Abstract
Adopting high penetration levels of electric vehicles (EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to
① the lack of utility-grade communication systems in many cases such as secondary (low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and ② existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators (DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.
THE rapid growth of electric vehicle (EV) deployment can significantly increase the charging load, which could lead to negative impacts on the existing infrastructure of power distribution systems [
In contrast to centralized and distributed energy management techniques, autonomous EV charging techniques can regulate the EV load without the need for a communication network [
There have been numerous studies in the literature that have proposed autonomous EV charging control schemes. A charging technique based on duty cycles and EV departure time is proposed in [
Several studies have considered the use of measured voltage at the point of common coupling (PCC) as a direct input to control the EV charging load. For example, [
Nevertheless, existing research works in this area have the following shortcomings and gaps. First, the communication-less controllers proposed in the literature are very conservative, causing unnecessarily slow charging without fully utilizing the capacity of the microgrid. In this regard, the frequency- and voltage-based controllers proposed in previous studies reduce the EV charging speed even when the system frequency and the bus voltages are above their respective nominal values.
Second, the charging control logic implemented in previous studies can result in unfair allocation of microgrid capacity among EVs. In this context, social charging fairness is defined as the equal share of limited microgrid capacity among EVs when power resources are in short supply [
Third, the frequency- and voltage-based controllers in [
To fill these gaps, this paper aims to develop a communication-less management strategy for EV charging in droop-controlled IMGs via an adaptive Sigmoid-based controller. The strategy considers social charging fairness during periods when the microgrid capacity is limited. The key contributions of this paper are as follows.
1) An adaptive Sigmoid-based controller that manages the charging rate based on the system frequencies and bus voltages is proposed. Compared with previous research works, the proposed controller provides more flexibility and better utilization of the power system capacity in EV charging without jeopardizing stability.
2) A social charging fairness system that assigns priority levels to EVs based on their past charging power allocation is developed. The priority level for each EV is autonomously lowered as its charging allocation in the historical time horizon increases. The priority level is utilized to adjust the Sigmoid-based controller to provide more system capacity to the EVs with higher priority levels.
3) The cut-off point in the controllers proposed in the previous research works has been replaced by a novel communication-less EV load shedding scheme that gets triggered when an under-voltage or under-frequency event occurs in the IMG when the generation does not meet the required demand. The proposed shedding scheme is coordinated with the priority level of EVs to ensure fair EV shedding.
4) A system violation index (SVI) is proposed to quantify the effectiveness of the proposed strategy in reducing violations of system operation constraints that result from EV charging.
Without loss of generality, the following assumptions are made during the development of this paper.
1) Similar to the research works in [
2) All chargers follow the constant current/constant voltage (CC/CV) charging profile, which is widely used in charging EV lithium-ion batteries [
3) Compared with other residential loads, EVs have a greater degree of flexibility due to the energy stored in their batteries. As a result, during periods when the microgrid is not operating normally, the normal load is prioritized while EV charging load is reduced or interrupted before any other loads. Supplemental approaches can be developed to consider the preferences of EV owners while ensuring the overall stability and resilience of the microgrid. This could involve dynamic pricing mechanisms, or predictive modeling that can enable more user-centered charging control, ensuring that the needs of EV owners are aligned with the microgrid stability and resilience goals.
This section describes the proposed communication-less management strategy for EV charging. The proposed strategy is applied locally by each EV charger in the system without any communication with the system operator or other EV chargers. The block diagram of the proposed strategy is shown in
Fig. 1 Proposed strategy for EV charging.
The proposed strategy continuously logs the charging process and calculates the SoC increase of the EV battery in the past time horizon to determine the priority level assigned to the EV. Let be the set of historical time steps and be the length of historical time horizon considered with . This means that if equals 4 hours for example, the smart charger will continuously calculate the total SoC increase in the last four hours. Parameter is programmed into all EV smart chargers and can be decided by the IMG operator depending on system requirements. In this regard, a higher SoC increase during the historical time horizon leads to a lower priority assignment in comparison to the EVs with less past charging. The priority level affects the degree to which the EV charging rate is reduced when the system frequencies and/or bus voltages are below their nominal values. In this regard, the choice of the parameter is guided by several factors that need to be considered. One primary factor is the desired level of responsiveness of the charging control strategy. A shorter allows for more immediate adjustments to priority levels based on recent charging behavior, whereas a longer captures a broader charging history, providing a more gradual response. Let be the time step at which various calculations are performed, where is used as an index to represent different moments in time during the charging process. The SoC increase of the EV at each time step is estimated by integrating the charging current and adding it to the previous state as [
(1) |
where is the rated capacity of the battery for the EV; and is the charger current of the EV at time step t. The total SoC increase during the historical time horizon for the EV is calculated as:
(2) |
The proposed strategy assigns a priority level to the EV at time step according to:
(3) |
where is the number of priority levels in the system that can be assigned to EVs; and is the total SoC increase that moves an EV from one priority level to another. The priority level is updated at each time step . In (3), is a higher priority level than because that latter has gained higher SoC in the past time horizon. In summary, the priority system embedded in each local charger uses the SoC increase of EV battery in the historical time horizon to assign an EV a priority level without the need for communication with system operator or other chargers. The priority level will affect the charging power that an EV is allocated through the adjustable parameter in Sigmoid-based controller as explained in the next subsection.
A Sigmoid-based controller is programmed on each EV charger. The EV charging power is controlled based on the adaptive charging speed factor , which is a multiplication of Sigmoid-based functions of voltage and frequency, as given by:
(4) |
where and are the voltage and frequency measured by the charger at the PCC, respectively; and is a function of the priority level .
Fig. 2 Example of how EV charging speed for frequency-based control function varies with priority level.
The Sigmoid-based controller is utilized in the proposed strategy because it provides three regions of continuous EV charging control, as illustrated in
The charging current of the EV at each time step is determined by:
(5) |
(6) |
(7) |
where and are the minimum and the maximum output current limits of the charger for the EV, respectively; and and are the minimum and the maximum limits of SoC for the EV, respectively. A minimum charging current is included based on the requirements of EV charging standards [
In the situations where the system frequencies and/or bus voltages go below their respective lower limit despite the reduction of the EV charging current to the minimum, the proposed strategy can deactivate the EV charger through its shedding scheme. While the EV is plugged in, a communication-less EV load shedding scheme continuously monitors the system frequency, bus voltage, and EV priority level at each time step , as shown in
Fig. 3 Flow chart of proposed communication-less EV load shedding scheme.
Algorithm 1 : proposed communication-less EV load shedding scheme |
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Input: priority level , system frequencies , and bus voltages measured at the EV PCC |
Output: shedding control signal |
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Function Main (, , ) |
while EV is plugged in do |
if then |
if then |
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else |
if then |
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else |
if then |
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else |
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By default, for any time step unless it is changed by the load shedding scheme. When the system frequencies or the bus voltages go below their respective acceptable limit or , the load shedding scheme checks whether the EV is being charged at the current time step .
If the EV is charging, the EV is assigned a time delay based on the function , which is detailed in
Algorithm 2 : time delay function |
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Function Time_Delay() |
if then |
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else if then |
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else if then |
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Algorithm 3 : shedding control function |
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Function Shedding_Control() |
if then |
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else if then |
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else if then |
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The effectiveness of the proposed strategy to reduce the violations of the system operation constraints that result from EV charging is quantified using the SVI as:
(8) |
(9) |
(10) |
where and are the frequency and voltage violation factors, respectively; is the time step; and are the parameters representing the minimum operation limits for system frequencies and bus voltages, respectively; and are the maximum limits for system frequencies and bus voltages, respectively; is the set of buses in the power system; and is the set of time steps in the studied duration. The acceptable frequency limit is typically within the range of to , while the acceptable voltage limit is usually within to . These values define the operation boundaries for system frequencies and bus voltages to ensure the stability and reliability of the power system [
In IMGs, DGs are the main components responsible for creating balanced power generation in the distribution systems [
(11) |
(12) |
where and are the droop control settings for DG i; is the frequency set for the DG with no load; is the voltage at no load for the DG; is the voltage of the bus connected to the DG; is the system operation frequency at time step t; and is a subset of buses with DGs. The drooped injected active power and reactive power are balanced with the load demands and through the power mismatch equations as:
(13) |
(14) |
where and are the Y-bus admittance magnitude and angle, respectively; and and are the voltage phase angle at any bus l and bus i at time step t, respectively.
Numerical simulations are performed in a MATLAB environment to test the effectiveness of the proposed strategy. The modified IEEE 33-bus test IMG system shown in
Fig. 4 Test IMG system. (a) IEEE 33-bus test IMG system. (b) CIGRE 14-node secondary network.
DG No. | (p.u.) | (p.u.) | (p.u.) | (p.u.) | (MVA) | Power factor |
---|---|---|---|---|---|---|
1 | 0.0486 | 1 | 1.03 | 2.5 | 0.80 | |
2 | 1.5 | 0.95 | ||||
3 | 0.1010 | 1 | 1.02 | 1.0 | 0.80 | |
4 | 0.1660 | 1 | 1.02 | 0.6 | 0.80 |
Fig. 5 Wind power profile for DG2.
Level-2 chargers with a maximum charging power of 6.6 kW are considered in this study, and the minimum charging rate is set to be 1.5 kW as per the IEC 61851 standard. A maximum of 200 EVs are assumed to be present in the IMG system, which sets the rated EV charging load to be around 35% of the total rated system load. The battery capacities for all EVs are set to be 62 kWh, which are similar to those of the Tesla Model 3, one of the most popular EVs in the market [
The initial battery SoCs of EVs at arrival follow a Gaussian distribution with and . All case studies are run from 12 p.m. to 12 a.m.. The time interval of simulation studies is 1 min. Without loss of generality, six priority levels are chosen based on hours and . The parameters of Sigmoid-based controller are listed in
Level | Level | ||
---|---|---|---|
L6 | 2000 | L3 | 2900 |
L5 | 2300 | L2 | 3200 |
L4 | 2600 | L1 | 3500 |
It should be noted that in
In case 1, the IMG is run without the presence of EV loads.
Fig. 6 Simulation results of case 1. (a) The minimum and maximum bus voltages. (b) System frequencies. (c) Normal load power.
Active and reactive power outputs of DG are shown in
Fig. 7 Active and reactive power of DGs. (a) Active power. (b) Reactive power.
In case 2, the IMG is simulated with the presence of EV load and the assumption that the EVs charge at their available rated power.
Fig. 8 Simulation results of case 2. (a) The minimum and maximum bus voltages. (b) System frequency. (c) Normal and EV charging load power. (d) Number of charging EVs.
The proposed strategy in Section II is simulated and compared with controllers from the state-of-the-art review. First, the proposed strategy is compared with the voltage-based controller proposed in [
(15) |
where is the charger power; is the minimum charger power; is a controller parameter for the EV; and is the sensitivity measured by the charger of the EV. in (15) is a reference voltage set to the lower acceptable limit, which is 0.95 p.u..
The performances of the two controllers during peak load period from the 1
Fig. 9 Simulation results for voltage-based controller and proposed strategy during peak load period. (a) The minimum bus voltage for voltage-based controller. (b) System frequency for voltage-based controller. (c) EV charging power for voltage-based controller. (d) The minimum bus voltage for proposed strategy. (e) System frequency for proposed strategy. (f) EV charging power for proposed strategy.
The SVI for opportunistic charging, voltage-based controller, and proposed strategy are listed in
Technique | SVI |
---|---|
Opportunistic charging | 0.0855 |
Voltage-based controller | 0.0196 |
Proposed strategy | 0 |
As described in Section II, the level at which charging power is reduced depends on those of each EV.
Fig. 10 Priority level distribution of EV during peak load period for proposed strategy.
The proposed strategy is also compared with the frequency-based controller implemented in [
(16) |
where is the controller droop gain.
Fig. 11 Simulation results for frequency-based controller during peak load period. (a) The minimum bus voltage. (b) System frequencies. (c) EV charging power.
Nonetheless, the controller applies equal charging reduction to all EVs without consideration of fairness, as can be observed from
Fig. 12 Priority level distribution of EV during peak load period for frequency-based controller.
The performances of the frequency-based controller and the proposed strategy are also compared during DG outages, which could cause a further drop in system frequency during the peak load period. A DG outage scenario is implemented where DG4 goes out of service at 6:30 p.m..
Fig. 13 Simulation results of frequency-based controller and proposed strategy in DG outage scenario. (a) The minimum bus voltage for frequency-based controller. (b) System frequencies for frequency-based controller. (c) EV charging power for frequency-based controller. (d) The minimum bus voltage for proposed strategy. (e) System frequencies for proposed strategy. (f) EV charging power for proposed strategy.
The simulation results of DG4 outage scenario are demonstrated in
The SVI for this scenario is calculated for the frequency-based controller and the proposed strategy, as shown in
Technique | SVI |
---|---|
Frequency-based | 0.0247 |
Proposed strategy | 0.0039 |
This study develops a communication-less management strategy for the EV charging in droop-controlled IMGs. The proposed strategy controls the EV charging rate based on both the system frequencies and bus voltages as well as the past charging power allocation. Further, a charging fairness system that assigns priority levels to EVs based on their past charging power allocation is developed. Moreover, a novel communication-less EV load shedding scheme is proposed that gets triggered when an under-voltage or under-frequency event occurs in the IMG. Numerical simulations are conducted to validate the effectiveness of the proposed strategy. The results demonstrate the superiority of the proposed strategy to the state-of-the-art controllers in modulating the EV charging load. The results also show that the charging fairness system implemented in the proposed strategy does not degrade the performance of the EV charging control. During a DG outage scenario, the proposed strategy successfully curtailed EV loads to prevent further violations and bring back the system operation parameters to acceptable limits. The effectiveness of the proposed strategy in controlling EV charging without the presence of communication is significant because it introduces an implementable and cost-effective solution that reduces the anticipated upgrades in power systems that are required for the seamless integration of EVs.
In future, optimizing the values of and can be a focus of research to enhance the effectiveness of the control strategy. Advanced machine learning techniques could be involved to analyze historical data and identify patterns in EV charging behavior that have the most significant impact on grid stability.
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