Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

Di Lu, Yonggang Peng () and Jing Sun
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Di Lu: Powerchina Huadong Engineering Corporation, Hangzhou 310030, China
Yonggang Peng: College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China
Jing Sun: College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China

Energies, 2024, vol. 17, issue 3, 1-19

Abstract: To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid.

Keywords: renewable energy system; hybrid energy storage; multi-objective optimization; dual-stage optimization; electrolyzer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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