Abstract
The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED) problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution requires powerful methods. A new finite action-set learning automata (FALA)-based approach that has the ability to adapt to a stochastic environment is proposed. The feasibility of the proposed approach is demonstrated in a modified IEEE 30 bus system. It is compared with continuous action-set learning automata and particle swarm optimization-based approaches in terms of convergence characteristics, computational efficiency, and solution quality. Simulation results show that the proposed FALA-based approach was indeed capable of more efficiently obtaining the approximately optimal solution. In addition, by using an optimal dispatch schedule for the interaction between electric vehicle stations and power systems, it is possible to reduce the gap between demand and power generation at different times of the day.
Highlights
Economic dispatch (ED) is defined as the allocation of generation levels to different electrical generation units, so that the system load may be supplied entirely and most economically
We have developed a stochastic ED model that considers a high penetration of Electric vehicle (EV)
Both the normal interaction (NI) and battery exchange (BE) modes are assumed to be schedulable in our model
Summary
Economic dispatch (ED) is defined as the allocation of generation levels to different electrical generation units, so that the system load may be supplied entirely and most economically. Electric vehicle (EV) fleets and renewable energy sources (such as wind power) have brought two new dimensions to this problem, along with the challenges introduced by their uncertainty. In most cases, during the daytime the wind output is small while the power load is heavy, and the situation reverses during the night-time This dislocation between renewable resources and power demand can only be mitigated when large energy storage facilities are available [10,11,12]. A simulation-based approach is used to study the probability distributions of the charge/discharge behaviors of PEVs. Yu et al [16] addressed the economic dispatch problem for distribution systems that contain wind power and mixed-mode EVs. Three interaction modes were introduced and compared, but only the battery exchanger mode is schedulable. We analyze and model all of these stochastic factors
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