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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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Mathematical programming

During system restoration, it is critical to utilize the available black-start (BS) units to provide cranking power to non-black-start (NBS) units in such a way that the overall system generation capability will be maximized. The corresponding optimization problem is combinatorial with complex practical constraints that can vary with time. This paper provides a new formulation of generator start-up sequencing as a mixed integer linear programming (MILP) problem. The linear formulation leads to an optimal solution to this important problem that clearly outperforms heuristic or enumerative techniques in quality of solutions or computational speed. The proposed generator start-up strategy is intended to provide an initial starting sequence of all BS or NBS units. The method can provide updates on the system MW generation capability as the restoration process progresses. The IEEE 39-Bus system, American Electric Power (AEP), and Entergy test cases are used for validation of the generation capability optimization. Simulation results demonstrate that the proposed MILP-based generator start-up sequencing algorithm is highly efficient.

Optimal Generator Start-Up Strategy for Bulk Power System Restoration

In power distribution systems, with their great vastness and various outage causes, one of the most important problems of power distribution companies is to select a suitable maintenance strategy of system elements and method of financial planning for the maintenance of system elements with the two objectives of decrease in outage costs and improvement of system reliability. In this article, a practical method is introduced for the selection of a suitable system elements maintenance strategy; moreover, to plan the preventive maintenance budget for the system elements, two methods are offered: the cost optimization method and the fuzzy Analytic Hierarchy Process (AHP) method. In the former method, a new model of system maintenance cost is offered. This model, based on system outage information, the elements maintenance costs are determined as functions of system reliability indices and preventive maintenance budget. The latter method, too, a new guideline is introduced for considering the cost and reliability criteria in the trend of preventive maintenance budget planning. In this method, the preventive maintenance budget for the elements is determined based on relative priority of elements with reliability criteria. © 2015 Wiley Periodicals, Inc. Complexity 21: 70–88, 2016

Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods

Multiobjective optimal placement of switches and protective devices in electric power distribution systems using ant colony optimization

The fundamental goal of an electric utility is to serve its customers with a reliable and low cost power supply. This goal has led to a number of standard protection engineering practices that ensure adequate service; however, with deregulation of the utility industry and increased competitive pressures, there is a desire to further improve service reliability and reduce costs. In this vein, this work proposes a binary programming optimization to identify type and location of the protective devices on a distribution feeder. The proposed algorithm uses engineering heuristics to minimize computational time. Numerical examples highlight the proposed approach.

/pdf/optimized-distribution-protection-using-binary-programming-50q9u2ckdy.pdf

Optimized distribution protection using binary programming

The number, type and location of the protective devices on a distribution feeder have a direct effect on the system reliability. In earlier work, a technique was developed to design a protective system in order to minimize the SAIFI index. This paper extends earlier results by using a goal programming approach to achieve compromises among various engineering objectives. The design goals are: (1) to minimize the SAIFI and ASIFI indices by identifying types and locations of protective devices; and (2) to achieve a reasonable trade-off between a decrease in the SAIFI index and an increase in MAIFI index by identifying where a fuse saving scheme should be applied. Numerical examples highlight the approach.

/pdf/optimal-trade-offs-in-distribution-protection-design-3664m2tzc2.pdf

Optimal trade-offs in distribution protection design

https://isiarticles.com/bundles/Article/pre/pdf/22386.pdf

Optimal allocation of distribution maintenance resources with limited information

https://web.eecs.utk.edu/~ktomsovi/Vitae/Publications/SOUD99.pdf

F. Soudi

Papers

Optimized distribution protection using binary programming

Optimal trade-offs in distribution protection design

Optimal allocation of distribution maintenance resources with limited information

Optimal distribution protection design : quality of solution and computational analysis