Abstract
Massive popularity of plug-in electric vehicles (PEVs) may bring considerable opportunities and challenges to the power grid. The scenario is highly dependent on whether PEVs can be effectively managed. Dynamic economic dispatch with PEVs (DED with PEVs) determines the optimal level of online units and PEVs, to minimize the fuel cost and grid fluctuations. Considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear, and non-convex characteristics. High efficient DED method provides a powerful tool in both power system scheduling and PEVs charging coordination. In this study, firstly, PEVs are integrated into the DED problem, which can carry out orderly charge and discharge management to improve the quality of the grid. To tackle this, a novel real-coded genetic algorithm (RCGA), namely, dimension-by-dimension mutation based on feature intervals (GADMFI), is proposed to enhance the exploitation and exploration of conventional RCGAs. Thirdly, a simple and efficient constraint handling method is proposed for an infeasible solution for DED. Finally, the proposed method is compared with the current literature on six cases with three scenarios, including only thermal units, units with disorderly PEVs, and units with orderly PEVs. The proposed GADMFI shows outstanding advantages on solving the DED with/without PEVs problem, obtaining the effect of cutting peaks and filling valleys on the DED with orderly PEVs problem.
Highlights
The Optimization ProblemOver the last few decades, the rapid increase in the use of fossil fuel has led to a consequential worldwide reduction of the resource; its optimal utilization in power generation has become an important research topic (Niu et al, 2014; Yang et al, 2015)
From mean and Std, it can be seen that GADMFI is the most stable and grey wolf optimizer (GWO), secondly, which benefits from dimension mutation based on feature intervals (DMFI) has the ability to be directed fine-grained to develop near the current optimal population
As can be obtained from Winner, only in F6, problem is inferior to artificial bee colony (ABC), and F6 of 100 dimensions is inferior to GWO, but the difference is very small
Summary
The Optimization ProblemOver the last few decades, the rapid increase in the use of fossil fuel has led to a consequential worldwide reduction of the resource; its optimal utilization in power generation has become an important research topic (Niu et al, 2014; Yang et al, 2015). The DED with PEVs plays an important role in power systems operation and control. Coupling with space and time, it is a complicated optimal decision problem, and its goal is to minimize the fuel cost and fluctuation of the power grid, on the premise of satisfying a series of constraints. The fuel cost function is a highly discontinuous, nonlinear, and nonconvex curve, due to the valve-point effect (VPE) of the steam turbine (Shen et al, 2019), and the transmission loss should not be ignored on a large scale of the power system. VPE, transmission losses, and PEVs make the DED model more complicated, but more accurate
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