Metaheuristic Optimization in Power Engineering
A metaheuristic is a consistent set of ideas, concepts, and operators to design a heuristic optimization algorithm, that can provide a sufficiently good solution to an optimization problem with incomplete or imperfect information. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation, are an important application for such methods. This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm. Applications covered include power flow calculation; optimal power flow in transmission networks; optimal reactive power dispatch in transmission networks; combined economic and emission dispatch; optimal power flow in distribution networks; optimal volt/var control in distribution networks; optimal placement and sizing of distributed generation in distribution networks; optimal energy and operation management of microgrids; optimal coordination of directional overcurrent relays; and steady-state analysis of self-excited induction generators.
Inspec keywords: optimisation; power systems
Other keywords: power engineering; power system problems; power system optimization; metaheuristic optimization methods
Subjects: Optimisation techniques; General electrical engineering topics; Power systems
- Book DOI: 10.1049/PBPO131E
- Chapter DOI: 10.1049/PBPO131E
- ISBN: 9781785615467
- e-ISBN: 9781785615474
- Page count: 535
- Format: PDF
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Front Matter
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1 Overview of metaheuristic optimization
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Metaheuristic optimization methods have become significant tools and often the only way of solving practical optimization problems. The basic requirement from these methods is to obtain the global solution or a solution close to the global optimum in a reasonable time.
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2 Overview of genetic algorithms
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Genetic algorithms (GAs) belong to the group of metaheuristic optimization methods. They are based on mimicking the process of evolution in nature. Evolution refers to constant adaptation of living beings to varying conditions in the environment. Individuals with the largest ability to adapt have the best chances to survive. There is an ongoing ruthless fight for survival in nature resulting in the survival of the fittest and perishing of the weakest individuals. In order for a species to survive during evolution, it must adapt to the surrounding conditions and the environment for these constantly changes. Each succeeding generation of any species must retain the good properties of the preceding generation while improving and altering them so that the quality of individuals in the population is continuously enhanced.
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3 Overview of particle swarm optimization
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Particle swarm algorithm (PSO) is the most general of all swarm intelligence algorithms. The task of the algorithm is finding the global optimum in a multidimensional search space. Kennedy and Eberhart developed PSO based on the analogy of swarm of bird and fish school. Some of the most popular applications of PSO are related to power system problems, such as optimal operation, control, and planning.
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4 Overview of gravitational search algorithm
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Gravitational search algorithm (GSA) belongs to the nature-inspired metaheuristic optimization methods. A metaheuristic optimization method consists of a generalized set of rules that can be applied to solve a variety of optimization problems. Many metaheuristic optimization methods have been developed on the model of some well-known processes in nature. For example, well-known genetic algorithm is based on mimicking of the process of evolution in biology; simulated annealing emulates the physical process of annealing, etc.
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5 Power-flow calculation
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This chapter describes the classical methods for power-flow computation in transmission and distribution networks. The efficiency of the power-flow algorithm has crucial impact on the efficiency and robustness of methods in the functions ensuring optimal operation and planning of transmission and distribution networks. In this context, this chapter presents an introduction in the next chapters related to the optimal power flow in transmission and distribution networks, optimal reactive power dispatch, optimal siting and sizing of distributed generation in distribution networks, etc.
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6 Optimal power flow in transmission networks
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This chapter has been focused on application of metaheuristic population-based optimization methods to solution of the OPF problem.
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7 Optimal reactive power dispatch in transmission networks
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This chapter presents the applications of hybrid PSOGSA algorithm and hybrid GSA-SQP algorithm to the ORPD problem. Performances of the algorithms for the ORPD problem are studied and evaluated on standard IEEE 30-bus and IEEE-118 test systems. The simulation results are compared with those of other metaheuristic optimization algorithms reported in the literature recently. In addition, a critical analysis of the ORPD results is also presented.
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8 Combined economic and emission dispatch
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This study deals with the application of hybrid PSO and GSA (hybrid PSOGSA) algorithm to solve the CEED problem. The PSOGSA algorithm profits from the abilities of both PSO and GSA algorithms. The performance of the proposed algorithm is tested on three standard test systems, with different constraints and various cost curve natures. Numerical results obtained by the proposed approach were compared with other optimization results reported in the literature recently.
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9 Optimal power flow in distribution networks
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In this chapter, a generalized approach for the OPF analysis in distribution networks is presented. This implies the inclusion of different types of DG units, taking into account the uncertainties of the input variables, the multiobjective optimization, and application of efficient metaheuristic methods for solving the OPF problem.
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10 Optimal Volt/Var control in distribution networks
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This chapter deals two methodologies for optimal voltage control. The first methodology related to determining the optimal transformer tap settings in radial (rural) distribution networks is based on the space and time decomposition of the voltage-control problem. Thereafter, the Volt/Var control is formulated as a nonlinear complex optimization problem with constraints and solved by using metaheuristic optimization methods.
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11 Optimal placement and sizing of distributed generation in distribution networks
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Procedure for optimal siting and sizing of DG units requires two levels. The first level aims to reduce the solution space by introducing a list of preliminary locations based on the sensitivity of power losses with power injections into the network buses. This significantly reduces the number of possible variants of the solution, which are searched in the second stage of the procedure. Two search techniques were used for searching: partial search of variants and metaheuristic optimization methods-GA. Simulation results on two test networks confirm the efficiency and robustness of this approach for both search techniques. However, it should be noted that the partial search technique is suitable for small distribution networks, while the GA also provides high-quality solutions for real systems with a large number of buses and potential locations for DG units.
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12 Optimal energy and operation management of microgrids
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In this chapter, an efficient PSO-based approach has been proposed and successfully applied to solve EOM problem in an MG. The Weibull and normal distributions are employed to model the input random variables, namely, the output power of the WT and PV units, the load demand, and the market price. The 2m+1 point estimate method and the Gram-Charlier expansion theory are used to obtain the statistical moments and the PDFs of the EOM results. The proposed approach has been tested and investigated on two grid-connected MGs including different DG units and energy storage. The simulation results show the efficiency of the proposed approach to solve both deterministic and probabilistic EOM problems under different operational scenarios of the MGs. Moreover, the results obtained using the proposed PSO algorithm are either better or comparable to those obtained using other techniques reported in the literature. As such, it can serve as a useful decision-making supporting tool for MG operators and help to find out how the input random variables affect the statistical characteristics of the EOM results.
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13 Optimal coordination of directional overcurrent relays
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The coordination between DOCRs is nonlinear and highly constrained optimization problem in which two settings, namely, TDS and PCS of each relay are considered as decision variables. The main objective is to minimize the sum of OTs of all the primary relays, which are expected to operate in order to clear the faults of their corresponding zones. In this chapter, a hybrid GSA-SQP optimization algorithm has been proposed and successfully applied to solve the DOCRs coordination problem. The SQP routine is incorporated in GSA as a local search mechanism to improve the performance of the conventional GSA algorithm. The proposed approach has been tested and investigated on three different test systems. Simulation results show that the hybrid GSA-SQP algorithm provides effective and robust high-quality solution. Moreover, the results obtained using hybrid GSA-SQP are either better or comparable to those obtained using other techniques reported in the literature. The proposed hybrid GSA-SQP algorithm is suitable to find the global optimal solution for the DOCRs coordination problems, because it benefits from the advantages of both GSA and SQP methods.
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14 Steady-state analysis of self-excited induction generators
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The aim of this chapter is to show how a metaheuristic optimization technique such as GA can be applied in solving a nonoptimization problem, i.e., in determining the steady-state performances of a SEIG for general operating conditions. GA-based optimization procedure is applied to the analysis of SEIG feeding balanced/unbalanced and static/dynamic loads. The GA has been applied to the computation of the unknowns by minimizing the total impedance module of the equivalent passive circuit.
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Back Matter
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Supplementary material
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Supplementary Material for 'Metaheuristic Optimization in Power Engineering'
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Supplementary Matlab Material for this title is available, please download the zipped file below.
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