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Owing to the increasing penetration of single‐phase small generation units and electric vehicles connected to distribution grids, system operators are facing challenges related to local unbalanced voltage rise or drop issues, which may lead to a violation of the allowed voltage band. To address this problem, distribution transformers with on‐load tapping capability are under development. This study presents model and experimental validation of a 35 kVA three‐phase power distribution transformer with independent on‐load tap‐changer control capability on each phase. With the purpose of investigating and evaluating its effectiveness under different operative conditions, appropriate scenarios are defined and tested considering both balanced and unbalanced situations, also in case of reverse power flow. The experimental setup is built starting from an analysis of a Danish distribution network, in order to reproduce the main feature of an unbalanced grid. The experimental activities are recreated in by carrying out dynamics simulation studies, aiming at validating the implemented models of both the transformer as well as the other grid components. Phase–neutral voltages' deviations are limited, proving the effectiveness of the phase‐independent tap operations. Furthermore, minor deviations of the results from simulations and experiments confirm that all the system components have been properly modelled. [ABSTRACT FROM AUTHOR]

The design and implementation of management policies for plug-in electric vehicles (PEVs) need to be supported by a holistic understanding of the functional processes, their complex interactions, and their response to various changes. Models developed to represent different functional processes and systems are seen as useful tools to support the related studies for different stakeholders in a tangible way. This paper presents an overview of modeling approaches applied to support aggregation-based management and integration of PEVs from the perspective of fleet operators and grid operators, respectively. We start by explaining a structured modeling approach, i.e., a flexible combination of process models and system models, applied to different management and integration studies. A state-of-the-art overview of modeling approaches applied to represent several key processes, such as charging management, and key systems, such as the PEV fleet, is then presented, along with a detailed description of different approaches. Finally, we discuss several considerations that need to be well understood during the modeling process in order to assist modelers and model users in the appropriate decisions of using existing, or developing their own, solutions for further applications. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a multi-agent based modeling technology for simulating and operating a hierarchical energy management of a power distribution system with focus on EVs integration. The proposed multi-agent system consists of four types of agents: i) Distribution system operator (DSO) technical agent and ii) DSO market agents that both belong to the top layer of the hierarchy and their roles are to manage the distribution network by avoiding grid congestions and using congestion prices to coordinate the energy scheduled; iii) Electric vehicle virtual power plant agents are in the middle level of the hierarchy and their roles are to manage the charge process of the electric vehicles; iv) Electric vehicle agents are placed at the bottom layer of the hierarchy and they represent electric vehicle owners with different users’ profiles. To demonstrate the coordination behavior of the proposed system, a multi-agent simulation platform is developed based on the co-simulation environment of JACK, Matlab and GAMS. The aim of the multi-agent system is to simulate the collaborative (all agents contribute to achieve an optimized global performance) but also competitive environment (each agent will try to increase its utilities or reduce its costs). [ABSTRACT FROM AUTHOR]

Electric vehicles can become integral parts of a smart grid, since they are capable of providing valuable services to power systems other than just consuming power. On the transmission system level, electric vehicles are regarded as an important means of balancing the intermittent renewable energy resources such as wind power. This is because electric vehicles can be used to absorb the energy during the period of high electricity penetration and feed the electricity back into the grid when the demand is high or in situations of insufficient electricity generation. However, on the distribution system level, the extra loads created by the increasing number of electric vehicles may have adverse impacts on grid. These factors bring new challenges to the power system operators. To coordinate the interests and solve the conflicts, electric vehicle fleet operators are proposed both by academics and industries. This paper presents a review and classification of methods for smart charging (including power to vehicle and vehicle-to-grid) of electric vehicles for fleet operators. The study firstly presents service relationships between fleet operators and other four actors in smart grids; then, modeling of battery dynamics and driving patterns of electric vehicles, charging and communications standards are introduced; after that, three control strategies and their commonly used algorithms are described; finally, conclusion and recommendations are made. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely regarded as valuable assets in the smart grid as distributed energy resources in addition to their primary transportation function. However, connecting EVs to the distribution network and recharging the EV batteries without any control may overload the transformers and cables during peak hours when the penetration of EVs is relatively high. In this study, a two level hierarchical control method for integrating EVs into the distribution network is proposed to coordinate the self-interests and operational constraints of two actors, the EV owner and Distribution system operator (DSO), facilitated by the introduction of the fleet operator (FO) and the grid capacity market operator (CMO). Unlike the typical hierarchical control system where the upper level controller commands the low level unit to execute the actions, in this study, market based control are applied both in the upper and low level of the hierarchical system. Specifically, in the upper level of the hierarchy, distribution system operator uses market based control to coordinate the fleet operator׳s power schedule. In the low level of the hierarchy, the fleet operator use market based control to allocate the charging power to the individual EVs, by using market based control, the proposed method considers the flexibility of EVs through the presence of the response-weighting factor to the shadow price sent out by the FO. Furthermore, to fully demonstrate the coordination behavior of the proposed control strategy, we built a multi-agent system (MAS) that is based on the co-simulation environment of JACK, Matlab and Simulink. A use case of the MAS and the results of running the system are presented to intuitively illustrate the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

As increasing numbers of electric vehicles (EVs) enter into the society, the charging behavior of EVs has got lots of attention due to its economical difference within the electricity market. The charging cost for EVs generally differ from each other in choosing the charging time interval (hourly), since the hourly electricity prices are different in the market. In this paper, the problem is formulated into an optimal control one and solved by dynamic programming. Optimization aims to find the economically optimal charging solution for each vehicle. In this paper, a nonlinear battery model is characterized and presented, and a given future electricity prices is assumed and utilized. Simulation results indicate that daily charing cost is reduced by smart charing. [ABSTRACT FROM AUTHOR]

• A data-driven prediction method of EVs' charging behavior is developed. • Uncertainties are analyzed to estimate the performance-based payment of providing ancillary service. • In optimization model, the non-parametric distribution function is converted to a solvable multivariable polynomial function. Electric vehicle batteries can be a flexible resource for providing regulation reserves in ancillary service markets. But uncertainty exists both in EVs' charging behavior and system's real-time regulation demand, which may cause the deficiency of regulation power of EVs, thus lowering the performance in ancillary service provision. Based on historical data of EVs' charging behavior and system's regulation signal, this paper proposes data-driven approaches to optimize EVs participation in ancillary service market. Firstly, the EV charging behavior uncertainty is described using a probability prediction model, and the uncertainty of regulation signal is analyzed as well. Secondly, based on the uncertainty description, a day-ahead schedule model is proposed to maximize the income, in which the estimated performance-based payment of providing ancillary service is considered. The model is tested with three EV aggregators, and the results are compared to analyze the ability and characteristics of the EV aggregators when providing ancillary service. [ABSTRACT FROM AUTHOR]

• A multi-time scale energy management for virtual battery-based prosumer was presented. • Proposal of a virtual battery model to describe aggregated electric vehicles flexibilities. • Combination of MPC and virtual battery model to determine prosumers' real-time power. • Comparison of the different models in terms of modelling accuracy and cost variation. The increasing number of small and medium-sized prosumers with distributed energy resources (DERs) has led to the need for innovative operational strategies at the distribution system level. Among them, microgrid (MG) energy management for peer-to-peer power sharing between prosumers is a promising approach. In this study, we develop a multi-time scale optimization method for virtual battery model-based prosumer energy management. First, a day-ahead optimal scheduling model is established based on an integrated virtual battery model that aims to minimize the total dispatching cost of a prosumer-oriented MG without impacting the privacy of individual end-prosumers. Then, close to real-time operation, model predictive control is applied to minimize the deviation between the real-time power and the day-ahead optimal schedule over the control horizon for prosumers with energy storage resources. The simulation results show that the proposed approach employing the integrated virtual battery model to quantitatively characterize the resource flexibility of prosumers yields a good optimization performance and high computing efficiency, compared to those of the approaches modelling traditional DER flexibilities. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs' uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations. [ABSTRACT FROM AUTHOR]