Data-driven Decision Making with Probabilistic Guarantees (Part 2): Applications of Chance-constrained Optimization in Power Systems

TL;DR

This paper reviews and compares data-driven chance-constrained optimization methods applied to power systems, emphasizing their ability to handle renewable energy uncertainties with probabilistic guarantees, supported by numerical simulations.

Contribution

It offers a comprehensive review and critical comparison of chance-constrained optimization techniques specifically applied to power system decision-making under uncertainty.

Findings

Data-driven methods effectively manage renewable energy uncertainties.

Numerical simulations compare the performance of different approaches.

Chance-constrained optimization enhances grid reliability with probabilistic guarantees.

Abstract

Uncertainties from deepening penetration of renewable energy resources have posed critical challenges to the secure and reliable operations of future electric grids. Among various approaches for decision making in uncertain environments, this paper focuses on chance-constrained optimization, which provides explicit probabilistic guarantees on the feasibility of optimal solutions. Although quite a few methods have been proposed to solve chance-constrained optimization problems, there is a lack of comprehensive review and comparative analysis of the proposed methods. Part I of this two-part paper reviews three categories of existing methods to chance-constrained optimization: (1) scenario approach; (2) sample average approximation; and (3) robust optimization based methods. Data-driven methods, which are not constrained by any particular distributions of the underlying uncertainties, are of particular interest. Part II of this two-part paper provides a literature review on the applications of chance-constrained optimization in power systems. Part II also provides a critical comparison of existing methods based on numerical simulations, which are conducted on standard power system test cases.