Demand Response in Electric Vehicles Management Optimal Use of End-user Contracts

Demand Response (DR) has gained attention in the past few years. With the mass introduction of Electric Vehicles (EVs) the opportunity for DR programs can grow even more. This paper concentrates on a specific type of DR, namely evaluating different scenarios with respect to end-user EVs tariffs. The effects of different tariffs are compared in several scenarios in terms of the Virtual Power Plant (VPP) operation costs and also the EVs' perspective, i.e. considering the optimal use of end-user contracts. To solve the Mixed Integer Non-Linear Problem (MINLP) a modernized optimization approach is used by combining a deterministic method in the first stage, relaxing the problem to a Mixed Integer Linear Problem (MILP) with the use of a computational intelligence method in the second stage, namely Particle Swarm Optimization (PSO). The case study presents different price scenarios, namely, single-tariff, bi-tariff, tri-tariff, tetra-tariff, and Real-Time Pricing (RTP). The network used for this application is a 33-bus distribution network with high penetration of renewables and a fleet of 30 electric buses.