Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services

Abstract

Electric vehicles and renewable energy sources are collectively being developed as a synergetic implementation for smart grids. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are seen as a way forward to achieve economic, technical and environmental benefits. The implementation of these technologies requires the cooperation of the end-electricity user, the electric vehicle owner, the system operator and policy makers. These stakeholders pursue different and sometime conflicting objectives. In this paper, the concept of multi-objective-techno-economicenvironmental optimisation is proposed for scheduling electric vehicle charging/discharging. End user energy cost, battery degradation, grid interaction and CO2 emissions in the home micro-grid context are modelled and concurrently optimised for the first time while providing frequency regulation. The results from three case studies show that the proposed method reduces the energy cost, battery degradation, CO2 emissions and grid utilisation by 88.2%, 67%, 34% and 90% respectively, when compared to uncontrolled electric vehicle charging. Furthermore, with multiple optimal solutions, in order to achieve a 41.8% improvement in grid utilisation, the system operator needs to compensate the end electricity user and the electric vehicle owner for their incurred benefit loss of 27.34% and 9.7% respectively, to stimulate participation in energy services. Highlights  Optimisation of energy cost, battery degradation, grid utilisation and CO2 emission  The conflicts among objectives were addressed with multi-objective optimisation  A multi-criteria decision making process was tailored to the stakeholders

Impact of Smart Charging for Consumers in a Real World Pilot

“…A quantitative analysis of how many users or sessions are affected by smart charging is largely lacking. This is particularly relevant as most pilots relate to residential (or private) charging rather than public charging [26,27]. Given that residential charging with one EV is more predicable than public charging (with several EVs with different charging characteristics on one charging station), applying smart charging on public charging infrastructure requires more research.…”

GGlueVaR‐based participation of electric vehicles in automatic demand response for two‐stage scheduling

“…The shortest time required to charge the vehicle l Q u à l ð Þ The load transfer generated by the vehicle l K 1 The quadratic term coefficient of incentive-based demand response K 2…”

“…The uptake of electric vehicles (EVs) is rapidly growing around the world, 1 and the high penetration of EVs will potentially lead to high loads on current grids. Controlled charging of EVs will significantly affect the power grid load, 2,3 which can also provide an approach to deal with the high loads that the grid faces.…”

A Quadratic Programming-Based Power Dispatch Method for a DC-Microgrid

“…Therefore, the optimal operation of the MG having ESSs have been investigated [6], [7], [16], [17], [8]- [15]. It has shown that BESS can effectively work as a solution for daily power intermittency and improve grid stability from many existing MGs test sites.…”