A Review on Energy Management System for Grid-Connected Utility-Scale Renewable Hybrid Power Plants.

Abstract

In recent years, renewable hybrid power plants (HPPs) have experienced rapid expansion. Energy management systems (EMSs) are vital to these facilities, helping maximize economic returns for owners and shaping operational strategies across various time scales. However, a comprehensive review of advancements in this field is still lacking. This paper presents an in-depth analysis of EMS research tailored for grid-connected, utility-scale renewable HPPs. It begins by outlining common HPP configurations, which form the foundation for EMS modeling. Five key EMS approaches are then discussed in detail, namely, rule-based methods, mathematical optimization, model predictive control, deep reinforcement learning, and stochastic dynamic programming. Following that, the paper categorizes the types of market participation and uncertainties addressed by EMS, and it introduces several industrial EMS tools. Finally, the discussion highlights existing gaps in EMS research for HPPs. Overall, this paper provides cutting-edge insights into EMS for HPPs, serving as a valuable resource for both researchers and industry professionals involved in HPP EMS development.