Integration of Distributed Generation in Power Networks Considering Constraints on Discrete Size of Distributed Generation Units

Abstract

Abstract—An optimization algorithm based on a novel discrete particle swarm optimization technique is proposed in this article for optimal sizing and location of distributed generation in a power distribution network. The proposed algorithm considers distributed generation size and location as discrete variables substantially reducing the search space and, consequently, computational requirements of the optimization problem. The proposed algorithm treats the generator sizes as real discrete variables with uneven step sizes that reflect the sizes of commercially available generators, meaning that it can handle a mixed search space of integer (generator location), discrete (generator sizes), and continuous (reactive power output) variables while substantially reducing the search space and, consequently, computational burden of the optimization problem. The validity of the proposed discrete particle swarm optimization algorithm is tested on a standard 69-bus benchmark distribution network with four different test cases. Two optimization scenarios are considered for each test case: a single objective optimization study where network real power loss is minimized and a multi-objective study in which network voltages are also considered. The proposed algorithm is shown to be effective in finding the optimal or near-optimal solution to the problem at a fraction of the computational cost associated with other algorithms.

Keywords:

• Power system optimization
• distributed generation
• dichotomy search algorithm
• evolutionary computation
• particle swarm optimization
• power loss reduction

Table A1 Commercially available generator sizes used in the study

Additional information

Notes on contributors

Idris Musa

Idris Musa received his B.Eng. and M.Eng. in electrical engineering from Bayero University, Kano, Nigeria, in 1996 and 2002, respectively. He is currently a Ph.D. student with the School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, UK. His current research interest is on stochastic optimization techniques applied to integration of distributed generation in power distribution networks.

Shady Gadoue

Shady Gadoue received his B.Sc. (hons.) and M.Sc. in electrical engineering from Alexandria University, Alexandria, Egypt, in 2000 and 2003, respectively, and his Ph.D. from Newcastle University, Newcastle upon Tyne, UK, in 2009. From 2009 to 2011, he was an assistant professor with the Department of Electrical Engineering, Alexandria University, where he was a demonstrator in 2000 and an assistant lecturer in 2003. In 2011, he joined the School of Electrical and Electronic Engineering at Newcastle University, UK, as a lecturer on control systems and electric drives. His main research interests include state estimation and control and optimization of power conversion systems with applications of computational intelligence techniques.

Bashar Zahawi

Bashar Zahawi received his B.Sc. and Ph.D. in electrical and electronic engineering from Newcastle University, UK, in 1983 and 1988. From 1988 to 1993, he was a design engineer with a U.K. manufacturer of large variable-speed drives and other power conversion equipment. In 1994, he was appointed as a lecturer in electrical engineering at the University of Manchester, and in 2003, he joined the School of Electrical and Electronic Engineering at Newcastle University, UK, as a senior lecturer. He is a recipient of the Crompton Premium awarded by the Institution of Electrical Engineers (IEE) and the Denny Medal awarded by the Institute of Marine Engineering, Science & Technology (IMarEST). His research interests include distributed generation, power conversion, and the application of non-linear dynamical methods to electrical circuits and systems.