Electric Vehicles In Microgrids Research Articles

Learning-based hierarchical control for a net-zero commercial building with solar plus storage and high EV Chargers Penetration

The integration of renewable energy resources and electric vehicles in microgrids presents a significant challenge due to generation and demand uncertainty. However, our technical and market research in Solar Plus Storage microgrids, carried out as part of a US Department of Energy's Solar Prize project, demonstrates the efficacy of advanced microgrid controls in managing this uncertainty while saving billions of dollars. The control system must be equipped with advanced optimization tools and adaptive forecasting models that minimize planning errors for future optimal operation to achieve this. This study contributes to the literature by quantifying the impact of integrating adaptive learning-based forecasting tools for PV power and electrical load at the tertiary level of a hierarchical control system. Additionally, we use the developed learning forecasters and optimization algorithms of the tertiary level to solve intertwined optimal sizing and operation subproblems as a combined problem for a solar plus storage system.

Event-triggered model predictive control for dynamic energy management of electric vehicles in microgrids

The rapidly increasing charging demand of electric vehicles (EVs) represents a significant new load with myriad uncertainties. This paper proposes an event-triggered model predictive control (ET-MPC) method for dynamic energy management of EVs in microgrids (MGs). The study novelties lie in the forecasting models of EV status in an environment of MGs and the event-triggered mechanism for dynamic energy management of EVs. Firstly, the characteristics of EVs from different types of MGs are analysed by fitting the EV states with different probability distributions to improve the accuracy of forecasting of their status. Secondly, an ET-MPC method focusing on the energy management of EVs is first proposed to achieve coordination between the computational efficiency and optimisation impact of EVs. The event-triggered mechanism is achieved by monitoring the errors between the real EV states and their forecast values, which is only required to carry out optimisation when the forecast error of the EV states meet a set trigger level. Numerical simulations show that unlike time-triggered MPC methods—with long calculation times and fixed mechanisms—the ET-MPC method proposed achieves nearly the same energy management impact with a significantly shorter calculation overhead.

Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles

The increased intensity and frequency of natural disasters have attracted the attention of researchers in the power sector to enhance the resilience of power systems. Microgrids are considered as a potential solution to enhance the resilience of power systems using local resources, such as renewable energy sources, electric vehicles (EV), and energy storage systems. However, the deployment of an additional storage system for resilience can increase the investment cost. Therefore, in this study, the usage of existing EVs in microgrids is proposed as a solution to increase the resilience of microgrids with outages without the need for additional investment. In the case of contingencies, the proposed algorithm supplies energy to islanded microgrids from grid-connected microgrids by using mobile EVs. The process for the selection of EVs for supplying energy to islanded microgrids is carried out in three steps. Firstly, islanded and networked microgrids inform the central energy management system (CEMS) about the required and available energy stored in EVs, respectively. Secondly, CEMS determines the microgrids among networked microgrids to supply energy to the islanded microgrid. Finally, the selected microgrids determine the EVs for supplying energy to the islanded microgrid. Simulations have shown the effectiveness of the proposed algorithm in enhancing the resilience of microgrids even in the absence of power connection among microgrids.

A coordinated charging scheduling method for electric vehicles considering different charging demands

The uncoordinated charging of large amounts of electric vehicles (EVs) can lead to a substantial surge of peak loads, which will further influence the operation of power system. Therefore, this study proposed a coordinated charging scheduling method for EVs in microgrid to shift load demand from peak period to valley period. In the proposed method, the charging mode of EVs was selected based on a charging urgency indicator, which can reflect different charging demand. Then, a coordinated charging scheduling optimization model was established to minimize the overall peak-valley load difference. Various constraints were considered for slow-charging EVs, fast-charging EVs, and microgrid operation. Furthermore, Monte Carlo Simulation (MCS) was used to simulate the randomness of EVs. The results have shed light on both the charging modes selection for EV owners and peak shaving and valley filling for microgrid operation. As a result, this model can support more friendly power supply-demand interaction to accommodate the increasing penetration of EVs and the rapid development of flexible microgrid.

A Computing Budget Allocation Method for Minimizing EV Charging Cost Using Uncertain Wind Power

The idea of using wind power to charge electric vehicles (EVs) has attracted more and more attention nowadays due to the potential in significantly reducing air pollution. However, this problem is challenging on account of the uncertainty in the wind power generation and the charging demand from the EVs. Simulation-based policy improvement (SBPI) has been an important method for decision-making in stochastic dynamic programming and, in particular, for charging decisions of EVs in microgrids. However, the problem of allocating the limited computing budget for the best decision-making in online applications is less discussed. We consider this important problem in this work and make the following three major contributions. First, we show that the significant uncertainty in wind power generation forecasting could make the policy that is the outcome of an SBPI worse than the base policy. Second, we apply two existing methods to address this issue, namely, the optimal computing budget allocation (OCBA) for maximizing the probability of correct selection (OCBA_PCS) and the OCBA for minimizing the expected opportunity cost (OCBA_EOC). The asymptotic optimality is briefly reviewed. Third, we numerically compare the performance of OCBA_PCS and OCBA_EOC with the equal allocation (EA), a principle-based method, and a stochastic scenario-based method on small-scale and large-scale experiments. This work sheds light on the EV charging decision in general. Note to Practitioners-Together with the growing adoption of EVs in modern societies, there goes the challenge of how to satisfy the charging demand. Given the high uncertainty both in the wind power generation and in the charging demand, it is important to make decisions online using up-to-date estimation on the renewable power generation and the charging demand. Simulation-based policy improvement (SBPI) is shown both theoretically and practically to be useful to improve a given base policy in various applications, including this EV charging problem. However, the high uncertainty in forecasting could sometimes make the output of SBPI worse than that of the base policy. In this work, we first use numerical experiments to demonstrate the risk for such scenarios. Then, we propose to use two computing budget allocation procedures to address this issue. The asymptotic optimality of both algorithms is briefly reviewed. We demonstrate their performance on numerical experiments when there are only several EVs and when there are 100 EVs.

Integrating Electric Vehicles in Microgrids: Overview on Hosting Capacity and New Controls

This article reports an overview of main issues related to hosting capacity and harmonic disturbances caused by electric vehicle (EV) penetration in a smart grid, taking into account renewable energy sources and energy storage systems as well as nonlinear loads. A new mixed deterministic and probabilistic method based on algorithms and new control strategies, able to perform EVs charging controls, is presented. The methodology shows that it is possible to achieve high levels of power quality, thanks to a pre-evaluation of harmonic disturbance. Studies on real networks are reported in order to carry out indices able to define a preliminary assessment of the network hosting capacity. Suggestions and guidelines about the management of EVs charging stations are proposed and discussed in order to properly integrate them in a smart grid concept.

A frequency control strategy of electric vehicles in microgrid using virtual synchronous generator control

In recent years, with the increase in energy resource penetration into power systems, more technical requirements regarding the frequency control capability of renewable energy have become required. Plug-in electrical vehicles (PEVs) have become increasingly commercialized, and vehicle-to-grid technology has received extensive attention. This paper proposes a novel frequency control strategy for EVs to participate in the primary and secondary frequency regulations of microgrids. Virtual inertia and virtual damping are introduced using a virtual synchronous generator through AC/DC control. Thus, the charging/discharging device has the characteristics of inertia and damping, which are similar to those of a synchronous generator. According to the droop characteristic of an EV’s charging frequency, the designed auxiliary frequency regulation method can be used to calculate the power adjustment to change the reference value of the EV’s charging/discharging power, allowing the EV to participate in the frequency regulation of a microgrid. The models and connected microgrid were established using MATLAB/Simulink software, and the proposed control strategy was tested. The proposed frequency control strategy can provide virtual inertia, damping, and frequency support; improve the dynamic stability; and realize an effective frequency regulation when the microgrid works in an independent operational mode.

Optimal Charging Scheduling of Electric Vehicles in Micro Grids Using Priority Algorithms and Particle Swarm Optimization

The large-scale integration of electric vehicles (EVs) into modern power grid brings both challenges and opportunities to the performance of the systems. This paper presents an optimal static (when EV is stationary) charging scheduling scheme of EVs to minimize the charging cost while complying with the constraints related to the status of the charging station. The proposed systematic charging scheme is based on “Particle Swarm Optimization (PSO)”. It is compared with well-established algorithms such as “Arrival Time-Based priority (ATP) algorithm” and “SOC-Based Priority (SBP) algorithm”. In addition, a microgrid scenario is further considered for reducing the consumption of energy from the grid and also, reducing the charging cost by properly shifting the EV load. Based on the study carried out for a sample test cases considered, it is found that the proposed scheme has better performance compared to the existing schemes.

A Price Optimization Method for Microgrid Economic Operation Considering Across-Time-and-Space Energy Transmission of Electric Vehicles

In recent years, electric vehicles (EVs) have drawn great attention due to their potential of mitigating pollution emission. Household plug-in electric vehicles (PEVs) are key devices discussed in this article, and their potentiality as mobile energy storage is brought into fully consideration. This article presents a price optimization method for the microgrid economic operation, considering across-time-and-space energy transmission of EVs in microgrids. The object of the proposed price optimization method is to minimize the total cost of microgrid economic operation. Different study cases are investigated to analyze the impact of charging/discharging prices on the microgrid economic operation. The results show that the proposed price optimization method can reduce total cost of the microgrid system as well as provide some benefits for PEV owners. Specially, the optimization method is able to be applied to the pricing of EV charging and discharging in real systems.

On Location Privacy-Preserving Online Double Auction for Electric Vehicles in Microgrids

In this paper, we address the issue of demand response (DR) in microgrids via vehicle-to-vehicle technology in the smart grid with consideration for location privacy protection supported by Internet of Vehicles. To enable effective DR, the online double auction is a viable approach to support energy trading between electric vehicles (EVs) that have surplus or insufficient energy, while the utility of each participant can be considered. Nonetheless, there are three primary challenges in designing such an online double auction approach. First, as EVs are allowed to enter the market at any time, the auctioneer should make the best decision without further information about bids and asks. Second, as EVs are allowed to enter the market in different places, the auctioneer should perform routing optimization for EV charging after determining the winner. Third, there is a risk of leakage in the location of EVs that needs to be protected. To tackle these issues, we present a new truthful online double auction scheme, which features multiunit energy trading among EVs, routing optimization for EV charging, and location privacy protection. We conduct a theoretical analysis and demonstrate that our online double auction scheme is capable of achieving several important economic properties as well as the privacy guarantee (i.e., k -anonymity). Our experimental results show that the proposed scheme can achieve good performance with respect to social welfare, satisfaction ratio, total profit of EV owners, peak load shifting, state of charge, driving distance satisfaction, and computing time, and can further ensure location privacy protection.

An Electric Scooter (G2V/V2H/V2G) With Renewable Energy Harvesting Functions

Smart grids are advancing the management efficiency and security of power grids with the integration of energy storage, distributed controllers, and advanced meters. In particular, with the increasing prevalence of residential automation devices and distributed renewable energy generation, residential energy management is now drawing more attention. Meanwhile, the increasing adoption of electric vehicle (EV) brings more challenges and opportunities for smart residential energy management. This project analyzes the impact of photovoltaic (PV) systems on storage and electric vehicles in micro-grids. As these kinds of systems are becoming increasingly popular in the residential sector, the development of a new generation of equipment, such as more efficient batteries or solar panels, makes further study necessary. These systems are especially interesting in commercial or office buildings, since they have a more repetitive daily pattern of electricity consumption, which usually occurs within the maximum solar radiation hours. This project proposes an electric scooter with grid-to vehicle/ vehicle-to-home (V2H)/vehicle-to-grid (V2G) and energy harvesting functions.

A predictive discrete event approach for the optimal charging of electric vehicles in microgrids

Distributed generation, renewables (RES), electric vehicles (EVs), storage systems and microgrids are increasing widespread all over the world owing to the necessity of applying policies for sustainable development. In particular, the progressive shift from traditional vehicles to EVs is considered as one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. One of the major problem to be solved to make EVs a viable solution for the sustainable mobility is the development of effective facilities for vehicles. In this context, besides to technological aspects, one of the most important issues is the definition of fair and efficient policies for the sequencing and scheduling of the vehicle charging. In fact, scheduling problems are widely recognized as representing one of the most challenging class of optimization problems. Besides, the additional presence of specific features concerning vehicle charging systems (like controllable execution times, presence of intermittent energy sources, etc.) make even more difficult the vehicle charging problem. In this framework, despite the fact that optimization problems regarding energy systems are generally considered within a discrete-time setting, in this paper a discrete event approach is proposed. The reasons for this choice are essentially two. The first one is the necessity of containing the number of the decision variables, which grows beyond reasonable values when a small-time discretization step is chosen. The second is the impossibility of an accurate tracking of process and events using a discrete-time approach.The considered optimization problem regards the charging of a series of vehicles by a charging station that is integrated in a microgrid. Such a microgrid includes also renewable and traditional energy sources, storage systems and a local load. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the cost related to the use of fossil fuels, and the overall tardiness of the services provided to the customers. The effectiveness of the proposed approach is tested on a real case study.

Cooperative Optimization of Electric Vehicles in Microgrids Considering Across-Time-and-Space Energy Transmission

Plug-in electric vehicles (PEVs) tend to be treated as a new form of the mobile energy storage system with the potentiality to promote energy management in microgrids (MGs) and smart grid. However, a conflict of interest exists regarding the optimal capacity configuration and the optimal economic dispatch of PEVs. On one hand, MGs can maximize their benefits by mobile energy transmission as much as possible. On the other hand, MGs can also minimize total cost by reducing the investment cost of PEV charging/discharging infrastructures. Both objective functions need to be considered simultaneously. This paper presents a cooperative optimization method for capacity configuration and economic dispatch of electric vehicles in MGs considering across-time-and-space energy transmission (ATSET). Considering the differences of electricity prices in different time and space, the impact of ATSET on MG economic operation is analyzed, and a two-loop optimization model is utilized to optimize capacity configuration and economic dispatch of PEVs. Improved particle swarm optimization is used to implement cooperative optimization procedure, and the effectiveness of the proposed method is validated by detailed case studies. Results show that the total costs of the MG system and PEV owners can be reduced by the proposed cooperative optimization method.

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Microgrid as an important part of smart grid comprises distributed generators (DGs), adjustable loads, energy storage systems (ESSs) and control units. It can be operated either connected with the external system or islanded with the support of ESSs. While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the unbalance between the daily load curve and generation curve. In this paper, a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge (SOC) of EV batteries. The optimization problem is proposed to obtain the economic operation for the microgrid based on this model. In day-ahead scheduling, with the estimated power generation and load demand, the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming. With the optimal charging/discharging scheduling of EVs, the daily load curve can better track the generation curve. The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.

Energy Management Strategy for Micro-Grids with PV-Battery Systems and Electric Vehicles

This paper analyzes the impact of photovoltaic (PV) systems on storage and electric vehicles in micro-grids. As these kinds of systems are becoming increasingly popular in the residential sector, the development of a new generation of equipment, such as more efficient batteries or solar panels, makes further study necessary. These systems are especially interesting in commercial or office buildings, since they have a more repetitive daily pattern of electricity consumption, which usually occurs within the maximum solar radiation hours. Based on this need, a novel control strategy aimed at efficiently managing this kind of micro-grid is proposed. The core of this strategy is a rule-based controller managing the power flows between the grid and the batteries of both the PV system and the electric vehicle. Through experimental data and simulations, this strategy was tested under different scenarios. The selected testbed consisted of the laboratory of a research center, which could be easily scalable to the entire building. Results showed the benefits of using an electric vehicle as an active agent in energy balance, leading to a reduction of the energetic costs of a micro-grid.

Optimal Integration of Plug-In Hybrid Electric Vehicles in Microgrids

Plug-in hybrid electric vehicles' (PHEVs) integration can result in additional electricity consumers and electricity suppliers in microgrids (MGs). This paper presents a new method for optimal integration of PHEVs in MGs, which considers the optimal number of parking numbers under optimal scheduling of PHEVs. Due to the uncertainty of solar energy, the radial basis function network (RBFN) techniques are used for forecasting photovoltaic (PV) power output. Monte Carlo simulation is used to deal with the uncertainties associated with the daily distance driven of the PHEVs, load values, and electricity market price. The objective is the minimization of total cost (TC) and optimal set of the optimization problem is found using genetic algorithm (GA) method. In order to verify the effectiveness of the proposed method, two market policies are used as case studies. The computation results can be used to evaluate the impact of PHEVs' integration on economical performance of MGs.

Optimal Charging Control for Plug-in Electric Vehicles in Smart Microgrids Fueled by Renewable Energy Sources

There is a growing interest in plug-in electric vehicles (EVs). Charging EVs from smart microgrids fueled by renewable energy resources is becoming a popular green approach. Although some works have been done on renewable energy sources and EVs in smart microgrids, the stochastic characteristics and the dynamic interplay between these two important green solutions should be carefully considered. Furthermore, one of the important performance metrics, service availability for EVs, is largely ignored in the existing works. In this article, we study the charging policies in smart microgrids with EVs and renewable energy sources. We analyze service availability under different charging policies. Based on the renewable energy sources states, battery states, and the number of charging EVs, an optimal charging policy is obtained to maximize the energy utilization with service availability constraints. We formulate the optimal charging problem as a stochastic decision process. Maximizing the energy utilization with service availability constraints is the objective in our model. Extensive simulation results are presented. It is illustrated that the proposed scheme can significantly improve the service availability for EVs in microgrids fueled by renewable energy sources.