Smart Monitoring of Microgrid-Integrated Renewable-Energy-Powered Electric Vehicle Charging Stations Using Synchrophasor Technology

<jats:p>With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring.</jats:p>

section_content: The power sector stands as a crucial pillar of any nation's economic advancement, with a significant role to play in India's journey towards becoming a developed nation. Ensuring equal accessible affordability and sustainable power has remained a cornerstone principle guiding the sector. Furthermore, the power sector will be instrumental in addressing climate change challenges and fulfilling India's commitments on the global stage. Over the years, the government has undertaken various initiatives aimed at transforming India from a power-deficit to a power-surplus nation. These efforts include enhancing the proportion of renewable energy within the total capacity blend, establishing a unified national grid, enhancing distribution grids, and attaining electrification for every household universally. As India undergoes economic development, the demand for power is increasing. The demand for electricity in the nation has grown at a Compound Annual Growth Rate (CAGR) of about 4. 1% over the last ten years. Based on projections from the 20th EPS Report, this demand is expected to rise at a CAGR of 7. 18% during the next five years. By 31 December 2022, the installed capacity of power plants (also known as utilities) had increased to 410,339 MW from just 1362 MW in 1947. Similarly, from roughly 5. 1 billion units (BU) in 1950 to 1491. 9 BU (including imports) in the fiscal year 2021-2022, there has been a notable growth in the production of electricity [1]. Since 31 December 2013, the country's electricity transmission has been made easier by the unification of regional grids into a single national grid via strong inter-regional AC and HVDC linkages. As a result, the nation has seen a consistent decrease in both the energy (MU) not delivered and the peak MW demand not satisfied. During the fiscal year 2021-2022, the peak power deficit was 1. 2%, while the energy deficit was only 0. 4%. This marginal shortage is attributed to factors other than the availability of generation capacity. 

India has made significant progress in transitioning towards renewable energy resources, and installed capacity significantly increased during the fiscal year 2006-2007. Renewable energy sources, including large hydro schemes, have seen their contribution to installed capacity rise from 5. 8% in 2006-2007 to 41% as of 31 December 2022. Furthermore, its share in total energy generation rose to 21. 54% in 2021-2022 from 1. 5% in 2006-2007 [2]. 

The 21st century witnesses a notable surge in global energy demand, driven by economic advancement and population growth. In India, the energy generation landscape mostly relies on fossil fuels, with coal comprising 49. 1%, natural gas 6%, nuclear 1. 6%, and diesel 0. 1%, while renewable energy sources make up 41. 6%, as depicted in Figure 1 [3]. However, this reliance on fossil fuels is depleting the nation's reserves of coal and natural gas rapidly as it strives to fulfil the escalating electricity needs of an increasing population and swift urbanization. Furthermore, this increasing demand for power exacerbates environmental challenges such as global warming and heightened greenhouse gas emissions. Consequently, promoting non-conventional energy sources such as wind, photovoltaic (PV), biomass, and hydroelectric power emerges as a practical solution to address the surging energy demand while mitigating greenhouse gas emissions [4] [5] [6] [7]. Nevertheless, India boasts abundant solar and wind energy resources, as depicted in Figure 2. Beyond manufacturing, researchers are diligently pursuing advancements to harness solar energy more effectively. This includes the development of hybrid energy systems integrating various renewable sources of energy and storage technologies alongside PV panels. Various hybrid configurations have been proposed, such as PV-wind-battery [8,9], PV-battery-fuel cell [10, 11], PV-wind-fuel cell [12], PV-biomass [13], and others. Even though many of these hybrid structures are made for stand-alone uses without connection to the electrical grid, integrating them with grid-connected systems presents a viable solution to reduce reliance on storage devices and enhance power supply reliability [14] [15] [16] [17] [18]. Grid-connected renewable energy systems offer numerous advantages, such as harmonic compensation, reactive power supply, load distribution, peak power reduction, regulation of voltage and frequency, and the capability to sustain essential loads during utility disruptions. 

In response to increasing consumer demand and the imperative of addressing climate change, electricity utilities are integrating various renewable energy sources alongside conventional ones. Each type of renewable generation exhibits unique behavior patterns, which the current measuring system struggles to capture adequately due to its slow data-capture rate. SCADA systems are about to be replaced by synchrophasor technology because of their very high sample rates (25-50 samples per second). In the near future, microgrids, energy storage, and electric vehicle charging are expected to become key elements of the power grid. With the growing penetration of renewables, rapid measurement with control action for renewable generation becomes essential. Synchrophasor technology in this application greatly improves power system network architecture, design, and functionality. 

Also, the adoption of electric power in the transportation sector using renewable energy resources (RES) represents a promising and relatively novel research frontier. Utilizing RES for power generation in Electric Vehicle Charging Stations (EVCS) could lead to a substantial 433% increase in their utilization by EV users [19]. As Electric Vehicles (EVs) become more prevalent, accommodating their increasing numbers could be facilitated through renewable distributed generation (DG) [20]. The focus of numerous investigations in the literature is on Grid-to-Vehicle (G2V) connection, with emerging interest in Vehicle-to-Grid (V2G) connections due to their potential for peak power savings and the enhancement of power system reliability and efficiency [21] [22] [23]. An experimental study demonstrated the feasibility of a PV-grid-connected EVCS with V2G capabilities, showing its ability to charge EVs while also contributing to grid load management with surplus electricity [24]. Nevertheless, optimizing the development of EVCS has been explored, considering different electricity generation alternatives including photovoltaic (PV), diesel generators, and batteries, with wind power notably absent as a choice in previous studies [25, 26]. Residential microgrids (MGs) equipped with renewable energy sources (RESs), energy storage, and EV charging capabilities offer a key solution, as RESs help balance energy demand while addressing the issue of emissions [27]. The optimal system for an EV charging station powered by renewable energy in Korea was explored in [28]. In [29], an overview of electric vehicles and their impacts, various charging modes, and different design approaches for EVCS is presented. In India, various investigations have explored the use of RES in rooftop buildings, mosques, and hospitals, primarily focusing on economic and environmental impacts [30] [31] [32]. In response to increasing consumer demand and the imperative of addressing climate change, electricity utilities are integrating various renewable energy sources alongside conventional ones. Each type of renewable generation exhibits unique behavior patterns, which the current measuring system struggles to capture adequately due to its slow data-capture rate. SCADA systems are about to be replaced by synchrophasor technology In response to increasing consumer demand and the imperative of addressing climate change, electricity utilities are integrating various renewable energy sources alongside conventional ones. Each type of renewable generation exhibits unique behavior patterns, which the current measuring system struggles to capture adequately due to its slow This study aims to develop an Electric Vehicle Charging Station (EVCS) that incorporates renewable energy sources (RES) to effectively manage load demand and lower system costs and emissions. For determining the most optimal feasible scheduling and design of renewable energy technologies, the HOMER simulation program is used in this research [33]. System inputs are chosen according to market availability references, with design variables including RES size and grid electricity exchange quantities. The proposed model yields optimal system configurations, energy scheduling, component sizing, cost estimates, and emissions. The limitation of multi-objective optimization lies in its inability to offer a singular solution satisfying all criteria [34] ; thus, trade-offs among constraints are inevitable to attain a optimal solution. This study presents a comparison of different scenarios and suggests a suitable solution meeting EVCS designer's requirements. Also, synchronized phasor measurements are increasingly acknowledged as a promising technology for the development of smart grid applications. Over the past few years, electric utilities worldwide have prioritized the installation of Phasor Measurement Units (PMUs) to continuously monitor power system features. The Gujarat energy transmission corporation limited (GETCO) has taken a pioneering step by installing 118 PMUs at 25 locations-including 4 locations abundant in renewable energy resources-within the Gujarat grid [35]. In this study, voltage magnitudes are measured from PMUs and compared with the conventional monitoring method. 

However, there remains a significant research gap concerning the integration of RES into EVCS, necessitating further studies and investigations in this area. Hence, this study aims to make a key contribution by: (i) Investigating both hybrid stand-alone and grid-connected systems and analyzing them from technical, economic, and environmental perspectives; (ii) Comparing various system setups and recommending the most suitable sizes for different hybrid components; (iii) Examining the incorporation of PV-Wind and PV-only systems that satisfy similar load demands on the grid, and contrasting them with standalone Hybrid Energy Systems (HESs); (iv) Analyzing how the cost of energy (COE) is affected by grid sell-back prices; (v) Conducting an analysis of the IEEE 33 bus architecture using micro-pmu (µPMU) with Electric Vehicle Charging Stations (EVCS) and Distributed Generators (DGs). 

This study is organized as follows: the suggested technique is explained in Section 2; in Section 3, the outcomes of system configurations are presented; in Section 4, smart monitoring of the IEEE 33 bus architecture incorporated with EVCS and DGs using µPMUs is depicted; and in the final section, the research conclusion is presented and some recommendations are proposed.
section_name: Introduction

section_name: Materials and Methods

section_content: An initial investment is necessary for a PV-Wind integrated power system, which yields power generation derived from both PV panels and wind turbines. The mathematical expression for calculating the total power produced by this combined system [36] is as follows:

where I T is the solar radiation that strikes the solar array (kW/m 2 ); P PV is PV output power (kW); Y PV is PV rated output (kW); f PV is the PV derating factor; I T,STC is the incoming solar radiation in kW/m 2 under nominal test conditions (NTC); β is the temperature coefficient (%/ • C); solar cell temperature (K C ) is defined as • C; and cell temperature (STC) is denoted as K C,STC. 

Wind energy is given as:

where ρ represents air density, A W denotes the swept region by the air, and V indicates the wind speed. Solar energy is given as:

where In(t) represents the insulation at time t in kW/m 2 ; A S represents the area of a single PV panel; and η(PV) indicates the overall efficiency of the PV panels. The overall efficiency is expressed as follows:

where PR denotes the performance ratio and K represents the annual average solar radiation acting on the panels.
section_name: Mathematical Modeling 2.1.1. PV-Wind Module

section_content: The converter efficiency is assumed to be 90% and is connected to the charging assemblies [37], given as follows:

where PDC is converter's output power, η is converter efficiency, and PAC is the converter's input power.
section_name: Converter

section_content: The battery bank's storage capacity is determined as follows:

where V is the voltage and Q constitutes the electric charge. The energy required for battery charging is determined as follows:

where E denotes the total energy needed to charge the battery, C is the capacity of the battery, η charger is the battery charger efficiency, and SOC is the percentage of remaining charge in the battery.
section_name: Battery

section_content: The Cost of Energy (COE) represents the charging cost for Electric Vehicles (EVs) per kilowatt hour (kWh) and is defined as follows:

where C base and C variable represent the basic and variable costs associated with EV charging, respectively. According to peak or off-peak hours, the COE changes.
section_name: Financial Components (a) Cost of Energy

section_content: Computation of the Net Present Value (NPV) is as follows:

where R t represents the net input or outflow of cash for a specific period, i denotes the discount rate, and t signifies the project's duration in years.
section_name: (b) Net Present Value

section_content: The net grid purchases in a grid-linked power plant are determined by HOMER and computed as the total grid purchases minus the total grid sales. The pollution emissions linked to the net grid purchasing are then calculated by the software by multiplying the emission parameter (in g/kWh) divided by the net grid expenditure (in kWh) associated with each pollutant. The Indian electrical grid's emission factor is reported as 0. 968 tCO 2 /MWh [38].
section_name: CO 2 Emissions

section_content: India is the world's seventh-largest country, covering 3,287,263 square kilometers (1,269,219 square miles), and boasts abundant solar energy resources [39]. With solar irradiance ranging from 4 to 7 kWh/m 2 /day and an average of 5 kWh/m 2 /day, coupled with mean summer temperatures of 25-45 • C and winter temperatures of 5-25 • C, India's tropical climate within its vast peninsula renders it rich in solar and wind power reserves. Despite this, planning and formulating policies frequently depend on governmental assessments of renewable energy potential [40]. Based on these estimates, the solar potential is 748. 98 GW, utilizing only 3% of accessible wasteland [41]. In a similar vein, a prudent assessment of wind power potential at 120 m above sea level stands at 695 GW for the country [42]. Figure 3 depicts the chosen location, indicating its solar and wind capabilities. area is home to numerous solar-and wind-power plants, and its hills are adorned with windmills-a prominent feature visible upon entering the city, as depicted in Figure 4. This study focuses on Chitradurga, Karnataka, India, characterized by a solar irradiance of 5. 55 kWh/m 2 day and a wind speed of 3. 97 m/s. Positioned at 14. 23 • N, 76. 4 • E, Chitradurga sits at an average elevation of 737. 9 m (2420. 93 ft) above sea level, experiencing a subtropical steppe climate classified as BSh by the Köppen-Geiger system. With an annual temperature averaging 27. 75 • C (81. 95 • F), which is 1. 78% higher than India's average, the region maintains a temperate climate throughout the year due to its elevated position. Moreover, Chitradurga also benefits from consistent wind currents, rendering it conducive for the establishment of both photovoltaic (PV) and wind farms. Notably, the area is home to numerous solar-and wind-power plants, and its hills are adorned with windmills-a prominent feature visible upon entering the city, as depicted in Figure 4. The National Renewable Energy Laboratory's (NREL) monthly mean global radiation chart was used to evaluate the solar system's potential capacity. Figure 5 illustrates the monthly mean solar global horizontal irradiance (GHI) for the Chitradurga district. The monthly average solar GHI stands at 5. 55 kWh/m 2 /day, with the highest recorded in April (6. 81 kWh/m 2 /day) and the lowest in August (4. 77 kWh/m 2 /day). Regarding wind resources, the average monthly wind speed in the area is typically 3. 39 m/s. Figure 6 presents input data for Chitradurga from January to December, depicting the wind speed for each month. Notably, June experiences the highest wind speed at 5. 58 m/s, while November witnesses the lowest at 3. 02 m/s. 

The monthly average solar GHI stands at 5. 55 kWh/m 2 /day, with the highest recorded in April (6. 81 kWh/m 2 /day) and the lowest in August (4. 77 kWh/m 2 /day). Regarding wind resources, the average monthly wind speed in the area is typically 3. 39 m/s. Figure 6 presents input data for Chitradurga from January to December, depicting the wind speed for each month. Notably, June experiences the highest wind speed at 5. 58 m/s, while November witnesses the lowest at 3. 02 m/s. the monthly mean solar global horizontal irradiance (GHI) for the Chitradurga district. The monthly average solar GHI stands at 5. 55 kWh/m 2 /day, with the highest recorded in April (6. 81 kWh/m 2 /day) and the lowest in August (4. 77 kWh/m 2 /day). Regarding wind resources, the average monthly wind speed in the area is typically 3. 39 m/s. Figure 6 presents input data for Chitradurga from January to December, depicting the wind speed for each month. Notably, June experiences the highest wind speed at 5. 58 m/s, while November witnesses the lowest at 3. 02 m/s.
section_name: Selected Site Description

section_content: The initial step in simulating a renewable-energy-based EVCS is to design the system architecture. In this scenario, the system design includes energy sources provided by the utility, photovoltaic (PV) panels, wind turbines, batteries, converters, and the load and proposed system architecture, as depicted in Figure 7. This EVCS system's primary aim is to consistently provide the load with the necessary power. When the grid supply is needed and the PV and wind energy are collectively unable to satisfy the load demands, the grid supply is used. Additionally, an important aspect is that the grid has the capability to purchase surplus energy generated by the PV-wind system through a sell-back policy. The cost of net exported energy is usually less than the average cost of power. According to the Indian central electricity regulatory commission (CERC), the government power tariff is 0. 078 $/kWh, whereas the commercial electricity tariff is 0. 10 $/kWh. For this study, the highest tariff value of $0. 10/kWh is taken into consideration. 

ing to the Indian central electricity regulatory commission (CERC), the government power tariff is 0. 078 $/kWh, whereas the commercial electricity tariff is 0. 10 $/kWh. For this study, the highest tariff value of $0. 10/kWh is taken into consideration. 

The load curve obtained from the EVCS, which is located in a typical part of Chitradurga, is depicted in Figure 7. It is noted from Figure 8 that charging primarily takes place from 6 AM to 6 PM daily, with lower activity during other hours. This results in an average yearly EV charging station operating frequency of 651 kWh/day. The EV demand pattern is nearly constant throughout the year. Therefore, the proposed PV-wind system could be a possible solution for effectively meeting the demand of 27 kW/day. The load curve obtained from the EVCS, which is located in a typical part of Chitradurga, is depicted in Figure 7. It is noted from Figure 8 that charging primarily takes place from 6 AM to 6 PM daily, with lower activity during other hours. This results in an average yearly EV charging station operating frequency of 651 kWh/day. The EV demand pattern is nearly constant throughout the year. Therefore, the proposed PV-wind system could be a possible solution for effectively meeting the demand of 27 kW/day.
section_name: System Description

section_content: This study assessed the viability of stand-alone Hybrid Energy Systems (HESs) for the designated site. The findings were assessed based on metrics such as the Cost of Energy (COE), Net Present Cost (NPC), capacity of photovoltaic (PV) panels, battery storage,
section_name: Results and Discussion

section_content: This study assessed the viability of stand-alone Hybrid Energy Systems (HESs) for the designated site. The findings were assessed based on metrics such as the Cost of Energy (COE), Net Present Cost (NPC), capacity of photovoltaic (PV) panels, battery storage, bidirectional converter, surplus energy generation, and Renewable Energy (RE) integration. Additionally, this study explored the integration of HESs with the grid in comparison with standalone systems. This study delves into the responsiveness of different grid purchase scenarios and prices for selling back energy. 

Table 1 outlines the technical specifications of the PV-wind powered EVCS, highlighting key components including PV modules, wind generators, converters, and batteries. To conduct techno-economic and environmental assessments, it is imperative to ascertain the quantity of units required as well as associated costs for installation, maintenance, and replacement. Extensive investigations were conducted involving multiple configurations for both standalone and grid-connected systems. Data regarding energy generation from individual components are provided in Tables 2 and 3. As Table 3 indicates, grid purchases decreased from 8% to 6. 64% for the hybrid model. This resulted in reduced operational costs and emissions. Therefore, the PV-wind generator functions to reduce grid purchases. The cost breakdown of the different system components for the grid-tied hybrid PV-wind setup is shown in Figure 9. First and foremost, the PV module requires the largest financial investment, with the battery coming in second. Table 4 compares standalone and grid-connected systems to give an overview of optimized results for various system setups in a typical area. It is evident that the gridintegrated PV-wind system exhibits the lowest Cost of Energy (COE) and Net Present Cost (NPC) at $0. 385/kWh and $2. 45 million, respectively, attributed to reduced initial, replacement, maintenance, and operation costs. Figure 10 depicts the monthly electricity generation throughout the year from both the electric grid and the PV-wind energy system, which is critical for sustaining the EVCS. Initially, the charging system prioritizes the utilization of PV and wind energy, only resorting to grid power when necessary. The PV-wind system demonstrates peak electricity generation during June and July, with diminished output during October and November. Figure 11 presents time-series data illustrating the performance of various system components aimed at fulfilling load demands. 

The proposed EVCS offers a lower cost of energy compared to a stand-alone charging station. Although the initial installation costs may be high, the system becomes profitable after a payback period of 6. 8 years. Since this payback period is much shorter than the system's overall lifespan, this clearly demonstrates the long-term financial viability of the system. Table 4 compares standalone and grid-connected systems to give an overview of optimized results for various system setups in a typical area. It is evident that the grid-integrated PV-wind system exhibits the lowest Cost of Energy (COE) and Net Present Cost (NPC) at $0. 385/kWh and $2. 45 million, respectively, attributed to reduced initial, replacement, maintenance, and operation costs. Figure 10 depicts the monthly electricity generation throughout the year from both the electric grid and the PV-wind energy system, which is critical for sustaining the EVCS. Initially, the charging system prioritizes the utilization of PV and wind energy, only resorting to grid power when necessary. The PVwind system demonstrates peak electricity generation during June and July, with diminished output during October and November. Figure 11 presents time-series data illustrating the performance of various system components aimed at fulfilling load demands. Table 4 compares standalone and grid-connected systems to give an overview of optimized results for various system setups in a typical area. It is evident that the grid-integrated PV-wind system exhibits the lowest Cost of Energy (COE) and Net Present Cost (NPC) at $0. 385/kWh and $2. 45 million, respectively, attributed to reduced initial, replacement, maintenance, and operation costs. Figure 10 depicts the monthly electricity generation throughout the year from both the electric grid and the PV-wind energy system, which is critical for sustaining the EVCS. Initially, the charging system prioritizes the utilization of PV and wind energy, only resorting to grid power when necessary. The PVwind system demonstrates peak electricity generation during June and July, with diminished output during October and November. Figure 11 presents time-series data illustrating the performance of various system components aimed at fulfilling load demands.
section_name: Results and Discussion

section_content: The advantages of grid-integrated Hybrid Energy Systems (HESs) largely hinge on government policies concerning the integration of RE into the country's power grid and the tariffs associated with sell-back options. Currently, there is no governmental policy in place concerning sell-back options for small-scale solar installations (<5 MW) and Hybrid Energy Systems (HESs). Hence, this study undertook an analysis of the potential benefits across various sell-back tariff scenarios for grid-connected HESs. The data depicted in Figure 12 indicate substantial benefits are achievable with a sell-back price of $0. 11/kWh. In this scenario, the COE experiences a remarkable decline of over 111% from the baseline scenario (COE = $0. 341/kWh). Moreover, escalating the sell-back price to $0. 15/kWh results in a 126% reduction in the COE. Nevertheless, very little change in the COE is observed when the tariff is less than the grid purchase price ($0. 10/kWh). Therefore, any sell-back price over the grid purchase price could be profitable and could incentivize the development of grid-connected hybrid power plants. The proposed EVCS offers a lower cost of energy compared to a stand-alone charging station. Although the initial installation costs may be high, the system becomes profitable after a payback period of 6. 8 years. Since this payback period is much shorter than the system's overall lifespan, this clearly demonstrates the long-term financial viability of the system.
section_name: Impacts of Sell-Back Price

section_content: The advantages of grid-integrated Hybrid Energy Systems (HESs) largely hinge on government policies concerning the integration of RE into the country's power grid and the tariffs associated with sell-back options. Currently, there is no governmental policy in place concerning sell-back options for small-scale solar installations (<5 MW) and Hybrid Energy Systems (HESs). Hence, this study undertook an analysis of the potential benefits across various sell-back tariff scenarios for grid-connected HESs. The data depicted in Figure 12 indicate substantial benefits are achievable with a sell-back price of $0. 11/kWh. In this scenario, the COE experiences a remarkable decline of over 111% from the baseline scenario (COE = $0. 341/kWh). Moreover, escalating the sell-back price to $0. 15/kWh results in a 126% reduction in the COE. Nevertheless, very little change in the COE is observed when the tariff is less than the grid purchase price ($0. 10/kWh). Therefore, any sellback price over the grid purchase price could be profitable and could incentivize the development of grid-connected hybrid power plants.
section_name: Impacts of Sell-Back Price

section_content: India's environmental policies have significant influence in curtailing carbon emissions [43]. The PV-based Electric Vehicle Charging Station (EVCS) system offers tangible advantages in mitigating pollutant emissions, notably decreasing levels of carbon dioxide, sulfur dioxide, and nitrogen oxide. Therefore, it demonstrates noteworthy environmental advantages. Figure 13 illustrates the levels of CO 2 gas emissions across all scenarios.
section_name: Environmental Analysis

section_content: India's environmental policies have significant influence in curtailing carbon emissions [43]. The PV-based Electric Vehicle Charging Station (EVCS) system offers tangible advantages in mitigating pollutant emissions, notably decreasing levels of carbon dioxide, sulfur dioxide, and nitrogen oxide. Therefore, it demonstrates noteworthy environmental advantages. Figure 13 illustrates the levels of CO2 gas emissions across all scenarios.
section_name: Environmental Analysis

section_content: The line diagram of the 33-bus test system with EVCS and DG integration and with µPMUs placed at optimal locations is shown in Figure 14. Three EVCSs and type 1, 2, 3, and 4 DGs were used to categorically study the impact of EVCSs and DGs on bus voltage and system losses, as follows:

• Type 1 only inject active power into the system;
section_name: Smart Monitoring of Distribution Networks Using µPMUs with EVCSs and DGs

section_content: The line diagram of the 33-bus test system with EVCS and DG integration and with µPMUs placed at optimal locations is shown in Figure 14. Three EVCSs and type 1, 2, 3, and 4 DGs were used to categorically study the impact of EVCSs and DGs on bus voltage and system losses, as follows:

• Type 1 only inject active power into the system; • Type 2 only inject reactive power;

• Type 3 inject both active and reactive power;

• Type 4 inject active power but absorb reactive power. 

The following scenarios are presented in the following sections: The rated line voltage for all test systems was set at 12. 66 kV, with a Base MVA of 100 MVA. The total real and reactive demand on the system amounts to 3. 715 MW and 2. 3 MVar, respectively. Additionally, the overall active power loss of the system was recorded at 0. 19061 MW, while the cumulative reactive loss was 0. 14123 MVAr.
section_name: Smart Monitoring of Distribution Networks Using µPMUs with EVCSs and DGs

section_content: Assuming the incorporation of a total of three Electric Vehicle Charging Station (EVCS) loads in the system, a combined power demand of 2925 kW (975 kW per EVCS) is imposed, resulting in a new loading condition for the test system. Consequently, the active power load rose from 3715 kW to 6640 kW. The corresponding performance of the test system is detailed in Table 5. With the increased EVCS load, active power losses escalated to 0. 27459 MW from 0. 19061 MW, while reactive power losses increased to 0. 19431 MVar from 0. 14123 MVar.
section_name: Scenario 2: IEEE 33-bus system with EVCS load and without integrating DGs

section_content: The line diagram of the 33-bus system with integration of EVCSs and DGs is shown in Figure 14. The integration of three EVCSs at optimal locations in the test system (buses 2, 19, and 25) was assumed with active power. Installing multiple DGs in a power system significantly impacts both the system's voltage profile and its voltage stability. Table 6 shows the voltage magnitudes measured from the forward/backward sweep power flow algorithm, and Table 7 shows the voltage magnitudes obtained from PMUs placed at optimal locations [44]. From Table 7, it is evident that there was significant improvement in the observability of voltage magnitudes from the PMUs. By integrating DGs, there was an improvement in the voltages of all nodes to some extent, as shown in Figure 15.
section_name: Scenario 3: IEEE 33-bus system with EVCS load and type-1 DGs

section_content: The integration of three EVCSs was assumed with type-2 DG connection. When pla ing multiple DGs with reactive power injection, the total power losses were reduced b 19. 68%, 23. 03%, and 24. 58%, respectively. The voltage magnitudes obtained from the fo ward/backward sweep power flow algorithm and µPMUs are shown in Tables 8 and From Table 8, it is prominent that the voltage profile was improved significantly fro PMU measurements when compared to the forward/backward sweep method, as show in Figure 16.
section_name: Scenario 4: IEEE 33-bus system with EVCS load and type-2 DGs

section_content: The integration of three EVCSs was assumed with type-2 DG connection. When placing multiple DGs with reactive power injection, the total power losses were reduced by 19. 68%, 23. 03%, and 24. 58%, respectively. The voltage magnitudes obtained from the forward/backward sweep power flow algorithm and µPMUs are shown in Tables 8 and 9. From Table 8, it is prominent that the voltage profile was improved significantly from PMU measurements when compared to the forward/backward sweep method, as shown in Figure 16.
section_name: Scenario 4: IEEE 33-bus system with EVCS load and type-2 DGs

section_content: The integration of three EVCSs was assumed with type-3 DGs. Placing multiple DGs with active and reactive power injection resulted in the reduction of power losses by 55. 44%, 64. 44%, and 80. 99%, respectively. The voltage magnitudes obtained from the forward/backward sweep method and PMUs are shown in Tables 10 and 11, respectively.
section_name: Scenario 5: IEEE 33-bus system with EVCS load and type-3 DGs

section_content: The integration of three EVCSs was assumed with type-3 DGs. Placing multiple DGs with active and reactive power injection resulted in the reduction of power losses by 55. 44%, 64. 44%, and 80. 99%, respectively. The voltage magnitudes obtained from the forward/backward sweep method and PMUs are shown in Tables 10 and 11, respectively. Figure 17 shows the minimum and maximum voltages at different buses before and after placing type-3 DGs, showing that the voltage profile was improved considerably by connecting three DGs.
section_name: Scenario 5: IEEE 33-bus system with EVCS load and type-3 DGs

section_content: The integration of three EVCSs was assumed with type-4 DGs. Placing multiple DGs with active power injection and reactive power absorption resulted in the reduction of total power losses by 22. 8%, 34. 38%, and 43. 53%, respectively. The voltage magnitudes obtained from the forward/backward sweep power flow algorithm and the PMUs are shown in Tables 12 and 13, respectively, and the voltage profile is shown in Figure 18. The integration of three EVCSs was assumed with type-4 DGs. Placing multiple DGs with active power injection and reactive power absorption resulted in the reduction of total power losses by 22. 8%, 34. 38%, and 43. 53%, respectively. The voltage magnitudes obtained from the forward/backward sweep power flow algorithm and the PMUs are shown in Tables 12 and 13, respectively, and the voltage profile is shown in Figure 18. The technical specifications of the computer used for the simulations are an Intel 2. 4 GHz CPU with 16 GB RAM. The time taken to obtain the PMU measurements from the conventional method is shown in Table 14. 

Incorporating DGs into the distribution power system decreased system overloading, offering increased released capacity and enhanced voltage regulation. Table 15 shows the total power losses obtained by integrating different types of DGs and the percentages of loss reduction. The active power losses and reactive power losses for all the scenarios are shown in Figures 19 and 20, respectively. The technical specifications of the computer used for the simulations are an Intel 2. 4 GHz CPU with 16 GB RAM. The time taken to obtain the PMU measurements from the conventional method is shown in Table 14. 

Incorporating DGs into the distribution power system decreased system overloading, offering increased released capacity and enhanced voltage regulation. Table 15 shows the total power losses obtained by integrating different types of DGs and the percentages of loss reduction. The active power losses and reactive power losses for all the scenarios are shown in Figures 19 and 20, respectively.
section_name: Scenario 6: IEEE 33-bus system with EVCS load and type-4 DGs

section_content: This paper developed a PV-wind based Electric Vehicle Charging Station (EVCS) for Karnataka, addressing the significant contributions of the transport and power sectors to global carbon emissions. This study aimed to optimize microgrid-integrated EVCSs using PV-wind systems, revealing an 85% reduction in emissions and a notable decrease in the cost of energy. CO₂ emissions from the hybrid PV-wind EVCS were significantly lower,
section_name: Conclusions

section_content: This paper developed a PV-wind based Electric Vehicle Charging Station (EVCS) for Karnataka, addressing the significant contributions of the transport and power sectors to global carbon emissions. This study aimed to optimize microgrid-integrated EVCSs using PV-wind systems, revealing an 85% reduction in emissions and a notable decrease in the cost of energy. CO 2 emissions from the hybrid PV-wind EVCS were significantly lower, reducing from 150,327 kg/yr to 21,921 kg/yr compared to grid-only systems. As renewable energy sources like PV and wind are highly intermittent in nature, the existing measurement system struggles to capture the dynamics of the system because of a slow data rate. However, synchrophasor technology, with a high sampling rate, offers a solution for dynamic monitoring and is poised to replace SCADA systems. Microgrids, energy storage, and EV charging are expected to be key elements in future power grids. Among the four types of power injection studied, injecting both real and reactive power (type 3) reduced power losses by 80. 99%. Simulations on a 2. 4 GHz processor with 16 GB RAM showed that µPMUs significantly outperformed conventional monitoring, with execution times of 5. 64 s versus 19. 07 s. This method enhances situational awareness for smart grid applications, and future work could explore network observability in case of µPMU failure.
section_name: Conclusions

section_name: V2G

section_content: Vehicle to Grid COE Cost of Energy NPV Net Present Value
section_name: V2G

section_name: Data Availability Statement:

section_content: The raw data supporting the conclusions of this article will be made available by the authors on request.
section_name: Data Availability Statement: