A systematic survey on demand response management schemes for electric vehicles

【Author】 Kakkar, Riya; Agrawal, Smita; Tanwar, Sudeep

【Source】RENEWABLE & SUSTAINABLE ENERGY REVIEWS

【影响因子】16.799

【Abstract】The unprecedented proliferation of electric vehicles is envisioned to revolutionize the Intelligent Transportation System as an energy-efficient and environment-friendly alternative to fossil-fuel vehicles. Despite the indispensable benefits of electric vehicles, the enormous and fluctuating energy demand for electric vehicles can imperil the efficiency and stability of the electric grid, which further necessitates the discussion on balancing the demand and supply for electric vehicle efficient charging at the charging station. Some of the research studies focus on the electric vehicle demand response management but without considering various critical aspects such as security, optimality, and fluctuating parameters (electric load or traffic condition) that impact the net-zero emission or carbon neutrality that is required to fulfil some of the United Nation Sustainable Development Goals. Thus, this research study presented an exhaustive meta-survey to secure electric vehicles' demand response management towards a smart grid environment. Consequently, the research in the meta- survey discussed the taxonomy of demand response for electric vehicles, considering various aspects such as electric vehicle modelling, load forecasting, and optimization techniques. Furthermore, this review studied the holistic discussion on various security techniques such as encryption, authentication, and consensus protocols for protected and preserved electric vehicle demand response management through the smart grid environment. Moreover, a case study is presented that focuses on the security and optimality aspects of demand response management for electric vehicles using blockchain and the reinforcement learning approach. The proposed case study focuses on optimizing electric vehicle energy trading based on optimized energy consumption for secure and efficient demand response management with the smart grid. Finally, the meta-surveys research challenges and future opportunities have been targeted and discussed for secure and optimal electric vehicle demand response management for future works.

【Keywords】Electric vehicle; Demand response management; Smart grid; Optimization techniques; Load forecasting; Reinforcement learning; Blockchain

【发表时间】2024 OCT

【收录时间】2024-08-07

【文献类型】实证数据

【主题类别】

区块链应用-实体经济-车辆领域