An Efficient Multi-objective Evolutionary Approach for Solving the Operation of Multi-Reservoir System Scheduling in Hydro-Power Plants

TL;DR

This paper introduces a novel multi-objective evolutionary algorithm, MESH, to optimize hydro-power reservoir operations, maximizing energy output and water volume, outperforming existing methods in efficiency and accuracy.

Contribution

The paper presents the MESH algorithm, a new swarm intelligence-based method, for multi-reservoir hydro-power scheduling, demonstrating superior performance over established evolutionary algorithms.

Findings

MESH outperforms NSGA-II, NSGA-III, SPEA2, and MOEA/D in efficiency and accuracy.

The approach yields an estimated profit of $412,500 per month.

Results validated on a real Brazilian hydro-power system.

Abstract

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of \$412,500 per month in a projection analysis carried out.