Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

Abstract

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (DEEDIWEv) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEEDIWEv model.

Novel Global Harmony Search Algorithm for General Linear Complementarity Problem

“…For these reasons, HS has been demonstrated in several studies to be a useful optimization algorithm for various engineering applications owing to its convenient implementation, rapid convergence, and reasonable computational cost. This can be found in the literature [34][35][36][37][38][39][40].…”

A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids

“…where E(P i ) is the emission cost of the microgrid system; w is the emission gas generally CO 2 , SO 2 , and NO x ; V w is the volume of the w gas emitted by the micropower source; µ w is the emission penalty price of w gas; α, β, γ, ζ, λ are the fuel cost coefficients of the micropower supply. Dynamic economic dispatching is a multi-objective optimization problem that combines two contradictory goals of reducing the system operating cost and reducing the pollution emission amount [57,58]. Its form can be expressed as follows:…”