An Improved Surrogate Method for Solving the Energy Storage Optimal Bidding Problem

TL;DR

This paper introduces an enhanced surrogate-based approach with spatial-temporal entropy to efficiently solve the complex bilevel optimization problem for energy storage bidding in renewable-rich markets.

Contribution

It proposes a novel surrogate method incorporating spatial-temporal entropy for better accuracy and scalability in energy storage bidding optimization.

Findings

Method demonstrates high scalability and efficiency.

Achieves accurate solutions in complex bilevel problems.

Outperforms existing approaches in numerical tests.

Abstract

Energy storage is expected to play an increasingly important role in mitigating variations that come along with the growing penetration of renewable energy. In this paper, we study the optimal bidding of an energy storage unit in a semi-centralized market. The energy storage unit offers its available storage capacity and maximum charging/discharging rate to the operator; then the operator clears the real-time market by minimizing the total cost. The energy storage unit is paid/charged at locational marginal price (LMP). The problem casts down to a bilevel optimization problem with a mixed-integer lower-level. An improved surrogate-based method with the combined spatial-temporal entropy term is developed to solve this problem. Numerical examples demonstrate the scalability, efficiency, and accuracy of the proposed method.