Abstract
This article presents a two-stage framework for optimal Electric Vehicle (EV) charging/discharging strategy for DC Microgrid (MG) with Distributed Generators (DGs). A multi-objective optimisation task aimed at minimising system losses and EV battery degradation with Vehicle-to-Grid (V2G) peak shaving service has been realised. This coordinated EV integration into the DCMG was formulated as a directed weighted single source shortest path problem that was solved using a modified Dijkstra's algorithm. The weights of the edges were obtained using primal-dual interior point method. The proposed framework has been experimentally verified using simulations with a test DCMG system with practical IEEE European low voltage test feeder load profiles. Results show realisation of peak demand shaving leveraging on EV discharge with minimal on-board battery degradation as well as reduced system losses. It is also shown that the proposed two-stage framework reduces the battery state of charge (SOC) sample space requirements in the analysis, thus, reducing the computational burden.
Highlights
R ECENTLY, there is increased awareness of the adverse impacts of fossil fuel consumption on environmental sustainability as well as the threats this poses to national economies and their energy security
Since this study focuses on providing ancillary service to MG operators, battery degradation is not considered during Electric Vehicle (EV) charging, which means the number of objectives is different between charging and discharging goals
WORK In this paper, an EV charging/discharging strategy based on two-stage optimisation framework which minimises DCMG network losses and EV battery degradation has been presented
Summary
R ECENTLY, there is increased awareness of the adverse impacts of fossil fuel consumption on environmental sustainability as well as the threats this poses to national economies and their energy security. When connected to distributed networks, GEVs could be considered as controllable loads during charging, called grid-to-vehicle (G2V) and as DGs during discharging, called vehicle-to-grid (V2G) [7] These GEVs can be manipulated as remote Energy Storage Systems (ESSs) to participate in the operation and control of the MG, thereby improving performance and reliability of MG by providing optimal charging and ancillary peak demand shaving service. Weighting method is considered the most suitable method for optimisation with aggregate objectives because of its capability of identifying non-inferior solutions that are not obtainable on a non-convex boundary [15] To solve such problems, several algorithms have been adopted in the literature. Optimal EV charging/discharging strategy for ancillary service is proposed for a test DCMG. Where SOHt,i indicates bus i battery State-of-Health at time t
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