Multi-Objective Operational Optimization of Energy Storage Systems in User-Side Microgrids

TL;DR

This paper presents a multi-objective optimization framework for energy storage systems in user-side microgrids, improving cost, emissions, and renewable use based on real data and scalable computational methods.

Contribution

It introduces a user-centric, multi-objective optimization strategy with dynamic weighting, validated on real data, enhancing flexibility and practical applicability for microgrid ESS operation.

Findings

Achieves an average 13.47% reduction in electricity costs.

Demonstrates accurate modeling of real system constraints.

Enables flexible operational modes through dynamic weight adjustment.

Abstract

An operational optimization strategy for microgrid energy storage systems (ESSs) is developed to address practical user-oriented application requirements, and its effectiveness is validated using real-world operational data. First, a fundamental ESS model is established to characterize system dynamics and operational constraints, providing a theoretical basis for optimization. Subsequently, a multi-objective operational optimization framework is formulated to simultaneously minimize electricity cost, reduce carbon emissions, and enhance renewable energy utilization. To ensure computational efficiency and scalability, the commercial optimization solver Gurobi is employed. The proposed strategy is evaluated using actual microgrid operational data, demonstrating that the developed ESS model accurately represents real system constraints. Compared with existing user operational strategies, the proposed approach achieves an average reduction of 13.47% in electricity cost. Moreover, by dynamically adjusting the weighting factors of the multi-objective formulation, the strategy enables flexible operational modes and significantly improves adaptability to varying operating scenarios. In addition, the proposed framework provides decision support for user-side microgrids participating in surplus electricity feed-in policies. The main contribution of this work lies in its user-centric optimization design, which enhances operational flexibility and scenario adaptability through multi-objective weight allocation, offering a practical and scalable solution for real-world microgrid ESS operation.