total integracon cost : $18 million $0.66/MWh Value of the energy provided by wind: $1.3 Billion For a system like ERCOT that efficiently dispatches wind and uses energy dispatch and non- spin for wind uncertainty, the cost of wind variability and uncertainty is very modest (1% - 2% of the energy value)

16 Value Points - Wind as a Full Market Participant Wind in dispatch (real time operations) A robust real time market encourages wind to offer day ahead Wind in day-ahead market (day-ahead unit commitment) Enhanced importance of better day-ahead forecasts Increases the value of improved forecasts to system operators, market participants and traders! Wind forecast changes within the day Ramping and reducing day-ahead forecast error Value to system operators for rolling commitment decisions Value to market participants or traders?

17 Aligning Forecasting & Scheduling The goal is to align timing to exploit best forecasts and optimize system operations Must reflect the implicit nature of weather and weather forecasting Key elements: Involvement in real-time dispatch / market Involvement in day-ahead unit commitment / market

18 Implications for Forecasting Value 1. With good dispatch methods and ample system flexibility, the value for improving very short term (intra-hour) forecasts is small But many systems don t currently have this, or while flexibility may physically exist, rules many not make it available to use 2. Efficient dispatch encourages participation in day ahead and intraday financial markets, thereby increasing the value of day ahead and hours ahead forecasts 3. This is where probabilistic forecasts and methods will dominate, and their value is probably much more than the spinning reserve calculation would suggest, especially with higher amounts of wind and solar

19 Discussion Mark Ahlstrom