As part of a sustainable power system, a synergy between electric mobility and renewable energy sources (RESs) can play a crucial role on mitigating the nature of RESs and defer costly grid upgrades via smart-charging. This paper presents a distributed autonomous control architecture for electric vehicle (EV) chargers and a clustering method for charging coordination. The architecture framework is detailed depending on the number of chargers and specific location properties. Moreover, the framework unveils the communication, measurement and power flow. The aforementioned approach aims at simplifying the overall charging experience for the EV owners while coupling it with a healthy grid behavior. The proposed control architecture is simulated on a prosumer case with two EVs. The performance of the controller is considerably affected by observability capabilities of current smart-meters. Faster measurement cycles of smartmeters can reduce the overshoot time span but not prevent it.
The mass penetration of electric vehicles (EVs) could develop grid stability problems due to the increase of peak loads created by coincident charging factors. Smart charging is the control of the EV charging loads and has long been identified as a potential solution. Smart charging could also contribute to grid stability by mitigating the intermittent nature of renewable energy generation. This paper describes the current status of EV flexibility services at the distribution level. The analysis of the smart charging status is done considering the technological, economic and regulatory frameworks, and presenting what the different barriers of each of these aspects are. Additionally, the paper introduces the ACDC project (Autonomously Controlled Distributed Charger), which aims at developing an EV clustering method based on distributed smart charging control logic for flexibility services. For divulgation purposes, the scheduled test case scenario of the parking lot at the Technical University of Denmark is described. The paper concludes on some of the most relevant actions to overcome the most imminent barriers and to push further the rollout of EV charging infrastructure towards the target EV penetration planned by policymakers.
Smart charging has a strong potential to mitigate the challenges in security of supply caused by the increasing reliance on renewable energy sources (RESs) and electric vehicles (EVs). This paper describes the performances of an autonomous distributed control for coordinating the charge of four parking lots as part of a virtual power plant. The virtual power plant consists of a wind farm and four parking lots located in different areas of the grid and connected to two different feeders. The control architecture is applied to a 24-hour simulation with input data from a wind park, the loading data of two feeders, and user behavior from 68 EVs. The objectives of the architecture are: maximization of the wind power usage to charge the EVs; minimization of feeders overloading; minimization of energy imported from the grid; assurance of sufficient charging fulfillment; wind power variability mitigation. Under simulated conditions, the control architecture keeps the feeder loading below 80% by reducing the power allowance to the parking lot during peak demand. Nonetheless the four parking lots guarantee an energy charged of 10.7 kWh for all EVs starting the charging session with less than 60% state of charge (SOC). The total energy produced by the wind power plant is 4.36 MWh, of which 1.34 MWh is used to charge EVs. The remaining 3.07 MWh is exported to the grid, and only 92 kWh is imported from the grid for charging. Further investigation is needed regarding the wind power variability mitigation, as its reduction is only marginal under simulated conditions.
De-coupling transport sector from the use of petroleum is giving way to the rise of electric mobility. As compromising the user's comfort is not an option managing the power system becomes a tall challenge, especially during peak hours. Thus, having a smart connection to the power system, such as an electric vehicle (EV) smart charger, is considered part of the solution. This paper focuses on assessing the capabilities of smart chargers in the context of helping the electrical network without compromising the user's comfort. By using a Tesla Model S P85, Renault Zoe, and Nissan LEAF, the paper first evaluates differently controlled (centralized and distributed) smart chargers against the IEC 61851 standard. Second, it tests smart features such as peak-shaving, valley-filling, and phase balancing. Being representatives of the state-of-the-art, both chargers exceed standard requirements and offer new grid service possibilities. However, the bottleneck for providing faster grid services remains the EV on-board charger. The results from this article can help to better simulate the dynamic charging behaviors of EVs.
This paper proposes an autonomous distributed control design for coordinating the charging process of parking lots for electric vehicles (EVs). The focus of this paper is to investigate the performance of the modeled architecture. The model simulates for 24 hours an office parking lot scenario with real input data from 16 EVs. The primary objective is to quantify the fulfillment of the demand and the guaranteed amount of energy for each user. The secondary objective is to analyze the effects of restricting the power capacity of the parking lot from 88 kW to 43 kW. In the constrained charging scenario, the system guarantees a minimum energy of 12.2 kWh (roughly 61 km) to each car connected for at least 5 hours and 54 minutes. In the unconstrained charging scenario 12 EVs reach maximum state-of-charge (SOC), while 11 EVs reach it in the constrained one. Demand fulfillment is only marginally different between the two scenarios because the final SOC values of the EVs are nearly the same. On the other hand, constraining connection capacity reduces significantly the idle state of six chargers.
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