Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

Abstract

Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system's economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

“…MSCLPSO is a decomposition based multiswarm MOPSO [ 23 ]. As Fig 1 shows, M swarms are used and each swarm m (1 ≤ m ≤ M ) focuses on optimizing objective f m using CLPSO.…”

“…MSCLPSO takes the decomposition based multiswarm architecture and updates each particle i ’s velocity purely based on the search experience of the particles in i ’s host swarm because information determined based on Pareto dominance or some other single objective might not contribute to the optimization on i ’s associated objective. MSCLPSO was applied to the 2-objective sustainable operation of China’s Three Gorges cascaded hydropower system in [ 23 ]. This paper gives a detailed description of MSCLPSO and presents the algorithm’s performance on a variety of benchmark MOPs.…”

Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

“…The optimal operation of such a hydropower reservoir aims to improve the generation policy for production maximization or other purposes [1]. For a multiple-reservoir cascaded hydropower system, the coordination among the reservoirs can create additional opportunities to further improve their operational policies [2]. When multiple cascaded hydropower systems on different rivers are connected by a power network, the joint operation provides great potential for increasing cooperative generation production of the entire system using the complementarities of hydrology, storage capacity, and generation capacity.…”

Multi‐Swarm Multi‐Objective Optimizer Based on p‐Optimality Criteria for Multi‐Objective Portfolio Management

“…Indeed, multi-swarm strategy could restrict such rapid convergence and increase diversity effectively due to cooperation and exchange between swarms. In literatures [10][11][12][13], several MOEAs introduce a multi-swarm strategy. These proposed algorithms contain multiple slave swarms, and the quantity of slave swarms is equal to that of objective functions.…”