Optimal planning of grid-connected energy storage and renewable energy sources integration using an improved whale algorithm

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Chinese explanation / 中文解读

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Original abstract

The integration of renewable energy sources (RESs) into energy systems poses considerable operational challenges, due to their intermittent and stochastic nature. Grid-connected energy storage systems (ESSs) present a compelling alternative for reliably accommodating various RESs. This paper presents an optimal scheduling for allocating wind-storage system capacity in high mountain regions with abundant wind resources and irrigation pumping demand. To capture uncertainty in wind and solar generation, a scenario-free stochastic formulation is adopted, enabling tractable and scalable uncertainty modelling. To reduce the overall lifecycle comprehensive cost (COC), a synergistic optimization model is developed by integrating features of wind energy, pumping unit parameters, and storage configurations. The model considers startup/shutdown losses, optimal flow distribution, and operating expenses of the pumping station while optimizing energy storage capacity. A single-battery system is compared with a hybrid battery-hydrogen storage strategy. Results show the hybrid solution reduces COC by 5.4%, alleviates battery operational stress, and maintains pumping station efficiency. The framework incorporates a reliability model for wind power generation, ensuring robust lifecycle cost optimization. This approach demonstrates the financial and technological benefits of hybrid storage in renewable energy-driven irrigation systems. Simulations in the case study validate the flexibility and effectiveness of the proposed approach across various practical settings.

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