Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. (November 2015)

• Record Type: Journal Article
• Authors: Dubey, Hari Mohan; Pandit, Manjaree; Panigrahi, B.K.
• Is Part Of: Renewable energy. Volume 83(2015)
• Page Start: 188
• Page End: 202
• Publication Date: 2015-11
• Subjects: Dynamic multi-objective optimal dispatch (DMOOD) -- Gaussian membership function -- Hybrid flower pollination algorithm (HFPA) -- Time varying fuzzy selection mechanism (TVFSM) -- Pareto diversity -- Wind power uncertainty; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.04.034
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: To maintain security and reliability of wind integrated power grid, additional spinning reserve is required to meet the demand under changing loads and unpredictable wind power generation. This paper presents a solution of dynamic multi objective optimal dispatch (DMOOD) for wind-thermal system using a hybrid flower pollination algorithm (HFPA). Simultaneous minimization of cost, emission and losses is carried out with complex constraints like valve point loadings, ramp limits, prohibited zones and spinning reserve. The cost of wind power uncertainty is also included in the cost function by using a probability density function model. The proposed HFPA improves the exploration and exploitation potential of the flower population which is conducting the search. In the HFPA the flower pollination algorithm (FPA) and differential evolution (DE) algorithm are integrated to preserve good solutions and to stop premature convergence. A 5-class, 3-step time varying fuzzy selection mechanism (TVFSM) is integrated with HFPA for solving multi-objective problems. The TVFSM finds a fuzzy selection index (FSI) by aggregating different conflicting objectives. The FSI is adopted as the merit criterion while updating the population. Guassian membership function is applied to compute FSI in such a manner that extreme solutions are filtered out and trade off solutions on the central portion of the Pareto-front are obtained. The HFPA-TVFSM approach effectively searches the best compromise solution (BCS) which satisfies all the three objectives maximally. The proposed approach is tested and validated on two wind-thermal test systems from literature. Highlights: A hybrid flower pollination method is proposed for cost/emission/loss minimization. A fuzzy selection index (FSI) is proposed as merit criterion for the problem. Five class fuzzy selection mechanism (FSM) creates solution diversity. Time varying 3-step FSM is used for exploration/exploitation. Wind uncertainty costs and other complex operational constraints are included.