Multiobjective ray optimization algorithm as a solution strategy for solving non-convex problems: A power generation scheduling case study

Beirami, Amin; Vahidinasab, Vahid; Shafie-khah, Miadreza; Catalão, João P.S.

Abstract

Economic generation scheduling (EGS) is a non-convex optimization problem for allocating optimal generation among the committed units that can meet given real-world practical limits such as ramp rate limits, prohibited operating zones, valve loading effects, multi-fuel options, spinning reserve and transmission system losses at the
minimum fuel cost. Moreover, considering environmental issues results in an environmental/economic generation scheduling (EEGS) problem that is a multiobjective optimization model with two non-commensurable and contradictory objectives. In this paper, a novel method has been presented in order to minimize production cost and emission of the steam power plants in short term periods. The obtained results showed that the proposed method can be used in short-term decision making of steam power plants which will be absolutely effective in long-term emission target oriented strategies. A framework is proposed for solving single objective EGS and multiobjective EEGS problems considering the aforementioned constraints. The problem is solved by a new meta-heuristic optimization called Ray Optimization (RO) to determine the optimal power generation. The performance of the proposed algorithm is investigated by applying it to solve diverse test systems having non convex solution spaces. Numerical results have been comprehensively compared with some of the most recently published research works in the area in order to validate the results and confirm the potential of the proposed approach. The obtained results show the application of the proposed framework and effectiveness of the solutions.

• Journal Article Type: Article
• Acceptance Date: Feb 24, 2020
• Online Publication Date: Mar 4, 2020
• Publication Date: Mar 4, 2020
• Deposit Date: Mar 5, 2025
• Journal: International Journal of Electrical Power & Energy Systems
• Print ISSN: 0142-0615
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 119
• Article Number: 105967
• DOI: https://doi.org/10.1016/j.ijepes.2020.105967