Bidirectional V2G Research Articles

The impact of a bi-directional V2G charging station to the frequency dependent grid impedance up to 500 KHZ

The impact of a bi-directional V2G charging station to the frequency dependent grid impedance up to 500 KHZ

Electric Vehicles and Vehicle–Grid Interaction in the Turkish Electricity System

Electric vehicles and energy storage systems are technologies in the stage of intensive development. One of the innovative ways to use electric cars is the Vehicle to Grid (V2G) concept. V2G charging points are characterized by the ability of bidirectional energy flow while charging EV/BEV (Electric Vehicles/Battery Electric Vehicles). In periods of low energy consumption and the presence of the highest shares of renewable sources, the cleanest electricity is drawn from the grid at the lowest prices and stored in a “mobile warehouse”, which is an electric car. During the reported peaks in electricity demand and the presence of high tariffs, the previously stored energy may be sold back to the distribution network operator. Thanks to this application, the technology determines the highest profitability of the system and assigns EV/BEV the ability to manage electricity flows, while improving the energy balance of the economy. The prospects for the spread of V2G have increased along with the growing requirements for domestic economies, closely related to the significant share of renewable energy sources. The vision of connecting EV/BEV with the power grid creates completely new ways of managing energy and makes it possible to build smart agglomerations in line with the Smartcity idea. Especially since Turkey is one of the countries promoting this idea. The scientific aim of the study is to maximize the aggregator’s profits for V2G by creating a coalition with renewable energy producers and combining the capacities of many EVs and offering their total capacities to the electricity markets. The subject of the research was to obtain extensive knowledge about the vehicle–grid interactions taking place in the Turkish power system. For this purpose, an analysis is conducted to determine the optimal preferred operating points and the amount of regulation proposals that maximize the profit of the EV users while satisfying the constraints of each stochastic parameter. The results show the system benefits from the implementation of the algorithms are significant to optimal bidirectional V2G impacts on distribution systems with high penetration of EVs. The research can find practical applications in assessing the role of electric vehicles and their integration in the vehicle–grid system in power systems. At the same time, pointing to the benefits related to the implementation of such solutions for Turkey and other countries in the field of electromobility, stability of energy systems, and energy independence through the possibility of achieving the desired synergy effect.

Supraharmonic and Harmonic Emissions of a Bi-Directional V2G Electric Vehicle Charging Station and Their Impact to the Grid Impedance

Bidirectional electric vehicle supply equipment and charging stations (EVSE) offer new business models and can provide services to the electrical grid. The smart grid lab in Vienna gives unique testing possibilities of future smart grids, as different type of electrical equipment can be operated at a reconstructed, well-known distribution grid. In this work the harmonic and supraharmonic emissions of a bidirectional EVSE are measured according to IEC61000-4-7 and IEC61000-4-30 Ed3 standard as well as the high-frequency grid impedance. In addition, the efficiency and the power factor are determined at various operating points. Although THDi at nominal power (10 kW) is very low and the efficiency and power factor is very high, at low power levels the opposite situation arise. Supraharmonic emissions remain stable independent of the charging/discharging power, and both wideband and narrowband emissions occur. The additional capacitance when connecting the EVSE impacts the high-frequency grid impedance substantially and generates resonance points.

Economic and climate benefits of vehicle-to-grid for low-carbon transitions of power systems: A case study of China's 2030 renewable energy target

More and more attention has been given to the role of electric vehicles in reducing the economic cost and carbon emissions of the power system through the vehicle-to-grid (V2G) mode, as well as helping to integrate renewable energy power. The promotion of V2G mode needs to consider the effectiveness of policy costs, so it is urgent to estimate the potential benefits of V2G. In this paper, a multi-regional power dispatch and expansion model is constructed from the perspective of power system by taking China's plan to reach 1.2 billion kW of installed wind and photovoltaic power generation capacity in 2030 as an example, combining with the development planning of electric vehicles and power systems. The results show that by 2030, the implementation of unidirectional V2G and bidirectional V2G in China can reduce the total cost of power system by 2.02% and 2.08%, reduce the annual carbon emissions of the power system by 2.27% and 2.95%, and increase the total proportion of wind and photovoltaic power generation by 1.33% and 1.28% in the power mix. An increasing proportion of electric vehicles participate in V2G increases the proportion of new-installed wind generation and decreases that of photovoltaic power. Due to the high cost of battery degradation and the current power mix dominated by coal-fired generation, additional benefits of implementing bidirectional V2G over unidirectional V2G are limited.

Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior

Due to their great potential for energy conservation and emission reduction, electric vehicles (EVs) have attracted the attention of governments around the world and become more popular. However, the high penetration rate of EVs has brought great challenges to the operation of the Active Distribution Network (ADN). On the other hand, EVs will be equipped with more intelligent chargers in the future, which supports the EVs’ high flexibility in both active and reactive power control. In this paper, a distributed optimization model of ADN is proposed by employing the collaborative active and reactive power control capability of EVs. Firstly, the preference of EV users is taken into account and the charging mode of EVs is divided into three categories: rated power charging, non-discharging, and flexible charging–discharging. Then, the reactive power compensation capacity of the plugged-in EV is deduced based on the circuit topology of the intelligent charger and the active–reactive power control model of the EV is established subsequently. Secondly, considering the operation constraints of ADN and the charging–discharging constraints of EVs over the operation planning horizon, the optimization objective of the model is proposed, which consists of two parts: “minimizing energy cost” and “improving voltage profile”. Finally, a distributed solution method is proposed based on the Alternating Direction Method of Multipliers (ADMM). The proposed model is implemented on a 33-bus ADN. The obtained results demonstrate that it is beneficial to achieve lower energy cost and increase the voltage profile of the ADN. In addition, the energy demand of EV batteries in their plugin intervals is met, and the demand preference of EV users is guaranteed.

Assessing the ancillary service potential of electric vehicles to support renewable energy integration in touristic islands: A case study from Balearic island of Menorca

The potential of e-mobility to participate in intermittency flattening ancillary services (AS) to support solar photo-voltaic (PV) grid integration is analysed in this paper. First, the capability of Plug-in Electric Vehicles (PEVs) to act as Energy Storage Systems (ESS) based on daily energy storage potential (ESP) with and without vehicle-to-grid (V2G) services is studied. Mobility behaviour and penetration levels of various categories of vehicles-cars, vans, trucks, buses and motorcycles in the island are modeled using traffic data from Menorca island. Next, ancillary service (AS) potential of 1000 PEVs to support solar PV grid integration is analysed. A crucial parameter of PEVs to offer uni-V2G services under aggregator to participate in hour-ahead AS market, termed achievable power capacity (APC), is explored. Results showed that, for bidirectional V2G services in summer, ESP can reach up to 100 MWh for minimum (P1) and 375 MWh for maximum (P6) penetration levels considered. The regulation potential for every 1000 private PEVs aggregated to a maximum of 4.6 MW and reserve potential to a maximum of 5.25 MW under coordinated charging. This reveals the great potential of PEVs in offering AS in small touristic islands.

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

The bi-directional linkage between the power grid and electric vehicles (EVs) enables flexible, cheap and fast-responding use of vehicle batteries in the grid. However, the battery aging effects due to the additional operation cycles caused by Vehicle-to-Grid (V2G) service and the concern of the battery degradation are the main reason that keeps the customer from being the named prosumer of the grid. This paper proposes a novel active battery anti-aging V2G scheduling approach. Firstly, to evaluate the battery aging effect in V2G service, the battery degradation phenomenon is quantified by a novel use of rain-flow cycle counting (RCC) algorithm. Then, the V2G scheduling is modeled as a multi-objective optimization problem, in which the minimal battery degradation and grid load fluctuation are designed as the optimization objectives. Finally, a multi-population collaborative mechanism, which is particularly designed for the V2G scheduling problem, is also developed to improve the practicability and performance of the heuristic optimization based V2G scheduling method. The proposed methodologies are verified by numerical analysis, which highlights that the proposed V2G scheduling method can minimize battery charge/discharge cycles by optimizing the time and scale of each V2G participant while providing the same services to the grid as expected.

A Calculation Model of Charge and Discharge Capacity of Electric Vehicle Cluster Based on Trip Chain

The rapid response characteristics and high-speed growth of electric vehicles (EVs) demonstrate its potential to provide auxiliary frequency regulation services for independent system operators through vehicle-to-grid (V2G). However, due to the spatiotemporal random dynamics of travel behavior, it is challenging to evaluate the ability of EV cluster to provide ancillary services under the premise of reaching the expected state of charge (SOC) level. To address this issue, a novel calculation model of charge and discharge capacity of EV cluster based on trip chain with excellent parallel computing performance is presented in this work. Following the introduction of the characteristic variables of the proposed trip chain model, the user’s continuous travel behavior in a time scale of several weeks is simulated. In particular, a bidirectional V2G scheduling strategy based on the five-zone map is designed to guide the charging and discharging behavior of EVs, where the expected SOC levels are guaranteed. The results of a 3-week travel simulation verify the effectiveness of the presented model in coordinating the V2G scheme and calculating the charge and discharge capacity of the EV cluster.

Technical investigation on V2G, S2V, and V2I for next generation smart city planning

The paper investigates a few of the major areas of the next generation technological advancement, “smart city planning concept”. The areas that the paper focuses are vehicle to grid (V2G), sun to vehicle (S2V), and vehicle to infrastructure (V2I). For the bi-directional crowd energy single entity concept, V2G and building to grid (B2G) are the primary parts of distributed renewable generation (DRG) under smart living. This research includes an in-depth overview of this three major areas. Next, the research conducts a case analysis of V2G, S2V, and V2I along with their possible limitations in order to find out the novel solutions for future development both for academia and industry levels. Lastly, few possible solutions have been proposed to minimize the limitations and to develop the existing system for future expansion.

Ancillary Services Bidding for Uncertain Bidirectional V2G Using Fuzzy Linear Programming

The operation of bidirectional V2G participating in ancillary services markets is particularly challenging due to various uncertainties. This work proposes an algorithm to optimize the uncertain operation of bidirectional V2G for an electric vehicle (EV) aggregator. The proposed algorithm maximizes the profits of the aggregator while providing additional system flexibility and low charging costs to the EV owners. The proposed algorithm considers electricity market and EV mobility uncertainties using fuzzy linear programming. These uncertainties include those of the regulation and responsive reserve prices, deployment signals, and the energy used for EV trips. Unlike other works in the literature, the proposed algorithm is capable of considering a large number of uncertain parameters without greatly impacting the problem's complexity or simulation time. Also, the algorithm does not result in conservative solutions. The simulation results show the superiority of the proposed fuzzy algorithm over its deterministic counterpart in terms of higher realized profits. Also, the proposed approach results in lower real-time penalties, which ensures its superiority to provide the EVs with the required trip energy at the required time. In addition, it results in lower battery degradation costs, which helps increase the life time of the EVs.

Energy Management Strategy for Plug-In Hybrid Electric Vehicles via Bidirectional Vehicle-to-Grid

As a paradigm of the incoming smart grid, vehicle-to-grid (V2G) has been proposed as a promising solution to increase the adoption rate of plug-in hybrid electric vehicles (PHEVs). In this paper, we investigate the energy management strategies for PHEVs via bidirectional V2G. We first follow a cost-conscious approach from the PHEV owner point of view. To minimize the daily energy cost, we formulate the energy management problem via dynamic programming (DP). However, the “well-known” complexity in solving DP poses a computational challenge even for a small number of iterations. Therefore, we propose a state-independent four-threshold $(s,S,s^{\prime},S^{\prime})$ battery charging/discharging policy and theoretically prove the optimality of the proposed energy management strategy based on stochastic inventory theory. A backward iteration algorithm is further developed to practically implement the $(s,S,s^{\prime},S^{\prime})$ feedback policy. Second, from the distribution system operator's perspective, we aim to shave the peak load and flatten the overall load profile. To this end, we propose an optimal PHEV charging scheme and further derive a reminiscent “water-filling” solution for this scenario. Realistic PHEV battery models, time-of-use electricity pricing rate, and real data of household demand are integrated into our formulated V2G system model. The theoretical analysis and proofs are instrumental to the future large-scale PHEV adoption in smart grids.

Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals

This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations.

Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services

Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks.