Evolutionary algorithm-based multiobjective reservoir operation policy optimisation under uncertainty

Abstract

Reservoir operation optimisation is a decision support tool to assist reservoir operators with water release decisions to achieve management objectives, such as maximising water supply security, mitigating flood risk, and maximising hydroelectric power generation. The effectiveness of reservoir operation decisions is subject to uncertainty in system inputs, such as inflow and therefore, methods such as stochastic dynamic programming (SDP) have been traditionally used. However, these methods suffer from the three curses of dimensionality, modelling, and multiple objectives. Evolutionary algorithm (EA)-based simulation-optimisation frameworks such as the Evolutionary Multi-Objective Direct Policy Search (EMODPS) offer a new paradigm for multiobjective reservoir optimisation under uncertainty, directly addressing the shortcomings of SDP-based methods. They also enable the consideration of input uncertainty represented using ensemble forecasts that have become more accessible recently. However, there is no universally agreed approach to incorporate uncertainty into EA-based multiobjective reservoir operation policy optimisation and it is not clear which approach is more effective. Therefore, this study conducts a comparative analysis to demonstrate the advantages and limitations of different approaches to account for uncertainty in multiobjective reservoir operation policy optimisation via a real-world case study; and provide guidance on the selection of appropriate approaches. Based on the results obtained, it is evident that each approach has both advantages and limitations. A suitable approach needs to be carefully selected based on the needs of the study, e.g., whether a hard constraint is required, or a well-established decision-making process exists. In addition, potential gaps for future research are identified.