An Alternating Direction Method of Multipliers (ADMM) Based Optimal Electric Vehicle Fleets Charging In Active Electric Distribution Network

This paper presents an ADMM (Alternating Direction Method of Multipliers) based scheduling algorithm for electric vehicle charging considering power grid impact. Different scheduling approaches for vehicle charging are developed that allow for demand management, such as valley filling and peak shaving by considering real-time pricing (RTP). The main advantage of the proposed algorithm is that it allows dividing a large number of vehicle and grid nodes into decentralized sub-problem which allows for a fast and efficient solution. The methods used in this research attempt to change consumers' electricity consumption behavior in order to lower peak demand and thereby increase energy efficiency and power system stability. Results show that improvement features a vital impact on demand management and cost objectives.