As already mentioned, this work proposes a new methodology for analyzing the hosting capacity of EVs on low- and medium-voltage distribution grids, based on the real characteristics of the system and on the combination of deterministic and stochastic methods for evaluating the hosting capacity. The methodology is separated into two stages, low and medium voltage, which can be simulated either together or separately. Through use of the methodology, it is possible to initially determine the hosting capacity of the EVs for different types of chargers downstream of the transformer, that is at low voltage. Following this, based on these results, we carried out the verification of the impact upstream of the transformer, namely on the medium-voltage feeder.
3.1. Methodology for Low Voltage
In the methodology for low voltage, deterministic and stochastic methodologies were used together in order to evaluate the capacity of hosting EVs by the secondary distribution grid.
The five performance criteria were composed using the deterministic method, with the user specifying the desired value. The deterministic methodology was used to select both the three-phase consumer units that receive the EV such as the fixed period for carrying out the slow recharge, normally over night, at the discretion of the user. Therefore, the load associated with the EV charger was added to a maximum load condition of the Customer User (CU), which corresponds to the worst-case scenario from a charging point of view. The period considered was that between 19:00 and 20:00, consistent with the peak hours of the distribution system; however, the user can select any other period.
When modeling LV distribution grids, the locations of the CU connections are known; however, it is unknown which customers will have EVs. EV location is a common source of uncertainty, and its loading can significantly affect the simulation results. Consequently, the stochastic methodology was used to select the CUs that will receive the EV, while producing a random draw, using a uniform distribution, without repetition, for the gradual allocation of the EVs. The maximum number of EVs that transformers can host was determined according to the following grid performance criteria:
Undervoltage (UV);
Conductor Thermal limit (CT);
Transformer Overload (TO);
Total Harmonic Distortion of Voltage (THDV);
Voltage Unbalance (VU).
These criteria were selected according to the proposition of Prodist [
37] and according to the BDGD (The BDGD contains various information concerning the DSO, from which the parameters for the simulation in OpenDSS were extracted. Among these parameters is the grouping of types of conductors existing in the distribution system, “SEGCON”, which contains information on the gauge, insulation, resistance, nominal current, etc.), in accordance with
Table 2.
According to the flowchart in
Figure 2, the methodology applied to the low-voltage grid was divided into three stages.
In Step 1, the interaction between Python and the data entered by the user takes place, such as the type of charger, undervoltage limit, overload allowed in the transformer, power of the transformer, and the number of simulated scenarios. Next, Python commands the simulation of the power flow through the OpenDSS software for the low-voltage grid and obtains all the initial input data, such as active and reactive power, the number of buses (nodes), along with the maximum and minimum voltage, etc.
In Step 2, with the initial data stored, a random draw is carried out among the transformers with the power previously selected by the user, and all possible scenarios are created, while performing the enumeration of states, which is combined with the data entered by the user. Subsequently, the selection of the three-phase CUs, which are able to receive the EV, begins. It was assumed in this work that these three-phase consumers would be in better financial conditions to acquire an EV.
In the low-voltage grid, the node and the connection phase of the VE have the same probability, allowing the LV allocation to be modeled as random. To define a two-phase EV allocation scenario in a node, on the three-phase grid, a customer connection point was selected by random sampling within a list of CUs. Therefore, the allocation of EVs in the CUs was carried out; however, for each CU drawn, the list was left without a replacement, and a new draw was carried out from among the remainder, while the distribution of the chargers was performed in a balanced way between the phases.
In Step 3, in each simulation of power flow at 60 Hz and its harmonic, the violation of the performance criteria already mentioned in low voltage is verified. Thus, the hosting capacity downstream of the transformer is defined until there is a violation of any of the five established criteria.
As the simulations generated a significant number of results, instead of saving all the variables for each repetition, only the final results were saved and recorded, highlighting as such the number of allocated vehicles, apparent power, reactive power, losses, voltage magnitudes, THD, and the bars (nodes) where the violations occurred. These data were stored and can be plotted and visualized together or individually through the Pandas in Python. Geo-referenced EVs and transformer location data (latitude and longitude) were plotted on the map and correlated with simulation data through the library Folium (Folium is a library for Python that facilitates the visualization of data that have been manipulated in an interactive map).
As the hosting capacity is directly related to the characteristics of each electrical system in which the EV will be installed, Python performs a new draw from among the selected CUs and simulates the grid again until it reaches the number of scenarios established by the user. Accordingly, the number of EVs admitted to the grid is determined by calculating the mode of the set of simulations performed.
3.2. Methodology for Medium Voltage
The medium-voltage methodology aims at investigating the impact on the medium-voltage grid by loading the secondary transformer with the insertion of the EVs, which differs from all the methodologies presented in Item II. The proposed methodology uses the allocation of EVs on the secondary of the MV/LV transformers, from which the data were extracted from the results of the low-voltage methodology for evaluating the medium-voltage grid. The flowchart in
Figure 3 presents the steps for simulating this methodology.
In Step 1, Python reads the results obtained through the low-voltage methodology and stores these in a virtual database to be used during simulations. Then, the user input data are fixed, and a new parameter is added, i.e., the percentage of EVs loading on the secondary transformer. This parameter has the objective of verifying the variation in the number of EVs admitted through the secondary loading of the transformers in the medium-voltage grid. Therefore, the observation points of the performance criteria are inserted through monitors in the medium-voltage buses, thus ignoring the influence of the medium grid on the low-voltage grid, and the simulation of the power flow is performed through the OpenDSS software, which obtains the initial data of the medium-voltage grid.
In Step 2, the scenarios are formed, and the selection of the transformers is carried out through a random drawing, using a uniform distribution, without replacement. For each transformer selected, the number of EVs admitted is retrieved from the database, and the buses where the EVs were inserted in the low-voltage methodology are stored in a list. As the number of vehicles and the list of bars follows a statistical distribution, the scenario chosen to constitute the simulation is given by calculating the mode of these variables, as long as there is no violation of any of the five performance criteria used.
In Step 3, the medium-voltage grid is analyzed following the same procedure as for the low-voltage methodology. The transformers are allocated one by one, randomly, with their respective EVs, until there is a violation of any of the five performance criteria in the medium-voltage grid; in this way, the maximum hosting capacity is obtained.
In order to verify and compare the results obtained in the medium-voltage grid, the transformers are loaded with a percentage of vehicles defined by the user in the chosen scenario. In this way, the number of repetitions of the simulations is given by the combination of the number of scenarios and percentages chosen.
Finally, through the Pandas and Seaborn library, the obtained results were saved to a spreadsheet and plotted with an issuance of per-transformer hosting capacity report containing the main information.