Abstract
In the case of the widespread adoption of electric vehicles (EV), it is well known that their use and charging could affect the network distribution system, with possible repercussions including line overload and transformer saturation. In consequence, during periods of peak energy demand, the number of EVs that can be simultaneously charged, or their individual power consumption, should be controlled, particularly if the production of energy relies solely on renewable sources. This requires the adoption of adaptive and/or intelligent charging strategies. This paper focuses on public charging stations and proposes methods of attribution of charging priority based on the level of charge required and premiums. The proposed solution is based on model predictive control (MPC), which maintains total current/power within limits (which can change with time) and imparts real-time priority charge scheduling of multiple charging bays. The priority is defined in the diagonal entry of the quadratic form matrix of the cost function. In all simulations, the order of EV charging operation matched the attributed priorities for the cases of ten cars within the available power. If two or more EVs possess similar or equal diagonal entry values, then the car with the smallest battery capacitance starts to charge its battery first. The method is also shown to readily allow participation in Demand Side Response (DSR) schemes by reducing the current temporarily during the charging operation.
Highlights
According to Hardman et al [1], the widespread use of electric vehicles (EV) presents many benefits to the environment, but it can pose significant operational challenges to existing power networks
Here we proposed to attribute a priority to each charging EV by appropriate selection of the Q matrix in an model predictive control (MPC) cost function
This paper proposed a method based on MPC combined with a multi-agent system to schedule EV charging operation at public stations with a constraint on the available peak energy
Summary
According to Hardman et al [1], the widespread use of electric vehicles (EV) presents many benefits to the environment, but it can pose significant operational challenges to existing power networks This large-scale adoption of EVs could lead to uncontrolled charging and cause a range of power network problems, including shortage of power, voltage limit violations, component overloads, power system losses, phase imbalance, and issues with power quality and stability. Some control strategies benefit both EV users and the grid This is the case of valley-filling charging proposed in [11], where EVs are charged during valley times according to the load profile to minimize energy losses. In [19], Shamsdin et al use linear programming in four policies, including random charging, lowest state-of-charge, and shortest parking time These algorithms place some limitations on large problems and the quality of their solutions. This paper, focuses on example public EV stations with charge scheduling based on allocation priorities to each vehicle whilst maintaining maximum total current/power constraints and being receptive to participation in DSR events when requested
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