Application of Hybrid Multiagent-based Particle Swarm Optimization to Optimal Reactive Power Dispatch

Abstract

Reactive power dispatch (RPD) is an optimization problem that determines variables, such as reactive power outputs of generators, output of shunt capacitors/reactors, etc., and minimizes the transmission losses, satisfying a given set of constraints. To solve the optimal reactive power dispatch (ORPD), this article proposes a new method, called hybrid multiagent-based particle swarm optimization, which does not allow the search to struck at local optima and search in different zones of the search space. Simulation results show that the proposed approach converges to better solutions faster than the earlier approaches when evaluated on IEEE 30-bus and IEEE 118-bus systems.

Optimal control method for power system reactive power management considering real‐time uncertainties

“…Intelligent search-based approaches such as genetic algorithms 3 and evolutionary algorithms 4 have been proposed to solve this limitation. Some feasible evolutionary algorithms have shown success in solving single-objective optimization problems, such as hybrid multiagent-based particle swarm optimization, 5 self-balanced differential evolution, 6 quasi-oppositional differential evolution, 7 novel teaching-learning-based optimization, 8 improved GSA-based algorithm, 9 differential evolution, 10 and swarm intelligent techniques. 11 Concentrating on single-objective optimization for absolute increase in power system performance looks insufficient in reality.…”

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

“…PSO is easily fallen into local optimal zones, and the possibility of jumping out the zones and moving to global optimal zones is low. However, it is not denied that PSO has been widely and successfully applied for many optimization problems, and its improved versions have increased constantly such as MLCL-PSO for ELD problem [11], hybrid particle swarm optimization (HPSO) for power loss optimization problem in transmission networks [34], hybrid multiagent-based particle swarm optimization (HMPSO) [35], PG-PSO for ELD problem [36], time-varying acceleration coefficients-based particle swarm optimization (TVAC-PSO) for combined heat and power dispatch (CHPD) problem [37], and four PSO methods such as constriction factor-based particle swarm optimization (CF-PSO), inertia weight factor-based particle swarm optimization (IW-PSO), CF-PSO with local best particle and IW-PSO with local best particle for CHPD problem [38]. As shown in the studies, these PSO methods could reach good result with higher performance than other metaheuristic methods like DE, hybrid DE (HDE), improved DE (IDE), GA, hybrid GA (HGA), TSA, gravitational search algorithm (GSA), harmony search algorithm (HSA), evolutionary programming algorithm (EPA), simulated annealing algorithm (SAA) and biogeography-based optimization algorithm (BBOA).…”

Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi‐objective model at the presence of uncertain wind power generation

“…In [15], a hybrid algorithm combining firefly algorithm (FA) and Nelder–Mead (NM) simplex method is presented for solving ORPD problem in power systems. Combination of shuffled frog leaping algorithm (SFLA) and NM algorithm in [16], gravitational search algorithm (GSA) in [17], penalty‐based discrete method in [18], quasi‐oppositional DE (QODE) in [19], exchange market algorithm (EMA) [20], ant colony optimisation (ACO) algorithm in [21], modified DE (MDE) in [22], constriction factor‐based PSO in [23], hybrid multi‐agent‐based PSO (HMAPSO) in [24], biogeography‐based optimisation (BBO) in [25], opposition‐based GSA (OGSA) in [26] and improved pseudo‐gradient search‐PSO (IPG‐PSO) in [27] are also utilised to solve single‐objective ORPD problem in recent years.…”