Multi-Objective Optimal Gate Control and Hydropower Scheduling of Haditha Dam Using the Improved Harris Hawks Multi-Objective (IMOHHO) Algorithm with Long Short-Term Memory (LSTM)

Effective water management requires accurate forecasting of dam inflows, rainfall, and actual electricity demand. In this paper, we propose a framework for intelligent control of Haditha Dam gates using the improved Harris Hawks Multi-Objective algorithm (IMOHHO) with Long Short-Term Memory (LSTM). The study aims to develop a framework for scheduling the operation of the gates that strikes a balance between maximizing hydropower generation, maintaining storage levels close to the target value, reducing unused discharge, and optimizing the operations of the gates. This ensures that a minimum net head of a 147 m water level in the dam reservoir is maintained. The proposed model was applied to the Haditha Dam using operational gates and a 48-step time horizon with an interval of 30 min that could be changed as needed. In the IMOHHO-LSTM algorithm, each filtered solution represents an integrated timeline for the opening of the six gates throughout the runtime; the algorithm relies on the concept of non-dominant solutions and creates an archive of optimal solutions using the Pareto interface to achieve a balance between different operational objectives. The final results showed the potential of the proposed algorithm to ensure the efficient operation of the dam, with a total power generation of 10,434.14 MWh, while the average output power was 434.76 MW. The maximum capacity reached 660 MW, which represents the maximum design capacity of a modern dam. Furthermore, the model kept the storage level within a very narrow range of 2199 to 2206 Mm3, centering around the target storage value of 2200 Mm3. This confirms the passing of the IMOHHO-LSTM algorithm in balancing energy production with maintaining the operational requirements of the dam lake’s water level.