Day-Ahead Nonlinear Optimization Scheduling for Industrial Park Energy Systems with Hybrid Energy Storage

Abstract

Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks, while lowering the industrial parks’ carbon emissions and accommodating diverse load demands from users. However, most optimization research on hybrid energy storage has adopted rule-based passive-control principles, failing to fully leverage the advantages of active energy storage. To address this gap in the literature, this study develops a detailed model for an industrial park energy system with hybrid energy storage (IPES-HES), taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks. An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day. An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm II (NSGA-II). A method using the improved NSGA-II is developed for day-ahead nonlinear scheduling, based on configuration optimization. The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD (5.5%) and 1600.3 kW (43.7%), respectively, compared with an operation strategy based on proportional electricity storage on a typical summer day. Overall, the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.