A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems. (15th July 2016)

• Record Type: Journal Article
• Authors: Ghasemi, Mojtaba; Aghaei, Jamshid; Akbari, Ebrahim; Ghavidel, Sahand; Li, Li
• Is Part Of: Energy. Volume 107(2016)
• Page Start: 182
• Page End: 195
• Publication Date: 2016-07-15
• Subjects: MAED (Multi-area economic dispatch) -- RCMAED (reserve constrained multi-area economic dispatch) -- RCMAEED (reserve constrained environmental/economic dispatch) -- DEPSO (differential evolution particle swarm optimization); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.04.002
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes a new, efficient and powerful heuristic-hybrid algorithm using hybrid DE (differential evolution) and PSO (particle swarm optimization) techniques DEPSO (differential evolution particle swarm optimization) designed to solve eight optimization problems with benchmark functions and the MAED (multi-area economic dispatch), RCMAED (reserve constrained MAED) and RCMAEED (reserve constrained multi area environmental/economic dispatch) problems with reserve sharing in power system operations. The proposed hybridizing sum-local search optimizer, entitled HSLSO, is a relatively simple but powerful technique. The HSLSO algorithm is used in this study for solving different MAED problems with non-smooth cost function. The effectiveness and efficiency of the HSLSO algorithm is first tested on a number of benchmark test functions. Experimental results showe the HSLSO has a better quality solution with the ability to converge for most of the tested functions. Highlights: Solving multi-area economic dispatch with tie-line constraints. A comparative study of proposed DEPSOs for MAED problem. The HSLSO has a better quality solution with the ability to converge.