Model-free predictive allocation control of large-scale pumped-storage clusters for active grid balancing via cutting-plane method

INTRODUCTION: Pumped-storage hydropower plants (PSHPs) are indispensable for maintaining grid balancing amid high-penetration renewable energy integration, yet optimizing their predictive control is severely hindered by the scale of interconnected units and the resulting decision-making complexity. OBJECTIVES: This paper aims to develop a computationally efficient and operationally scalable assignment predictive control framework tailored for modern PSHP fleets. METHODS: To alleviate the computational burden and enhance real-time responsiveness, a model-free assignment predictive control scheme incorporating a cutting-based method is proposed to effectively streamline the solution space while guaranteeing grid stabilization. RESULTS: Furthermore, the framework is extended to multi-PSHP clusters, establishing a cooperative model-free predictive architecture that maximizes collective balancing capacity across diverse geographical regions. Numerical simulations validate the proposed method's efficacy. CONCLUSION: The results indicate that the proposed cooperative framework provides a practical and highly scalable solution for active grid balancing and renewable integration in large-scale PSHP systems.