A Multi-Objective Optimization Framework for Planning and Operation of Wind-Photovoltaic-Energy Storage Systems in Smart Grids

Abstract

Driven by the “dual carbon” goals, the global energy structure is transforming toward clean energy. It aggravates the resource depletion and environmental pollution faced by traditional fossil fuel power systems. Nowadays, the share of wind energy and solar energy is constantly increasing. But due to natural conditions, they still exist characteristics like intermittency. Energy storage systems, become a key solution to address these challenges by balancing supply and demand and enhance grid stability. Nevertheless, existing integrated energy planning models often fail to accurately capture the spatiotemporal correlation between wind and solar resources. To address these issues, this paper raises a multi-objective framework aiming to minimize annual total cost and renewable energy curtailment rate. This framework integrates K-Means clustering used for typical scenario extraction, the Frank-Copula function to model wind-PV correlation, and Kantorovich distance-based scenario reduction to improve efficiency. Taking the Sanjiangyuan Project as an example, after optimization, indicators like wind and solar curtailment rate, and life-all-cycle cost have obviously decreased. At the same time, the probability of insufficient power supply has been reduced, verifying the effectiveness of the model. This work provides a practical and supportive tool for the planning and stable operation of future high-renewable energy systems.