SYSTEM AND METHOD FOR DETERMINING LOCATION OF ELECTRIC VEHICLE CHARGING STATIONS

Abstract

An embodiment of the present invention relates a method (100) and a system (200) for determining the location of an electric vehicle charging station (EVCS) (800) and a renewable energy distributed generation (REDG) unit (700) in a power distribution network (PDN) (900) is disclosed. The method (100) involves the step of- i. obtaining (101) information associated with the EVCS (800) and REDG unit (700), ii. initializing (102) one or more attributes for determining the coordinates, iii. calculating (103) fitness parameters, and further iv. updating (105) the attributes iteratively to determine the location and capacity of the EVCS (800) and REDG unit (700). The method (100) ensures optimal placement by minimizing power loss and balancing load based on predefined fitness parameters.

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the processes of determining optimal locations for electric vehicle charging stations (hereinafter "EVCS"). More particularly, the present invention relates to a method and a system for determining optimal locations and capacities of the electric vehicle charging stations (EVCS) and renewable energy distributed generation (hereinafter "REDG") units in a power distribution network (hereinafter "PDN").

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the present invention. This section may include certain aspects of the art that may be related to various features of the present invention. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present invention, and not as admissions of the prior art.
[0003] Improper placement of EVCS and REDG units may have opposite impacts such as reverse power flow, power losses, complicated protective relay coordination, voltage increase, unstable voltage, and also face problem of power quality. Therefore, the ideal quantity, location, and size of REDG is identified as a global challenge for business and academic sectors. Therefore, an approach for selection of optimal size and site of REDG and EVCS in distribution system is presented in the present invention.
[0004] The integration of electric vehicles (EVs) and renewable energy sources into existing power distribution networks are increased the demand for well-placed electric vehicle charging stations (EVCS) and renewable energy distributed generation (REDG) units. Optimal placement of EVCS and REDG units are critical for maintaining the balance between energy demand and supply, minimizing power losses, and reducing environmental impact. Conventional methods for determining the location of EVCS and REDG units rely on empirical or manual techniques, which lack precision and scalability. As the demand for EV infrastructure grows, there is a need for automated, data-driven methods to ensure optimal placement of these units within a PDN.
[0005] A literature reference titled "A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system", published in Applied Soft Computing Journal, vol. 91, Jun. 2020, discloses distributed generation (DG) which is utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application This paper uses a hybrid technique to optimize the position and size of DG units to reduce losses in the distribution system. The hybrid technique is the joined execution of both the Grasshopper Optimization Algorithm (GOA) and Cuckoo Search (CS) technique. Here, the GOA optimization behavior is upgraded by utilizing the CS technique. Here, the perfect position of the DG unit is settled with respect to the power loss, line power flow, and voltage profile using the proposed system. To improve the dynamic execution, the limit of DG is directed by the proposed technique with respect to the cost of work. The motivation behind the proposed system is to produce optimal capacity to lessen aggregate power loss and enhance the voltage profiles of power distribution networks. The proposed hybrid technique is executed in MATLAB/Simulink working platform and the dynamic dependability execution is tested and considered with IEEE 33-bus distribution networks and IEEE 69-bus system. The stability of the distribution system by diminishing loss is investigated by executing different load states. The execution of the proposed system is analyzed and compared with different existing techniques. A prior reference is analyzed and compares disclosed hybrid technique with existing art and not particularly discloses the determination of the optimal locations of EVCS or DG units.
[0006] Another literature reference titled "Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II, vol. 30, no. 2, pp. 569-578, Apr. 2015, discloses an improved nondominated sorting genetic algorithm-II (INSGA-II) for optimal planning of multiple distributed generation (DG) units in this literature. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of the population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved.
[0007] Thus, there is a need in the art to provide a method and a system for determining optimal locations of the EVCS and the REDG unit in the PDN.

OBJECTS OF THE PRESENT INVENTION
[0008] Some of the objects of the present invention, which at least one embodiment herein satisfies are as listed herein below.
[0009] It is an object of the present invention to provide a method for determining the location of an electric vehicle charging station (EVCS) and a renewable energy distributed generation (REDG) unit in a power distribution network.
[0010] It is another object of the present invention to provide a method and a system that obtains information related to electric vehicle charging stations and renewable energy distributed generation units for each zone in the power distribution network.
[0011] It is another object of the present invention to provide a method that initializes attributes to set coordinates for electric vehicle charging stations and renewable energy distributed generation units based on the obtained information.
[0012] It is another object of the present invention to provide a method that iteratively updates the position and velocity of the attributes until the optimal location for electric vehicle charging stations and renewable energy distributed generation units is determined.
[0013] It is another object of the present invention to provide a method that validates the determined location of electric vehicle charging stations and renewable energy distributed generation units using a power system simulation tool.
[0014] It is another object of the present disclosure to provide a system and method that uses particle swarm optimization or other optimization techniques to improve the accuracy of the location determination for electric vehicle charging stations and renewable energy distributed generation units.

SUMMARY
[0015] Within the scope of this application, it is expressly envisaged that the various aspects, embodiments, examples, and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.
[0016] In an aspect, the present invention provides a method for determining the location of an electric vehicle charging station (EVCS) and a renewable energy distributed generation (REDG) unit within a power distribution network (PDN).
The method includes obtaining information related to the EVCS and REDG unit, such as load profiles and renewable energy generation, using a processor (101) embedded in a computing device. Based on the obtained information, the processor initializes one or more attributes to assign coordinates for the EVCS and REDG units. The processor further calculates fitness parameters, evaluates and updates the attributes iteratively, and determines the location and capacity of the EVCS and REDG unit using updated fitness parameters.
[0017] In another aspect, the present invention discloses a system for determining the location of an electric vehicle charging station (EVCS) and a renewable energy distributed generation (REDG) unit in a power distribution network (PDN). The system performed the various steps to determine optimal location using a processor embedded in the computing device.
[0018] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that the invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0020] FIG. 1 illustrates an exemplary flowchart of a method for determining the location of an electric vehicle charging station (EVCS) and a renewable energy distributed generation (REDG) unit in a power distribution network (PDN), in accordance with an embodiment of the present invention.
[0021] FIG. 2 illustrates an exemplary flowchart for the selection of optimal site and size of EVCS and REDG (PV panel), in accordance with an embodiment of the present invention.
[0022] FIGs. 3 (a-c) illustrates an exemplary graphical representation that represents a power loss corresponding to REDG (Single PV) (a), optimal size and site of REDG (Single PV) (b), and profile of voltage improvement after injecting solar power (c), in accordance with an embodiment of the present invention.
[0023] FIGs. 4 (a-d) illustrates an exemplary graphical representation that represents voltage deviation at each bus i.e. case I (a), case II (b), and Case III (c), and a comparative analysis of all the cases i.e. I to III (d), in accordance with an embodiment of the present invention.
[0024] FIGs. 5 (a-c) illustrates an exemplary graphical representation of detailed stability analysis for each phase plotted in a-c, in accordance with an embodiment of the present invention.
[0025] FIG. 6 illustrates an example representation of a circuit diagram under Dig SILENT power factory software with integration of EVCS and REDG load, in accordance with an embodiment of the present invention.
[0026] FIG. 7 illustrates an exemplary representation of a power distribution network (PDN) i.e. IEEE-33 distributed network, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION
[0027] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent, however, that embodiments of the present invention may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0028] The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0029] The present invention relates to a method and a system for determining optimal locations and capacities of the electric vehicle charging stations (EVCS) and renewable energy distributed generation (hereinafter "REDG") units in a power distribution network (hereinafter "PDN").
[0030] Various embodiments of the present invention will be explained in detail with respect to FIGs. 1-7.
[0031] FIG. 1 illustrates an exemplary flowchart (100) of a method (100) for determining a location of an electric vehicle charging station (EVCS) and a renewable energy distributed generation (REDG) unit in a power distribution network (PDN), in accordance with an embodiment of the present invention.
[0032] In first embodiment of the present invention, the present invention discloses a method (100) for determining the location of an electric vehicle charging station (EVCS) (800) and a renewable energy distributed generation (REDG) unit (700) in a power distribution network (PDN) (900). The method (100) is implemented by a processor (101) embedded in a computing device (102). The computing device (102) may include any suitable device such as a laptop, desktop, tablet, smartphone, or a dedicated device. The method (100) facilitates the optimal placement of the EVCS (800) and the REDG unit (700) in the PDN (900) to minimize power loss, balance load, and ensure efficient integration of renewable energy resources. The method (100) includes below steps:
[0033] At block 101, the processor (101) is configured to obtain information related to the EVCS (800) and the REDG unit (700). The obtained information includes but is not limited to load profiles, voltage constraints, power balance, branch current limits, power loss minimization, charging capacity of EVCS (800), renewable energy generation profiles, network topology, zonal division, and cost factors. The quantity of EVCS (800) and REDG units (700) is set for each zone in the PDN (900), enabling the processor (101) to identify the zones that require EV charging infrastructure and renewable energy support. In one example embodiment, the information is obtained using MATLAB Simulink, which simulates various scenarios in the PDN (900). The simulation data helps in predicting the EV load demand, renewable energy generation potential, and network capacity for each zone.
[0034] At block 102, based on the obtained information, the processor (101) initializes one or more attributes. The attributes are parameters that define the placement and operation of the EVCS (800) and REDG units (700), such as geographical coordinates, power demand, renewable energy potential, and load balancing requirements. The initialization is aimed at setting one or more coordinates for the EVCS (800) and REDG unit (700) in the PDN (900). For instance, in one embodiment, the processor (101) initializes the attributes by considering geographical factors such as proximity to EV users and grid accessibility.
[0035] At block 103, the processor (101) calculates one or more fitness parameters based on the initialized attributes. The fitness parameters are selected from power loss, voltage profile, voltage deviation, branch loading, current capacity, load balancing, cost function, reliability index, environmental impact, and environmental emission reduction. In one embodiment, the fitness parameters help the processor (101) evaluate the suitability of the EVCS (800) and REDG unit (700) placement within the PDN (900) by minimizing power loss and ensuring optimal voltage levels.
[0036] At block 104, once the fitness parameters are calculated, the processor (101) evaluates them to update the position and velocity of the attributes.
[0037] At block 105, the processor (101) updates the fitness parameters iteratively to refine the location and capacity of the EVCS (800) and REDG unit (700). The method (100) employs particle swarm optimization (PSO) to iteratively update the fitness parameters until a predefined number of iterations is completed or the optimal location for the EVCS (800) and REDG unit (700) is determined. PSO is used to ensure that the parameters converge towards the best solution for EVCS (800) and REDG unit (700) placement.
[0038] At block 106, the processor (101) determines the location of the EVCS (800) and REDG unit (700) based on the updated fitness parameters. The final location is selected to maximize renewable energy utilization, reduce power losses, and meet the demand for EV charging in each zone of the PDN (900). The REDG unit (700), such as a photovoltaic (PV) solar panel system or a wind turbine system, is integrated with the EVCS (800) to provide clean energy for EV charging, thus reducing grid dependency. The determined location of the EVCS (800) and REDG unit (700) is validated using the Dig SILENT Power Factory tool, ensuring compatibility with the PDN (900).
[0039] In the exemplary implementation of the first embodiment, the PDN (900) used in the method (100) may be an IEEE 33-bus distribution network. The PDN (900) is divided into one or more zones based on technical parameters such as voltage constraints and current capacity, as well as consumer behavioral parameters like EV usage patterns. The zones are assigned specific EVCS (800) and REDG unit (700) configurations, considering the unique requirements of each zone.
[0040] In the exemplary implementation of the first embodiment, the processor (101) calculates the current limit for each zone of the PDN (900) using the following equation:
[0041] = 1,2,3……….n
where, is a zone current flows between the buses and is the maximum allowable current in the zone. This ensures that the current flow in each zone is within the safe operating limits of the PDN (900).
[0042] In the exemplary implementation of the first embodiment, the processor (101) also calculates the voltage limit in each zone of the PDN (900) using the following equation:
[0043] =1,2,3……..n
Where is the minimum allowable voltage and is the maximum allowable voltage, wherein a range of allowable voltage is between 0.95 pu to 1.05 pu. This ensures voltage stability across the PDN (900) during the operation of EVCS (800) and REDG units (700).
[0044] Working example: the method (100) is applied to a city's PDN (900) where there is a need to establish EVCS (800) in different zones. The processor (101) gathers load profile data and renewable energy generation potential for each zone. Based on this data, the processor (101) initializes attributes, such as geographical coordinates and load demand, and calculates fitness parameters like power loss and cost efficiency. The particle swarm optimization technique is employed to iteratively update these attributes. Finally, the method (100) determines that Zone A requires two EVCS (800) with integrated solar panels (REDG units) to meet the demand, while Zone B requires one EVCS (800) with wind energy support (REDG unit). The determined locations are validated using a Dig SILENT power factory tool that ensures efficient energy distribution.
[0045] In a second embodiment of the present invention, the system (200)
for determining the location of an electric vehicle charging station (EVCS) (800) and a renewable energy distributed generation (REDG) unit (700) within a power distribution network (PDN) (900) is disclosed. The system (200) is designed to optimize the placement of EVCS (800) and REDG units (700) in order to reduce power losses, balance network loads, and maximize the use of renewable energy sources. The system (200) is implemented by a computing device (102) and a processor (101) embedded within the computing device (102), with the processor (101) configured to execute various functions.
[0046] In an exemplary implementation of the second embodiment, the system (200) includes a computing device (102) that is capable of executing the functions of the system. Examples of computing devices (102) may include but are not limited to a laptop, desktop, tablet, smartphone, or any other dedicated computing device. The computing device (102) houses the processor (101) that performs the core operations of the system (200).
[0047] In an exemplary implementation of the second embodiment, the processor (101) is embedded within the computing device (102) and is responsible for executing the various functions of the system (200). The processor (101) is configured to obtain information related to the EVCS (800) and REDG unit (700) from the PDN (900), initialize attributes, calculate fitness parameters, and determine the optimal location and capacity of the EVCS (800) and REDG unit (700) based on the updated parameters.
[0048] In an exemplary implementation of the second embodiment, the processor (101) is configured to obtain information associated with the EVCS (800) and REDG unit (700), wherein the quantity of the EVCS (800) and REDG units (700) is set for each zone within the PDN (900). The information comprises a wide array of data, including load profiles, voltage constraints, power balance, branch current limits, power loss minimization, charging capacity of the EVCS (800), renewable energy generation profiles, network topology, zonal division, and cost factors. By analyzing this data, the system (200) is able to assess the needs of each zone and determine where the EVCS (800) and REDG units (700) should be placed to provide optimal performance.
[0049] In an exemplary implementation of the second embodiment, based on the obtained information, the processor (101) initializes one or more attributes that define the potential locations of the EVCS (800) and REDG unit (700). The attributes include parameters such as geographical coordinates, load demands, renewable energy potential, and network conditions. These attributes are then used to set one or more coordinates for the EVCS (800) and REDG unit (700) in the PDN (900). The initialization ensures that the placement is optimized based on the technical and consumer requirements of each zone.
[0050] In an exemplary implementation of the second embodiment, the processor (101) calculates one or more fitness parameters based on the initialized attributes. These fitness parameters are selected from a range of performance indicators, such as power loss, voltage profile, voltage deviation, branch loading, current capacity, load balancing, cost function, reliability index, environmental impact, or environmental emission reduction. In one embodiment, the fitness parameters help the system (200) evaluate how well the proposed locations for the EVCS (800) and REDG unit (700) perform in minimizing power losses and ensuring a stable voltage profile within the PDN (900).
[0051] In an exemplary implementation of the second embodiment, once the fitness parameters are calculated, the processor (101) evaluates the calculated parameters and updates the position and velocity of the attributes associated with the EVCS (800) and REDG unit (700). The evaluation is conducted iteratively, with the system (200) continuously updating the fitness parameters until a set threshold is reached. The iterative updates are necessary to refine the proposed locations and capacities, ensuring that the final placement maximizes efficiency and minimizes power losses in the PDN (900).
[0052] In an exemplary implementation of the second embodiment, the system (200) utilizes a particle swarm optimization (PSO) technique to update the fitness parameters iteratively. The PSO technique mimics the social behavior of particles, where each particle adjusts its position based on its own best solution and the collective best solution of the swarm. The processor (101) applies the PSO technique to iteratively refine the location and capacity of the EVCS (800) and REDG unit (700) until the optimal placement is determined.
[0053] In an exemplary implementation of the second embodiment, the final step of the system (200) is determining the location and capacity of the EVCS (800) and REDG unit (700) based on the updated fitness parameters. The processor (101) selects the coordinates and capacity for the EVCS (800) and REDG unit (700) that provide the best performance in terms of load balancing, minimizing power loss, and maximizing the utilization of renewable energy. In one example embodiment, the REDG unit (700) may be a photovoltaic (PV) solar panel system or a wind turbine system integrated with the EVCS (800). This integration allows the system (200) to harness renewable energy for charging electric vehicles, thus reducing reliance on the grid.
[0054] In an exemplary implementation of the second embodiment, the system (200) validates the determined location of the EVCS (800) and REDG unit (700) using a Dig SILENT Power Factory tool. This validation ensures that the selected location and capacity are compatible with the actual conditions of the PDN (900) and that the system (200) meets the performance criteria. The validation process helps confirm that the system's calculations are accurate and that the proposed locations may be implemented effectively within the PDN (900).
[0055] In an exemplary implementation of the second embodiment, the system (200) obtains information for calculating the fitness parameters using MATLAB Simulink. The MATLAB Simulink provides a dynamic simulation environment that allows the processor (101) to model different scenarios and evaluate the performance of the EVCS (800) and REDG unit (700) within the PDN (900). The simulation data helps the system (200) predict EV load demand, renewable energy generation, and network capacity for each zone.
[0056] Working example: The system (200) is applied to a power distribution network (PDN) (900) with multiple zones. The processor (101) obtains load profiles and renewable energy generation potential for each zone using MATLAB Simulink. The system (200) then initializes attributes based on the obtained data and calculates fitness parameters, including power loss and cost efficiency. The PSO technique is employed to iteratively update the fitness parameters, refining the location of EVCS (800) and REDG units (700). The system (200) determines that two EVCS (800) with integrated solar panels (REDG units) should be placed in Zone A, while one EVCS (800) with a wind energy REDG unit should be placed in Zone B. The results are validated using the Dig SILENT Power Factory tool to ensure compatibility with the PDN (900).
[0057] The implementation of the method (100) and the system (200) disclosed in the present invention and relevant test data is illustrated in below embodiments.
[0058] FIG. 2 illustrates an exemplary flowchart (250) for the selection of the optimal site and size of EVCS and REDG (PV panel), in accordance with an embodiment of the present invention.
[0059] In a third embodiment of the present invention, the IEEE 33 radial distribution network is considered for analysis of system performance is disclosed with a flowchart as mentioned in FIG. 2. The distribution network is divided into 5 zones by considering technical and consumer behavioral parameters. An algorithm i.e. PSO is applied to find the optimal location of EVCS on the distribution network with the help of MATLAB as a flow chart is presented in FIG.2. Each zone of the network has one fixed EVCS load as mentioned in Table 1 which reveals that the optimal location is obtained using PSO technique.


Table 1: Real and Reactive Power Loss
Zone EVCS Optimal Location Other Location 1 Other Location 2 Other Location 3
Zone-1 EVCS-1 19 4 19 22
Zone-2 EVCS-2 3 7 23 25
Zone-3 EVCS-3 4 27 5 33
Zone-4 EVCS-4 9 31 8 12
Zone-5 EVCS-5 13 3 14 18
Real and Reactive Power Loss in (kW) and (KVAR) P=250.00
Q=165.00 P: 255.10
Q: 167.50 P: 253.40
Q: 167.60 P: 281.80
Q: 190.60

[0060] In an exemplary implementation of the third embodiment, if EVCS are placed in optimal locations at each zone, then minimum losses are obtained for the distribution network. Once, the EVCS load is placed at the optimal location, and then the losses may be further reduced if it is integrated with the optimal size and location of REDG (PV panel is used in the present invention). Table 2 shows REDG (or DG) size and their corresponding losses at each bus. Fig. 2 shows data of power loss at each bus by considering data from Table 2.

Table 2: Optimal Placement of REDG (Single PV) on IEEE-33 Bus System with integrated EVCS load.
BUS Bus1 Bus2 Bus3 Bus4 Bus5 Bus6 Bus7 Bus8 Bus9 Bus10 Bus11
P loss 263.471 250.591 198.372 180.834 166.566 136.706 137.333 140.47 142.346 144.23 144.680
DG Size 0.544 3.9842 3.947 3.600 3.248 2.901 2.754 2.08 1.874 1.598 1.555
BUS Bus12 Bus13 Bus14 Bus15 Bus16 Bus17 Bus18 Bus19 Bus20 Bus21 Bus22
P loss 145.658 149.628 153.787 156.355 161.183 170.05 174.124 256.77 260.3 260.55 260.982
DG Size 1.512 1.3316 1.14 1.203 1.150 1.111 0.952 2.07 0.52 0.491 0.3477
BUS Bus23 Bus24 Bus25 Bus26 Bus27 Bus28 Bus29 Bus30 Bus31 Bus32 Bus33
P loss 212.282 219.333 226.64 139.9 143.99 153.8 158.10 161.0 169.009 171.951 176.412
DG Size 2.75 1.846 1.326 2.7592 2.535 2.027 1.7762 1.653 1.4782 1.413 1.328
[0061] FIGs. 3 (a-c) illustrates an exemplary graphical representation (350) that represents a power loss corresponding to REDG (Single PV) (a), optimal size and site of REDG (Single PV) (b), and profile of voltage improvement after injecting solar power (c), in accordance with an embodiment of the present invention.
[0062] In the exemplary implementation of the third embodiment, Bus 6 shows minimum loss which indicates the optimal location and requirement of REDG rating. Fig. 3 shows the pictorial representation requirement of REDG size at each bus. The suitable rating of solar distributed generation is also measured with the proposed control approach. The voltage deviation is lesser as compared to without including the PV system as results shown in Fig. 3b. The results i.e. loss and voltage deviation with interconnection of multi-PV are also measured as presented in Table 3.

Table 3: Multi PV Location at IEEE 33 Bus System
Optimal Location Bus 16 Bus 24 Bus 26
Optimal Size(MVA) 0.6012 1.1265 1.446
Power Loss(KW) 81.125
VD (pu) 0.020






[0063] In a fourth embodiment of the present invention, the validation of simulated results under balanced load conditions using Dig SILENT power factory software is disclosed.
[0064] In an exemplary implementation of the fourth embodiment, the simulated results are obtained with the integration of REDG and EVCS load in the IEEE 33 bus system. Furthermore, the optimal locations of load and suitable rating of REDG are validated with Dig SILENT power factory software.
[0065] FIGs. 4 (a-d) illustrates an exemplary graphical representation (450) that represents voltage deviation at each bus i.e. case I (a), case II (b), and Case III (c), and a comparative analysis of all the cases i.e. I to III (d), in accordance with an embodiment of the present invention.
[0066] In a fifth embodiment of the present invention, the six different cases are considered to validate the proposed control scheme as mentioned in Table 4. Case I to case III to depict the ideal case i.e. under balanced load conditions and case IV to case VI depicts real-world scenarios with consideration of unbalanced load conditions. Fig. 4a to 4c represent voltage deviation at each bus and FIG. 4d reveals a comparative analysis of all the cases i.e. I to III. Case III which is the integration of EVCS and multi-PV load has the least voltage variation under balanced load conditions in contrast to other cases e.g. case I and case II.

Table 4: Six different cases for placement of EVCS and REDG load
Case Single PV or Single REDG Multi PV or Multi REDG EVCS Balance Load Unbalance Load
Case-I ✘ ✘ ✓ ✓ ✘
Case-II ✓ ✘ ✓ ✓ ✘
Case-III ✘ ✓ ✓ ✓ ✘
Case-IV ✘ ✘ ✓ ✘ ✓
Case-V ✓ ✘ ✓ ✘ ✓
Case-VI ✘ ✓ ✓ ✘ ✓

[0067] FIGs. 5 (a-c) illustrates an exemplary graphical representation (550) of detailed stability analysis for each phase plotted in a-c, in accordance with an embodiment of the present invention.
[0068] In a sixth embodiment of the present invention, the simulated results under unbalanced load conditions using Dig SILENT power factory software is disclosed.
[0069] In an exemplary implementation of the sixth embodiment, the unequal impedances of branch, load increase in one phase or two phases, and unequal connection in one or more phase situations may create unbalancing conditions in the distribution system which impacts on power quality, power losses, and voltage profile in the distributed system. Therefore, three different cases IV to VI are applied in the system to depict the real world. Single PV, Multi PV, and all types of EVCS loads e.g. Type 1 or Type 3 chargers are placed at optimal places with suitable ratings to be searched by the PSO algorithm. The data having minimum losses and flat voltage profile for an unbalanced IEEE 33 bus system is taken from Table 5 to show the effectiveness of the optimization technique. The data is implemented in Dig SILENT power factory software for placement of REDG and EVCS as results are presented in Fig. 5 (a-c).

Table 5:
Test System Unbalance three-phase test system
Bus No Type Active Demand(MW) Reactive Demand(MVAR) Minimum Voltage(p.u.) Maximum Voltage(p.u.) Number of Phases Connection Type Number of wire
1 Reference 0 0 1 1
2 PQ 0.1 0.06 1.05 0.95 2 (AB) Y 3
3 PQ 0.09 0.04 1.05 0.95 1 (A) Y 3
4 PQ 0.12 0.08 1.05 0.95 2 (BC) Y 3
5 PQ 0.06 0.03 1.05 0.95 1 (B) Y 3
6 PQ 0.06 0.02 1.05 0.95 1 (C) Y 3
7 PQ 0.2 0.1 1.05 0.95 3 (ABC) ∆ 3
8 PQ 0.2 0.1 1.05 0.95 3 (ABC) Y 3
9 PQ 0.06 0.02 1.05 0.95 1 (A) Y 3
10 PQ 0.06 0.02 1.05 0.95 1 (B) Y 3
11 PQ 0.045 0.03 1.05 0.95 1 (C) Y 3
12 PQ 0.06 0.035 1.05 0.95 1 (A) Y 4
13 PQ 0.06 0.035 1.05 0.95 1 (B) Y 4
14 PQ 0.12 0.08 1.05 0.95 2 (AC) Y 4
15 PQ 0.06 0.01 1.05 0.95 1 (C) Y 4
16 PQ 0.06 0.02 1.05 0.95 1 (A) Y 4
17 PQ 0.06 0.02 1.05 0.95 1 (B) Y 4
18 PQ 0.09 0.04 1.05 0.95 1 (C) Y 4
19 PQ 0.09 0.04 1.05 0.95 1 (A) Y 3
20 PQ 0.09 0.04 1.05 0.95 1 (B) Y 3
21 PQ 0.09 0.04 1.05 0.95 1 (C) Y 3
22 PQ 0.09 0.04 1.05 0.95 1 (A) Y 3
23 PQ 0.09 0.05 1.05 0.95 1 (B) Y 3
24 PQ 0.42 0.2 1.05 0.95 3 (ABC) Y 3
25 PQ 0.42 0.2 1.05 0.95 3 (ABC) ∆ 3
26 PQ 0.06 0.025 1.05 0.95 1 (C) Y 3
27 PQ 0.06 0.025 1.05 0.95 1 (A) Y 3
28 PQ 0.06 0.02 1.05 0.95 1 (B) Y 3
29 PQ 0.12 0.07 1.05 0.95 2 (AB) Y 4
30 PQ 0.2 0.6 1.05 0.95 1 (C) Y 4
31 PQ 0.15 0.07 1.05 0.95 2 (BC) Y 4
32 PQ 0.21 0.1 1.05 0.95 3 (ABC) Y 4
33 PQ 0.06 0.04 1.05 0.95 1 (A) Y 4

[0070] FIG. 6 illustrates an example representation of a circuit diagram (650) under Dig SILENT power factory software with integration of EVCS and REDG load, in accordance with an embodiment of the present invention.
[0071] Referring to FIG. 6, the circuit diagram under Dig SILENT power factory software with integration of the EVCS and the REDG load is disclosed.
[0072] In a seventh embodiment of the present invention, the present disclosed method (100) is executed under MATLAB Simulink environment and Dig SILENT power factory software. The simulated results are validated under Dig SILENT power factory software.
[0073] In the exemplary implementation of the seventh embodiment, the considered IEEE bus system is divided into 5 zones, therefore 5 EVCS of type 1 and Type 3 chargers. Type 1 chargers can charge 1 car and Type 3 chargers have facilities to charge 5 cars at a time.
[0074] In the exemplary implementation of the seventh embodiment, the five REDG (PV systems) are placed in each zone.
[0075] In the exemplary implementation of the seventh embodiment, the Dig SILENT Power Factory 2024 SP1(x64)/Build 24.03.0(24037) / Rev. 109576. Institute Licensed version is used.
[0076] In the exemplary implementation of the seventh embodiment, the comparative analysis between the proposed approach and previous planning approaches is presented in Table 5.
Table 5: Comparison between the proposed approach and previous planning approaches
Research Article [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] This invention
Optimization Model Single Objective ✘ ✘ ✘ ✓ ✘ ✓ ✘ ✓ ✘ ✘ ✘ ✘ ✘ ✘ ✘
Multi Objective ✓ ✓ ✓ ✘ ✓ ✘ ✓ ✘ ✓ ✓ ✓ ✘ ✓ ✓ ✓
Network Configuration Initial ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✓ ✘ ✘ ✘ ✘ ✓ ✘ ✘
Optimal ✓ ✓ ✓ ✓ ✓ ✓ ✘ ✘ ✓ ✓ ✓ ✓ ✘ ✓ ✓
Balance ✘ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✘ ✓ ✓ ✓ ✓ ✓ ✓
Unbalance ✓ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✓ ✘ ✘ ✘ ✘ ✘ ✓
DG Integration Renewable PV Energy ✓ ✓ ✓ ✓ ✘ ✘ ✓ ✓ ✓ ✘ ✓ ✓ ✘ ✘ ✓
EVCS Integration ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Type of Charging Type 1& Type 3 ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✓ ✘ ✓
Validation of Software ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✓

[0077] FIG. 7 illustrates an exemplary representation (750) of a power distribution network (PDN) i.e. IEEE-33 distributed network, in accordance with an embodiment of the present invention.
[0078] In an eighth embodiment of the present invention, the present invention shows more practical approach with validation of results using Dig SILENT power factory software. The present invention considers balanced as well as unbalanced real-world condition at each phase to depict real time environment. The maximum feasibility is considered with connection of Type I and Type 3 charger option in IEEE-33 bus system. The results not only shown with single PV interconnection, but also considers multiple PV scenario in the distribution system. To show accessibility to number of consumers, the complete IEEE 33 bus system is categorized into 5 zones e.g. residential, industrial and commercial based on types of loads as presented in Table 6 so that interconnection of each source i.e. EVCS, REDG may be approachable to maximum number of consumers as shown in FIG. 7.
Table 6: 5 Zones in IEEE 33 Bus System
Zones Buses Type of Load
Zone 1 2 19 20 21 22 Residential
Zone 2 3 23 24 25 Industrial
Zone 3 4 5 6 26 27 28 29 30 31 32 33 Residential
Zone 4 7 8 9 10 11 12 Commercial
Zone 5 13 14 15 16 17 18 Residential

[0079] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the invention herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the invention and not as a limitation.

ADVANTAGES OF THE PRESENT INVENTION
[0080] The present invention provides a method and a system for determining optimal locations and capacities of the electric vehicle charging stations (EVCS) and renewable energy distributed generation units in a power distribution network.
[0081] The present invention provides a distribution system that uses two types of chargers to show practical scenarios in the distribution system.
[0082] The present invention provides a method and a system that includes the number of renewable resources i.e. single PV, and multiple PV are integrated in the IEEE 33 bus distribution system.
[0083] The present invention provides a system and a method that provides balanced and unbalanced conditions too at each phase are incorporated to validate the optimization approach to depict practical scenario.
[0084] The present invention provides a system and a method that the obtained parameters are validated with the Dig SILENT power factory tool.
[0085] The present invention provides a system and a method that integrates the EVCS with Type 1 as well as type 3 chargers and REDG includes single as well as multiple PV panels under balanced load conditions and the same multiple scenarios under unbalanced load conditions are implemented while a satisfying number of constraints e.g. power balance, voltage deviation, and branch current limit.
, Claims:1. A method (100) for determining location of an electric vehicle charging station (EVCS) (800) and a renewable energy distributed generation (REDG) unit in a power distribution network (PDN) (900), wherein at least one REDG unit (700) is placed in the EVCS (800), the method (100) comprising:
obtaining (101), by a processor (101), information associated with the EVCS and the REDG unit (700), wherein a quantity of the EVCS (800) and the REDG unit (700) is set for each zone in the PDN, wherein the processor is embedded in a computing device (102);
initializing (102), by the processor (101), based on the obtained information and the quantity of the EVCS (800) and the REDG unit (700), one or more attributes, the one or more attributes are configured to set one or more coordinates for the EVCS (800) and the REDG unit (700);
calculating (103), by the processor (101), one or more fitness parameters based on the one or more initialized attributes;
evaluating (104), by the processor (101), the one or more calculated fitness parameters to update a position and a velocity of the one or more attributes;
updating (105), by the processor (101) iteratively the position and the velocity of the one or more evaluated fitness parameters; and
determining (106), by the processor (101), the location and the capacity of the EVCS (800) and the REDG unit (700) based on the one or more updated fitness parameters in the PDN (600).

2. The method as claimed in claim 1, wherein:
the information comprises load profiles, voltage constraints, power balance, branch current limits, power loss minimization, charging capacity of EVCS (800), renewable energy generation profiles, network topology, zonal division, or cost factor; and
the one or more fitness parameters are selected from any or a combination of power loss, voltage profile, voltage deviation, branch loading, current capacity, load balancing, cost function, reliability index, or environmental impact, or environmental emission reduction.

3. The method (100) as claimed in claim 1, wherein:
the information is obtained on the MATLAB Simulink;
the determined location of the EVCS (800) the REDG unit (700) are validated using a Dig SILENT power factory tool; and
the computing device is selected from any of laptop, desktop, tablet, or smartphone or a dedicated device.

4. The method (100) as claimed in claim 1, wherein the one or more evaluated fitness parameters are updated iteratively using a particle swarm optimization (PSO) technique until a number of pre-set iterations are over or the location for the EVCS (800) and the REDG unit (700) is determined.

5. The method (100) as claimed in claim 1, wherein:
the PDN (600) is an IEEE 33-bus distribution network, wherein the PDN (600) having a type 1 charger or a type 3 charger;
the REDG unit (700) is a photovoltaic (PV) solar panel system or a wind turbine system; and
the PDN (600) is divided into one or more zones based on one or more technical or one or more consumer behavioral parameters.

6. The method (100) as claimed in claim 1, wherein a current limit in the each zone of PDN (600) is calculated using:
= 1,2,3……….n
where, is a zone current flows between the buses and is a maximum allowable current in the zone.

7. The method (100) as claimed in claim 1, wherein a voltage limit in the each zone of PDN (600) is calculated using:
=1,2,3……..n
where is a minimum allowable voltage and is a maximum allowable voltage, wherein a range of allowable voltage is between 0.95 pu to 1.05 pu.

8. A system (200) to determine location an electric vehicle charging station (EVCS) (800) and a renewable energy distributed generation (REDG) unit in a power distribution network (PDN) (900), wherein at least one REDG unit (700) is placed in the EVCS (800), the system (200) comprising:
a processor (101) embedded in a computing device (102), the processor is configured to:
obtain information associated with the EVCS (800) and the REDG unit (700), wherein a quantity of the EVCS (800) and the REDG unit (700) is set for each zone in the PDN (600);
initialize, based on the obtained information and the quantity of the EVCS (800) and the REDG unit (700), one or more attributes, the one or more attributes being configured to set one or more coordinates for the EVCS (800) and the REDG unit (700);
calculate one or more fitness parameters based on the one or more initialized attributes;
evaluate the one or more calculated fitness parameters to update a position and a velocity of the one or more attributes;
update iteratively the position and the velocity of the one or more evaluated fitness parameters until a set threshold is achieved; and
determine the location and capacity of the EVCS (800) and the REDG unit (700) based on the one or more updated fitness parameters in the PDN (600).

9. The system (200) as claimed in claim 8, wherein
the information is obtained on a MATLAB Simulink; and
the determined location of the EVCS (800) the REDG unit (700) are validated using a Dig SILENT power factory tool; and
the computing device (102) is selected from any of laptop, desktop, tablet, or smartphone or a dedicated device.

10. The system (200) as claimed in claim 8, wherein:
the information comprises load profiles, voltage constraints, power balance, branch current limits, power loss minimization, charging capacity of EVCS (800), renewable energy generation profiles, network topology, zonal division, or cost factor; and
the one or more fitness parameters are selected from any or a combination of power loss, voltage profile, voltage deviation, branch loading, current capacity, load balancing, cost function, reliability index, or environmental impact, or environmental emission reduction.

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