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Uncertainty Prediction Tools for System Operation

Uncertainty Prediction Tools for System Operation. Y. V. Makarov (PNNL), P. V. Etingov (PNNL), and C. Loutan (CAISO) Presentation for WECC VGS Salt Lake City, Utah December 8, 2011. Renewables Integration Work at PNNL. New low-impact control algorithms.

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Uncertainty Prediction Tools for System Operation

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  1. Uncertainty Prediction Tools for System Operation Y. V. Makarov (PNNL), P. V. Etingov (PNNL), and C. Loutan (CAISO) Presentation for WECC VGS Salt Lake City, Utah December 8, 2011

  2. 2 Renewables Integration Work at PNNL • New low-impact control algorithms • Coordination & consolidation of Balancing Areas • Regional & Utility studies • Methods to accommodate VG uncertainty • Tools to develop/ operate a grid MW Schedule Renewable Integration Model Time

  3. Acknowledgements • This work and its parts were supported by • U.S. Department of Energy • California Energy Commission • California Independent System Operator (CAISO) • California Institute for Energy and Environment (CIEE) • Bonneville Power Administration (BPA) • ColumbiaGrid Corporation • AREVA • Nevada Energy, and • Laboratory Directed Research and Development grants at PNNL.

  4. Current work for CAISO on Uncertainty Prediction Tools for System Operation Wind and Solar: Probabilistic Analysis and Visualization Ramp and Uncertainty Prediction Tool Transmission Tool: Probabilistic limits for Voltage, Stability, Congestion, Reactive Power Margin Capacity Ramp Rate and Ramp Duration

  5. Focus Areas • Balancing process: • Day-ahead schedules • Unit commitment • Economic dispatch • Real-time dispatch • Unit commitment • Economic dispatch • Spinning reserve • Regulation • Contingency reserve Sources of uncertainty (discrete and continuous): • Traditional: • Load forecast • Unexpected forced outages • Generation units failure to startup • New challenges: • Wind generation forecast • Solar generation forecast • Wind ramps • Control performance • Reliability

  6. 95 90 85 80 Probabilistic Tool Concept Maximum capacity P, MW Confidence intervals, % Generation is not able to follow the demand Net load forecast Available Capacity Schedule Time, min or h Net Load =Load – Wind – Solar - Interchange

  7. Models for Balancing Requirements Uncertainty: Multidimensional Uncertainty Analysis (1) • Existing approaches are frequently limited to one dimension of the uncertainty problem – capacity. • But capacity is not a single sufficient descriptor of the problem. • Operational performance of a power system can be demonstrated through four basic metrics, forming the “first performance envelope”. • Capacity (π). • Ramp rate (ρ. • Ramp duration (δ). • Energy (Є). * Y.V. Makarov, C. Loutan, J. Ma, and P. De Mello. “Operational impacts of wind generation on California power systems,” IEEE Transactions on Power Systems, vol. 24, pp.1039–1050, May, 2009.

  8. Real-Time Requirements (1)

  9. Real-Time Requirements (2)

  10. Model Self-Validation

  11. Price Spike Prediction Example

  12. CAISO Real Time Price Monitor, 06/24/2011 * Available at http://www.aiso.com/Documents/June%202011/DailyMarketWatch_Real-Time_Jun_24_2011.pdf

  13. Summary • The ramp and uncertainty tool has been developed and deployed at CAISO control room. • The tool received a positive feedback from the CAISO specialists. • For instance, it has been found that the tool is capable to predict intra-hour deficiency in generation capability. • Based on a preliminary CAISO assessment, the tool was able to predict more than 90% price spikes in the real-time market. • We are open for a continuing discussion in the WECC system.

  14. Future Work • New uncertainty prediction approaches based on modern statistical methods. • Feed in uncertainty and ramp information for wind and solar generation weather-based forecasts. • Investigate the impact of BAAL standard, interchange control and inadvertent interchange accumulations on the uncertainty analysis and propose solutions. • Implement active and pro-active integration approaches to incorporate uncertainty information back into the unit commitment and dispatch processes. • Quantify the cost of uncertainty and develop mechanisms for allocating the cost to specific sources and their aggregates.

  15. Bids/production cost: Energy Regulation Up/Down Spinning Reserve Non-spinning Reserve Constrains/Requirements Transmission Constrains Load Forecast Units’ characteristics Reserve Requirements Wind Forecast Pmin Ramping Requirements Pmax Solar Forecast Ramp Rate ….. Start up time …. Security Constrained Unit Commitment Uncertainty Tool Advisory Visualization “Active” “Proactive” Generation Schedule On-line units Available Reserves “Active” and “Proactive” Integration Approaches with Unit Commitment

  16. Transmission Uncertainty Tool

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