Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

TL;DR

This paper introduces practical methods for determining power system operating reserves using probabilistic forecasts, improving risk management over traditional deterministic approaches, validated with real CAISO data.

Contribution

It proposes new approaches that incorporate probabilistic forecast data into reserve dimensioning, enhancing reliability and risk reduction in power system operations.

Findings

Probabilistic methods reduce system risk compared to deterministic approaches

Validation with CAISO data demonstrates improved reserve adequacy

Incorporating uncertainty thresholds enhances operational reliability

Abstract

The rapid integration of variable 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 (e.g., DynADOR, 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 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.