Abstract-In this paper, three new particle swarm optimization (PSO) algorithms are compared with the state of the art PSO algorithms for the optimal steady-state performance of power systems, namely, the reactive power and voltage control. Two of the three introduced, the enhanced GPAC PSO and LPAC PSO, are based on the global and local-neighborhood variant PSOs, respectively. They are hybridized with the constriction factor approach together with a new operator, reflecting the physical force of passive congregation observed in swarms. The third one is based on a new concept of coordinated aggregation (CA) and simulates how the achievements of particles can be distributed in the swarm affecting its manipulation. Specifically, each particle in the swarm is attracted only by particles with better achievements than its own, with the exception of the particle with the best achievement, which moves randomly as a "crazy" agent. The obtained results by the enhanced general passive congregation (GPAC), local passive congregation (LPAC), and CA on the IEEE 30-bus and IEEE 118-bus systems are compared with an interior point (IP)-based OPF algorithm, a conventional PSO algorithm, and an evolutionary algorithm (EA), demonstrating the excellent performance of the proposed PSO algorithms.

Abstract-In this paper an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm is introduced for solving the optimal economic load dispatch (ELD) problem in power systems. In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively and the particles search the decision space with accuracy up to two digit points resulting in the improved convergence of the process. The ICA-PSO algorithm is tested on a number of power systems, including the systems with 6, 13, 15, and 40 generating units, the island power system of Crete in Greece and the Hellenic bulk power system, and is compared with other state-of-the-art heuristic optimization techniques (HOTs), demonstrating improved performance over them.

A new formulation and solution of probabilistic constrained load flow (PCLF) problem suitable for modern power systems with wind power generation and electric vehicles (EV) demand or supply is represented. The developed stochastic model of EV demand/supply and the wind power generation model are incorporated into load flow studies. In the resulted PCLF formulation, discrete and continuous control parameters are engaged. Therefore, a hybrid learning automata system (HLAS) is developed to find the optimal offline control settings over a whole planning period of power system. The process of HLAS is applied to a new introduced 14-busbar test system which comprises two wind turbine (WT) generators, one small power plant, and two EV-plug-in stations connected at two PQ buses. The results demonstrate the excellent performance of the HLAS for PCLF problem. New formulae to facilitate the optimal integration of WT generation in correlation with EV demand/supply into the electricity grids are also introduced, resulting in the first benchmark. Novel conclusions for EV portfolio management are drawn.Index Terms-Constrained load flow, correlation model, electric vehicles integration, planning period, stochastic learning automata, wind power penetration.

This paper presents an evolutionary algorithm based on quantum computation for bid-based optimal real and reactive power (P-Q) dispatch. The proposed quantum-inspired evolutionary algorithm (QEA) has applications in various combinatorial optimization problems in power systems and elsewhere. In this paper, the QEA determines the settings of control variables, such as generator outputs, generator voltages, transformer taps and shunt VAR compensation devices for optimal P-Q dispatch considering the bid-offered cost. The algorithm is tested on the IEEE 30-bus system, and the results obtained by the QEA are compared with those obtained by other modern heuristic techniques: ant colony system (ACS), enhanced GA and simulated annealing (SA) as well as the original QEA. Furthermore, in order to demonstrate the applicability of the proposed QEA, it is also implemented in a different problem, which is to minimize the real power losses in the IEEE 118-bus transmission system. The comparisons demonstrate an improved performance of the proposed QEA.

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