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

Optimal location and sizing of DSTATCOM in distribution systems by immune algorithm

Polygeneration microgrids: A viable solution in remote areas for supplying power, potable water and hydrogen as transportation fuel

DSTATCOM allocation in distribution networks considering load variations using bat algorithm

Optimal allocation of shunt Var compensators in power systems using a novel global harmony search algorithm

Heuristic approaches are traditionally applied to find the size and location of Flexible AC Transmission Systems (FACTS) devices in a small power system. Nevertheless, more sophisticated methods are required for placing them in a large power network. Recently, the Particle Swarm Optimization (PSO) technique has been applied to solve power engineering optimization problems giving better results than classical methods. This paper shows the application of PSO for optimal sizing and allocation of a Static Compensator (STATCOM) in a power system. A 45 bus system (part of the Brazilian power network) is used as an example to illustrate the technique. Results show that the PSO is able to find the best solution with statistical significance and a high degree of convergence. A Detailed description of the method, results and conclusions are also presented.

Optimal STATCOM Sizing and Placement Using Particle Swarn Optimization

This study shows step by step the application of the particle swarm optimization (PSO) method to solve the problem of optimal allocation and sizing of multiple static compensators (STATCOM) in a medium size power network (45 bus system, part of the Brazilian power network). The PSO is proposed as an alternative methodology for traditional heuristic approaches and complicated mixed integer linear and non linear programming methods. Simulation results show the suitability of the PSO technique in finding multiple optimal solutions to the problem (Pareto front) with reasonable computational effort. As a part of this study, the optimal setting of PSO parameters is investigated and different power system load conditions are tested to determine the impact over the location and size of each STATCOM unit

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Multiple STATCOM Allocation and Sizing Using Particle Swarm Optimization

Grid-connected photovoltaic (PV) systems with relatively high capacity effectively reduce peak load, but because of their reverse power flow, they can cause overvoltage along a feeder that can exceed five percent of the rated voltage Modern PV systems with the capability of Volt/Var control can mitigate overvoltage by either injecting or absorbing reactive power Thus, the objectives of this study are to (1) model a distribution network (feeder J1 located in the northeastern United States and enhanced by thirteen PV systems) using OpenDSS; (2) use actual collected load profile data as input data to the simulation model and PV generation output data at 15-minute intervals; and (3) calculate the time-series steady-state power flow of the distribution network when PV systems generate or absorb reactive power Finally, it addresses overvoltage resulting from the deployment of high-capacity grid-connected renewable PV systems and mitigation techniques for such overvoltage issues, particularly Volt/Var management and control

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The effect of the volt/var control of photovoltaic systems on the time-series steady-state analysis of a distribution network

This paper validates the effectiveness of an enhanced particle swarm optimizer (Enhanced-PSO) method in solving the problem of optimal allocation of FACTS devices in a power system. The performance of the Enhanced-PSO method is compared with classical optimization approaches using a simple but realistic case study of optimal allocation of STATCOM devices, considering steady state and economic criteria. This paper also discusses the concepts and details about the optimization process that tend to be overlooked in the literature since the selection of an optimization algorithm highly depends on them.

Comparison of Enhanced-PSO and Classical Optimization Methods: A Case Study for STATCOM Placement

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Yamille del Valle

Papers

Optimal STATCOM Sizing and Placement Using Particle Swarn Optimization

Multiple STATCOM Allocation and Sizing Using Particle Swarm Optimization

The effect of the volt/var control of photovoltaic systems on the time-series steady-state analysis of a distribution network

Comparison of Enhanced-PSO and Classical Optimization Methods: A Case Study for STATCOM Placement

Optimal Allocation of a STATCOM in a 45 Bus Section of the Brazilian Power System Using Particle Swarm Optimization