Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

doi:10.1109/TPWRS.2022.3163106

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

2023-01-01

6

OA

AI

N

Napoleon Costilla-Enriquez *

M

Miguel A. Ortega‐Vazquez

DOI：10.1109/TPWRS.2022.3163106

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摘要

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The rapid integration of variable renewable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process such as Dynamic Assessment and Determination of Operating Reserve (DynADOR), Dynamic Reserves Dimensioning (DRD), and RESERVE, among others. While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the California Independent System Operator (CAISO) system. Results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

Keyword:

Probabilistic logic

Uncertainty

Wind forecasting

Power system dynamics

Standards

Correlation

Production

Generation scheduling

operating reserve

probabilistic forecasts

uncertainty

variable renewable energy sources

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

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