Exploiting V2G to optimize residential energy consumption with electrical vehicle (dis)charging

Abstract

Abstract-The potential breakthrough of pluggable (hybrid) electrical vehicles (PHEVs) will impose various challenges to the power grid, and esp. implies a significant increase of its load. Adequately dealing with such PHEVs is one of the challenges and opportunities for smart grids. In particular, intelligent control strategies for the charging process can significantly alleviate peak load increases that are to be expected from e.g. residential vehicle charging at home. In addition, the car batteries connected to the grid can also be exploited to deliver grid services, and in particular give stored energy back to the grid to help coping with peak demands stemming from e.g. household appliances. In this paper, we will address such so-called vehicle-to-grid (V2G) scenarios while considering the optimization of PHEV charging in a residential scenario.In particular, we will assess the optimal car battery (dis)charging scheduling to achieve peak shaving and reduction of the variability (over time) of the load of households connected to a local distribution grid. We compare (i) a business-as-usual (BAU) scenario, without any intelligent charging, (ii) intelligent local charging optimization without V2G, and (iii) charging optimization with V2G. To evaluate these scenarios, we make use of our simulation tool, based on OMNeT++, which combines ICT and power network models and incorporates a Matlab model that allows e.g. assessing voltage violations. In a case study on a threefeeder distribution network spanning 63 households, we observe that non-V2G optimized charging can reduce the peak demand compared to BAU with 64%. If we apply V2G to the intelligent charging, we can further cut the non-V2G peak demand with 17% (i.e., achieve a peak load which is only 30% of BAU).

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

“…In this case, the EVs are assumed to be equipped with technology capable of providing energy stored in the battery to the grid (V2G) when they are parked at home; therefore, EVs can act as loads and generators. This new capability enables EVs to offer ancillary services, such as spinning reserves and regulation, to the system operators by working as flexible energy storage resources [13]. Smart scheduling with a combination of G2V and V2G offers the possibility of managing EVs as V2G to the smart grid aggregator, offering peak savings.…”

“…An EV can be considered a flexible load that can be charged throughout the day instead of following a rigid charging schedule [9,10]. The flexibility of the EV demand will improve the operation of power systems in terms of flattening the load curve on main substation transformers, providing, in addition, peak shaving services, reduced power system losses, reduced aging of transformers and lines, and increased renewable energy penetration [11] as well as providing financial support [9][10][11][12][13][14][15][16][17][18]. In an uncontrolled, or "dumb", scenario, EVs should be charged when the owner arrives at his home [16,19]; however, potential problems such as sudden peak demand or sudden overloading could be shaped or flattened by using a smart charging schedule for the EV's batteries [20].…”

Smart Home Activities: A Literature Review

“…The possibility of assessing optimal car battery charging and discharging to achieve peak shaving and reduce the variability over time of household loads interconnected to a local distribution system was explored in [70]. Three case studies were compared: business as usual with no smart charging, smart local charging optimization with no vehicle-to-grid charging, and charging optimization with a vehicle-to-grid connection.…”

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

“…The aim is to minimize energy losses, and maximize the grid load factor. In earlier work [5,6], we also explored approaches based on quadratic programming, that reduce peak load and load variability. An example of a multi-agent system is PowerMatcher [7], which is based on virtual markets, where agents bid on an electronic market to determine an equilibrium price matching demand and supply.…”

“…The contributions of this paper are: (i) an extensive analysis (beyond [5,6]) of quadratic programming (QP) based assessment of attainable peak load reduction, (ii) including associated effects on power quality, and (iii) benchmarking of a fully distributed market-based multi-agent system against the optimal QP results.…”