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How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Journal

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY
Volume 9, Issue -, Pages 437-450

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/OAJPE.2022.3217909

Keywords

Probabilistic logic; Uncertainty; Renewable energy sources; Schedules; Procurement; Costs; Contingency management; Power markets; probabilistic solar forecasts; renewable energy; operating reserves; California; flexiramp

Funding

  1. U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy under Solar Forecasting [IIDE-FOA-0001649]
  2. Topic Area 3: Power Forecasts and Operational Integration, under Agreement [EE0008215]

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Net load uncertainty in electricity spot markets is increasing rapidly. Probabilistic forecasts of wind, solar, and load can be used by system operators and market participants in five general approaches to manage this uncertainty. These approaches include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. The paper reviews these approaches and presents a case study on using probabilistic solar forecasts to define reserve needs.
Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

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