Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

Abstract

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

“…Large datasets management, analytics and control can be managed by smart control algorithms to integrate PEVs, smart home metering and increased distributed renewable generation and any environmental constraints holistically. The algorithm proposed by this study is further developed in Mets et al [98] where a more intelligent global algorithm is designed to schedule a full days charging rather than calculate iteratively in small time slots. The results showed additional 22%, 63% and 100% energy improvements could be achieved in light, medium and heavy scenarios respectively, and the extra peak load of 1.5, 2.4, and 3.3 times the original load for these three scenarios could be removed by 99.64%, 90.64% and 77.15% respectively.…”

“…Quadratic programming methods have been implemented to model and solve this problem. In Mets et al [98], the problem was formulated to minimise the variance between load variable and optimal load variables. Similarly, in [100,123] the minimisation of variance was also implemented to develop an optimal PEV strategy.…”

“…Year Objectives Opt-Methods Tools or platforms Clement-Nyns et al [53] 2009 Power loss minimisation QP Not stated Mets et al [97] 2010 Variance minimisation QP OMNeTþ þ Sortomme et al [124] 2011 Power loss minimisation QP Matlab,CVX Mets et al [97] 2011 Variance minimisation QP OMNeTþ þ He et al [127] 2012 Variance minimisation QP Matlab,CVX Mets et al [98] 2012 Variance minimisation QP Not stated Zhang et al [100] 2012 Variance minimisation QP Matlab Jian et al [125] 2013 Variance minimisation QP Not stated Jian et al [126] 2015 Variance minimisation QP Not stated Acha et al [129] 2010 Power loss minimisation NLP gPROMS Yao et al [99] 2013 Variance minimisation NLP IPOPT Honarmand et al [102] 2014 SOC maximisation NLP CONOPT,GAMS He et al [130] 2013 Welfare maximisation NLP-Lag CONOPT Kim et al [131] 2013 Welfare maximisation NLP-Lag Not stated Liu et al [132] 2013 Operational cost minimisation NLP-Lag Not stated Zhang et al [133] 2014 Welfare maximisation NLP-Lag Matlab Qi et al [134] 2014 Welfare maximisation NLP-Lag CPLEX function was designed to maximise the revenue gained by the aggregator based on the state of charge of PEV batteries. Two vectors, including the weights determining the ratio of charging/ discharging as well as the delivered power are both taken as variables in forming the quadratic terms.…”

Charging Electric Vehicles in the Smart Grid

“…In particular we consider a single-shot, multi-unit auction market mechanism (see Mets et al, 2012b). This means that the control signal that will steer power consumption, is a price signal.…”

Machine‐to‐machine communications infrastructure for smart electric vehicle charging in private parking lots

“…Nevertheless, EVs still have three main drawbacks that hamper their complete introduction into the current vehicle market, namely Current electricity grids are not prepared for a scenario with a high penetration of EVs because its demand for a huge amount of electricity may coincide with current daily demand peaks . As a result, much research is being carried out on coordinated charging control mechanisms that mitigate such a problem . Despite the fact that EVs do not emit CO 2 , they consume electricity, whose generation, especially during demand peaks, is one of the main sources of CO 2 emissions . Although the data on average commuting distances and daily displacements are encouraging, it is difficult for current users of internal combustion engine vehicles to sacrifice the flexibility offered by a large driving range and an already deployed refueling infrastructure. Furthermore, the charging time of EVs may take up to several hours, which can be uncomfortable for some users and not feasible for others.…”