Decentralized trading of plug-in electric vehicle aggregation agents for optimal energy management of smart renewable penetrated microgrids with the aim of CO_2 emission reduction

摘要

With a significant increase in concerns about global warming and lack of fossil fuels reserves as well as the requirements for clean energy production, the electrification of transportation in smart microgrids (SMG) has become an undeniable solution to respond to existing challenges. The distribution network operator (DNO), as responsible utility for optimal management of SMG, tries to optimize the procurement costs of network in such a way that all technical constraints of network are thoroughly met considering environmental obligations. In so doing, this paper proposes a novel eco-friendly scheme for optimal charging/discharging scheduling of plug-in electric vehicles (PEV) aggregators in SMG to minimize the procurement costs of system as well as reducing CO2 emission with taking into account various types of PEV (i.e., BEV and PHEV). The Vehicle to Grid (V2G) capability as well as the actual patterns of drivers are taken into account in the proposed model. The impact of PEVs aggregation agents on the operation costs, purchasing power from upstream network, air pollution as well as technical specifications of system such as power losses and voltage profile has been investigated under practical constraints of PEVs, heterogeneous DERs and distribution network. In addition, the uncertainties subject to renewable generations are handled by stochastic programming to mitigate their minus effects on the profit of system. The weighted sum approach is employed to convert the multi-objective problem into a single-objective MINLP model and subsequently is minimized by collaborative grey wolf optimizer and Taguchi test method to acquire a satisfactory solution. Finally, an illustrative case study is provided to acknowledge the sufficiency of the proposed framework by performing it on the modified IEEE 69-bus system with integration of renewable resources. (C) 2018 Elsevier Ltd. All rights reserved.