Charging Electric Vehicles in the Smart Grid

The increasing integration of renewable energy sources, electric vehicles (EVs), and demand-side flexibility presents both new opportunities and problems for future energy systems. This work proposes a smart grid-based charging architecture that is intelligent and robust to combine EV charging, demand response (DR), and renewable energy generation under a stochastic control mechanism. Unpredictable EV charging patterns, fluctuating electricity consumption, and the inherent uncertainty in solar and wind output are all addressed via probabilistic modeling and optimization techniques. By combining energy storage devices with real-time demand projections, the system ensures efficient energy distribution while lowering grid stress and operational costs. Deep learning and reinforcement learning approaches are used to forecast energy consumption and schedule charging behavior, while fairness-based algorithms keep the distributed EV loads balanced. By adapting demand to grid conditions, demand response programs increase system flexibility. Simulation findings against uncertain and changeable energy conditions verify the system's stability, emission minimization, and dependable operation. Future green energy systems, also known as grid-responsive, adaptable, and sustainable ones, would look just like this. The suggested method is adaptable to city distribution networks and community microgrids, and it is scalable. It also supports policy-driven energy prices and environmental regulations. Research Difficulties Future research challenges will include online hardware deployment and interoperability with national grid architectures. This opens up new possibilities for creating environmentally friendly, intelligent, and robust EV charging infrastructure. Keywords: Demand Response, Renewable Integration, Electric Vehicles, Smart Grid, and Stochastic Strategy

With the increase of power receiving proportion and insufficient peak regulation capacity of local units, receiving-end power grid is facing the difficulty of peak regulation in valley time. In order to solve this problem, while considering the potential of large scale charging load of electric vehicles (EVs), an aggregator-based demand response (DR) mechanism for EVs participating in peak regulation in valley time is proposed in this paper. In this aggregator-based DR mechanism, profits for power grid’s operation and participation willingness of EV owners are both taken into account. Based on characteristics of EV charging process and day-ahead unit generation scheduling, a rolling unit commitment model with DR is established to maximize the social welfare. In addition, to improve the efficiency of optimization problem solving process and communication between the independent system operator (ISO) and aggregators, clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving costs and reducing peak-valley difference of receiving-end power grid are verified through case studies.

There are a variety of flexible energy demand loads in modern buildings. In particular, heating, ventilation and air conditioning (HVAC) system accounts for around 50% of buildings' total energy consumption and is a major source for energy scheduling flexibility. Electric vehicle (EV) charging is an emerging load in many buildings, and may also provides scheduling flexibility depending on the charging requirements. In addition, the usage of energy storage systems such as batteries further increases the flexibility in scheduling building energy demands. At the power grid level, it is important to leverage such flexibility for improving energy efficiency. To this end, we have proposed a proactive demand response scheme that enables building customers to actively participate in the electricity market clearing process. Compared to the conventional passive demand response strategy, the proactive scheme is shown to be more effective to take advantage of buildings' flexible load demand and enhance the energy efficiency of the overall power system. In this paper, the scheduling of various flexible building energy loads such as HVAC control and EV charging, as well as the usage of battery storage system, are jointly modeled and optimized in the proactive demand participation framework. We conduct experiments on the IEEE 5-bus system to evaluate the impact of heterogeneous flexible loads on the energy efficiency of the proactive scheme.

The adoption of Electric Vehicle (EV) can shave the peak load and flatten the load profile in an urban area. However, the uncontrolled patterns of simultaneous and randomised EV charging may increase the peak load and thus destabilise the power grid. To address this problem effectively, this paper proposes an optimal charging scheme with the objective of lowering the power fluctuation level. The charging scheme takes into account the uncertainty of EV's driver behaviour and EV charging demand model with minimal impacts on daily routine of EV users. Firstly, a power fluctuation level problem is formulated and a novel EV charging scheme based on Genetic Algorithm is further proposed to solve the problem. The simulation results have shown the robustness of the proposed scheme in lowering the power fluctuation level and the overall peak demand significantly in the grid system.

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings. Note to Practitioners —This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.

The global upsurge in research of smart grid and demand response (DR) is opening up new opportunities and challenges to China's power industry in recent years. The smart grid emphasize the interaction with user particularly, including the information interaction and power interaction, which are achieved through the deployment of various types of DR programs. Meanwhile, the development of smart grid also depends on demand response mechanism in the open market environment. DR coincides with the goal of smart grid which is to achieve a high-security, economical, reliable and efficient power system. So the collaboration between smart grid and demand response is talked in this paper. First,we analyze the relationship between smart grid and demand response systematically.Second,the development conditions of DR project in China and abroad are expounded. Furthermore, the necessity and significance of pushing DR project into smart grid is discussed and summarized, finally, methods and suggestions for achieving the collaboration are presented. (4 pages)

Smart grid research in New Zealand – A review from the GREEN Grid research programme

While the number of the vehicle actuated with liquid fuels are settled, the count of electric vehicles is increasing. For the present moment most of them are scheduled for daily urban usage. This paper presents an analytical approach for estimation of the impact of electrical vehicle (EV) battery charging on the distribution grid. Based on the EV charge profile, load curve and local distributed generation the grid nodes, the time variation of grid parameters is obtained. A set of typical load profiles of EV charging modes is studied and presented. A software implementation and a 24h case study of low voltage distribution network with EV charging devices is presented in order to illustrate the approach and the impacts of EV charging on the grid. In the current paper an approach using variable nonlinear algebraic equations for dynamic time domain analysis of the charge of the electric vehicles is presented. Based on the results, the challenges due to EV charging in distribution networks including renewable energy sources are discussed. This approach is widely applicable for various EV charging and distributed energy resources studies considering control algorithms, grid stability analysis, smart grid power management and other power system analysis problems.

Electric power systems are in state of transition as they attempt to evolve to meet new challenges provided by growing environmental concerns, increases in the penetration of distributed renewable energy sources (DRES) as well as the challenges associated with integrating new technologies to enable smart grids. New techniques to improve the electrical power system, including the distribution system, are thus needed. One such technique is dynamic distribution system reconfiguration (DNSR), which involves altering the network topology during operation, providing significant benefits regarding the increased integration of DRES. This paper lays out an improved model which aimed to optimize the system operation in a coordinated way, where DRES, energy storage systems (ESS) and DNSR are considered as well as the uncertainty of these resources. The objective function was modeled to incentivize the uptake of DRES by considering the cost of emissions to incentivize the decarbonization of the power system. Also, the switching costs were modeled to consider not only the switching, but also the cost of degradation of these mechanisms in the system operation. Two systems are used to validate the model, the IEEE 119-bus system, and a real system in São Miguel Island. The results of this paper show that using DNSR, DRES, and ESS can lead to a significant 59% reduction in energy demand through a 24-hour period. In addition, using these technologies results in a healthier, more efficient, and higher quality system. This shows the benefits of using a variety of smart grid technologies in a coordinated manner.

<div class="section abstract"><div class="htmlview paragraph">The adoption of electric vehicles (EVs) has been announced worldwide with the aim of reducing CO<sub>2</sub> emissions. However, a significant increase in electricity demand by EVs might impact the stable operation of the existing power grid. Meanwhile, EV charging is acceptable to most users if it is completed by the time of the next driving event. From the viewpoint of power grid operators, flexibility for shifting the timing of EV charging would be advantageous, including making effective use of renewable energy.</div><div class="htmlview paragraph">In this work, an EV model and simulation tool were developed to make clear how the total charging demand of all EVs in use will be influenced by future EV specifications (e.g., charge power) and installation of charging infrastructure. Among the most influential factors, EV charging behavior according to use cases and regional characteristics were statistically analyzed based on the real-world usage data of over 14, 000 EVs and incorporated in the simulation tool. Using the resultant statistical model, the Monte Carlo method was applied to conduct a parameter simulation study of continuous EV usage. The impact of EV charging demand on the power grid in a future scenario and also the benefit of controlling EV charging were evaluated quantitatively in the study. Important findings indicated that active smart charging will be necessary rather than passive control based on time-of-use tariffs.</div></div>

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

In this paper an optimal strategy for electric vehicle (EV) charging is proposed to achieve the best network operating conditions, with the help of battery energy storage systems (BESS), while integrating photovoltaic power plants (PV). The electric vehicles can both participate in demand response (DR) and serve as energy storage facilities. They can respond to DR signals, such as price variations or direct control messages by modulating their power consumption, thus providing necessary flexibility to the grid operator. The simulations are performed on a test network, and the mathematical model has been implemented in Microsoft's CPLEX tool. The results have shown that in order to cope with the challenges generated by a large number of EVs, coordination of the EV charging not only reduces the difference between the peak and valley of the load demand, but also generates a number of favorable factors and improving the network operation.

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

The reduction of air pollutants for the purpose of maintaining or improving air quality across the globe is a fundamental concern to which all modern governments are allocating varying amounts of attention and resources. The successful amelioration of air pollution requires strategic investments in the commercialization and adoption of “clean energy technologies” by both private and public entities, the conversion of contemporary houses to “smart houses”, the diffusion of Renewable Energy Sources (RES) including photovoltaic systems (PV), wind farms, and different forms of bioenergy, and the integration of electric-powered vehicles. In concert with these ideas, this paper aims to discuss the possibility of undertaking a feasibility study in two countries Canada and Italy concerning the integration of electric vehicles (EVs) and electric motorcycles (EMs). The proposed feasibility study would seek to assess the prospect of replacing the current vehicle fleets in these two countries with EVs in a manner that utilizes renewable energy sources and, thus, does not generate new toxic emissions. In conclusion, this study demonstrated that a pronounced introduction and distribution of RES, EVs, and EMs can operate as a great opportunity for both the environment and the capacities and needs of energy production. Today, the EV is not widespread. With this contribution, it is shown how EVs can be well integrated with renewable energy. Therefore, it is the duty of governments to implement policy strategies, in order to spread them across more territory.