Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Abstract—In this paper, three new particle swarm optimization (PSO) algorithms are compared with the state of the art PSO al-gorithms for the optimal steady-state performance of power sys-tems, 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, respec-tively. They are hybridized with the constriction factor approach together with a new operator, reflecting the physical force of pas-sive 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 af-fecting 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 con-gregation (LPAC), and CA on the IEEE 30-bus and IEEE 118-bus systems are compared with an interior point (IP)-based OPF al-gorithm, a conventional PSO algorithm, and an evolutionary algo-rithm (EA), demonstrating the excellent performance of the pro-posed PSO algorithms. Index Terms—Coordinated aggregation (CA), particle swarm optimization (PSO), passive congregation, reactive power control, voltage control. I.

Microgrids represent an emerging paradigm of future electric power systems that can utilize both distributed and centralized generations. Two recent trends in microgrids are the integration of local renewable energy sources (such as wind farms) and the use of co-generation (i.e., to supply both electricity and heat). However, these trends also bring unprecedented challenges to the design of intelligent control strategies for microgrids. Traditional generation scheduling paradigms rely on perfect prediction of future electricity supply and demand. They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand). In this paper, we study online algorithms for the microgrid generation scheduling problem with intermittent

Abstract—In a smart grid, effective distributed control algorithms could be embedded in distributed controllers to properly allocate electrical power among connected buses autonomously. By selecting the incremental cost of each generation unit as the consensus variable, the Incremental Cost Consensus (ICC) algorithm can solve the economic dispatch problem in a distributed manner instead of using conventional centralized approaches. In this paper, we further decrease the requirement of the communication network by using an average consensus algorithm to acquire the system power mismatch information. The mathematical formulation of the algorithms is also presented in this paper. In addition, the results of several case studies are presented to show the convergence rate of the ICC algorithm and the comparison between the previous ICC and the new two-level ICC algorithm. Index Terms—Distributed control, consensus, economic dispatch, multi-agent system, smart grid I.

Economic Load Dispatch (ELD) is an important and difficult optimization problem in power system planning. This article aims at addressing two practically important issues related to ELD optimization: (1) analyzing the ELD problem from the perspective of evolutionary optimization; (2) developing effective algorithms for ELD problems of large scale. The first issue is addressed by investigating the fitness landscape of ELD problems with the purpose of estimating the expected performance of different approaches. To address the second issue, a new algorithm named ‘‘Estimation of Distribution and Differential Evolution Cooperation” (ED-DE) is proposed, which is a serial hybrid of two effective evolutionary compu-ation (EC) techniques: estimation of distribution and differential evolution. The advantages of ED-DE over the previous ELD optimization algorithms are experimentally testified on ELD problems with the number of generators scaling from 10 to 160. The best solution records of classical 13 and 40-generator ELD problems with valve points, and the best solution records of 10, 20, 40, 80 and 160-generator ELD problems with both valve points and multiple fuels are updated in this work. To further evaluate the efficiency and effectiveness of ED-DE, we also compare it with other state-of-the-art evolutionary algorithms (EAs) on typical function optimization tasks.

... interest in the recent years. Success of the V2G research depends on efficient scheduling of gridable vehicles in limited parking lots. V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for thermal units. Particle swarm optimization (PSO) is used to solve the UC with V2G, as PSO can reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO is used to optimize the on/off states of power generating units and in the same model, discrete version of PSO is used to optimize the scheduling of the gridable vehicles in the parking lots to reduce the dimension of the problem. Finally, simulation results show a considerable amount of profit for using V2G after proper UC with V2G scheduling of gridable vehicles in constrained parking lots.

Abstract—This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods. Index Terms—Bacterial foraging technique, particle swarm optimization, economic load dispatch. I.

Institute of Information Management I-Shou University This thesis presents a new approach to the support vector learning for dynamic clustering based on particle swarm optimization. Support vector clustering requires solving a constrained quadratic optimization problem. This problem often involves a matrix with an extremely large number of entries, which make off-the-shelf optimization packages unsuitable. Several methods have been used to decompose the problem, of which many require numeric packages for solving the smaller sub-problems. Support vector clustering solves the unsupervised clustering problem by searching for a minimal sphere enclosing all data images in feature space. Data points are mapped

This paper presents a new approach for solution of the economic load dispatch (ELD) problem with valve-point effect using a modified particle swarm optimization (MPSO) technique. The practical ELD problems have non-smooth cost function with equality and inequality constraints, which make the problem of finding the global optimum difficult when using any mathematical approaches. In this paper, a modified particle swarm optimization (MPSO) mechanism is proposed to deal with the equality and inequality constraints in the ELD problems through the application of Gaussian and Cauchy probability distributions. The MPSO approach introduces new diversification and intensification strategy into the particles thus preventing PSO algorithm from premature convergence. To demonstrate the effectiveness of the proposed approach, the numerical studies have been performed for three different test systems, i.e. six, thirteen and forty generating unit systems, respectively. The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature.