Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation (2024)

1. Introduction

Increasingly destructive environmental impacts have been observed, alongside the urgent need to stop climate change, especially driven by the large volume of greenhouse gas emissions (GHGs) [1,2]. One of the contributions that stands out in the quest for reducing these emissions is e-mobility, referring to the use of vehicles powered by electricity, whether from the grid or distributed energy resources (DERs), replacing traditional vehicles that use combustion engines. The transportation sector plays a significant role in GHG emissions due to its high dependence on fossil fuels such as gasoline and diesel [3,4]. A massive transition to electric-driven transports can significantly reduce the carbon footprint and gradually contribute to sectoral decarbonization. However, a greater number of public charging points is increasingly necessary in order to accomplish such a transition [5].

Furthermore, e-mobility provides greater energy efficiency. Electric vehicles (EVs) convert a larger portion of grid energy into useful work, compared to combustion engine vehicles [6,7]. When the increase in renewable energy generation in electricity production is considered, the environmental footprint of electric vehicles becomes even more positive, making them a more sustainable alternative. That is the case for Brazil, where renewable energy generation represents more than eighty percent of electricity offer [8,9]. Although e-mobility is remarked as a promising solution when massively applied in countries where renewable energy generation is significant, several studies address the considerable impact of EVSE insertion in the energy distribution systems. Even though several charging and integration methodologies have emerged in the last decade, including the possibility for vehicle-to-grid operations (V2G), these impacts on the distribution grid are still expected to be significant in the following years [7,10].

A second issue on the path throughout e-mobility advance is related to the importance of the business models applied [11]. Whereas public or private endeavors, distribution system operators (DSOs) and charge point operators (CPOs) must pace with the long-term economic outcomes of gradually increasing the number of charge points (CPs). The increase in the local grid capacity and maintenance costs are also significant and must be considered. Hence, a business model in the sector must cover the costs related to infrastructure scalability, energy consumption and CP maintenance while still enabling an economically viable service for the EV drivers.

DSOs and CPOs can rely on microgrid-based solutions (MGs) with local photovoltaic energy generation (PV) and demand-response strategies [12], along with dynamic pricing in order to mitigate the impacts on the energy grid and to create an economically viable service [13]. MGs constitute a reliable and robust complement to ensure resilience and reliability in power supply to consumers, owing to issues such as instability in the main grid, including degradation of electrical power quality, periods of high load demand, and islanding occurrences [14].

Until this publication, there was only one publication that addressed solutions for at least part of these issues, using microgrid-based solutions. In [15], the authors demonstrate an e-park controlled by an energy management system that optimizes EV charging and energy resources. However, their work does not consider islanded operation nor consider energy costs or dynamic pricing on dispatch solutions. Part of the reason for the reduced number of investments in this type of research in Brazil is its scenery characteristics, which completely differ from current reviews of European, Asian and North American EV markets [16].

In Brazil, the advance of e-mobility must yet overcome particular difficulties. In this context, the low demand for charging infrastructure and EV market prices (which are not yet truly competitive when traditional combustion engine vehicles are considered) can be highlighted, as discussed by the authors in [17].

There are governmental policies in Brazil that support initiatives designed to overcome the aforementioned issues. For instance, The Brazilian Electricity Regulatory Agency (ANEEL), through its Research and Development (R&D) programs, promotes the advancement of new technologies and business models in order to innovate regulations and benefit society, supporting cases developed by companies in the energy sector. This paper presents a case of development in the context of one of these ANEEL initiatives, which is financed by Equatorial Energia, an energy distribution company from Equatorial Group. The Equatorial Group is a major DSO that also operates in the energy generation and transmission sectors. This specific case (hereby called Equatorial case) aims to implement EV charging infrastructure at strategic locations, also piloting smart integration of DERs, pursuing new approaches for a more sustainable operation in the e-mobility sector as well as, ultimately, corroborating GHG emission reductions.

The Equatorial case will implement e-mobility hubs (e-MHs) based on MGs with grid-tied and islanded capabilities as well as the validation with HIL. The validation scheme is designed to accelerate the testing process of the solution and mitigate risks through real-time simulation, ensuring the effectiveness and reliability of the strategy before its practical implementation.

In this sense, the major contribution of this paper is a cost-driven, real-time management strategy for e-MHs in Brazil, which aggregates the following characteristics:

Mitigation of e-MH overload in case of sudden consumption increase and reduction on local protective device triggering occurrence;
The capacity to prioritize EV charging operations and overlook their power limits according to local grid conditions and dynamic pricing;
EVSE scalability, minimizing infrastructure alterations for future installation of new charging stations;
Islanded operation with a set of control rules that guarantees safe operation for photovoltaics, avoiding generation surplus.

This paper is structured as follows. In Section 2, an overview of the e-MH structure considered in the project is presented. Section 3 describes the management strategy developed. Next, Section 4 describes the implementation using HIL. Section 5 describes and analyzes the test scenarios considered. Finally, Section 6 outlines the conclusions of this work.

2. Foreseen Microgrid and Their Structure—The Equatorial Case

The case presented in this paper will be implemented in two different regions in Brazil. Both are attended by Equatorial:

Region 1: Macapá, the capital of Amapá state, located in the North area;
Region 2: Goiânia, the capital of Goiás state, is located in the Central-West area.

Figure 1 shows the e-MH installation locations as well as city locations in Brazil. In Amapá City, an e-MH will be installed with a CP composed of two charging stations. In Region 2, on the other hand, the e-MH will be installed at a public location, with a single charging station.

Implementing such e-MHs along with their management system may have significant implementation costs, particularly in installing and maintaining DERs and EVSE infrastructure. Initial expenses include purchasing hardware, and software, and integrating systems into existing infrastructure. Ongoing maintenance is essential for optimal operation, involving regular inspections, updates, and repairs. Additionally, regulatory compliance adds to costs, with adjustments needed to meet legal and environmental standards.

Despite these high initial costs, a robust control system yields long-term benefits such as reduced operational costs once the structure allows local energy generation and standardized communication protocols allow easier equipment substitution. For such cases, the DSO and CPO can have a return on their investment and potent revenue streams from EV charging systems.

It is important to observe that achieving interoperability is, by itself, a significant challenge in this project as long as such a solution considers an MG that incorporates different commercially available devices. These devices must communicate and exchange data in an arrangement that shall include DERs, charging stations, and meters and operate with remote monitoring. That challenge is also posed due to the use of devices from different manufacturers, the necessity to meet minimal performance requirements (e.g., small communication delays) and the necessity for robustness.

To overcome these challenges, embedded systems can act as unified communication gateways on the field, dealing with different protocols and data formats while communicating with various devices. It is also important to rely as far as possible on the industry standards in order to interoperability issues. Additionally, following communication and programming standards improves maintenance and scalability. In that sense, a programmable logic controller (PLC) was selected as the best option for the Equatorial case due to a series of qualities when compared to microcontrollers and System-on-Modules (SOMs) including robustness, reliability, compliance to programming, communication and mechanical standards and also because of its compatibility with other industrial devices in terms of voltage levels for I/O and energy supply.

Regarding the monitoring systems and cloud-based services for charging stations, middleware (such as databases and object request brokers) can be used in order to facilitate seamless message translation and mediation, ensuring all components communicate effectively. For the Equatorial case, an OCPP server (from the Open Charge Point Protocol acronym) is used as specialized middleware for various charging stations. OCPP is an open communication protocol used in charging infrastructure. It defines a standardized way for charging stations and cloud servers to communicate. This protocol specifies how data and commands are exchanged, facilitating interoperability among charging stations and management systems from different manufacturers. Additionally, using the OCPP protocol also facilitates the inclusion of new charging stations in the e-MH.

In order to present all infrastructure devices that will be used in the project, Figure 2 shows how its components are distributed in the e-MH. Figure 2 also shows the communication mesh that links these components to the PLC which will host the centralized controller. This controller has all the functionalities to manage the e-MH, discussed in Section 3.

In this context, the PLC is responsible for acquiring and monitoring data from energy meters, EVSE and DERs: battery energy storage system (BESS), with a Battery Management System (BMS), Photovoltaic (PV) and Dispatchable Generator (DG). Moreover, the PLC is also used to establish the parameters and restrictions for energy consumption and energy generation.

The energy meters allow tracking of the power provided by the e-MH primary energy source (distribution grid) and secondary source. A third energy meter tracks the power drawn by the local loads. This tracking, used for both operation modes (islanded or connected) is essential for the controller in order to execute the decision-making. When the primary source is absent, the secondary source is started by the automatic transfer switch (ATS). The ATS also sends a signal to the PLC through its digital input module, indicating if the e-MH is operating in connected or islanded mode.

There are two AC-DC converters planned for each e-MH site, for BESS and PV, respectively. The PLC receives the power measurements provided by both converters and controls their operation. For the PV, the PLC may limit the energy generation by saturating the output for the maximum power point tracking algorithm (MPPT), which acts on the converter control. As for the BESS, PLC is responsible for deciding its operation mode (charging or discharging) and for controlling its power reference.

In the upper portion of Figure 2 it is possible to observe the PLC application structure. This architecture is divided into two sections: real-time (RT) and Linux operational system. The communication between PLC, meters, converters and DG is implemented through RT functions, following the IEC 61131-3 [18] specification. The RT portion also hosts a TCP modbus server with equivalent memory space for all maps from the equipment which will be installed on-site. Therefore, all values read from the equipment on site are copied to the modbus server map. On the Linux portion, a Docker container is used to execute the Python application. This application includes the controller functionalities for the e-MH energy management (acquisition, processing and data monitoring), TCP modbus client, an API client, the customer information and the system logger. With this implementation, it is possible to exchange information between RT and Linux through TCP modbus.

Therefore, the modbus client at Linux executes a request command when it is necessary to read or write a value to the modbus server running at the RT portion. For example, if a meter measurement is necessary for the energy management running in the Python application, its modbus client will read the last measurement available for that meter at the modbus server, running at the RT portion.

For the operation, the PLC communicates with two cloud-based servers: the OCPP server and a SFTP server (from Secure File Transfer Protocol). The communication between EVSE and the controller does not depend on the RT functions, as observed in Figure 2. This communication is handled by the OCPP server, which is, in fact, a SteVE instance. SteVE (name derived from Steckdosenverwaltung, socket administration, in German) is a Java-based software distributed under a general public license (GPL). Its development started at the RWTH Aachen University in 2013 and its aim is to support the deployment of electric mobility [19]. This software package contains all major functionalities described in the OCPP protocol, hence, allowing the remote control of EVSE as well as authentication and transaction-related data storage. SteVE also has an adjacent API implemented, which will be extended in order to allow requests to be made automatically by the PLC, using a 4G modem.

For monitoring consumption and energy generation, as well as providing alarms and fault identification, the Python application gathers essential information such as energy consumption, generation, and event logs, and device status, which are then securely sent to an SFTP server. Power BI processes these data into interactive dashboards and reports, allowing for detailed real-time monitoring and analysis of the energy system.

Table 1 shows installed and contracted capacity values for the e-MH considered. The equipment operational limits are shown in Table 2. These constitute some of the major parameters considered by the controller while solving the power dispatch calculation and making decisions concerning e-MH management.

Finally, with the e-MH structure of the project presented, the following section aims to detail the implementation of the centralized controller, demonstrating how its functionalities were designed to operate while managing the e-MH.

3. Management Strategy

This section will explore the e-MH management strategy for the Equatorial case. This strategy has two distinct functionality groups: protection functionalities and economic functionalities. In Figure 3, the protection features ensure the e-MH safety by prioritizing the detection of possible issues and preventing the e-MH from danger or compromising events. That includes alarm monitoring and fault identification, which can mitigate possible problems in the operation of the e-MH. Thus, given its importance, the protection group receives the higher priority.

The economic group is focused on efficiency and optimization while using energy resources. For this group, the management strategy considers variables such as e-MH grid status (connected or islanded), peak-demand hours, contracted capacity, energy prices (which may vary dynamically), and BESS state of charge (SoC). Other factors are also considered part of the strategy as well as reducing the consumption of diesel and maximizing BESS life expectancy.

It is also important to establish the inner-group priorities. For the protection group, alarm and fault monitoring have more priority than installed capacity limiting even when e-MH is islanded, given its importance in case, for example, of a failed protective device. Similarly, considering the economic aspects group, the e-MH status monitoring is the rule with higher priority, followed by contracted capacity limiting, cost-related rules and EVSE prioritization rule. This hierarchy seeks to balance economic aspects, operation reliability and yet to allow charge prioritization. The following sections give a detailed view of each of the functionalities.

3.1. Alarm and Fault Monitoring

Table 3 summarizes the eight alarm types monitored that could be activated by the proposed controller. Table 3 also shows the alarm activation conditions. The controller constantly evaluates its activation conditions. Besides logging its occurrence, the controller also tries to mitigate the problem detected in case an alarm is triggered. This section continues by explaining each alarm.

3.1.1. Alarm 1

The first alarm concerns the identification of an overcurrent occurrence, as illustrated by Figure 4. The consumer unit meter records the measured currents, and the controller compares them with a preconfigured value. If the measured value exceeds the configured maximum value, the alarm is activated and the power available to the CP is reduced to a protective value. This reduction should be followed by a decrease in the measured currents at the input. Currents are then evaluated for a time interval, from $t_{1}$ to $t_{2}$ . The power available to the CP remains at a protective value if there is no more overcurrent occurrence in this interval. Otherwise, the power available to the CP will be reduced to zero, stopping its operation.

3.1.2. Alarm 2

Alarm 2 is triggered whenever a meter sends an error status. The power available to the CP is reduced to a protective value whenever such an error is identified. Also, in that case, DER power references are reduced to zero if the e-MH is operating in connected mode. For the islanded case, the BESS reference is only reduced after the PV power is checked to be zero.

3.1.3. Alarm 3

Alarm 3 is activated when there is a fail status coming from the PV converter. After its activation, the controller sets the PV power limit to zero. If the e-MH is operating in islanded mode, the controller also reduces the BESS power reference to zero in case PV power actually reduces to zero.

3.1.4. Alarm 4

Alarm 4 is similar to alarm 3. It is triggered whenever the BESS converter sends an error status. In that case, if e-MH is operating in connected mode, the controller only reduces the BESS power reference. For the islanded mode, on the other hand, it is necessary to reduce PV power to zero prior to reducing BESS power to zero.

3.1.5. Alarm 5

Alarm 5 is triggered when an error status is captured for the DG. In that case, the controller will set its power reference to zero. By default, the DG is only used when e-MH is in connected mode; therefore, this alarm can only occur while in connected mode. The DG is not considered for islanded mode in order to keep power balance and avoid energy generation exceeding the power drawn by the local loads and CP in case one of them has a sudden reduction in their use.

3.1.6. Alarm 6

Alarm 6 is triggered when a faulted status is captured for a charging connector, being immediately removed from the prioritization rule. Its power limit is also reduced to zero. This occurrence is logged and later used to schedule corrective maintenance. These data may also be used to inform the drivers that connectors are not available in the area through driving assistance applications.

3.1.7. Alarm 7

Alarm 7 is activated whenever communication is lost between PLC and meters or between PLC and DERs. Loss of communication with meters will also result in the activation of alarm 2 and its protective measures. This approach is also used for any of the DERs, which would trigger alarms 3 to 5 as well as their protective measures, as described in Section 3.1.3, Section 3.1.4 and Section 3.1.5.

3.1.8. Alarm 8

Alarm 8 is triggered when there is a communication loss between the OCPP server and PLC. In this situation, the controller is not able to send requests to the API. Therefore, in that case, the controller must consider the last connector status received and the last power references set for the connectors until communication resumes. By looking at the monitoring data, the CPO should be able to identify this communication fault in case becomes persistent, reconfigure CP for stand-alone operation and schedule corrective maintenance.

3.2. e-MH Status Monitoring

The controller identifies whether the e-MH is operating in connected or islanded mode. In connected mode, contracted capacity limiting, cost-related rules and EVSE prioritization functionalities are applied. In islanded mode, the functionalities of installed capacity limiting and EVSE prioritization are applied. The controller also incorporates a strategy that allows BESS to be recharged using the energy generated by the PV plant when the e-MH is operating in islanded mode, as represented by Figure 5. The goal is to ensure that the BESS is recharged almost exclusively with energy produced by the PV plant, in a safe manner.

Upon identifying that the e-MH is in islanded mode, the first action taken by the controller is to limit the available power for the CP to a maximum value, which is previously configured by the CPO in the consumer information parameters. Initially, the BESS power reference and PV generation limit are reduced to zero. From this moment on, the BESS begins to increase its reference power. When the measured power of the BESS reaches the reference, the controller increases the generation limit power of the PV system to 90% of the increment made in the BESS. In Figure 5, it can be observed that the PV generation limit is always smaller than the BESS power reference. This increment is repeated periodically for both BESS and PV generation while there is enough solar irradiation for PV generation to reach its limit. In that case, the BESS power reference may be increased until reaching 60% of its rated power. However, if a decrease in solar irradiance occurs during this process of increment, the controller starts to decrease the PV generation limit, as shown at time instant $t_{1}$ in Figure 5. In that case, the reference power for BESS is also decreased, subsequently. The values of the decrements made for the PV system and BESS are the same as those applied during the increment. At $t_{2}$ , BESS charging power is again close to power provided by PV generation.

In the example given in Figure 5, after $t_{2}$ , the controller starts increasing the BESS reference again. Once BESS power becomes close to the reference power, the PV generation limit is incremented again to check if the PV can increase its generation. However, unlike the condition before $t_{1}$ , the PV power does not increase because the solar irradiation is still low. After this moment, the PV generation limit is decremented again, followed by a decrement in the BESS reference. In this way, the controller always checks if there is enough PV generation to recharge the BESS, assessing if the power generated by the PV is following the power measured in the BESS as desired. This check is conducted by periodically increasing the BESS power reference followed by an increase in the PV generation limit, as shown between the intervals $t_{2}$ and $t_{3}$ .

At $t_{3}$ , after the increment in the PV generation limit, the generated power increased, allowing another increment in the reference power of the BESS, and so the cycle repeats. It is worth noting that the BESS recharge is not entirely conducted by the power generated by the PV. However, as mentioned earlier, the strategy aims to minimize the use of the secondary source as much as possible to recharge the BESS in islanded mode.

Lastly, in islanded mode, the BESS is discharged only when the sum of the measured power in the load and EVSEs exceeds the discharge limit configured, established at 60% of the nominal power of the BESS.

3.3. Safety and Protection Rules

The contracted capacity must not be exceeded while e-MH is operating in connected mode. That is essential to avoid extra costs due to penalties for excessive energy consumption. On the other hand, the installed capacity must not be exceeded while e-MH is operating in islanded mode. That is important to prevent overload that could compromise all equipment in the circuit. Thus, both functionalities play crucial roles, whether e-MH is connected or islanded.

The strategy explained below applies to both functionalities, i.e., both operating modes. Equation (1) is used to determine the threshold value to activate the functionality. The value of $p_i n_l i m i t$ represents a power limit, calculated by multiplying two factors. The first factor, $p_i n_c o n f i g$ , represents the value of the installed or contracted capacity, depending on the context in which the equation is applied. For example, if we are dealing with the installed capacity limiting functionality, $p_i n_c o n f i g$ would represent the installed capacity. Similarly, if we are dealing with the contracted capacity limiting functionality, $p_i n_c o n f i g$ would represent the contracted capacity. The second factor, $t x_l i m i t$ , is a ratio that indicates how far the power limit is from the actual contracted or installed capacity. For example, if $t x_l i m i t$ = 0.9, this means that the power limit is 10% of the contracted or installed capacity. This equation provides a flexible way to adjust the power limit based on the specific requirements of the e-MH and its operational conditions. Figure 6 provides a better understanding of what has been presented and also helps to understand the next equations of this strategy.

$p_i n_l i m i t = p_i n_c o n f i g \cdot t x_l i m i t$

(1)

As shown in Figure 6, the strategy aims to constantly check if the input power of the e-MH ( $p_i n_m e a s u r e$ ) has exceeded $p_i n_l i m i t$ . The controller intervenes in BESS power reference (Equations (2) and (3)) and in the CP available power (Equations (4) and (5)) if $p_i n_m e a s u r e$ exceeds $p_i n_l i m i t$ .

In case of overload, if the BESS is in discharge mode, its reference power ( $p_b e s s_r e f$ ) is reduced to decrease $p_i n_m e a s u r e$ . On the other hand, if the BESS is in charge mode and overload occurs, its reference power is increased to help compensate for the increase in e-MH demand, always respecting BESS-rated power ( $p_b e s s_r a t e d$ ). Subsequently, the controller also reduces the available power to the CP ( $p_l i m i t_c p$ ). The value calculated for the CP is divided among its busy connectors using the prioritization rule, as will be detailed in Section 3.4. Therefore, both adjustments result in $p_i n_m e a s u r e$ returning to a value below $p_i n_l i m i t$ .

In Equations (4) and (5), the value zero is assigned to $f l a g_b e s s_m a x$ if there is a need to increase $p_b e s s_r e f$ and this increase causes $p_b e s s_r e f$ to be greater than $p_b e s s_r a t e d$ . Otherwise, the value 1 is assigned. On the other hand, $f l a g_r e a d y_b e s s$ indicates whether the BESS is able to change $p_b e s s_r e f$ . These conditions are listed below:

Conditions for $f l a g_r e a d y_b e s s$ = 0:
–
BESS is inactive;
–
The controller needs to increase BESS discharge power but SoC is below its inferior operation limit;
–
The controller needs to increase BESS charge power but the SoC is over its upper operation limit;
–
The controller needs to increase BESS power but it is already at rated power.
Conditions for $f l a g_r e a d y_b e s s$ = 1:
–
BESS is active;
–
The controller needs to increase BESS discharge power and the SoC is still above its inferior operation limit;
–
The controller needs to increase BESS charge power and the SoC is still below its upper operation limit;

$p_b e s s_r e f = p_b e s s_m e a s u r e \pm α \cdot p_b e s s_m e a s u r e$

(2)

$α = \frac{p_i n_m e a s u r e - p_i n_l i m i t}{p_e v s e_m e a s u r e + p_b e s s_m e a s u r e}$

(3)

$\begin{matrix} p_l i m i t_c p & = p_e v s e_m e a s u r e - [β \cdot p_e v s e_m e a s u r e \\ + (p_b e s s_r e f - p_b e s s_r a t e d) \cdot f l a g_b e s s_m a x \cdot f l a g_r e a d y_b e s s] \end{matrix}$

(4)

$β = \frac{p_i n_m e a s u r e - p_i n_l i m i t}{p_e v s e_m e a s u r e + (p_b e s s_m e a s u r e \cdot f l a g_r e a d y_b e s s)}$

(5)

The power limit acts as a safety margin to prevent $p_i n_m e a s u r e$ from reaching values close to the contracted or installed capacity. However, it is important to highlight that this margin does not guarantee that input power will never exceed the limits in transient situations since it depends on factors such as the value chosen for $t x_l i m i t$ , load variation and communication delay for the measurements. The appropriate choice of $t x_l i m i t$ should be evaluated based on the e-MH characteristics, considering its responsiveness to load variations.

Finally, since the contracted capacity must be below the installed capacity, both functionalities will prevent the measured power from reaching the installed capacity.

3.4. EVSE Prioritization Rule

The recharge prioritization functionality creates a list with all busy connectors, which are sorted according to the time since each authentication. The first element of this list corresponds to the connector that has been busy for the longest time. The last element corresponds to the connector which is busy for the shortest time. Additionally, the functionality establishes that each connector in this list should receive a weight, with the value of each weight related to the time the connector is occupied. For example, the first element of the list has a higher weight, while the last element of the list has a lower weight.

3.5. Cost-Related Rules

This functionally defines which criteria are used to determine the dispatch of power from the DG to charge and discharge the BESS and to establish the CP power limit in connected mode. These criteria use the energy tariffs for the electrical grid, DG, BESS, and CP. The controller verifies whether the e-MH is operating during peak hours or not, the amount of fuel in the DG, BESS SoC and e-MH input power.

The tariffs can be changed during the operation of the e-MH, e.g., in dynamic pricing application. These changes also prompt the controller to adjust the power balance during the operation. Table 4 presents the description of the parameters and variables used to define the minimum criteria. Subsequently, the minimum criteria for the use of the DG and BESS are presented.

Minimal criteria for DG use:

If $d g_f u e l_m e a s u r e d > 0.1 \cdot d g_f u e l_c a p a c i t y$ :
- If $p_i n_m e a s u r e d + p_p v_m e a s u r e d \leq p_i n_l i m i t$
  –
  $t r_d g < t r_g r i d$ and $p_b e s s_r a t e d < p_i n_m e a s u r e d$ or $s o c < s o c_m i n$ ) for ( $h_c u r r e n t < h_p e a k_s t a r t$ and $h_p e a k_e n d < h_c u r r e n t$ )
  –
  $t r_d g < t r_g r i d$ for $h_p e a k_s t a r t < h_c u r r e n t < h_p e a k_e n d$
- If $p_i n_m e a s u r e d + p_p v_m e a s u r e d > p_i n_l i m i t$
  –
  $p_b e s s_r a t e d < p_i n_m e a s u r e d + p_p v_m e a s u r e d$ or $s o c < s o c_m i n$

Minimal criteria for BESS use in discharge mode:

If $s o c > s o c_m i n$
- If $h_c u r r e n t < h_p e a k_s t a r t$ and $h_p e a k_e n d < h_c u r r e n t$
  –
  $t r_g r i d > t r_b e s s$ and $s o c > 50 %$
- If $h_p e a k_s t a r t < h_c u r r e n t < h_p e a k_e n d$
  –
  $t r_g r i d > t r_b e s s$

Minimal criteria for BESS use in charge mode:

$s o c < s o c_m a x$
$p_p v_m e a s u r e d > 0$ or $(p_i n_m e a s u r e d + p_p v_m e a s u r e d) < p_i n_l i m i t$

After defining the minimum criteria, the cost rules are used to control the e-MH power dispatch. Table 4 presents the description of the parameters for this functionality.

When $t r_d g \geq t r_g r i d$ , no power should be dispatched by the DG. In that case, only the CP power limit and BESS reference are defined, based on the tariffs $t r_r e c h a r g e_c p$ and $t r_b e s s$ .

On the other hand, if $t r_d g < t r_g r i d$ , DG power reference is also defined:

If $t r_d g \geq t r_g r i d$ :
- $p_d g_r e f = 0$
- If ( $n_e v s e_b u s y \cdot p_e v s e_r a t e d) > p_i n_l i m i t - p_l o a d_m e a s u r e d$ :
  (a)
  If $t r_r e c h a r g e_c p > t r_b e s s$ :
  –
  $p_b e s s_r e f = M I N (p_b e s s_r a t e d, ((n_e v s e_b u s y \cdot p_e v s e_r a t e d) - p_i n_l i m i t - p_l o a d_m e a s u r e d))$
  –
  $p_l i m i t_c p = p_i n_l i m i t - p_l o a d_m e a s u r e d + p_b e s s_r e f$
  (b)
  If $t r_r e c h a r g e_c p \leq t r_b e s s$ :
  –
  $p_b e s s_r e f = - 1 \cdot M I N (t x_s l o w_r e c h a r g e \cdot p_b e s s_r a t e d, p_p v_m e a s u r e d)$
  –
  $p_l i m i t_c p = p_i n_l i m i t - p_l o a d_m e a s u r e d + p_b e s s_r e f$
- If ( $n_e v s e_b u s y \cdot p_e v s e_r a t e d) \leq p_i n_l i m i t - p_l o a d_m e a s u r e d$ :
  (a)
  $p_l i m i t_c p = (n_e v s e_b u s y \cdot p_e v s e_r a t e d)$
  (b)
  $p_b e s s_r e f = - 1 \cdot M I N (t x_s l o w_r e c h a r g e \cdot p_b e s s_r a t e d, (p_i n_l i m i t - p_l o a d_m e a s u r e d - p_l i m i t_c p + p_p v_m e a s u r e d))$
If $t r_d g < t r_g r i d$ :
- If $(n_e v s e_b u s y \cdot p_e v s e_r a t e d) > p_i n_l i m i t - p_l o a d_m e a s u r e d$ :
  (a)
  If $t r_r e c h a r g e_c p > t r_b e s s$ :
  –
  $p_b e s s_r e f = M I N (p_b e s s_r a t e d, ((n_e v s e_b u s y \cdot p_e v s e_r a t e d) - p_i n_l i m i t - p_l o a d_m e a s u r e d))$
  –
  If $p_b e s s_r e f + p_i n_l i m i t - p_l o a d_m e a s u r e d < (n_e v s e_b u s y \cdot p_e v s e_r a t e d)$ :
  i.
  $p_d g_r e f = M I N (p_d g_r a t e d, (n_e v s e_b u s y \cdot p_e v s e_r a t e d) - p_b e s s_r e f + p_i n_l i m i t - p_l o a d_m e a s u r e d)$
  ii.
  $p_l i m i t_c p = p_b e s s_r e f + p_d g_r e f + p_i n_l i m i t - p_l o a d_m e a s u r e d$
  –
  If $p_b e s s_r e f + p_i n_l i m i t - p_l o a d_m e a s u r e d \geq (n_e v s e_b u s y \cdot p_e v s e_r a t e d)$ :
  i.
  $p_d g_r e f = 0$
  ii.
  $p_l i m i t_c p = p_b e s s_r e f + p_i n_l i m i t - p_l o a d_m e a s u r e d$
  (b)
  If $t r_r e c h a r g e_c p \leq t r_b e s s$ :
  –
  $p_b e s s_r e f = - 1 \cdot M I N (t x_s l o w_r e c h a r g e \cdot p_b e s s_r a t e d, p_p v_m e a s u r e d)$
  –
  $p_d g_r e f = M I N (p_d g_r a t e d, (n_e v s e_b u s y \cdot p_e v s e_r a t e d) - (p_i n_l i m i t - p_l o a d_m e a s u r e d))$
  –
  $p_l i m i t_c p = p_d g_r e f + p_b e s s_r e f + p_i n_l i m i t - p_l o a d_m e a s u r e d$
- If $(n_e v s e_b u s y \cdot p_e v s e_r a t e d) \leq p_i n_l i m i t - p_l o a d_m e a s u r e d$ :
  (a)
  $p_l i m i t e_c p = (n_e v s e_b u s y \cdot p_e v s e_r a t e d)$
  (b)
  $p_b e s s_r e f = - 1 \cdot M I N (t x_s l o w_r e c h a r g e \cdot p_b e s s_r a t e d, (p_i n_l i m i t - p_l o a d_m e a s u r e d - p_l i m i t_c p + p_p v_m e a s u e r d))$
  (c)
  $p_d g_r e f = 0$

3.6. Data Acquisition and Data Flux

A significant part of the control strategy proposed relies on the communication between the PLC, e-MH equipment, and cloud servers. In general, data from equipment on site will be exchanged to PLC by using modbus or using the PLC digital I/O channels. EVSE communicates with the server using standard OCPP messages (as specified in [19]) and their content can be accessed through requests sent to the API. The API can also receive commands with data to be sent to the charging stations. Lastly, measurements, status and other important information as alarms, collected by the PLC, are sent to the SFTP server using comma-separated value files (CSV) for monitoring purposes. Figure 7 presents a simplified sequence diagram for this communication in the project. This figure is focused on the communication between the same components presented in Figure 2. More specifically, the diagram shows the order in which the PLC executes power control-related tasks (including the EV charging cycle) and which type of information is exchanged while the application is running.

For this sequence diagram, let us consider that EVSE is previously registered in the OCPP server, i.e., all activities in charging connectors are recognized in the server and can be provided through the API. Also, for this case, let us consider that an EV is connected to one of these connectors for a regular charging transaction. For each PLC control cycle, the PLC main function will gather the power measurements and status from meters and DERs on site, using the modbus client implemented. The e-MH islanding status is obtained from the ATS through a digital I/O. This status is then copied to the modbus server in the RT portion and, once more, read by the modbus client.

Once all the necessary data are acquired and copied to the SFTP server, the PLC application calculates the power dispatch and sends the DER references using modbus. In the sequence, the prioritization rule is executed using the CP power limit computed so the charging connector setpoint can be updated, sending it back to the OCPP API. Then, the server relays it to the charging connector using the OCPP protocol.

It is important to observe that communication delays must be considered in the control processes. Communication delay may be attributed to a range of factors including physical distance between equipment, processing capacity, and communication bandwidth among other causes [20,21,22,23]. Although unlikely to happen, a large delay in communication (>>5 s) could cause safety issues. To mitigate problems in such cases, the controller implementation considers an upper limit on the communication delay. If the delay exceeds this threshold, an alarm signal is generated by the controller and the appropriate actions are taken, as detailed in Section 3.1.

4. Hardware-in-the-Loop Implementation

This section presents a HIL implementation for the project infrastructure and management solution proposed. The HIL technology was introduced in the last decades and its use is becoming a standard step in design and optimization for automatic control development. This technology is especially useful for automatic power systems, where, in practice, equipment replacement capacity is low, tests may involve risks of varied types and, at the same time, the controller behavior needs to be systematically and repeatedly tested. In many cases, tests using simulations in real-time with HIL also require less time and spend less financial resources when compared to practical tests [24,25]. For the very same reasons, the Equatorial case validation is conducted using HIL. This approach significantly reduces time and financial resources.

Thus, the e-MH described in Section 2 was modeled to be used with Typhoon HIL 602+ (HIL device), as represented in Figure 8, where the blue bus represents the four-wire, three-phase connections between MG components. The model implemented (HIL model), presented in Figure 8, was created using components from the 2023.3 SP1 Typhoon HIL component library. The primary and secondary sources are modeled as three-phase Grid components. The ATS was modeled using two triple-pole, single-throw contactors along with a C code block which contains the code for its functional logic, i.e., connecting the MG to the secondary source whenever a primary source is not available. The DERs and MG loads are modeled as voltage behind reactance variable loads. For the PV, the model also includes an input for solar irradiation in W/m². The CP is also represented using a variable load. However, in this case, each connector has its own C code block implementing the logic to simulate its respective EV charging operations. Hence, the CP represents the sum of active power being drawn by the EVs in charge state. Lastly, the three-phase meters were configured for RMS measurement of the active power in the same locations projected in Figure 2.

For the HIL model, all communication between the PLC and HIL model components was implemented using the HIL modbus device, as also represented in Figure 8. This device acts as a modbus server with multiple memory maps. In this case, each meter, DER, charging station and ATS has its own modbus map, which stores measurements and status and can also be used to receive modbus commands. Each modbus map has its own IP address, communication port and slave ID declared independently. This independence is useful while validating the communication methods executed by the implementation at the PLC.

Specifically, regarding the communication between PLC and CP charging stations, the use of modbus does not change the type of the data payload exchanged between PLC and charging stations but significantly reduces the experiment complexity by skipping the OCPP server communication. The negative impact, in terms of realism, is that the delay expected for the API requests and OCPP communication would not be represented. However, since transport delay is an important aspect to be considered in the management strategy proposed, time delay blocks were included for all components in the HIL model which communicate with the PLC in order to mitigate this impact when the realism aspect is considered. These blocks were configured to represent the total amount of time delay expected after the project commissioning.

For the validation, CSV files are used to input real data in the simulation. That is the case for the local load curve, the EV charge curve and solar irradiation. The initial time of the day is configurable through the SCADA main script so the tests can comprise a wide range of scenarios. These input values can also be overthrown if a fixed value is necessary.

For the tests, the PLC application periodically accesses data received from all meters, DERs, ATS and CP while the real-time simulation is running in the HIL device. The HIL device uses an FPGA-based multi-core processor optimized for time-exact simulation of electrical domain models [26]. In the HIL model, the electrical and signal processing components are distributed for different processing units (CPUs) in order to enable parallel computing. This partitioning reduces memory usage and allows for the use of a smaller base time step in the FPGA solver, which is important to faithfully reproduce the electric circuit behavior.

The base time step must be chosen observing the mathematical complexity and the level of detail targeted for the model. For the case presented in this paper, it is important to achieve accurate power flux for the 60 Hz electrical system and to represent the communication between PLC and each of the e-MH components. Also, the project does not include power converter design validation nor islanding detection algorithms, i.e., it is not necessary to analyze high-frequency switching effects. Moreover, the hardware components considered for the actual e-MH installation are commercially available already and thus can be considered individually validated. Hence, considering the development purposes for the Equatorial case, DERs used in the HIL model can be based on averaged models present in the Typhoon standard libraries without loss of significance for the test results. Therefore, the base time step chosen for the FPGA solver is $10 \times 10^{- 6}$ s. This value is small enough when compared to the electric system period ( $0.01 \bar{6}$ s).

The HIL device also uses various execution rates, which must be a base time step multiple number. This resource is useful because some model components have an inner control mesh or signal processing block that requires frequency decoupling, e.g., DG control or the PV MPPT. For the case presented in this paper, the main execution rate is $1 \times 10^{- 3}$ s and inner control meshes use $2 \times 10^{- 4}$ s.

Overall, during an HIL simulation, the proposed controller constantly calculates the power dispatch for all controllable components considering the data acquired from HIL and the functionalities described in Section 3.

5. e-MH Test Setup

In this section, five test scenarios are used to demonstrate e-MH operation while governed by the proposed controller using the HIL implementation described in Section 4. The experimental setup used can be observed in Figure 9. The PLC used is the Wago PFC200.

In all scenarios, the customer information is fed to the controller as described in Figure 2, which also includes the power limits to be considered. The results use the same signal convention from Section 3. The BESS power is represented as a negative value for charging operation and as a positive value for discharging operation. In contrast, the consumer unit meter gives a negative power measurement when the e-MH is delivering energy to the grid.

The controller constantly checks for possible modifications in the input parameters. Hence, items of the customer information can be modified during the operation, in order to modify the controller behavior itself, according to the features described in Section 3. For instance, in all scenarios of this section, the values for the energy tariffs start normalized such that $t r_r e c h a g e_c p > t r_d g > t r_g r i d > t r_b e s s$ . However, the prices could be modified on the run according to a DSO or CPO order. This modification would then lead to changes in the controller behavior, as commonly adopted in demand-response and dynamic pricing strategies.

For each of the scenarios described in the following sections, the HIL device is started through the SCADA panel, starting the real-time simulation and, at the same time, data collection, which is copied to the CSV file. After model initialization in the HIL device, the PLC is started, starting its communication with HIL, using the TCP modbus protocol. Lastly, unless explicitly mentioned otherwise, consumer unit parameters and the rated power of components are the same as in Table 1 and Table 2, respectively.

5.1. Scenario 1—Islanding and Re-Connection

The first scenario’s major objective is to demonstrate the general behavior of the controller for EV charging operations in connected and islanded modes. Figure 10 presents the results for Scenario 1. A real-world example of this scenario would be an unexpected grid outage event in the daytime. This scenario simulates the transition between grid-connected and islanded modes. It tests the ability of the management strategy proposed to maintain services through resource allocation.

When the operation starts, the controller increases the PV limit to its rated power at the same time as charging the BESS at a slow rate. Since the local loads are the only other component draining power from the e-MH and solar irradiation is high enough, the energy surplus is delivered to the grid. After 60 s, an EV charging operation starts, using connector 1, as observed in Figure 10b.

Initially, since there is only one connector in use, the controller allows maximum power (20 kW) for connector 1. However, as a second EV requests to recharge using connector 2 after 110 s, the controller decreases the power reference for connector 1 in order to keep the e-MH consumption within power restrictions. It is important to observe once more that allowable power for the CP does not account for current PV generation because its intermittence is expected.

An islanding event occurs at 175 s, stopping all operations for a few seconds. When ATS switches to the secondary source, the charging operations are resumed and the original priority order is kept. Local loads are also attended to. However, CP power is limited to a 10 kW maximum while the e-MH is operating in islanded mode. The controller also resets BESS charging and PV operation using the mechanism described in Section 3.2. This mechanism will be better detailed through the results shown in Section 5.3.

After 350 s, the EV at connector 2 stops its charging procedure so the controller can now increase the power allowed to connector 1 without increasing overall CP energy consumption, as observed in Figure 10a. When e-MH is reconnected, at 415 s, the controller can again set the PV connector 1 limits to their respective rated power. Also, the BESS charging operation is again set to slow charge.

Therefore, as observed through Figure 10a,b, the controller proposed prioritizes the charging operations according to the order of occupation and maintains this information as long as EVs are still connected to the CP. In terms of BESS use, slow charge operation is preferred when e-MH is connected to the grid in order to maximize battery life. In islanded operation mode, the controller reduces CP maximum power and regulates BESS charging according to PV power availability. Overall, in islanded mode, the proposed controller reduces energy consumption and avoids energy generation surpassing.

5.2. Scenario 2—Peak Demand Period

Figure 11 presents the results obtained for the test scenario 2. Its major objective is to demonstrate the proposed strategy concerning demand peak time and demand-side actions, i.e., using the cost-related rules presented in Section 3.5. In addition, for Scenario 2, the DG is also considered. This scenario replicates challenges faced in the real world which need cost optimization and resilience during peak demand times. Dispatch calculations and smart charging strategies shall be responsive to fluctuating energy prices and demand spikes.

As in the initial seconds of Scenario 1, the PV limit is set to its rated power at the same time as BESS is set to charge at a slow rate, as shown by Figure 11a. The behavior concerning EV charging is also similar to the previous test. More priority is given to EVs connected first, as can be observed in Figure 10b. At 125 s, solar irradiation starts to vary, which causes PV generation to oscillate between 5 and 20 kW.

After 150 s, the peak demand period starts. At this moment, the controller changes BESS to discharge mode, providing more power to the CP, and dispatching more power to the EVs, without increasing energy consumption from the grid. After 220 s, the grid energy tariff ( $t r_g r i d$ ) is increased. Also, the tariff value for DG use ( $t r_d g$ ) is decreased (i.e., $t r_d g < t r_g r i d$ ), simulating a demand-side action in order to compensate for solar generation decrease and overall consumption at the grid during peak demand time. Therefore, at this moment the controller sets the DG power reference to 10 kW in order to supply part of the energy consumed at the e-MH as PV generation decays.

When the peak demand period finishes at 275 s, all tariffs are set to their original values, i.e., $t r_d g > t r_g r i d$ . Therefore, there is no longer an advantage in DG use in terms of costs. Also, since BESS is no longer in discharging operation, the power allowed for EV charging is decreased to around 40 kW. After 330 s, CP consumption reduces and PV generation increases so the controller resumes the BESS slow charging operation setting in reference to −5 kW. The last charging connector is released at 355 s, making overall energy consumption decrease to under 5 kW with a slight increase in PV generation.

5.3. Scenario 3—Islanded Operation

The third scenario considered, shown in Figure 12 is focused on the islanded operation and gives a detailed view on BESS and PV operation. It simulates real-world scenarios like prolonged grid outages where renewable energy sources and energy storage are necessary for maintaining operational independence. It tests the ability of the proposed strategy to balance energy demand and supply.

For this scenario, the BESS model capacity at HIL was significantly reduced in order to demonstrate a complete cycle of charge within a short period of time. Also, BESS SoC was set to start at 85%.

Once more, at the beginning of the test, an EV is connected to the CP. After 45 s, islanding occurs, stopping all operations for a few seconds. When ATS switches to the secondary source, the EV charging operation is resumed, drawing 10 kW from the secondary energy source. Since the BESS is initially fully charged ( $S o C > = 85 %$ ) and PV generation is not considered on CP power calculation, the PV generation limit is kept at zero, avoiding undesirable generation surpass. The controller also sets the BESS reference to discharge mode in order to reduce the power drawn from the secondary source, which is reduced from 10 kW to 5 kW, as shown in Figure 12a.

After about 145 s, BESS SoC reaches its inferior operation limit (20%), as observed in Figure 12b. Therefore, the controller starts to charge the BESS using PV generation by slowly increasing BESS and PV power (as described in Section 3.2) in coordinated steps of 1 kW. After 240 s, solar irradiation reduces so PV generation starts to decay behind its limit for the next 70 s, as observed in Figure 12a. Thus, the controller also changes the PV limit and BESS reference in order to reduce power drawn from a secondary source (which, in this case, would be consumed by the BESS) at the same time guaranteeing that all power generated by the PV will be consumed by the BESS charging operation.

At 310 s, solar irradiation increases again and the BESS is not yet fully charged. Hence, the controller restarts to increase the BESS reference and PV power limit until the BESS is fully charged, which occurs at 395 s, as shown by Figure 12b. At this moment, the PV limit is again set to zero and BESS starts a new discharging cycle, which reduces the power drawn from the secondary source.

5.4. Scenario 4—Power Surpass

Scenario 4 is focused on actions taken by the controller in case of an unexpected increase in local loads, causing the power drawn from the grid to surpass the input power limit by using the safety and protection rules described in Section 3.3. This scenario reflects challenges like sudden load spikes or equipment failures that can strain grid connections. It tests the ability of the proposed strategy to reduce energy consumption where possible to maintain grid compliance under varying operational conditions. In order to cause it, the local load-rated power was increased to 20 kW in the HIL model and kept disconnected at the start of the test. The model was also configured to simulate solar irradiation in the early morning, maintaining PV generation under 5 kW at the start of the test. The results for such a test are demonstrated in Figure 13.

Five EVs were simultaneously connected to the CP (seen in Figure 13a), while BESS was kept in slow charge mode, which elevates overall consumption to 40 kW, as observed in Figure 13a. In order to provoke the limit to be surpassed, at 49 s, the local load is connected to the e-MH, rapidly increasing the power drawn from the grid. When the e-MH power consumption surpasses the input power limit set in the consumer unit parameters, the proposed controller stops charging the BESS and reduces the power allowed to the CP to about 24 kW. Consequently, all EVs have their power limit reduced while the prioritization order is maintained, as shown in Figure 13b.

By reducing the power destined for EV charging and stopping BESS operation the controller brings the overall power drawn from the grid back to acceptable values, around 40 kW. After 76 s, solar irradiation increases and PV generation reaches its rated power. Again, since PV power is not accounted for EV use, the power allowed to the CP is kept at 24 kW. In this case, PV generation is used to decrease e-MH energy consumption.

5.5. Scenario 5—Power Surpass in Islanded Operation

Figure 14 shows the results for test Scenario 5. Similar to the test shown in Section 5.4, this scenario is intended to validate the controller behavior when an unexpected increase in local loads occurs, causing the power drawn from the secondary source to surpass the input power limits configured for islanded operation. It mirrors the challenges faced in isolated or emergency situations where energy demand can exceed local supply capabilities. It tests the proposed strategy’s adaptive capabilities in managing unforeseen load variations without compromising operational integrity in islanded mode.

In this case, the local load-rated power was increased from 5 to 15 kW in the HIL model and kept disconnected at the start of the test. Also, the limit of power allowed to the CP in islanded mode was increased from 10 kW to 40 kW.

In the beginning, the PV power limit and BESS reference are slowly increased in a coordinated way, with 1 kW steps, avoiding excessive generation. After 25 s, the first EV is connected followed sequentially by the remaining connectors. In this case, because each EV is connected after a certain period of time, it is possible to observe that the controller proposed first reduces power already in use prior to allowing the power limit to recently connected EVs. This behavior can again be observed through the CP power oscillation in the first 60 s in Figure 14a.

After 65 s, the local load is connected to the e-MH, increasing the power drawn from the secondary source. When the power drawn surpasses the input power limit set, the proposed controller reduces the power allowed to the CP to about 32 kW. Once again, all EVs have their power limit reduced while the prioritization order is maintained, as shown in Figure 14b. In this event, the BESS charging operation and PV generation are also stopped. By reducing the power allowed for EV charging, the controller brings the overall power drawn from the secondary source back to acceptable values, around 46 kW until the local load is disconnected from the e-MH, after 94 s. At this moment, power allowed to the CP can again be increased from 32 to 40 kW and kept until EVs start to release the connectors after 110 s, as demonstrated in Figure 14b.

6. Conclusions

This paper proposed a centralized control strategy for managing e-MHs based on MGs capable of operating connected or islanded from the electrical grid composed of EVSE, DG, PV, and BESS through the Equatorial case, a real case study. This case is part of a wider effort supported by ANEEL, in Brazil, in order to achieve technological advances in the e-mobility sector, mitigate impacts on the energy distribution system, and contribute to a more sustainable operation while also contributing to GHG emission reduction.

By comparing the results from this paper to the results demonstrated by the authors in [15], it can be observed that although there are similar effects in terms of the setpoint variation for the charging stations, the solution proposed in this paper is significantly different, mainly because it considers that there is a possibility of sudden increase in load, with capacity to overcome contracted capacity. Furthermore, the solution proposed in this paper can sustain PV generation even in islanded operation, which does not occur in [15]. Lastly, the solution in this paper favors the driver which gets first to the CP in terms of providing more power.

Overall, the findings from this study show that the solution proposed effectively integrates DER technologies to the charge point and provides means to execute functionalities such as dynamic pricing and demand-response strategies while still considering energy costs. The results demonstrate that the proposed strategy can dynamically control the power made available for each charging connector, improving energy utilization by EVs, instead of simply dividing the available power between all the charging stations. Additionally, the management strategy proposed automatically adapts e-MH behavior in periods of peak demand and islanding operation.

In Section 3, the features that define the controller strategy were presented. Communication between the PLC and the equipment provides the functionalities with the necessary data to make decisions based on a series of management rules. As a result, the functionalities work by constantly defining the PV generation limit, BESS and DG power references and the power allowed to EVSE.

To validate the proposed strategy, the equipment from the real scenario was modeled using HIL. The most significant scenarios were considered, as shown in Section 5. It was possible to observe that the power allocated to the CP was distributed among the connectors according to their order of connection, thus validating the recharge prioritization rule.

In scenario 2, the changes that occurred in power dispatch during peak hours demonstrated that the proposed solution fits the use of demand-response strategies. Another important aspect addressed in this work was the incorporation of dynamic pricing mechanisms, as also demonstrated in scenario 2. A variation in the grid and DG tariffs was considered during HIL validation, which resulted in changes in the dispatch calculation.

In islanded operation, the proposed strategy prioritizes BESS charge with PV as much as possible, drawing less power from the secondary source. The strategy allows charging the BESS with a renewable energy source, avoiding excessive fuel consumption in cases where the secondary source is a grid-forming DG. Consequently, the proposed controller can also reduce costs while ensuring that the e-MH safely operates in islanded mode.

Considering scenarios 4 and 5, the power limit value configured in Figure 6 influences the safety margin so that the measured power is kept below the contracted capacity in connected mode and is also kept below the installed capacity for both operation modes. During a high load variation, a shorter safety margin allows greater risk during the transient. However, it also allows better use of the power capacity. A greater safety margin reduces the risk. However, it also reduces capacity use. Therefore, choosing the appropriate value for the threshold works as a regulating tool. It balances the operational security of the e-MH with efficiency and optimal utilization of the available resources. This regulation must be evaluated based on the specific characteristics of the e-MH structure and its operating conditions.

Author Contributions

The authors contributed equally in the research: Conceptualization, W.C.L., M.O.G. and R.A.S.K.; investigation and methodology, W.C.L. and M.O.G.; validation, W.C.L., M.O.G. and R.A.S.K.; writing—original draft preparation, W.C.L. and M.O.G.; writing—review and editing, M.O.G., B.B.C. and R.A.S.K.; team administration, B.B.C.; project administration, D.d.S.N., R.A.S.K. and M.I.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Research and Development project PD-06072-0700/2023, granted by the Brazilian Electricity Regulatory Agency (ANEEL) and Equatorial Energia.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank the Research Program and Technological Development of the electricity sector regulated by ANEEL and Equatorial Energia for their financial support. This paper is related to the project PD-06072-0700/2023, which is cooperated between CERTI Foundation and Equatorial Energia.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:

ANEEL	Brazilian Electricity Regulatory Agency
API	Application Programming Interface
ATS	Automatic Transfer Switch
BESS	Battery Energy Storage System
BI	Business Intelligence
BMS	Battery Management System
CP	Charge Point
CPO	Charge Point Operator
CPU	Processing Unit
CSV	Comma separated values
DER	Distributed Energy Resource
DG	Dispatchable Generator
DSO	Distribution System Operator
e-MH	e-Mobility Hub
EV	Electric Vehicle
EVSE	Electric Vehicle Supply Equipment
FPGA	Field-Programmable Gate Array
GHG	Greenhouse gas
GPL	General Public Licence
HIL	Hardware-in-the-loop
MG	Microgrid
MPPT	Maximum power point tracking
OCPP	Open Charge Point Protocol
PLC	Programmable Logic Controller
PV	Photovoltaics
RFID	Radio Frequency Identification
R&D	Research and Development
RT	Real-Time
SFTP	Secure File Transfer Protocol
SoC	State of Charge
SOM	System-on-Module
SCADA	Supervisory Control and Data Acquisition
UPS	Uninterruptible power supply
V2G	Vehicle-to-Grid

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Figure 1. Locations where e-MHs will be installed.

Figure 2. Architecture for the e-MH: DERs, data acquisition and processing, monitoring, communication and cloud servers.

Figure 3. Centralized controller features.

Figure 4. Activation criteria for alarm 1. (a) Overcurrent occurrence followed by current reduction (after CP power decrease); (b) Persistence of overcurrent followed by CP shutdown.

Figure 5. BESS and PV power management when e-MH is in islanded operation mode.

Figure 6. Capacity limiting functionality example. The controller reduces BESS and EVSE power in case overload is detected.

Figure 7. Simplified sequence diagram for the communication in the project.

Figure 8. HIL architecture and communication implemented for evaluation of the proposed management strategy.

Figure 9. Experimental setup. (a) PLC; (b) HIL device; (c) HIL model; (d) SCADA panel; (e) 4G communication modem; (f) UPS.

Figure 10. Test scenario 1—simultaneous charging of two EVs with islanded period (in pale red). (a) Contracted capacity (purple); Power measured by consumer unit meter (blue); Power measured by secondary source meter (orange); Charge point power (red); PV power (pink); BESS power (brown); Local load power (green); (b) Connector 1 charging power (blue); Connector 2 charging power (orange).

Figure 11. Test scenario 2—simultaneous charging of three EVs with peak demand period (in light gray). (a) Contracted capacity (purple); Input power limit (red); PV power (pink); Power measured by consumer unit meter (blue); Charge point power (green); BESS power (Brown); Local load power (orange); Dispatchable generator power (grey); (b) Connector 1 charging power (blue); Connector 2 charging power (orange); Connector 3 charging power (green).

Figure 12. Test scenario 3—Islanded operation (in pale red). (a) Power measured by consumer unit meter (blue); Power measured by secondary source meter (orange); Charge point power (red); PV power (brown); BESS power (purple); Local load power (green); BESS power reference (grey, dashed); PV power limit (pink, dashed); (b) BESS SoC (blue); BESS SoC upper operation limit (orange); BESS SoC inferior operation limit (green).

Figure 13. Test scenario 4—Simultaneous charge of five EVs with input power limit surpass. (a) Installation capacity (orange); contracted capacity (blue); Input power limit (brown); Consumer unit meter (green); Charge point power (purple); Local load power (red); BESS power (pink); PV power (grey); (b) Connector 1 (blue); Connector 2 (orange); Connector 3 (green); Connector 4 (red); Connector 5 (purple).

Figure 14. Scenario 5—Simultaneous charge of five EVs with input power limit surpassed in islanded mode. (a) Installation power (blue); Input Power limit (purple); Power measured by secondary source meter (orange); Charge point power (red); Local load power (green); BESS power (Brown); PV power (pink); (b) Connector 1 (blue); Connector 2 (orange); Connector 3 (green); Connector 4 (red); Connector 5 (purple).

Table 1. Consumer unit capacity.

Consumer Unit limitations	Rated Power (kW)
Installed capacity	60.0
Contracted capacity	50.0

Table 2. Equipment operational limits.

Device	Rated Power (kW)
PV	20.0
BESS	20.0
Load	5.0
CC-CA Converter (PV)	20.0
CC-CA Converter (BESS)	20.0
Dispatchable Generator (DG)	30.0
EVSE (per connector)	20.0

Table 3. Alarms with respective activation criteria and post-activation procedures.

Alarm	Activation	Post-Activation Procedures
1	Current A, B or C > max. value configured	Limit CP power to a protection value ¹ Reduce BESS reference to 0 Reduce PV power limit to 0 Reduce DG power reference to 0
2	Meter error status detected	Limit CP power to a protection value; Reduce BESS reference to 0 Reduce PV power limit to 0 Reduce DG power reference to 0
3	PV DC-AC converter error status detected	Reduce PV power limit to 0 Reduce BESS reference to 0 ²
4	BESS error status detected	Reduce BESS reference to 0 Reduce PV power limit to 0 ²
5	DG error status detected	Reduce DG power reference to 0
6	EVSE fault status detected	Reduce connector power limit to zero Exclude connector from EVSE prioritization
7	Communication timeout between PLC and e-MH components	Activate alarms 2, 3, 4, or 5
8	Communication timeout between PLC and OCPP server	Consider previous EVs status and power references

¹ CP power limit is reduced to zero if overcurrent persists for the evaluation period. ² Only executed in islanded operation mode.

Table 4. Parameters and variables used at the cost-related rules.

Parameters	Description
$t r_g r i d$	Utility grid energy tariff per kWh
$t r_d g$	DG use cost per kWh
$t r_b e s s$	BESS use cost per kWh
$t r_r e c h a r g e_c p$	EV charging tariff per kWh
$d g_f u e l_c a p a c i t y$	DG fuel tank capacity in liters
$p_i n_l i m i t$	Contracted capacity activation limit in kW
$s o c_m i n$	Inferior operation limit for BESS as percentage
$s o c_m a x$	Upper operation limit for BESS as percentage
$h_p e a k_s t a r t$	Peak demand period starting time
$h_p e a k_e n d$	Peak demand period ending time
$n_e v s e_b u s y$	Number of busy connectors at the CP
$t x_r e c h a r g e_s l o w$	BESS slow recharge ratio
$p_d g_r a t e d$	DG rated power in kW
$p_b e s s_r a t e d$	BESS rated power in kW
$p_e v s e_r a t e d$	Charging connector rated power in kW
Variables	Description
$d g_f u e l_m e a s u r e d$	DG remaining fuel as percentage
$p_i n_m e a s u r e d$	Measurement of the power drawn from the grid in kW
$s o c$	Current BESS SoC as percentage
$h_c u r r e n t$	Current time
$p_p v_m e a s u r e d$	PV power measurement in kW
$p_d g_r e f$	DG power reference in kW
$p_b e s s_r e f$	BESS power reference in kW
$p_l o a d_m e a s u r e d$	Power measured at the local load in kW
$p_l i m i t_c p$	Power allowed to CP in kW

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