Autonomously Distributed Control of Electric Vehicle Chargers for Grid Services

2022 57th International Universities Power Engineering Conference (UPEC)

Marinelli

et al. 2022

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.

“…This section describes the methodology implemented in this paper. The model developed uses as a starting point the model previously built in [13] and further developed in [14].…”

Section: Methodsmentioning

confidence: 99%

“…The model is created in Matlab/Simulink. The simulation runs with a variable time step, setting the minimum time step at 0.1 s. More information about the model's signal discretization and communication delays can be found in [13]. Figure 3 shows the layout of the grid to which the system is applied.…”

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

Wind Based Charging via Autonomously Controlled EV Chargers under Grid Constraints

2022 57th International Universities Power Engineering Conference (UPEC)

Marinelli

et al. 2022

“…This section describes the methodology implemented in this paper. The model developed uses as a starting point the model previously built in [12].…”

Section: Methodsmentioning

confidence: 99%

Autonomously Distributed Control of EV Parking Lot Management for Optimal Grid Integration

2022 International Conference on Renewable Energies and Smart Technologies (REST)

Andersen

et al. 2022

Self Cite

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.

“…Due to the implementation simplicity, the early stage of smart chargers belongs to the centralized control approach [12]. However, recent initiatives follow the distributed control approach introduced in [15]. The rationale behind exploiting distributed control is the increased robustness to malfunctions on a largescale deployment and the reduction of required communication together with the communication delays [16].…”

Section: Control Architecture a Smart Chargers State-of-the-artmentioning

confidence: 99%

“…This paper does not intend to be exhaustive on the grid service front but rather evaluates some important well-researched features such as peak-shaving, valley-filling, and phase-balancing actions. Based on previous work [12], [17], we take as state-of-the-art representatives for the centralized control approach the Zaptec charger, while the charger developed in the ACDC project for the distributed control approach from [15].…”

Section: Control Architecture a Smart Chargers State-of-the-artmentioning

confidence: 99%

Power Modulation and Phase Switching Testing of Smart Charger and Electric Vehicle Pairs

2022 57th International Universities Power Engineering Conference (UPEC)

Andersen

et al. 2022

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.