A Multi-Criteria Metaheuristic Algorithm for Distributed Optimization of Electric Energy Storage

TL;DR

This paper introduces a novel multi-criteria metaheuristic algorithm for distributed optimization of electric energy storage, capable of generating optimized schedules considering various local criteria for real-world household and industrial applications.

Contribution

It presents an extended distributed optimization framework, an evolutionary algorithm for schedule generation, and the inclusion of arbitrary local criteria, advancing energy storage scheduling methods.

Findings

Suitable for real household and industrial energy storage scheduling

Effective in handling multiple local optimization criteria

Demonstrates improved scheduling performance over existing methods

Abstract

The distributed schedule optimization of energy storage constitutes a challenge. Such algorithms often expect an input set containing all feasible schedules or respectively require to efficiently search the schedule space. It is hardly possible to accomplish this with energy storage due to its high flexibility. In this paper, the problem is introduced in detail and addressed by a metaheuristic algorithm, which generates a preselection of schedules. Three contributions are presented to achieve this goal: First, an extension for a distributed schedule optimization allowing a simultaneous optimization is developed. Second, an evolutionary algorithm is designed to generate optimized schedules. Third, the algorithm is extended to include an arbitrary local criterion. It is shown that the presented approach is suitable to schedule electric energy storage in real households and industries with different generator and storage types.