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

Particle swarm optimisation (PSO) has been enormously successful. Within little more than a decade hundreds of papers have reported successful applications of PSO. In fact, there are so many of them, that it is difficult for PSO practitioners and researchers to have a clear up-to-date vision of what has been done in the area of PSO applications. This brief paper attempts to fill this gap, by categorising a large number of publications dealing with PSO applications stored in the IEEE Xplore database at the time of writing.

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Analysis of the publications on the applications of particle swarm optimisation

Investment on generation system and transmission network is an important issue in power systems, and investment reversibility closely depends on performing an optimal planning. In this regard, generation expansion planning (GEP) and transmission expansion planning (TEP) have been presented by researchers to manage an optimal planning on generation and transmission systems. In recent years, a large number of research works have been carried out on GEP and TEP. These problems have been investigated with different views, methods, constraints and objectives. The evaluation of researches in these fields and categorising their different aspects are necessary to manage further works. This study presents a comprehensive review of GEP and TEP problems from different aspects and views such as modelling, solving methods, reliability, distributed generation, electricity market, uncertainties, line congestion, reactive power planning, demand-side management and so on. The review results provide a comprehensive background to find out further ideas in these fields.

Comprehensive review of generation and transmission expansion planning

A long-term, multiarea, and multistage model for the supply/interconnections expansion planning of integrated electricity and natural gas (NG) is presented in this paper. The proposed Gas Electricity Planning (GEP) model considers the NG value chain, i.e., from the supply to end-consumers through NG pipelines and the electrical systems value chain, i.e., power generation and transmission, in an integrated way. The sources of NG can be represented by NG wells, liquefied natural gas (LNG) terminals and storages of NG and LNG. The electricity generation may be composed by hydro plants, wind farms, or thermal plants where the latter represent the link between the gas and the electricity chain. The proposed model is formulated as a mixed-integer linear optimization problem which minimizes the investment and operation costs to determine the optimal location, technologies, and installation times of any new facilities for power generation, power interconnections, and the complete natural gas chain value (supply/transmission/storage) as well as the optimal dispatch of existing and new facilities over a long range planning horizon. A didactic case study as well as the Brazilian integrated gas/electricity system are presented to illustrate the proposed framework.

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A Model to Long-Term, Multiarea, Multistage, and Integrated Expansion Planning of Electricity and Natural Gas Systems

A modified differential evolution (MDE) algorithm, for solving short-term hydrothermal scheduling problem is presented. Hydrothermal scheduling involves the optimisation of a nonlinear objective function with a set of operational and physical constraints. Differential evolution, an improved version of a genetic algorithm, is a very simple, fast and robust global optimisation technique. The differential evolution algorithm is modified in order to handle the reservoir end volume constraints in the hydrothermal scheduling. The transmission losses are also accounted for through the use of loss coefficients. The study is extended for the combined economic emission dispatch. The performance of the proposed approach is validated by illustration with two test systems. The results of the proposed approach are compared with those of dynamic programming, nonlinear programming, genetic algorithm and evolutionary programming techniques. From the numerical results, it is found that the modified DE based approach is able to provide a better solution at a lesser computational effort.

Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution

This work presents both application and comparison of the metaheuristic techniques to generation expansion planning (GEP) problem. The Metaheuristic techniques such as the genetic algorithm, differential evolution, evolutionary programming, evolutionary strategy, ant colony optimization, particle swarm optimization, tabu search, simulated annealing, and hybrid approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods virtual mapping procedure (VMP) and penalty factor approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, intelligent initial population generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional dynamic programming and the effectiveness of each proposed methods has also been illustrated in detail.

Application and comparison of metaheuristic techniques to generation expansion planning problem

This paper presents the application of evolutionary computation (EC) techniques, improved evolutionary strategies (IES), improved evolutionary programming (IEP) and improved genetic algorithm (IGA) to least cost generation expansion planning (GEP) problem. Least-cost GEP problem is a highly constrained nonlinear discrete dynamic optimization problem. Several conventional non-linear optimization methods have been used to solve the GEP problem. These methods may fail to provide global optimal due to involvement of discrete variables in the constraints. Recently EC techniques are used to solve the combinatorial optimization GEP problems, due to its global search characteristics. The GEP problem is illustrated for a synthetic reliable test system with 4, 6 and 14 years planning horizon. The results obtained using IES, EEP and IGA are verified using dynamic programming (DP) for 4 and 6 year planning horizon. The problem with 14 year planning horizon is simulated using IES, IEP and IGA.

S.M.R. Slochanal

Papers

Application and comparison of metaheuristic techniques to generation expansion planning problem

Application of evolutionary computation techniques for generation expansion planning