Design of optimal operating rule curves for hydropower multi-reservoir systems by an influential optimization method

Abstract

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems.