The Application of Multi-Objective Particle Swarm Optimization in Economic Dispatch of Power System

Abstract

Economic dispatch (ED) is a typical power system operation optimization problem. But it has non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult. According to the characteristics of economic dispatch problem, a improved algorithm based on particle swarm optimization for solving economic dispatch strategy is researched in this paper. Multi-objective economic\environmental dispatch demands that the pollutant emission of power plants should reach minimum while the condition of least generation cost should be satisfied. According to this demand, this multi-objective problem is solved by improved particle swarm optimization (PSO) algorithm. Using particle position and speed of change in the familiar update, the multi-objective particle swarm algorithm based on test function of this algorithm, and the simulation results of simulation optimization. The effectiveness of the proposed algorithm is verified by Simulation.

Regulation Flexibility Assessment and Optimal Aggregation Strategy of Greenhouse Loads in Modern Agricultural Parks

“…Reference [8] establishes a physical model of the equivalent thermal parameters of residential air conditioning loads, and evaluates the maximum regulation potential of aggregated air conditioning loads based on an improved discrete particle swarm algorithm. In [9], a constant current-voltage variable power model is introduced to describe the charging and discharging processes of electric vehicles and a regulation potential assessment method is proposed that accounts for the difference in dispatch and user-side demand.…”