Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

Abstract

The intermittency of wind power and the large-scale integration of electric vehicles (EVs) bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED) model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G) power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user's travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

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

“…Note that it is not necessary to control the charging/discharging of each EV individually. The hierarchical dispatch model [23] is employed for the EVs, dividing EVs in the same area into several aggregates; these aggregates are controlled by the corresponding aggregation mechanism for charging and discharging. The charging and discharging states of the EVs are represented by a sign function at each dispatching interval: The description of EV charging and discharging states is shown in Table 2.…”

“…Because wind power generation is uncertain, it is not always consistent with the demand of the dispatching system [23,32]. Therefore, an interaction model between wind power and EVs is proposed to reduce the impact of the uncertainty and is specified as follows.…”

“…To address the uncertainty of wind power and take advantage of electric vehicles as energy storage elements, several studies were conducted combining EVs with wind power [22]. In [23], Qu et al considered EVs and wind power generation in dynamic power dispatch using the MOEA/D algorithm. Zhang et al in [24] combined EVs and wind power to develop a multi-objective hydrothermal wind power with the EV dispatch (MOHTWES) model.…”

Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation

“…However, these techniques can get stuck at local optimum points easily, are very sensitive to the starting point [21,22], and have difficulties with solving nonconvex problems as well as those with nonsmooth objective functions [21,23]. Stochastic techniques have the advantage that can solve nonconvex problems and those with nonsmooth objective functions [14,[16][17][18][24][25][26][27][28][29][30]. However, these techniques find a solution close to the Pareto set only [31], as opposed to the exact solution [14,16], and require a very long time of computation, especially when dealing with large problems [1,15,24], and the control parameters and diversity of the population introduce several degrees of freedom to the solution approach [14,15].…”

Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition

“…For such complicated MOPs, it would be always the focus of research to find a better solving algorithm and further improve the quality of the solutions according to the specific characteristics of the problems. The multiobjective evolutionary algorithm based on decomposition (MOEA/D), which is developed by Zhang and Li [33], has demonstrated potential in dealing with complex MOPs [34][35][36], and MOEA/D has been successfully implemented in many applications [37][38][39]. The author of this paper has also used MOEA/D in the field of static EED problem and obtained a better result [40].…”