Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method. (15th September 2019)

• Record Type: Journal Article
• Authors: Wang, Ying; Yang, Zhile; Mourshed, Monjur; Guo, Yuanjun; Niu, Qun; Zhu, Xiaodong
• Is Part Of: Energy conversion and management. Volume 196(2019)
• Page Start: 935
• Page End: 949
• Publication Date: 2019-09-15
• Subjects: Unit commitment -- Demand side management -- Plug-in electric vehicles -- Competitive swarm optimization -- Binary optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.06.012
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A new unit commitment framework with different levels of DSM for PEVs is proposed. A novel binary competitive swarm optimization method is proposed and utilized. The uncoordinated and coordinated charging is measured by a weighting factor. Numerical studies on various unit scales and levels of load are investigated. Abstract: Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed-integer variables and multiple constraints – typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit.