Solving Unit Commitment Problem Using Multi-agent Evolutionary Programming Incorporating Priority List

This paper presents an approach to solve the unit commitment problem using a newly developed Multi-agent Evolutionary Programming incorporating Priority List optimisation technique (MAEP-PL). The objective of this study is to search for generation scheduling such that the total operating cost can be minimised when subjected to a variety of constraints, while at the same time reducing its computational time. The proposed technique assimilates the concepts of Priority Listing (PL), Multi-agent System (MAS) and Evolutionary Programming (EP) as its basis. In the proposed technique, deterministic PL technique is applied to produce a population of initial solutions. The search process is refined using heuristic EP-based algorithm with multi-agent approach to produce the final solution. The developed technique is tested on ten generating units test system for a 24-h scheduling period, and the results are compared with the standard Evolutionary Programming (EP), Evolutionary Programming with Priority Listing (EP-PL) and Multi-agent Evolutionary Programming (MAEP) optimisation techniques. From the obtained results and the comparative studies, it was found that the proposed MAEP-PL optimisation technique is able to solve the unit commitment problem where the total daily generation cost is effectively minimised and the computation time is reduced as compared to other techniques.