A GSO-based algorithm for combined heat and power dispatch problem with modified scrounger and ranger operators. (25th June 2017)

• Record Type: Journal Article
• Authors: Davoodi, Elnaz; Zare, Kazam; Babaei, Ebrahim
• Is Part Of: Applied thermal engineering. Volume 120(2017)
• Page Start: 36
• Page End: 48
• Publication Date: 2017-06-25
• Subjects: Optimization -- Combined heat and power unit (CHP) -- Cogeneration -- Economic dispatch -- Group search optimizer algorithm (GSO); Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.03.114
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: This paper presents new group search optimizer approach for solving combined heat and power economic dispatch. This approach implements adaptive scrounger and ranger strategies for improving the GSO algorithm. The effect of valve-point in cost function considered and clearly formulated in the cost function. The proposed framework is applied for three different case studies. Results are compared with other recent methodologies and demonstrate the superiority and effectiveness of the proposed method. Abstract: In this paper a modified group search optimizer (MGSO) algorithm has been proposed for solving the combined heat and power economic dispatch (CHPED) problem with bounded feasible operating region. MGSO is a population-based optimization algorithm inspired by group-living, a typical phenomenon of the animal territory. The adaptive scrounger and ranger strategies have been applied in this algorithm in order to improve the performance of the basic group search optimizer (GSO). Unlike most of the previous approaches, valve-point loading effect has been considered and added to the classical polynomial objective function as an absolute sinusoidal term. The Simulations have been carried out on various case studies, including (i) small (5-unit test system with three different test scenarios), (ii) medium (24-unit and 48-unit test systems) and (iii) large (72-unit and 96-unit test systems) test systems in order to validate the effectiveness of the suggested framework. According to the numerical results, MGSO has a higher quality solution and superior performance in comparison to some of the recently presented approaches.