An Efficient Hybrid Approach to Solve Bi-objective Multi-area Dynamic Economic Emission Dispatch Problem. Issue 4 (28th July 2020)

• Record Type: Journal Article
• Authors: Azizivahed, Ali; Arefi, Ali; Naderi, Ehsan; Narimani, Hossein; Fathi, Mehdi; Narimani, Mohammad Rasoul
• Is Part Of: Electric power components and systems. Volume 48:Issue 4/5(2020)
• Page Start: 485
• Page End: 500
• Publication Date: 2020-07-28
• Subjects: economic-emission dispatch -- gray wolf optimizer (GWO) -- multiple fuel -- particle swarm optimization (PSO) -- ramp rate (RR) -- valve point effect (VPE); Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2020.1793830
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature.