Abstract
This paper proposes the shrink Gaussian distribution quantum-behaved optimization (SG-QPSO) algorithm to solve economic dispatch (ED) problems from the power systems area. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SG-QPSO maintains a strong global search capability at the beginning and strengthen its local search capability gradually. In this way, SG-QPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages. The performance of the SG-QPSO algorithm was obtained by evaluating three different power systems containing many nonlinear features such as the ramp rate limits, prohibited operating zones, and nonsmooth cost functions and compared with other existing optimization algorithms in terms of solution quality, convergence, and robustness. Experimental results show that the SG-QPSO algorithm outperforms any other evaluated optimization algorithms in solving ED problems.
Highlights
Solving economic dispatch (ED) problem is to ensure that the power production is safe, high-quality and meets the customer’s electricity demand by using various technical and management measures to make the power production equipment in the best working state and reach the lowest cost of the power system
These deterministic numerical methods do not work effectively for problems with hard constraints such as nonsmooth and nonconvex cost functions, or suffer “dimensional disasters.” erefore, in order to effectively address the issues of the nonlinear characteristics of practical power systems, many swarm intelligence algorithms or evolutionary algorithms are used to solve multiconstrained optimization problems, including genetic algorithms (GA) [5], particle swarm optimization (PSO) [6], differential evolution (DE) [7], evolutionary programming (EP) [8, 9], tabu search (TS) [10], neural network (NN) [11, 12], ant colony search algorithm (ACSA) [13], artificial immune system (AIS) [14, 15], honey bee colony algorithm [16], firefly algorithm [17], and the hybrid method [18]
When M 100 and Gmax 200, the worst performance optimization algorithm is antipredatory PSO (APSO), and the mean cost obtained in 100 runs is 15473.3164$/h
Summary
Solving economic dispatch (ED) problem is to ensure that the power production is safe, high-quality and meets the customer’s electricity demand by using various technical and management measures to make the power production equipment in the best working state and reach the lowest cost of the power system. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SGQPSO maintains a strong global search capability at the early search stage and strengthens the local search capability at the later stage In this way, the proposed SGQPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages.
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