Research on the Multi-Objective Cooperative Competition Mechanism of Jinsha River Downstream Cascade Reservoirs during the Flood Season Based on Optimized NSGA-III

Abstract

This paper analyzes the complex relationship among flood control, power generation and ecological maintenance for the four cascade reservoirs located on the lower reaches of the Jinsha River, China. A weighted flood control index is incorporated and a constraining method consisting of the combination of a constrained corridor and a penalty function is proposed. A comprehensive utilization model is established in this paper based on the objectives of flood prevention, power generation, and ecological maintenance of the downstream cascade reservoir group of the Jinsha River during flood season. In addition, based on the coalescent selection of reference points and vector angles, an optimized non-dominated sorting genetic algorithm (VA-NSGA-III) is proposed. The algorithm is applied to the constructed model to define the cooperative competition mechanisms among these three targets, resulting in a set of non-inferior scheduling schemes with more uniformity and better convergence acquired with VA-NSGA-III. The scheduling program shows that there is a non-linear competitive relationship between the power generation and ecological effects of the cascade reservoirs during flood season, and the competitiveness weakens as the power generation increases. Furthermore, when the flood control is at low risk, there exists a complex coupling relationship between competition and coordination of the flood control, power generation, and ecological maintenance. While the risk appears high, there is a competitive relationship between flood control and power generation, with flood control being in synergy with ecological maintenance.

Scenario‐Based Multiobjective Robust Optimization and Decision‐Making Framework for Optimal Operation of a Cascade Hydropower System Under Multiple Uncertainties

“…To overcome the acknowledged shortcomings, NSGA‐III was formulated by modifying the operators of NSGA‐II and adding the guidance of reference points to maintain solution diversity. Generally, the following steps are involved in the process (Ni et al., 2019). Initialization.…”

Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand

“…Based on the four types of PM models that include single-and bi-objective models with and without the constraint, PM strategies in the green industry were extensively investigated. For the bi-objective PM optimization, NSGA-III [24][25][26][27][28] was employed, which was previously adopted to solve various multi-objective optimization problems, with modifications made for specific problems [29][30][31][32][33]. The algorithm was further developed to optimize the competitive PM alternatives in the Pareto front in the form of maintenance activities in the degradation states of components.…”

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

“…The nondominated sorting genetic algorithm III (NSGA-III) [21][22][23] has been successfully adopted to solve various multiobjective optimisation problems, and variants have been developed for specific problems [24][25][26][27][28]. Bhesdadiya et al [21] applied NSGA-III to lower the emission value and fuel cost of fossil fuel power plants while providing a sustainable and reliable power supply.…”

“…Li et al [24] applied NSGA-III to solve a triobjective military optimisation problem for minimising expected damage and operation costs and maximising combat value for fighter jets. Ni et al [25] applied NSGA-III to optimise the complex coupling relationship between flood control, power generation, and ecological maintenance of a reservoir. Bi and Wang [26] proposed an improved NSGA-III with enhanced convergence.…”