Economic Dispatch Methods for Smart Grid Based on Improved SPEA2 and Improved NSGA2

Abstract

The severity of the ongoing environmental crisis has prompted the development of renewable energy generation and smart grids integration. The access of enewable energy makes the economic dispatching of smart grid complicated. Therefore, the economic dispatching model for smart grid is very necessary. This paper presents an economic dispatching model of smart power grid, which considers both economy and pollution emission. The smart grid model used for the simulation is construced of wind energy, solar energy, fuel cell, and thermal power, and the use of fuel cell enables the smart grid to achieve multi-energy complementar. To overcome the defect of the traditional centralized communication methods, which are prone to communication jams, this paper adopts a multi-agent inform ation exchange method to improve the stability and efficiency. In terms of the solution method for this model, this paper proposes Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2) and Improved Non-dominated Sorting Genetic Algorithm 2(INSGA2) that solves the economic dispatch problem of a smart grid. The strength Pareto evolutionary algorithm 2(SPEA2),non-dominated sorting genetic algorithm 2(NSGA2) and the improved algorithms are simultaneously applied to the proposed smart grid model for economic dispatching simulation. The simulation results show that ISPEA2 and INSGA2 are effective. ISPEA2 and INSGA2 have shown improvements over SPEA2 and NSGA2 in accuracy or running times.

Research on hydro-photovoltaic complementary analysis of Yangfanggou hydropower station on Yalong River

“…NSGA-II is one of the most popular multi-objective genetic algorithms, which was proposed by Srinivas and Deb in 2000 on the basis of NSGA (Li and Wang, 2021). As a representative of multi-objective optimization algorithms, it reduces the complexity of noninferiority sorting genetic algorithms.…”

“…Where, P P,t, max is the available maximum value of PV output in the tth period; P s is the installed capacity of PV generation (Li and Wang, 2021).…”

“…Where, P W,t, max and P W,t, min are the upper and lower limits of hydropower station output in the tth period respectively (Li and Wang, 2021).…”

Energy Dispatching Based on an Improved PSO-ACO Algorithm

“…Zeng et al [32] proposed a multiobjective dispatch approach based on hierarchical progressive parallel NSGA-II algorithm, which speeds up the convergence speed of energy dispatching and reduces the algorithm iteration time. Li and Wang [33] proposed an improved intensity pareto evolutionary algorithm 2 (ISPEA2) and improved nondominated sorting genetic algorithm 2 (INSGA2), which improve the accuracy of energy dispatching and reduce the dispatching time. Wang et al [34] proposed an improved harmony search algorithm to alleviate the time series constraint problem in dispatching.…”