Techno-Economic Analysis of Solar-Wind Powered EV Charging Stations at Train Station Parking Lots

2.1 Regional selection

The plan is for Public Electric Vehicle Charging Stations to use renewable energy, which will be placed at several train stations on the island of Java. Modeling was carried out at the train station because it is the island's leading mobility center for land transportation. The selected train stations include Tawang Station in the City of Semarang (Central Java Province), Kota Baru Station in the City of Malang (East Java Province), and Tanah Abang Station in Central Jakarta (DKI Jakarta Province). These three locations have distinct geographical and demographic characteristics: Tanah Abang Station is a metropolitan area with a high population density, and Tawang Station is in the lowlands near the harbor area. In contrast, Kota Baru Station is an administrative region with highland topography. These stations represent various regional conditions in different parts of Java Island, reflecting the island's diversity of ecosystems, administrative divisions, and transportation infrastructure.

The Global Energy Resources Prediction (POWER) tool from the National Aeronautics and Space Administration (NASA) was combined with HOMER software to identify potential locations for Public Electric Vehicle Charging Stations. This HOMER function allows for identifying and selecting ideal project locations based on geographic and environmental parameters. Table 1 details the geographic circumstances of the sites chosen to install this hybrid system.

Table 1. Geographic conditions of the station location

2.2 Model description

The suggested system's Lowest Cost of Energy (LCOE) value may be found by running simulations with the HOMER-Grid program to assess the PV-Wind hybrid power plant's potential. Numerous characteristics are considered in this process, such as solar and wind energy availability, load profile, technical and economic aspects, and location-related considerations [26]. The HOMER simulation process's framework is shown in Figure 1.

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Figure 1. The simulation framework HOMER [27]

This simulation aims to identify regions needing further technical and financial evaluation. The study focuses on three selected sites in Java. As depicted in Figure 2, the load profiles used in this analysis are tailored for office-scale energy consumption. These profiles are based on default parameters provided by the HOMER software, which serves as a widely recognized tool for energy modelling and optimization. The study does not designate a specific peak demand month, allowing for a more generalized analysis of energy use patterns across the selected sites. This approach ensures a comprehensive evaluation of potential opportunities.

Next, as indicated in Table 2, components appropriate for the hybrid system and information on cost, replacement, service life, and after-sale maintenance are chosen. The primary goal of this research is to evaluate the advantages of an EV-charging hybrid wind-solar system, such as the one shown in Figure 3. Wind turbines and solar panels serve as electrical energy sources to meet power requirements. This system is linked to the On-Grid General Energy System in the event of an excess or deficiency of energy supply. To utilize AC/DC inverters for household appliances, DC voltage must first be converted to AC. The HOMER program models the complete system and assesses related expenses. These kinds of off-grid systems rely on modest energy systems, such as solar panels, wind turbines, and other energy storage devices; therefore, evaluating prices has to be done differently. Life Cycling Cycle (LCC) analysis is considered a suitable technique for assessing the expenses of these systems [28].

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Figure 2. Electrical load in charging station

HOMER software automatically verifies the amount of power used for modest office requirements at train stops. This electrical load profile estimates the authorized consumption for office spaces in the HOMER Grid. It is assumed that the daily power usage of all the cities on the Java island under study is the same or around 231.45 kWh. About 21.89 kW is the most significant amount of power that is utilized. Monthly computations of electricity use are performed, as Figure 3 illustrates. Several components are needed for the wind turbine-solar panel hybrid system to function correctly, impacting the cost analysis. Table 2 enumerates the types and specifications of components required for this system.

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Figure 3. PV-Turbine hybrid system con figuration scheme

Table 2. System components projection

2.3 Component specification

1. PV modules

Solar panel modules function by directly converting sunlight into electrical energy through the semiconductor material's photovoltaic effect [29]. In this instance, the Peimar SG370-M solar panels are utilized in the PLTS system. Table 3 provides a detailed breakdown of this solar cell's specs.

Table 3. Solar panel specifications

When designing the overall capacity needed for solar panels to power electric vehicle (EV) charging stations, it is essential to take into account system losses, the total energy generated by the PV module, and the PV module's capacity, which is determined by the module's nominal power of 370 Wp as per specifications. Table 4 displays specifics on the system's losses.

Table 4. System failures

2. Wind turbines

A wind turbine is an apparatus that uses mechanical turbine action to convert wind energy into electrical energy [30]. We used a 6 kW XLR 6-type wind turbine for this project. To start this project, we estimate capital costs of around IDR 38,500,000.00. In addition, an additional fee of the same amount is required to replace the wind turbine if necessary. We also project annual operation and maintenance costs of IDR 3550,000.00. This wind turbine is projected to be operational for 20 years. Detailed wind turbine specifications in the system can be seen in Table 5.

Table 5. Specifications for wind turbines

3. AC/DC inverters

An inverter is a helpful tool for converting solar panels' direct electric current (DC) into alternating electric current (AC), which is usable by most domestic appliances [31]. The magnitude of the load connected to the inverter has no bearing on its output power. Still, the electricity is about the same as what the solar panels were producing at the time. The system needs a total of 21.89 kW of electrical loads. An inverter of the Dynapower IPS-500 type is used in this simulation. Table 6 displays the specs of this inverter.

Table 6. Inverter specifications

2.4 Environmental parameters

Numerous geographic and environmental factors are taken into account. Air temperature, wind speed, and solar radiation are determined using a database incorporating NASA's Global Energy Resources Prediction (POWER) into the HOMER program. In this regard, the research will center on several sites on Java Island. Environmental parameters like air temperature, wind speed, and solar radiation intensity may be found by modeling and analyzing NASA POWER data in HOMER. These statistics offer crucial planning information for constructing sustainable and effective regional power plants.

The average monthly temperature at several rail stops on Java Island is shown in Figure 4. With an average temperature of 25.71℃, Kota Baru station had the lowest average temperature, as seen in this image. The monthly average wind speed is seen in Figure 5. The most excellent average wind speed of 5.13 m/s was recorded in January at Tawang Station. Meanwhile, Figure 6 shows the monthly average solar radiation intensity. In St Tawang, particularly from August to October, the average solar radiation intensity is maximum, measuring 5.32 kWh/m². This data dramatically aids the design of EV charging stations at various stations around the island of Java.

HOMER collects environmental data such as solar radiation intensity, wind speed, and temperature, which are crucial in simulating and optimizing renewable energy systems. Solar radiation intensity, or Global Horizontal Irradiance (GHI), is used to calculate the electricity generation potential from photovoltaic (PV) panels, considering daily and seasonal variations to determine the optimal size and configuration of the panels. Wind speed is used to simulate energy production from wind turbines by modeling the wind speed distribution to evaluate the feasibility and energy potential of the location. Environmental temperature affects the efficiency of PV panels and battery performance, as higher temperatures can decrease panel efficiency and accelerate battery degradation. HOMER uses this data to ensure that energy supply from renewable sources can meet energy demand throughout the year, optimize system configuration, and reduce costs and environmental impact, enabling more efficient energy system design.

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Figure 4. Average ambient temperature for a month

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Figure 5. Average wind speed in a month

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Figure 6. Average intensity of solar radiation in a month

2.5 Economic analysis

The HOMER simulation findings include an economic evaluation comprising output power and cost analysis. The output power analysis captures the energy provided by wind turbines and solar panels [32]. The PV module's output power is determined using Eq. (1).

$P_{P V}=Y_{P V} \times f_{P V} \times\left(\frac{G_T}{G_{T, S T C}}\right) \times\left[1+\alpha_P\left(T_c-T_{c, S T C}\right)\right]$    (1)

where, $Y_{P V}$ denotes the PV module's nominal capacity, $f_{P V}$ is its reduction factor, $G_T$ represents the solar radiation it has received thus far, $G_{T, S T C}$ is its radiation received under standard test conditions, $\alpha_P$ is its temperature coefficient of power, $T_c$ represents its temperature as of right now, and $T_{c, S T C}$ is its temperature under standard test conditions [25]. To find a wind turbine's output power, use Eq. (2) below.

$P_{W T G}=P_{W T G, S T P} \times\left(\frac{\rho}{\rho_0}\right)$    (2)

where, $\rho$ is the actual air density and $\rho_0$ is the air density under standard circumstances, $P_{W T G, S T P}$ is the power generated by the wind turbine under standard conditions, and $P_{W T G}$ is the wind turbine generate power [33].

Cost analysis in HOMER is defined by two critical economic metrics commonly used for techno-economic analysis: the Net Present Cost (NPC) and the Levelized Cost of Energy (LCOE). Here’s how these are calculated and what assumptions are typically made:

1. Net Present Cost (NPC)

NPC is a fundamental economic metric used in the HOMER software to evaluate the total cost of an energy system over its entire operational lifespan. NPC aggregates all costs—capital expenditures, operation and maintenance (O&M) expenses, fuel costs, component replacement costs, and other relevant financial outlays—into a single, present-day value. This allows for a comprehensive assessment of the economic burden of a system. In the HOMER simulation process, where the operating process searches for the system configuration with the lowest Net Present Cost (NPC), consideration of economic value is crucial. HOMER uses the following formula to determine NPC [34]:

$N P C(R p)=\frac{c_{\text {ann }, \text { tot }}}{C R F . i . \text { Rproj }}$    (3)

where,

C_ann,tot  : total annual costs ($/year)

CRF: capital recovery factor

i: interest rate

Rproj: age/use period (years)

2. Levelized Cost of Energy (LCOE)

LCOE is another critical metric used in HOMER to quantify the average cost of generating one unit of electricity over the system's operational lifespan. It provides a normalized comparison across different energy technologies, considering all costs incurred over the project's life. The following formula is used to determine COE [35]:

$C O E=\frac{c_{\text {ann }, \text { tot }}}{L_{\text {prim }, A C}+L_{\text {prim }, D C}}$    (4)

L_prim,AC and L_prim,DC are the AC and DC loads on the system. The lifetime of this technology is estimated to be 25 years, with an estimated decline in value, as shown in Figure 7.

A comprehensive understanding of Net Present Cost (NPC) and Levelized Cost of Energy (LCoE) is essential for performing rigorous techno-economic analysis, as these metrics offer an integrative assessment of energy systems' financial viability and cost-effectiveness. NPC and LCoE provide quantitative benchmarks that facilitate the comparative evaluation of diverse energy technologies and system configurations. Systems with lower NPC and LCoE values indicate higher economic efficiency, making these metrics critical for identifying optimal solutions. The capability of HOMER to simulate and analyze these metrics across a range of scenarios enhances the precision of hybrid energy system design, ensuring that both technical performance and economic outcomes are effectively harmonized with the project's strategic objectives.

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Figure 7. Project assumptions

This research investigates the efficiency and economic aspects of using wind turbine-solar panel hybrid microgrids to provide electricity supply for Electrical Support Facilities for Public Services implemented at several train stations on the island of Java. The data used in this research was collected from the HOMER-Grid application and other supporting information obtained through the Indonesian Central Statistics Agency (BPS). Although the load required by each station is similar, the hybrid microgrid configuration applied at each station location can vary, influenced by unique geographic conditions in each area. Through simulation results, Tawang Station, located in Semarang, is proven to have the most optimal microgrid system with lower investment costs and significant electricity production.

Energy consumption at EV charging stations is still dominated by conventional electricity due to the low production of renewable energy fractions. This study used the same configuration at several locations, indicating that further development of optimized system configurations is still needed to increase energy production. Therefore, this research encourages further studies to identify and develop a hybrid microgrid system with solar panels that can maximize the potential of renewable energy in each region at an affordable cost while increasing the electricity supply capacity for EV charging stations. This research is likely to positively contribute to optimizing the use of solar energy as an alternative energy source that can address the electricity access challenges still faced by several EV charging stations on the island of Java.