A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

Abstract

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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.

Keywords

• dimension-by-dimension mutation
• horizontal vertical local search
• collaborative optimization
• constraints handling method
• dynamic economic dispatch
• plug-in electric vehicles