Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization

Abstract

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

Multiobjective Optimization on Hierarchical Refugee Evacuation and Resource Allocation for Disaster Management

“…e SA algorithm is the expansion of the local search algorithm. Different from the local search algorithm, SA accepts a worse solution with a certain probability in iterations to prevent local optimum [20]. Based on this optimization feature, SA has been used in the site selection research [21], manufacturing problem [22], and multiobjective problem [23].…”

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

“…However, the optimality of solution is compromised, and the algorithm is validated on static networks thereby deficient in evaluating the dynamic stability of the proposed work. An improved SA‐PSO is proposed in Glover and McMillan by introducing GA's mutation and crossover operators into the traditional SA‐PSO algorithm. This embodied the algorithm with the capabilities for global searching and local exploration to overcome the deficiencies in location selection and capacity finding of DGs such as local optimality and slow convergence speed.…”

Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

“…The capacity at a given year y and state s is considered to be constrained between maximum and minimum values. The active and reactive capacity limits of exiting REHDGs are given in (27) and 28, respectively. Equations (29) and (30) give the corresponding limits for the new REHDGs.…”

“…Intelligent search (IS) based methods are differently employed to solve the optimal sizing and placement of DGs problems. IS methods utilize artificial intelligence (AI) algorithms like the genetic algorithm (GA) [21,22], particle swarm optimization (PSO) [23,24], simulated annealing (SA) [25][26][27], harmony search (HS) [28,29], big bang crunch (BBC) [30,31], the fireworks algorithm (FA) [32,33], and the water drop algorithm (WDA) [34,35].…”