Abstract:

The medium- and long-term optimal scheduling of water resources is a complex optimization problem characterized by non-linearity, multi-stage high dimensionality, and multi-constraints. To address the local optimization and low convergence efficiency of classical intelligent algorithms, this paper introduce a novel algorithm named the Adaptive Chaotic Elite Mutation Differential Evolution (ACEDE) algorithm. The algorithm leverages a chaotic search strategy to enhance the algorithm’s exploration capabilities while revising the traditional mutation approach to learn from elite individuals, thereby accelerating convergence. The proposed algorithm is applied to the medium- and long-term scheduling of the Pearl River Delta Water Resources Allocation Project (PRD WRAP) as a case study and is compared with classical intelligent algorithms. Results indicate that: 1) The ACEDE algorithm improves significantly in global exploration capabilities and convergence accuracy and speed, demonstrating good adaptability. For the June 2030 and June 2040 level year dispatches, respectively, the ACEDE algorithm, saves ￥742 300 and ￥235 500 in electricity costs compared to the traditional DE algorithm, reducing the cost of electricity by 6.68% and 1.52%. 2) For the medium- and long-term optimal scheduling of PRD WRAP, fully utilizing the reservoir storage capacity to meet high water demand while slowing down the replenishment at the end of month could effectively control the smooth operation of the pumping station and minimize electricity costs.

Key words: optimization of water resources dispatching, differential evolution algorithm, chaotic mapping, elite mutation, Pearl River Delta Water Resources Allocation Project