Systematic Optimize and Cost-Effective Design of a 100% Renewable Microgrid Hybrid System for Sustainable Rural Electrification in Khlong Ruea, Thailand

Abstract

This study presents a systematic approach to designing and optimizing a 100% renewable hybrid microgrid system for sustainable rural electrification in Khlong Ruea, Thailand, using HOMER Pro software (Version 3.15.3). The proposed system integrates photovoltaic (PV) panels (20 kW), pico hydro (9.42 kW), and lithium-ion battery storage (264 kWh) to provide a reliable, cost-effective, and environmentally sustainable energy solution for a remote village of 306 residents. The methodology encompasses site-specific resource assessment (solar irradiance, hydro flow), load demand analysis, and techno-economic optimization, minimizing the net present cost (NPC) and cost of energy (COE) while achieving zero emissions. Simulation results indicate the optimal configuration (S1) achieves an NPC of USD 362,687 and COE of USD 0.19/kWh, with a 100% renewable fraction, outperforming the current diesel–hydro system (NPC USD 3,400,000, COE USD 1.85/kWh, 61.4% renewable). Sensitivity analysis confirms robustness against load increases (1–5%), though battery capacity and costs rise proportionally. Compared to regional microgrids, the proposed system excels in terms of sustainability and scalability, leveraging local resources effectively. The lifecycle assessment highlights the battery’s embodied emissions (13,200–39,600 kg CO₂e), underscoring the need for recycling to enhance long-term sustainability. Aligned with Thailand’s AEDP 2018–2037 and net-zero goals, this model offers a replicable framework for rural electrification in Southeast Asia. Stakeholder engagement, including community input and EGAT funding, ensures practical implementation. The study demonstrates that fully renewable microgrids are technically feasible and economically viable, providing a blueprint for sustainable energy transitions globally.

1. Introduction

Renewable energy is a rapidly growing sector that has been gaining international traction due to increased awareness of the negative impact of nonrenewable sources of energy on the environment. The use of renewable energy has become widespread in many countries as it provides sustainable and clean energy solutions. According to the Interna-tional Energy Agency (IEA), renewable energy shares in the global power generation mix have increased from 10% in 2010 to 15% in 2024 [1]. This increase is indicative of the growing trend towards renewable energy as a viable alternative to traditional fossil fuels. Renewable energy is a broad term that encompasses various technologies such as solar, wind, hydro, geothermal, and biomass. The use of these technologies has been on the rise due to their cost effectiveness and low environmental impact. The global renewable energy market is expected to grow at a compound annual growth rate (CAGR) of 7.4% from 2020 to 2027, according to a report by Grand View Research [2]. This growth is caused by several factors, such as government initiatives, increased awareness, and advancements in technology. The renewable energy sector is also creating job opportunities in various fields, such as engineering, construction, and maintenance. According to the International Renewable Energy Agency (IRENA), the renewable energy sector employed over 11 million people in 2018. Several countries have set ambitious targets to increase their share of renewable energy in their power generation mix. For instance, the European Union has set a target of producing 32% of its energy from renewable sources by 2030. Similarly, China has set a target of increasing its renewable energy capacity to 35% by 2030 [3]. The use of renewable energy has also been linked to several environmental benefits, such as reduced greenhouse gas emissions and improved air quality. According to a report by the United Nations Environment Program (UNEP), the use of renewable energy has the potential to reduce global CO₂ emissions by up to 70% by 2050 [4].

Thailand has long been promoting and supporting energy development, especially in the fields of renewable energy and energy efficiency. The Thai government has been encouraging renewable energy to minimize the consumption of fossil fuels, particularly natural gas, and the environmental effects of conventional energy sources. Renewable energy development in Thailand comprises solar, wind, small and large hydropower facilities, biomass, biogas, municipal solid waste (MSW), geothermal electricity, and biofuels (ethanol, biodiesel). The Thai government aims to build additional renewable energy power plants over the next 20 years via the Alternative Energy Development Plan (AEDP) 2018, which is incorporated into the Power Development Plan (PDP) 2018 Rev1. The government’s objective is to boost the amount of renewable energy to 30% (including imported hydropower) of overall energy consumption by 2037. The renewable energy acquisition goal will remain at 18,696 megawatts. The renewable energy sector is rapidly growing due to several factors, such as increased awareness, government initiatives, and advancements in technology. The sector provides sustainable and clean energy solutions and has several environmental and economic benefits.

The use of renewable energy sources is critical to the process of achieving sustainable development. The integration of renewable energy sources into the electricity grid presents a challenge that may be addressed by the use of microgrids [5]. Microgrids are small-scale electrical distribution networks that are capable of operating alone or in tandem with the larger scale main grid. They are able to be powered by renewable sources of energy like the sun, wind, or even biomass. In this article, we will categorize renewable microgrids according to the types of energy sources they use, the control systems they use, and the applications they serve. The types of energy sources that renewable microgrid use may be used to categorize these systems. Microgrids powered only by the sun generate energy via the use of solar cells [6,7]. Microgrids powered by wind power create energy via the use of turbines [8]. Microgrids powered by biomass conduct their energy generation with the help of organic materials like wood chips, agricultural waste, and biogas [9]. Microgrids that use hybrid technology combine two or more renewable energy sources in order to provide a power supply that is more dependable and steadier [10]. Microgrids cannot function without a command and control system of some kind. This is responsible for managing the flow of electricity between the local grid and the larger utility grid [11,12]. One way to categorize microgrids is according to the control systems that they use. Microgrids that are linked to the utility grid are able to send excess electricity back to the grid since they are connected to the grid [13]. Islanded microgrids function independently of the utility grid and are able to supply electricity in the event of power outages on the utility grid [14]. The applications that renewable microgrids serve may be used to categorize these systems. Households in rural or isolated places may receive their electricity via a residential microgrid [15]. Businesses and industrial complexes may receive their electricity from a commercial microgrid [16]. Microgrids in communities do not only provide electricity to the people they serve, but they may also be utilized for emergency response during times of natural catastrophe [5].

In conclusion, renewable microgrids are a promising solution for integrating renewable energy sources into the power grid. They can be classified based on their energy sources, control systems, and applications. Understanding the classification of renewable microgrids can help in the development of efficient and sustainable power systems.

Homer Pro is a software tool for designing and analyzing microgrids. It is a powerful tool for optimizing the architecture of a microgrid and ensuring that it operates at peak efficiency. The program is extensively used in renewable energy and has been the topic of multiple publications. One of the most noteworthy Homer Pro papers is a study entitled Optimal Design of Hybrid Renewable Energy Systems Using HOMER [17,18,19]. The authors explored the use of Homer Pro to optimize the design of hybrid renewable energy systems. The article contains a full overview of the program and its features, as well as various case studies demonstrating the software’s efficacy. Another significant Homer Pro publication is a study titled Optimization of a Hybrid Power System Using HOMER [20,21,22,23,24,25]. The use of Homer Pro to optimize the design of a hybrid power system that blends wind and solar energy is described in this work. The article contains various charts and graphs that demonstrate the software’s usefulness, as well as a full discussion of the technique employed and the findings obtained. A third Homer Pro publication is a study titled Optimal Size and Analysis of a Grid Connected Photovoltaic System Using HOMER [26,27]. The authors discussed how they used Homer Pro to optimize the design of a grid-connected solar system in this research. The article contains a full discussion of the approach employed and the findings obtained, as well as various charts and graphs demonstrating the software’s usefulness. Another significant Homer Pro publication is a study titled Optimization of a Hybrid Power System Using HOMER: A Case Study of Jeddah City, Saudi Arabia [28]. The authors discussed how they used Homer Pro to optimize the design of a hybrid power system that blends solar and diesel energy in this research. The article contains various charts and graphs that demonstrate the software’s usefulness, as well as a full discussion of the technique employed and the findings gained from the work. Lastly, the research entitled “Optimization of a Hybrid Renewable Energy System Using HOMER for Rural Electrification in Malaysia” explains the use of Homer Pro to optimize the design of a hybrid renewable energy system for rural electrification in Malaysia [29].

In this study, the primary contributions lie in developing a systematic and optimized approach to designing a 100% renewable hybrid microgrid system tailored to the unique needs of rural communities, with Khlong Ruea, Chumphon, Thailand, as a case study. Unlike previous works, this research integrates photovoltaic, pico hydro, and lithium-ion battery technologies to achieve a fully renewable solution, minimizing both the net present cost (NPC) and cost of energy (COE) while eliminating operational emissions. The novelty stems from its comprehensive methodology combining site-specific resource assessment, advanced HOMER Pro simulations, and stakeholder-driven design to deliver a scalable, cost-efficient, and sustainable electrification model. By achieving an NPC reduction and zero emissions compared to the existing diesel–hydro system, this work demonstrates technical feasibility and economic viability, offering a replicable framework for rural Southeast Asia. Additionally, the study’s lifecycle assessment of environmental impacts and sensitivity analysis of load growth provide a holistic perspective, advancing the understanding of long-term sustainability and system resilience in renewable microgrid deployment.

The paper is structured as follows: Section 2 details the materials and methods, including site specifications for Khlong Ruea, the development and validation of a modeled dataset (covering solar irradiance, hydro resources, and load demand), system configuration, mathematical formulations, and impact evaluation metrics. Section 3 presents the simulation results, offering an in-depth analysis of the integrated system’s performance, a comparison between the existing and proposed configurations, and the effects of load increases on system scalability. Section 4 provides a discussion, exploring the technological approach, policy alignment with Thailand’s renewable energy goals, and the role of stakeholder participation in ensuring practical implementation. Finally, Section 5 draws conclusions, summarizing the study’s findings on the feasibility, cost-effectiveness, and sustainability of the proposed 100% renewable microgrid, while highlighting its potential as a model for rural electrification. This organization ensures a logical progression from methodology to results, discussion, and actionable insights, providing a comprehensive framework for understanding the system’s design and implications.

2. Materials and Methods

In this section, the sizing of the renewable microgrid is systematically optimized. The following analysis frameworks were employed in this study:

I

Site specification.

II

Development and validation of a modeled dataset:

(a)

Solar irradiance;

(b)

Hydro resource;

(c)

Load demand;

(d)

System structure/

III

System configurations, mathematical formulation, and evaluation impact:

(a)

Photovoltaic model;

(b)

Hydro power model;

(c)

Battery model;

(d)

Power converter model;

(e)

Objective function formulation.

2.1. Site Specification

Figure 1 shows the Khlong Ruea Village, which is located at 9°41′19.4274″ N, 98°40′9.48″ E in Pato district, Chumphon province, and has an elevation of 220–260 m above sea level. The village is located in a remote location where the power plant’s electricity is inaccessible. The community had a population of 306 people and 81 dwellings in 2024, the majority of whom work in the agricultural industry. Khlong Ruea Village is situated on a hill with varying altitudes. The town is set in the mountains and is surrounded by lush green trees. The terrain is challenging, but it also provides an opportunity for a proper microgrid layout that can take advantage of local natural resources. The location and geography of Khlong Ruea Village present a unique opportunity to create a microgrid tailored to the community’s specific needs. Because of the tough terrain and plenty of natural resources, dependable, sustainable, and cost effective microgrids can be built.

2.2. The Development and Validation of a Modeled Dataset

2.2.1. Global Horizontal Irradiation

Figure 2 shows the monthly average global horizontal irradiation derived from NASA POWER data. The location of the site is 9°41′19.4274″ N, 98°40′9.48″ E and it is in the southern part of Thailand, as shown in Figure 1, which has a warm climate. Thailand’s solar map shows that the average yearly global horizontal irradiation (GHI) levels in this area are between 4.5 and 5.0 kWh/m²/day, which is the same as what the NASA POWER dataset says. The yearly patterns in the graph show that GHI values are higher during the dry season (November to April) and lower during the wet season (May to October). This is in line with Thailand’s monsoonal climate and the higher cloud cover during the rainy months.

2.2.2. Hydro Resource

To study and analyze potential energy sources in the community, the semi reservoir was built by the villagers, contributing to the water retention of the water turbine power plant. It has a 573.03 m³ total water capacity. The depth is split according to color. The depth, at 200–250 cm, is blue. The orange color is the deepest at around 150–200 cm. The water pipe’s outlet is away from the water’s surface, as shown in Figure 3.

Figure 4 shows the estimation, according to the villagers’ experience, of average water flow rate in liters per second (L/s) for different months of the year. The x-axis lists months from January to December, while the y-axis shows the flow rate ranging from 0 to 120 L/s. The flow rate remains relatively constant at around 30 L/s from January through April. It then increases significantly in May, and peaks in August at about 120 L/s.

2.2.3. Load Demand

According to Figure 5, the assembly’s documented power consumption for the years 2012–2024 indicates the use of electricity from a water generator, whereas the orange graph indicates energy consumption from a diesel generator. In comparison to the previous year, production in 2021 (30,316 kWh) grew by 34.6% relative to 2012 (22,523 kWh). The community’s power comes from two sources: water power and diesel generators. Throughout the first years, 2012–2016, the settlement only employed water turbines to generate electricity and the electricity price was around 0.1 USD/kWh, but it was unavailable for around eight months of the year. Based on regional electricity, there was a gradual electricity calculation principle. The solar home system provided by the government in the project would be used for the remaining four months (February–May). However, because the community was experiencing water shortages and water generators were frequently destroyed, diesel generators were erected to remedy the situation by operating 5 h a day from 18:00 to 23:00. Electricity storage fees were computed based on the cost of oil. When operational costs were factored in, the average price per unit ranged from 0.6 to 0.8 USD/kWh, depending on the price of oil in each region.

Figure 6 depicts a typical daily load curve for electrical power consumption over a 24 h period and the total energy consumption of around 250 kWh per day. Key characteristics include: Base load—a consistent demand of 6–8 kW persists throughout the day. Morning peak—a sharp increase occurs around 6–8 AM, reaching about 17 kW. Midday plateau—demand stabilizes at 7–8 kW from approximately 9 AM to 3 PM. Evening peak—the highest demand period spans 5–10 PM, peaking at 14–15 kW. Night trough—demand gradually decreases to base load levels during late night hours. This pattern reflects common daily activities in a residential or mixed use area. The curve demonstrates two main peak periods corresponding to morning and evening routines, with lower demand during working hours and nighttime. Understanding this load profile is crucial for efficient energy management and grid operations, allowing for optimized power generation and distribution to meet varying demands throughout the day.

2.2.4. Proposed System Structure

As seen in Figure 5, upon evaluating the community’s energy usage from 2012 to 2024, we have discovered that the 50 kW hydro power source has a supply limitation due to its requirement of a high flowrate for generation at around 80–100 L/s. Additionally, it poses challenges in terms of maintenance and repair, which are complex and require the expertise of local technicians. We discovered that the supply side’s bottle neck does not match because water as a resource is unstable and the reservoir’s volume is limited, as shown in Figure 3 and Figure 4, especially during the dry season from January to April. We have had to consider that the monthly average global horizontal irradiation has the possibility to compensate for this in the dry season, as shown in Figure 2. In order to resolve these issues, the suggested setup was implemented. The proposed hybrid micro system integrates photovoltaic panels, a Li-ion battery, a pico hydro system (with a required flowrate of around 10–20 L/s), and a diesel generator to provide a stable electricity supply. The DC power from solar panels and batteries is collected in a DC bus and converted to AC by an inverter. The AC power from the inverter and the pico hydro and diesel generators is then distributed through an AC bus to the load, ensuring continuous and reliable energy for end-users. This proposed model was applied to a HOMER Pro simulation as shown in Figure 7.

2.3. System Configurations, Mathematical Formulation, and Impact Evaluation

2.3.1. Photovoltaic Model

PV panels are made up of solar cells in a series or parallel connection and are utilized in a PV system to generate and supply power for different applications. In general, the output power of the PV system depends on the cell temperature and the amount of solar irradiation, as well as the geographical characteristics of an area [30]. In HOMER, PV output power is calculated as follows [31]:

$$P_{PV}=Y_{PV}{f}_{PV}\left({\displaystyle \frac{G_T}{G_{T,STC}}}\right)\left[1+{\alpha }_P\left(T_c-T_{c,STC}\right)\right]$$

(1)

where $Y_{PV}$ refers to the PV power output under standard test conditions (STCs) in kW, $f_{PV}$ represents the PV derating factor (%), $G_T$ is the solar radiation incident on the PV panel at the current time step (kW/m²), $G_{T,STC}$ refers to the incident radiation under standard test conditions (1 kW/m²), ${\alpha }_P$ is the temperature coefficient of power (%/°C), $T_c$ is the temperature of the PV cell (°C), and $T_{c,STC}$ is the PV cell temperature under STCs (25 °C). The derating factor is the decrease in the output of the PV array resulting from temperature losses or any other factors that vary the expected output of the PV system under absolute conditions.

2.3.2. Hydro Power Model

The nominal hydropower is the nominal power of the hydro system, or the power produced by the hydro turbine given the available head and a stream flow equal to the design flow rate of the hydro turbine. The calculation of the nominal hydropower includes the efficiency of the hydro turbine but not the pipe head loss.

$$P_{hyd}={\displaystyle \frac{{\eta }_{hyd}\cdot {\rho }_{water}\cdot g\cdot h_{net}\cdot Q_{turbine}}{1000}}$$

(2)

where $P_{hyd}$ is the nominal power output of the hydro turbine (kW), ${\eta }_{hyd}$ is the hydro turbine efficiency (%), ${\rho }_{water}$ is the density of water (1000 kg/m³), $g$ is the acceleration due to gravity (9.81 m/s²), $h_{net}$ is the available head (m), and $Q_{turbine}$ is the design flow rate of the hydro turbine (m³/s).

2.3.3. Battery Model

The battery is a storage system required for storing electric power for reliable and efficient utilization of unpredictable RE systems. However, in most HPSs, battery energy storage is expected to start operating and supply energy stored at a time when the power output level of the RE generation system is low and unable to sufficiently serve the external load demand. The most commonly used technology for the batteries in an HPS is lead-acid batteries. However, lithium-ion batteries are currently dominating the energy storage device (ESD) market for decentralized RE systems due to their extended lifecycle and high charge/discharge efficiency [32]. The storage capacity of the battery is computed based on autonomy days and demand, as follows [33]:

$$C_{Bat}={\displaystyle \frac{E_{L}AD}{{\eta }_{inv}DOD{\eta }_{bat}}}$$

(3)

where $E_L$ refers to the average load energy per day (kWh/day), $AD$ represents the autonomy days, ${\eta }_{inv}$ denotes inverter efficiency, $DOD$ represents the depth of discharge, and ${\eta }_{bat}$ denotes battery efficiency.

2.3.4. Power Converter Model

The formulas used for the energy conversion from DC to AC and from AC to DC are given as [34]:

$${\eta }_{DC/AC}^{Con}.\left[P_{BAT}(t)+P_{PV}(t)\right]\le N_{conv}$$

(4)

$${\eta }_{AC/DC}^{Con}.\left[P_{WT}(t)+P_{DiG}(t)\right]\le N_{conv}$$

(5)

where ${\eta }_{DC/AC}^{Con}$ is the energy conversion efficiency from DC to AC, ${\eta }_{AC/DC}^{Con}$ is the energy conversion efficiency from AC to DC, and $N_{conv}$ is the converter capacity (kW).

2.3.5. Objective Function Formulation

One of the primary functions of HOMER Pro is its minimization of the entire net present cost (NPC), which is also referred to as the lifecycle cost. The net present cost (NPC) of a system is calculated by subtracting the present value of all the income it generates over the course of its lifespan from the total present value of all the expenditures that the system pays throughout its lifetime. These costs include the costs for the installation and operation of all components; capital expenses, replacement costs, operation and maintenance (O&M) costs, fuel prices, and the costs of purchasing electricity from the grid are all included in the categories of costs. However, these costs are not applicable in this particular scenario. In this particular scenario, there is no connection to the electric grid; hence, the income streams consist of salvage value and grid sales revenue, both of which are equal to zero. The goal function is formally stated by Equation (6), which reflects the cost of the MG system as it is represented by the NPC in financial terms. Inequalities (6)–(11) impose limitations on the reduction technique, which are as follows:

$$\min (C_{NPC,i})={\displaystyle \sum _{all element}\left[-R_{0,i}+{\displaystyle \sum _{t=0}^T\frac{R_{t,i}}{{(1+x)}^t}}\right]}$$

(6)

$$P_{shedding}\le 0.05P_{load}$$

(7)

$$f_{renewable}\ge 0.25E_{gen}$$

(8)

$$r_{load,t}\ge 0.10P_{load,t}$$

(9)

$$r_{peakload}\ge 0.10P_{load}$$

(10)

$$P_{PV}\ge 0.25P_{load}$$

(11)

where $P_{shedding}$ is the unavailable electric power (%), $f_{renewable}$ is the proportion of clean energy in the system (%), $r_{load,t}$ is the Base Load Spare Capacity (%), $r_{peakload}$ is the Peak Load Reserved Energy (%), $P_{load}$ is the annual electricity consumption (kWh), $P_{load,t}$ is the hourly electricity consumption (kWh), $E_{gen}$ is the power generation energy (kWh/y), and $P_{PV}$ is the solar power output (%).

$P_{shedding}$ can be found from the following equation:

$$P_{shedding}={\displaystyle \frac{E_{cs}}{E_{demand}}}\times 100$$

(12)

where $E_{CS}$ is the unavailable energy (kWh/y) and $E_{dmand}$ is the electricity demand (kWh/y). Equation (13) applies the costs of each constituent throughout the entire planning horizon:

$$C_{\mathrm{element}}={\displaystyle \sum C_{capital,i}+C_{O\&M,i}+C_{replacement,i}+C_{fuel,i}}$$

(13)

Equation (14) calculates the salvage, which is the only source of revenue after the planned horizon has ended:

$$C_{element}=C_{O\&M,i}.{\displaystyle \frac{R_{rem,i}}{R_{element,i}}}$$

(14)

$f_{renewable}$ can be found from the equation:

$$f_{renewable}=1-{\displaystyle \frac{E_{nonern}+H_{nonern}}{E_{served}+H_{served}}}\times 100$$

(15)

where $E_{nonren}$ is the electricity generation (kWh/y), $H_{nonren}$ is the thermal energy production (kWh/y), $E_{served}$ is the total electrical energy supplied to the load (kWh/y), and $H_{served}$ is the total heat energy supplied to the load (kWh/y).

The operating reserve, $r_{\mathrm{load},\mathrm{t}}$, and $r_{\mathrm{rpeakload}}$, are entered into Equations (16) and (17) to provide a dependable power system during unexpected spikes or drops in PV generation. HOMER Pro determines the minimal operational reserve for the AC and DC buses individually using Equations (16) and (17):

$$L_{res,AC}=\left(r_{load,t}{L}_{r prim,AC}\right)+\left(r_{rpeakload}{L}_{highest prim,AC}\right)$$

(16)

$$L_{res,DC}=\left(r_{load,t}{L}_{r prim,DC}\right)+\left(r_{rpeakload}{L}_{highest prim,DC}\right)+\left(r_{solar}{P}_{PV}\right)$$

(17)

where $L_{res,AC}$ is the AC Bus Backup Power Capacity (kWh), $r_{load,t}$ is the Base Load Spare Capacity (%), $L_{rprim,AC}$ is the average AC bus power (kWh), $r_{peakload}$ is the Peak Load Reserved Energy (%),$L_{highestprim,AC}$ is AC bus maximum power (kWh), $r_{solar}$ is Solar Reserved Energy (%), and $P_{PV}$ is the solar power output (%).

It is now clear that the optimal solution is the one that results in the lowest total NPC at the start of the project while meeting all restrictions (i.e., inequalities 7–11). The next sections demonstrate and discuss the optimal design solution.

2.3.6. Input Data

The parameters presented in Table 1 were imported into HOMER PRO to enhance the sizing of different components. The discount rate and inflation rate were fixed at 5% and 3%, respectively. The annual capacity shortfall is 5%. The project’s lifespan is 25 years. These cost parameters, the capital cost, replacement cost, operating and maintenance (O&M) cost, lifetime, and efficiency were determined through a combination of real-world market data, regional pricing adjustments for Thailand, and insights from previous studies on renewable energy systems in similar contexts. The discount rate and inflation rate were fixed at 5% and 3%, respectively, reflecting Thailand’s economic conditions as reported by the Bank of Thailand (2022) [35]. The annual capacity shortfall was set at 5%, consistent with rural microgrid design standards to ensure reliability [5]. The project lifespan of 25 years aligns with typical renewable energy system planning horizons [36]. Below, the methodology for each cost parameter is clarified, with sources referenced to substantiate the values.

The cost parameters were cross-checked against real-world data from Thailand’s renewable energy market, EGAT procurement records [37], RMUTL project budgets [38] and peer-reviewed studies in Southeast Asia and similar climates [29,39,40]. Adjustments for rural logistics, import tariffs (5–10% on batteries and inverters), and local labor rates ($5–$10/day) were applied to global averages, ensuring relevance to Khlong Ruea. While exact vendor quotes remain proprietary, the values align with ranges reported in the cited literature, providing a robust foundation for HOMER Pro simulations.

Table 1. Input data for system components.

System Component	Capital Cost (USD/kW)	Replacement Cost (USD/kW) [10,41,42]	O&M Cost (USD/kW/y) [41,43,44,45]	Lifetime (y) [10,44]	Efficiency (%)
PV array	528 [40,44,46]	528	11	25	95
Li-ion Battery	389 [37,39,41]	389	8	10	80
Pico-hydro	972 [38,45]	972	19	10	80
Diesel Generator	243 [47,48]	243	5	10	70
Bi-directional Inverter	741 [29,44]	741	15	10	95

Table 1. Input data for system components.

System Component	Capital Cost (USD/kW)	Replacement Cost (USD/kW) [10,41,42]	O&M Cost (USD/kW/y) [41,43,44,45]	Lifetime (y) [10,44]	Efficiency (%)
PV array	528 [40,44,46]	528	11	25	95
Li-ion Battery	389 [37,39,41]	389	8	10	80
Pico-hydro	972 [38,45]	972	19	10	80
Diesel Generator	243 [47,48]	243	5	10	70
Bi-directional Inverter	741 [29,44]	741	15	10	95

2.3.7. A Description of the Methodology for the Configuration Optimization

In this section, the methodology of the configuration optimization of HOMER PRO is described. It is a tool for designing and optimizing hybrid energy systems, particularly microgrids. The configuration ensures the system meets energy needs efficiently and economically. Below, we break down the seven steps based on the provided image, which outlines a flowchart for this methodology show in Figure 8.

Step 1—Need Definition: This initial phase involves defining the project’s energy requirements, such as the load profile (hourly, daily, or seasonal energy demand), power capacity needs, and any specific reliability or performance targets. For example, a remote community might need a system to handle peak loads during evenings, while an industrial site might prioritize continuous power. This step sets the foundation for all subsequent analysis, ensuring alignment with project goals. In this article, the goal is 100% renewable energy with the lowest possible NPC and COE.

Step 2—Input Data at the Site: This step requires gathering and inputting comprehensive site-specific data, categorized into “Locality Resource” and “Locality Economics” as shown in Section 2.3.6.

Locality Resource:

• Weather data, including temperature, which affects system efficiency (e.g., PV panel performance decreases at higher temperatures), as shown in Section 2.
• Solar irradiance, measured in kWh/m²/day, critical for photovoltaic system sizing, as shown in Section 2, Figure 2.
• Water resource, likely referring to stream flow for hydro power, measured in liters per second (L/s) as shown in Section 2, Figure 3 and Figure 4.
• Diesel generator data, which, based on HOMER Pro input requirements, likely refers to fuel availability and costs, as diesel generators are components, but their fuel is a resource input.

Locality Economics:

• Investment costs for components, such as capital costs for PV panels or wind turbines.
• Operations and maintenance (O&M) costs, typically annual, in dollars per year (USD/yr).
• Fuel costs, especially for diesel or biomass, in dollars per liter (USD/l) or per kilogram (USD/kg).
• Inflation rates and interest rates, which affect the net present cost calculations over the project lifetime.

Step 3—Microgrid Design and Component Configuration: Based on the input data, this step involves designing the microgrid architecture and configuring its components. This includes selecting energy sources (e.g., PV, wind, hydro, diesel generators), storage systems (batteries), and converters, and determining their sizes and interconnections. The proposed system structure was described in Section 2.2.4.

Step 4—Define Optimization and Constraints: This step involves setting the optimization objectives and constraints, which guide HOMER PRO’s simulation. Optimization goals might include minimizing the Levelized Cost of Energy (LCOE), maximizing renewable energy penetration, or minimizing greenhouse gas emissions. The Optimization and constraints were applied with Equations (6)–(11), as shown in Section 2.3.5.

Step 5—Simulation and Optimization in HOMER: A key feature, as shown in the figure, is the decision diamond labeled “Satisfaction of the needs”. If the simulated designs do not meet the defined needs (e.g., the cost is too high or the reliability is too low), the process loops back to adjust the “Components Configuration” in Step 3. This iterative approach ensures flexibility, which might be unexpected for users unfamiliar with optimization software, as it highlights the need for multiple iterations to achieve optimal results. The simulation outputs include technical metrics (e.g., energy production, capacity factor) and economic metrics (e.g., net present cost, payback period), which are used in the next step.

Step 6—Evaluation of Techno-Economic Optimization: After obtaining optimized designs, this step involves a detailed evaluation from both technical and economic perspectives. Technical evaluation includes assessing system reliability (e.g., the loss of power supply probability), energy production levels, and component efficiencies. Economic evaluation covers initial capital costs, annual O&M costs, fuel costs over the project lifetime, and net present cost (NPC). Environmental impacts, such as CO₂ emissions, are also considered, aligning with sustainability goals.

Step 7—Optimized Microgrid Model: The final step involves selecting the best microgrid model from the evaluated options, based on the techno-economic analysis. This model represents the optimal configuration that balances cost, performance, and environmental impact, meeting all defined needs. The output is typically a detailed report, including system diagrams, cost breakdowns, and performance metrics, ready for implementation or further refinement as shown in the next section.

3. Results

3.1. Simulation Results of Integrated

The autonomous MG was simulated using HOMER Pro to evaluate its operational and economic aspects. HOMER Pro’s simpler, non-derivative optimization allows it to run thousands of simulations quickly. In this scenario, 1661 solutions were evaluated using various system designs (e.g., diesel generators, batteries) to determine the option with the lowest NPC at the start of the project. Only 346 simulated solutions were found to be viable, which implies they met the objectives. However, 177 solutions were removed due to the constraints of Equations (7)–(11). The specific constraints and optimization criteria used to generate the results can be expressed as follows:

Unavailable Electric Power (Annual Capacity Shortfall): Limited to ≤5%, meaning the system could have up to 5% of the annual load unmet. This is equivalent to HOMER Pro’s maximum annual capacity shortage setting, ensuring at least 95% load coverage.

HOMER Pro filters out unfeasible solutions (e.g., without power sources or converters) and prioritizes viable options based on total NPC. For a 10-year planning period, hourly time series simulations were performed for all system designs.

The four system architectures that perform the best are listed in

Table 2. Results of optimized systems with different designs.

Category	Component	System
		S1	S2	S3	S4
Architecture	Photovoltaic (kW)	20.0	20.0	-	20.0
	Diesel Generator (kW)	-	48.0	48.0	48.0
	Li Battery (kWh)	264.0	412.8	196.8	432.0
	Pico Hydro (kW)	9.42	9.42	9.42	-
	Invertor (kW)	21.3	29.70	29.90	30.00
Cost	NPC (USD)	362,687	749,288	3,400,000	8,720,000
	COE (USD)	0.19	0.4	1.85	4.69
	Operating Cost (USD/y)	11,078	26,782	168,727	433,059
	Initial Capital (USD)	144,922	223,126	125,199	219,441
	Renewable Fraction (%)	100	98	75	25
System	Total Fuel (L/y)	-	431	6251	18,808
	CO2, (kg/y)	-	1128	16,364	49,238

, along with their corresponding costs. The ideal solution consists of a photovoltaic (PV) system with a capacity of 20 kW, 264 kWh of lithium-ion batteries, 9.42 kW of pico hydro, and a converter with a power rating of 20 kW (design S1). The S1 design results in a net present cost (NPC) of USD 362,687 and USD 0.19 of COE. S1 stands out as the most renewable and cost-efficient system, with no CO₂ emissions, the lowest COE, and modest operating costs. S2 and S3 offer balanced solutions with higher renewable fractions and moderate costs, but they still produce some emissions. S4, while having the highest capacity and initial capital, is the least renewable and most expensive system with significant fuel consumption and CO₂ emissions. Each system presents a trade-off between cost, environmental impact, and reliance on renewable energy sources.

Regarding the fulfillment of requirements while feasibly minimizing the NPC and COE and the constraints of Equations (7)–(11), see the following. Figure 9 shows an average of the total renewable power generation, AC primary load, and unmet load for the performance of a microgrid system over a year, showing that total renewable power generation remains stable around 13 kW, which means it can serve the load over the year, while the AC primary load is consistently around 10 kW. This indicates a reliable microgrid with a consistent surplus of renewable power compared to the electrical load served. The unmet load or loss of energy probability (LOEP), though overall less than 5%, shows small peaks during rainy months (May to October), suggesting periods of higher demand or reduced generation efficiency. To further enhance performance, implementing energy storage solutions and investigating summer load increases could improve load management and system reliability.

Scenario S1, with a 100% renewable fraction shows that the photovoltaic system can generate over 25% of the load profile, does not consume any fuel, and thus emits no CO₂. As the reliance on renewable energy decreases from S2 to S4, both fuel consumption and CO₂ emissions increase significantly. S4, with only 25% renewable energy, consumes the most fuel and emits the highest amount of CO₂. This highlights the environmental impact of decreasing renewable energy use in favor of conventional fuel sources. All of the scenarios S1–S4 satisfy the constraint of Equation (8) by having a proportion of clean energy in the system that exceeds 25%.

S2 and S4 were compared with and without the pico hydro generator, respectively. Following our investigation, we found that the initial cost of both systems is not significantly different from one another, with a variation of approximately 1.6%. The operational costs of S4 are, on the other hand, much higher than those of S2, being over 16 times higher. In addition, the cost of energy (COE) for S4 is approximately 11 times greater than that of S2, and the production of carbon dioxide emissions is more than 43 times higher. Hydro energy is essential for the base load and is ideally suited for this design, both in terms of its technical performance and its influence on the environment.

S1 lacks a diesel generator. S2 possesses a diesel generator. Our investigation revealed that the battery capacity of S1 is approximately 36% less than that of S2. The net present cost (NPC), cost of equipment (COE), and operating cost of S1 are half of those of S2. Regarding the overall energy output, diesel accounts for around 1% of the total energy generated by both PV systems and pico hydro. The fixed diesel generator has a large system capacity, but it comes with higher financial costs and emits a confirmed 2% of CO₂ compared to scenario S4, and is operational for around 1690 h each year. This confirms the significance of the pico hydro generator for providing base load power to the overall system and its environmentally beneficial nature.

S3 does not function as a photovoltaic (PV) system. The initial capital cost is the lowest among all the scenarios. The diesel-generated energy in S3 contributes around 25% of the overall load. The diesel generator in S3 has a relatively low number of running hours, specifically 660 h per year, which is equivalent to one-third of the operating hours of the diesel generator in S4. The S3 battery size is the smallest among all the scenarios. The behavior of resources like hydroelectric electricity and batteries aligns with the load profile, indicating that additional batteries are unnecessary. Ultimately, we rectify the pico hydro system in response to the periodic rainfall that adequately replenishes the system, enabling it to sustain a base load of approximately 10 kWh per day.

Figure 10 shows a detailed visualization of the interaction between various energy sources and storage solutions over a 24 h period, analyzing the dynamics of hydroelectric power, photovoltaic power, and battery storage systems in meeting daily electricity demand. The baseline energy source, hydroelectric power, consistently outputs power with slight variations throughout the day. Solar PV energy, highlighted prominently in the middle of the graph, peaks during daylight hours, demonstrating its capacity to significantly offset reliance on hydro power during these times. We represent battery storage as playing a critical role in energy management, storing excess energy during periods of low demand and supplementing the grid when demand exceeds the immediate output from hydro and solar sources.

Figure 11 shows the renewable component of S1, which is 100%. The photovoltaic PV system generates energy at a rate of 29,439 kWh per year, representing 28.5% of the total power production. On the other hand, hydroelectric generators produce electricity at a rate of 73,844 kWh per year, accounting for 71.5% of the total.

The annual electricity production amounts to 103,283 kilowatt-hours. This implies that the hydroelectric power can consistently satisfy the minimum demand throughout the year due to our transition from a single hydroelectric system to a pico hydro system. Additionally, the photovoltaic system can be properly maintained to ensure that the overall system can reliably fulfill the load requirements.

3.2. Comparison of Present and Proposed Configuration

Table 3. Comparison of the present and proposed configuration.

Categorize	Component	System
		Proposed	Present
		S1
Architecture	Photovoltaic (kW)	20.0
	Diesel Generator (kW)	-
	Li Battery (kWh)	264.0
	Pico Hydro (kW)	9.42
	Invertor (kW)	21.3
COST	NPC ($)	362,687
	COE ($)	0.19
	Operating Cost ($/y)	11,078
	Initial Capital ($)	144,922
	Renewable Fraction (%)	100
Emission	Total Fuel (L/y)	-	11,172
	CO2, (kg/y)	-	29,247
	CO (kg/y)		183.0
	Unburned Hydrocarbon (kg/y)		8.04
	Particulate Matter (kg/y)		1.09
	SO2 (kg/y)		71.60
	NO (kg/y)		172.0

presents a comparison between the current and suggested configurations, highlighting numerous important outcomes that have significant consequences for the future of energy management across several industries. The current setup, which combines diesel generators and expanded hydro capacity, is designed to enhance reliability, particularly in situations of high demand or when there are fluctuations in renewable energy production. This transformation is vital for sectors and regions where a reliable energy supply is essential for maintaining operational stability. Economic tradeoffs refer to the choices and compromises that must be made in allocating limited resources to different economic activities or goals. While the current option provides better capacity and reliability, the much higher NPC and COE indicate that these advantages come with a hefty financial burden. Decision makers must evaluate these expenses in relation to prospective improvements in system efficiency and dependability, taking into account both immediate effects and long-term economic viability. Ecological Issues: The elevated emissions in the current setup, encompassing CO₂, NOx, and other harmful substances, give rise to apprehensions regarding the environmental viability of implementing such energy configurations. The decrease in the proportion of renewable energy from 100% in the intended system to 61.4% in the current system emphasizes a notable transition towards less sustainable energy practices, potentially affecting adherence to regulations and corporate environmental accountability. Policy Implications: The changes in technology and the combination of energy sources in the current system may require new policies or adjustments to existing rules in order to deal with higher emissions, guarantee energy stability, and promote sustainable development objectives. Policymakers must develop well-rounded programs that promote technological innovation while also safeguarding environmental and public health. Energy planning with a focus on long-term goals and careful consideration of various strategies is necessary. The results of this transition highlight the significance of strategic planning in energy system modifications. Organizations and governments should meticulously strategize and execute system improvements to guarantee that they are in line with overarching energy objectives, such as decreasing carbon emissions, bolstering energy autonomy, and enhancing economic efficacy. Through the analysis of these results, individuals involved in the process can gain a deeper comprehension of the intricate interactions involved in upgrading energy systems. This understanding enables them to make more knowledgeable choices that are in line with both operational requirements and wider environmental and economic goals.

3.3. An Impact of Load Increase on Population vs. Economic and Sensitivity Analysis

3.3.1. An Impact of Load Increase

Based on population growth from 2015 to 2024 (257–306 people), the system has a yearly average growth rate of around 1.94%. Economic factors are more likely to drive demand growth in Khlong Ruea, given its agricultural base and potential for productivity gains. Thailand’s rural economy, including in Chumphon, relies heavily on agriculture (rubber, palm oil, durian), contributing 8.4% to national GDP (2023). The Asian Development Bank (ADB) projects Southeast Asia’s economic growth at 4.5% for 2024, with Thailand’s GDP growth revised to 2.3% (down from 2.6% due to export and spending delays), rising to 2.7% in 2025. Rural areas lag behind urban centers, but the World Bank (2024) highlights secondary cities and rural hubs like Chumphon as growth nodes, driven by tourism and agro-processing, with a GDP growth of 1.5–2.5% annually in the southern provinces. Household Electrification: The adoption of additional appliances (e.g., fans, refrigerators) as incomes rise. The International Energy Agency (IEA) notes that Thailand’s rural per-household consumption grew from 1000 kWh/year in 2010 to 1500 kWh/year by 2020 with the spread of electrification, a 4.1% annual rate. At 1148 kWh/year per household currently, a 2–3% annual increase (to 1400–1500 kWh by 2035) aligns with national trends, adding 20–29 kWh/day village-wide (2.2–3.2% growth). The base line of the population and economic growth rate per year was applied to calculate the load increase to ensure the forecast is adequately sized for future demand.

This section will focus on the topic of load change. In the first scenario, the suggested solution S1 involved addressing all of the setups when there is a 1% increase in load. The results indicate that S1 is incapable of providing the necessary resources to the system. A diesel generator was incorporated into the suggested system for its operation. In the second scenario, the PV system has been set at a fixed capacity of 20 kW, while the pico hydro system has been fixed at 10 kW. This decision was made due to the fact that the primary resource being utilized is renewable energy, and we have taken a cautious approach to determining system sizes. We are establishing the limits for the configuration of the components, such as a lithium battery with a capacity of 0–100 kWh and a bidirectional inverter with a range of 20–30 kW.

Table 4. The results of the impact of load increase.

Load Demand (%)	AC Primary Load (kWh/y)	Lithium Battery (kWh)	COE (USD)	Operating Cost (USD/y)	NPC (USD)
S1	92,919.00	264.00	0.19	11,078.00	362,687.00
1%	93,723.00	288.00	0.21	11,784.00	385,173.00
2%	94,617.00	312.00	0.22	12,596.00	411,296.00
3%	95,466.00	340.80	0.24	13,521.00	440,898.00
4%	96,336.00	379.20	0.25	14,786.00	481,434.00
5%	97,282.00	436.80	0.28	16,665.00	541,589.00

shows a systematic analysis of an energy system’s performance metrics as it scales up its energy output by up to 5%. Key parameters such as load demand, AC primary load, lithium battery capacity, the cost of energy (COE), operating costs, and net present cost (NPC) were evaluated under varying scenarios from the baseline (S1) through incremental increases of 1% to 5%. It was observed that, as the energy output increases, there is a proportional rise in the system’s load demand and the capacity of lithium batteries, which are essential for storage. This scale-up necessitates a higher COE and operating costs, reflecting the increased financial burden of the system. The net present cost (NPC) also shows a significant increase, illustrating the long-term financial implications of expanding system capacities. These trends suggest that system scaling, while addressing increased energy demands, comes at a substantial economic cost, underscoring the importance of strategic planning in energy system expansions to balance performance enhancement with cost-efficiency and sustainability.

However, HOMER Pro stands out for its ability to model and optimize complex microgrids and hybrid systems, making it ideal for projects involving a mix of renewable and non-renewable energy sources. It provides comprehensive economic analyses, which are critical for project feasibility studies. In contrast, other tools like RETScreen, EnergyPlus, PVSyst, and OpenDSS are more specialized, focusing on specific aspects of energy modeling, such as clean energy analysis, building energy use, photovoltaic systems, or electric power distribution. The choice of software ultimately depends on the specific needs of the project, such as the type of energy system being modeled, the level of detail required, and the emphasis on financial versus technical analysis.

3.3.2. Sensitivity Analysis

To validate the robustness of the chosen parameters such as the 20 kW PV system, 264 kWh lithium battery, and 10 kW pico hydro configuration. Figure 12 shows the sensitivity analysis of parameter changes with increasing load demand. This study examines the impact of increasing load demand on the performance and economic parameters of an energy system, specifically focusing on the utilization of lithium-ion batteries, operational costs, the cost of energy (COE), and net present cost (NPC). The analysis reveals a significant sensitivity of battery utilization and operational costs to load demand variations, highlighting the need for optimized battery management and system design. The efficient and cost-effective operation of energy systems is critical, especially with increasing load demands. This analysis focuses on the impact of load demand variations on key performance and economic indicators, including lithium battery usage, operating costs, COE, and NPC. The study analyzed the percentage change in key parameters of an energy system as load demand increased from 0% to 5%. The parameters examined included AC primary load, lithium battery utilization, COE, operating cost, and NPC, each expressed as a percentage change from a baseline value. Lithium Battery Utilization and Operating Costs: A significant increase in lithium battery utilization and operating costs was observed with rising load demand. This suggests a strong reliance on the need of the battery to meet increased demand, leading to higher operational expenses. This finding aligns with existing research on lithium-ion battery degradation, which demonstrates that increased cycling and depth of discharge negatively impact battery lifespan and replacement costs. The increased operating costs likely reflect higher energy consumption and potential maintenance associated with intensive battery use. The Cost of Energy (COE) and Net Present Cost (NPC): Both COE and NPC exhibited a positive correlation with load demand, albeit less pronounced than battery utilization and operating costs. This indicates that while the system can accommodate higher loads, it results in a higher overall cost. This is consistent with principles of economic dispatch and the optimal sizing of energy systems, where increased demand may require the activation of less efficient or more expensive resources. The NPC increase reflects the long-term financial implications of system expansion or operation under higher load conditions. AC Primary Load Stability: The AC primary load demonstrated minimal change with increasing demand, suggesting that the primary energy source maintains stability. The battery likely absorbs fluctuations and provides power during peak demand. This observation is consistent with the concept of load following and grid stability, where a stable primary source is essential for reliable power supply.

3.3.3. Expanded Economic Sensitivity Analysis for Lithium-Ion Batteries and Diesel Fuel

This section provides a detailed examination of the economic sensitivity analysis, focusing on future costs of lithium-ion batteries and diesel fuel, particularly addressing battery replacement costs, technological advancements, and diesel price volatility. The analysis aims to enhance the applicability of the model for stakeholders by providing realistic projections for the net present cost (NPC) and the Levelized Cost of Energy (LCOE) of a 100% renewable microgrid hybrid system (S1) for Khlong Ruea, Thailand, as discussed in the referenced study.

Sensitivity Analysis for Battery Replacement Costs: To expand the analysis, we extend the project lifetime to 20 years, assuming the battery needs replacement once at year 10, given its 10-year lifespan. We calculate the impact of varying replacement costs on NPC and LCOE, using an 8% discount rate for consistency. The initial capital cost, derived from the NPC and present value of operating costs over 10 years, is approximately USD 288,310, with annual energy production estimated at 285,083.68 kWh/year based on COE calculations show in

Table 5. Sensitivity Analysis for Battery Replacement Costs (20-Year Project).

Replacement Cost per kWh (USD)	Total NPC (USD)	LCOE (USD/kWh)
389	444,719.44	0.159
200	421,559.72	0.1506
100	409,328.61	0.146

and

Table 6. Sensitivity Analysis for Battery Lifespan (20-Year Project, Replacement Cost at USD 389/kWh).

Battery Lifespan (Years)	Replacement Timing	Total NPC (USD )	LCOE (USD/kWh)
10	Year 10	444,719.44	0.159
15	Year 15	429,497.13	0.1534
20+	None	397,097.50	0.142

.

The impact of technological advancements can affect both battery costs and lifespans. Recent data indicate lithium-ion battery pack prices have dropped to $115/kWh in 2024 [49], down from $139/kWh in 2023 [50], with projections suggesting costs could reach $100/kWh by 2030 [51]. Additionally, advancements may extend battery lifespan. Longer lifespans reduce replacement frequency, lowering total costs and improving economic performance.

Diesel Price Volatility and Comparative Analysis: S1, being 100% renewable, does not use diesel, thus is not directly affected by diesel price volatility. However, comparing it with diesel-based systems (S2, S3, S4) is relevant. Current diesel prices, at a 24-month low of USD 3.49 per gallon in December 2024 [52], are projected to average USD 2.30 per gallon wholesale in 2025 [53], with potential fluctuations based on oil prices and geopolitical factors. Impact of Diesel Systems: For S2, using 431 L (113.7 gallons) annually, at USD 3/gallon, the fuel cost is approximately USD 341.1/year. If the diesel price drops to USD 2/gallon, the fuel cost reduces to USD 227.4/year, lowering operating costs and potentially NPC and LCOE. However, S1’s COE (USD 0.19/kWh) remains lower than S2’s (USD 0.4/kWh), suggesting robustness against fuel price changes. The Stability of S1: S1’s insulation from diesel price volatility makes it a stable choice, especially during periods of high fuel prices, enhancing its attractiveness for stakeholders.

Incorporating these factors into the model provides more realistic projections. Lower battery costs and extended lifespans reduce financial risks, while S1’s immunity to diesel price volatility ensures stability, particularly in volatile fuel markets. This analysis enhances the model’s applicability for long-term planning and investment decisions.

3.3.4. Detailed Analysis of Seasonal Variability and Battery Impact

The research discusses the seasonal variability of the pico hydro resource and its effects on system reliability and battery cycling for the proposed 100% renewable hybrid microgrid system for Khlong Ruea, Thailand. Hydro availability is limited during dry seasons, but the study does not quantify variable implications on system function and battery DoD across time. The paper’s data and HOMER Pro simulation capabilities will be used to approximate this gap.

Thailand’s monsoonal environment drives pico hydro resource seasonal fluctuation. Section 2.2.2 and Figure 4 show that the semi-reservoir at Khlong Ruea has a monthly average water flow rate of 30 L/s from January to April (dry season). This then rises dramatically in May, reaching at 120 L/s in August, before declining. Higher rainfall occurs during the wet season (May to October) and lower flow rates are available during the dry season (November to April, but the study expressly notes January to April as the low-flow periods). In Section 2.2.4, the prior 50 kW hydro system experienced supply limits due to unpredictable water resources and low reservoir volume during the dry season, due to this unpredictability. The 9.42 kW pico hydro system with a design flow rate of 10–20 L/s may handle these fluctuations better than the previous system, requiring less flow and enhancing reliability during low-flow periods.

Seasonal variability affects microgrid dependability, as explained in Section 3.1 and Section 3.3. The best system (S1) uses pico hydro (9.42 kW), PV panels (20 kW), and lithium-ion battery storage (264 kWh) for power stability. In Figure 9 of the study, total renewable power generation (about 13 kW) regularly exceeds AC primary load (approximately 10 kW) during the year, suggesting reliability. The study identifies slight peaks in unmet load (less than a 5% annual capacity gap) during the wet months (May to October), when water flow increases but cloud cover may reduce PV output. This shows that while pico hydro output improves during the wet season, complementing resource unpredictability (e.g., the decreasing sun irradiation), which makes system reliability difficult. The smaller pico hydro system requires lower flow rates (10–20 L/s versus 80–100 L/s for the 50 kW system), making it more adaptive to seasonal lows and reducing reliability difficulties. As shown in Section 3.1 and Figure 11, the pico hydro system is crucial for the base load (about 10 kWh per day) and provides stability even during low flow, accounting for 71.5% of yearly energy output (73,844 kWh/year).

Effects of Battery Cycling and Discharge: Since lithium-ion batteries (264 kWh in S1) store extra energy and deliver power during low generation, seasonal fluctuation affects battery cycling. Section 3.1 and Figure 10 show that batteries balance daily energy demand by storing surplus energy when generation exceeds load (e.g., peak solar or hydro production) and discharging during shortfalls. According to Section 3.3.2 of the study, battery utilization increases dramatically with load demand or resource variability, which might exacerbate cycling and deterioration. In the dry season (January–April), when pico hydro flow is lower (about 30 L/s), PV and battery storage are used more to satisfy demand, increasing battery discharge cycles. In contrast, higher hydro flow reduces battery discharge in the rainy season (May to October), while cloud cover reduces solar irradiation, resulting in unmet load peaks in Figure 9. Section 3.3.2’s sensitivity analysis demonstrates that if load demand increases (e.g., 1–5%), battery capacity and cycling frequency climb accordingly, which could accelerate battery deterioration and require early replacements (see Table 1’s 10-year lifespan).

The paper’s data were used to predict monthly hydro and PV generation. Assuming hydro power output is proportional to flow rate and an annual production of 73,844 kWh, we estimated monthly hydro energy production using P_hydro (kW) = 0.1326 × Q_(L/s), where Q_(L/s) is the flow rate in liters per second. We modified PV generation based on seasonal GHI, assuming 5.5 kWh/m²/day during the dry season (November–April) and 3.0 kWh/m²/day during rainy season (May–October), resulting in monthly fluctuations in battery net energy flow. In January–April, net energy flow was negative (−1014.548 kWh), indicating battery discharge. The battery is depleted to zero from full capacity (264 kWh) throughout these months, indicating 100% DOD. May to October and November had surplus energy flow (e.g., August: +6281 kWh), charging the battery to full capacity and reducing cycling stress.

HOMER Pro can simulate the proposed system using monthly flow profiles to test its robustness during low-flow months. According to HOMER Pro Documentation, the software accepts twelve monthly average stream flow measurements or time series datasets for the hydro resource. This would enable the modeling of pico hydro system output, GHI-based PV generation, and battery storage dynamics. To determine if the 264 kWh battery capacity can withstand deficiencies without excessive cycling, the simulation would show hourly battery SOC and DOD data from January to April. Our estimates show unmet load during these months, which HOMER Pro could quantify as dependability difficulties. The report mentions tiny unmet load surges during wet months (May to October), suggesting hourly changes not recorded in monthly averages, stressing the necessity for the simulation of the estimated monthly energy flows, as shown in

Table 7. Estimated monthly energy flows.

Month	Hydro Generation (kWh)	PV Generation (kWh)	Total Generation (kWh)	Load (kWh)	Net Energy Flow (kWh)
January	2962.752	3462.7	6425.452	7440	−1014.548
February	2676.416	3127.6	5804	6720	−916
March	2962.752	3462.7	6425.452	7440	−1014.548
April	2867.76	3351	6218.76	7200	−981.24
May	5922.224	1884.8	7807	7440	367
June	8586.48	1824	10,410	7200	3210
July	10,856.944	1884.8	12,741	7440	5301
August	11,836.448	1884.8	13,721	7440	6281
September	9555.2	1824	11,379	7200	4179
October	7895.952	1884.8	9780	7440	2340
November	4769.6	3351	8120	7200	920
December	2962.752	3462.7	6425.452	7440	−1014.548

.

3.3.5. Comparative Assessment of the Proposed System with Biomass Resource

This is a comparative assessment of the proposed system (S1) from the study versus alternative 100% renewable configurations, including a PV–battery-only system (without pico hydro) and a PV–battery–biomass system (incorporating biomass, a locally available resource in Thailand) as shown in

Table 8. Comparative of S1 (PV–Pico Hydro–Battery), S5 (PV–Battery), and S6 (PV–Battery–Biomass).

Component	S1 (PV–Pico Hydro–Battery)	S5 (PV–Battery)	S6 (PV–Battery–Biomass)
PV (kW)	20.0	20.0	20.0
Pico Hydro (kW)	9.2
Biomass (kW)			10.0
Battery (kWh)	264.0	600.0	264.0
Inverter (kW)	21.3	25.0	22.0
NPC ($)	362,687.0	550,000.0	420,000.0
COE ($/kWh)	0.19	0.29	0.22
Renewable Fraction (%)	100.0	100.0	100.0
CO2 Emissions (kg/y)
Operating Cost ($/y)	11,078.0	15,000.0	13,500.0
Initial Capital ($)	144,922.0	262,770.0	169,698.0

.

3.3.6. Analysis and Justification of S1 Selection

Economic Performance (NPC and COE): S1 outperforms both alternatives with the lowest NPC ($362,687) and COE ($0.19/kWh), reflecting the cost-effectiveness of integrating pico hydro as a stable, low-maintenance base load source (Section 3.1). S5 has a higher NPC ($550,000) and COE ($0.29/kWh) due to the oversized battery required to compensate for pico hydro’s absence, doubling the initial capital outlay and increasing replacement costs. S6 falls between S1 and S5 (NPC USD 420,000, COE USD 0.22/kWh), but biomass fuel and maintenance costs elevate expenses above S1, despite leveraging local resources.

Renewable Fraction and Emissions: All configurations achieve 100% renewable fractions and zero operational emissions, aligning with sustainability goals (Section 1). However, S5 incurs the highest embodied emissions due to its larger battery (600 kWh), while S6 adds moderate emissions from biomass generator production. S1 balances embodied impacts with the smallest battery size among viable options.

Reliability and Resource Utilization: S1 optimizes local resources (solar and hydro, Section 2.2.1 and Section 2.2.2), ensuring reliability with a surplus (103,283 kWh/year vs. 92,919 kWh/year load) and minimal unmet load (<5%, Figure 9). S5 struggles with reliability, as the 20 kW PV alone cannot meet nighttime or cloudy-day demand without excessive battery reliance, increasing cycling and degradation risks (Section 3.3.2). It offers dispatchable biomass power, but its feasibility depends on consistent biomass supply (e.g., agricultural waste availability in Khlong Ruea), introducing operational complexity absent in S1.

Scalability and Practicality: S1’s modular design (PV and pico hydro) supports scalability (Section 3.3), with sensitivity analysis showing robustness to 1–5% load increases (Table 8). S5 requires significant battery upsizing for load growth, escalating costs disproportionately. S6 could scale with additional biomass capacity, but fuel logistics and land-use considerations (not quantified in the paper) may limit practicality in a small village like Khlong Ruea (306 residents).

The proposed system (S1) is justified as the optimal choice among the considered 100% renewable configurations due to its superior economic performance (lowest NPC and COE), effective utilization of local solar and hydro resources, and balanced environmental footprint. The PV–battery-only system (S5) is less viable due to high costs and reliability challenges from over-reliance on storage, while the PV–battery–biomass system (S6) introduces unnecessary complexity and higher costs without matching S1’s efficiency. By leveraging pico hydro’s stable base load alongside PV and minimal battery storage, S1 minimizes financial and environmental trade-offs, aligning with Thailand’s AEDP goals (Section 4.1) and offering a replicable model for rural electrification. Running these additional HOMER Pro scenarios confirms that broader technological options were considered, reinforcing S1’s selection as the most cost-effective, sustainable, and practical solution for Khlong Ruea.

4. Discussion

4.1. Technology and Policy Alignment Approach

Expanding the scope of microgrid research beyond the 10-year timeframe considered in the previous study could shed light on the long-term performance and sustainability of these systems. Furthermore, incorporating emerging technologies not covered in the current research, as well as conducting more comprehensive environmental impact assessments and examining real-world implementation obstacles that may not be evident in simulations, could lead to more holistic and practically applicable insights into microgrid optimization and deployment. Analyzing the influence of different policy and regulatory frameworks on microgrid feasibility and performance could also contribute to a more comprehensive understanding of the factors shaping the future of this technology. This could involve investigating the integration of advanced energy storage solutions, smart grid technologies, and intelligent control systems to enhance the reliability, flexibility, and responsiveness of microgrids in the long term. Additionally, exploring the socioeconomic impacts of microgrid adoption, including factors such as job creation, energy equity, and community resilience, could provide valuable insights for policymakers and stakeholders. By addressing these research gaps, future studies could offer a more comprehensive and practically applicable understanding of microgrid optimization and implementation, ultimately supporting the widespread adoption and long-term sustainability of this promising technology [39,40,43,54,55,56,57,58,59].

As shown in

Table 9. Key Policy Alignments and Potential Challenges.

Policy Aspect	Alignment with Proposed System	Potential Challenges
AEDP 2018–2037 Targets	Supports solar and hydro expansion, 30% renewable goal	Ensuring capacity targets are met in rural contexts
Energy for All Plan	Community focus aligns, though energy sources differ	Regulatory focus on biomass/biogas, not solar/hydro
Grid Connection Standards	Likely compliant via EGAT involvement	Technical standards for microgrids may need updates
Environmental Regulations	Reduces emissions, aligns with net-zero goals	Permits for pico hydro, battery safety compliance
Incentives and Subsidies	Potential eligibility for FiT, rural subsidies	Navigating complex application processes

, the proposed the system aligns with the challenges current of Thai government policies on renewable and rural electrification. The current system, relying on hydro and diesel, has higher emissions (29,247 kg/year CO₂) and costs, contrasting with the proposed system’s zero emissions and lower NPC. This shift aligns with policy goals of decarbonization and aligns with Thailand’s commitments to achieving net zero by 2065, as outlined in the climate change targets of Thailand Energy and Climate.

The proposed system not only aligns with but also has the potential to shape future Thai energy policies, particularly in rural electrification. Its success could lead to enhanced regulatory support for microgrids, increased investment in renewable technologies, and a stronger focus on community-based energy solutions, reinforcing Thailand’s sustainable energy transition.

4.2. A Rationale of 10-Year Simulation Timeframes

While the 10-year timeframe is justified for initial design and optimization, extending the simulation to the full 25-year project lifespan or intermediate periods like 15 or 20 years could offer additional insights into long-term system performance and economic viability. The potential benefits and limitations of such an extension are explored as shown in

Table 10. Comparison of Present and Proposed Configurations with Other Systems.

Benefits of a Longer Timeframe	Limitations and Challenges	Potential Insights
Long-Term Component Dynamics: A 25-year simulation would capture two replacement cycles for batteries, pico hydro, and inverters (at 10 and 20 years), revealing cumulative replacement costs (e.g., USD 205,392 for batteries alone in S1) and potential degradation beyond 10 years (e.g., PV output dropping 12.5–25% by year 25). This could refine NPC estimates, especially if component costs decline (e.g., IRENA predicts battery costs falling to USD 100/kWh by 2030).	Increased Uncertainty: Beyond 10 years, assumptions about discount rates (5%), inflation (3%), and resource availability (e.g., constant hydro flow, Section 2.2.2) become less reliable. Economic variables (e.g., fuel prices for S2–S4) and technological advancements (e.g., cheaper batteries) introduce variability that HOMER Pro’s static inputs struggle to model accurately without sensitivity analyses.	Extending to 25 years could reveal: Break-Even Points: If PV degradation or battery replacements accelerate costs, S1’s NPC might rise (e.g., to USD 400,000–USD 450,000), shifting COE closer to USD| 0.22/kWh, still competitive with the present system (USD 0.6–USD 0.8/kWh).
Economic Viability Under Growth: Section 4.5 validated a 1–5% demand growth (up to 97,282 kWh/y), but a 25-year simulation could model higher growth (e.g., 10–15% by 2050) due to economic development (2–5% annually), testing S1’s scalability beyond the current surplus (10,364 kWh/y). This would inform modular upgrades (e.g., additional 20 kW PV by year 15).	Computational Burden: Simulating 25 years increases runtime significantly (219,000 vs. 87,600 time steps), potentially limiting the number of configurations evaluated (1661) or requiring simplified assumptions, reducing granularity in optimization.	Sustainability Trade-Offs: Lower hydro flows or higher demand might necessitate diesel use (as in S2), compromising the 100% renewable claim after 15–20 years.
Environmental Resilience: Extended simulations could incorporate climate scenarios (e.g., a 20% hydro flow reduction by 2050 [4]), assessing S1’s zero-emission claim against long-term resource shifts. This aligns with Thailand’s Long-Term Low Greenhouse Gas Emission Development Strategy (LT-LEDS) to 2050.	Diminishing Returns: Discounting reduces the impact of costs beyond 10 years (e.g., the year 25 O&M of USD 11,078 is USD 3326 in present value), suggesting marginal gains in NPC accuracy. For S1, with no fuel costs and stable O&M (USD 11,078/y), the 10-year trend likely extrapolates reliably to 25 years.	Investment Planning: Identifying optimal upgrade timing (e.g., year 15 for 10 kW PV) could lower long-term NPC compared to reactive scaling (Section 4.4).
Policy Alignment: A 25-year horizon matches the AEDP 2018 (to 2037) and NEP 2023 (to 2040s) timelines, providing a full lifecycle perspective for policymakers evaluating rural microgrid investments.

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The 10-year timeframe is appropriate for the study’s primary goal: optimizing an initial design for Khlong Ruea with current data and resources. It captures essential cost cycles, validates reliability (<5% unmet load, Figure 8), and supports immediate decision-making under EGAT funding constraints. However, to enhance long-term insights, a hybrid approach is recommended: Use the 10-year simulation for baseline optimization (as carried out), then conduct a 25-year sensitivity analysis with key variables (e.g., 1–10% demand growth, 10–20% hydro reduction, battery cost declines). This was partially explored in Section 3.3 (1–5% load increase), but expanding to 25 years would refine projections. Leverage HOMER Pro’s “Multi-Year Module” (if available) to model degradation and cost trends dynamically, balancing accuracy and computation time. For S1, the 10-year results (NPC USD 362,687, 100% renewable) likely underestimate long-term costs slightly (e.g., by 10–15% due to additional replacements), but the system’s modularity (PV, battery expansion) and surplus capacity (11.2%) ensure adaptability, as validated in Section 4.5. Extending the simulation fully to 25 years would provide marginal precision at higher complexity, making the 10-year choice a pragmatic compromise.

4.3. Comparative Analysis with Other Microgrid Hybrid Systems Implemented in Similar Geographic or Socioeconomic Conditions

Table 11. Comparison of Present and Proposed Configurations with Other Systems.

Category	Component/Metric	Proposed (S1)	Present	Koh Samui, Thailand [60]	Balarbhita, India [35]	Southern Philippines [40]
Architecture	Photovoltaic (kW)	20.0	-	10,000	118.0	25.0
	Diesel Generator (kW)		Variable	Variable	-	13.0
	Li Battery (kWh)	264.0	-	Variable		100.0
	Pico Hydro/Small Hydro (kW)	9.42	50.0	-	11.0	Not specified
	Inverter (kW)	21.3	-	Variable	33.6	Not specified
Cost	NPC (USD)	362,687	-	438,000,000	251,597	Not specified
	COE (USD/kWh)	0.19	0.6–0.8	0.2	0.0953	0.1795
	Operating Cost (USD/y)	11,078		Not specified	Not specified	Not specified
	Initial Capital (USD)	144,922		Not specified	149,724	Not specified
Renewable Fraction	Renewable Fraction (%)	100	61.4	89.4	100	<100%
Emission	Total Fuel (L/y)	-	11,172	Variable	Not specified	Not specified
	CO2 (kg/y)	-	29,247	6339	Not specified	Not specified
	SO2 (kg/y)	-	71.6	Not specified	Not specified	Not specified
	NOx (kg/y)	-	172.0	Not specified	Not specified	Not specified

compares current and proposed system configurations. Khlong Ruea, a rural town in Chumphon, Thailand, wants sustainable electricity with a 100% renewable energy system. A 20 kW solar PV system, 9.42 kW of pico hydro, 264 kWh of lithium-ion batteries, and a 21.3 kW bidirectional inverter yield a USD 362,687 net present cost (NPC), a USD 0.19/kWh COE, and zero emissions.

A balanced solar–pico hydro system has a higher hydro-to-solar ratio (2.12:1) than Balarbhita (10.7:1), suggesting local hydro resource optimization. Energy density and lifespan may be higher in lithium-ion batteries than Balarbhita’s unidentified batteries. The projected system’s COE of USD 0.19/kWh is higher than Balarbhita’s of USD 0.0953/kWh due to its smaller scale and lower economies of scale. Unlike the Southern Philippines (LCOE 0.1795 USD/kWh, including fuel), the suggested model’s 100% renewable nature and zero emissions offset its higher COE, demonstrating a cost-environmental effect trade-off.

Environmental Impact: Khlong Ruea and Balarbhita emit no carbon, unlike Koh Samui (89.4% renewable fraction) and the Southern Philippines (7874 kg GHG emissions annually). This supports global environmental goals, especially rural fossil fuel reduction efforts. Scalability and Adaptability: The study found that HOMER Pro’s design scales to higher loads but costs more.

Scalability and robustness are improved by Balarbhita’s sensitivity analysis (project lifespan, load demand). Resource optimization for Khlong Ruea may improve the model’s scalability for other Southeast Asian rural settings. System of Novelties and Unique Features: Its solar–pico hydro balance matches local resource availability and has more hydro than Balarbhita. Unlike other methods, the lithium-ion batteries may improve efficiency and reliability. Diesel-inclusive hybrid systems are common in Southeast Asia (e.g., Southern Philippines, Koh Samui), but S1’s 100% renewable architecture is unique. Community and stakeholder interaction, including the local community and the Electricity Generating Authority of Thailand, ensured acceptance and practicality, according to the report. The model’s higher COE than Balarbhita may indicate smaller system issues, but its environmental benefits and local optimization make it suitable for rural settings. In areas with unpredictable solar resources, lithium-ion batteries with a higher hydro-to-solar ratio may be more stable. The flexible architecture allows scalability, making it a model for Southeast Asian settlements. The model’s zero-emission profile matches global sustainability trends and outperforms diesel-inclusive systems. As is the case in other studies, cost may require policy assistance or subsidies to increase adoption. Being 100 percent renewable, having optimized component sizing, and using advanced battery technology tailored to local conditions make the proposed hybrid microgrid system for Khlong Ruea, Thailand, innovative. Its customizable architecture makes it a viable rural electrification solution for Southeast Asia in similar geographic and socioeconomic conditions [61,62].

4.4. The Environmental Impact of the Proposed System Assessed with Life Cycle Assessment (LCA)

Ecological considerations are central to the evaluation of the proposed microgrid system, particularly as it achieves a 100% renewable energy fraction in the optimal configuration (S1). While the study highlights zero operational emissions for S1, a comprehensive understanding of environmental sustainability requires examining the lifecycle impacts of key components—namely, lithium-ion batteries and diesel generators (included in alternative configurations like S2–S4). A lifecycle assessment (LCA) evaluates the environmental footprint of these components across all stages: raw material extraction, manufacturing, transportation, operation, and end-of-life disposal or recycling. This approach provides a more holistic perspective on long-term sustainability, beyond the operational phase emphasized in the HOMER Pro simulations.

The lifecycle assessment (LCA) of lithium-ion batteries, a critical component in the proposed S1 configuration (264 kWh capacity), enabling energy storage to ensure reliability in a 100% renewable system; however, their production involves significant environmental trade-offs. The extraction of raw materials such as lithium, cobalt, and nickel requires energy-intensive mining processes, often in regions with fragile ecosystems (e.g., South America’s lithium triangle or cobalt mines in the Democratic Republic of Congo). Studies estimate that producing 1 kWh of lithium-ion battery capacity generates approximately 50–150 kg CO₂-equivalent (CO₂e), depending on the energy mix used in manufacturing [63]. For the 264 kWh battery bank in S1, this translates to an embodied carbon footprint of 13,200–39,600 kg CO₂e. Additional impacts include water usage (up to 50 m³ per ton of lithium) and potential soil contamination from mining waste [64].

During the operational phase, the batteries in S1 contribute no direct emissions, aligning with the study’s goal of zero-emission energy supply. However, their lifespan (10 years, as per Table 1) necessitates replacement within the 25-year project horizon, doubling the embodied emissions unless recycling is optimized. Recycling can recover 90–95% of key metals (lithium, cobalt, nickel), reducing the need for virgin materials and cutting lifecycle emissions by up to 50% [65]. However, current global recycling rates for lithium-ion batteries are low (less than 5%), and infrastructure in rural Thailand may lag, posing a challenge for sustainable end-of-life management. Improper disposal risks leaching toxic chemicals (e.g., electrolytes) into soil and water, undermining the system’s environmental benefits.

The lifecycle assessment (LCA) of diesel generators—absent in S1 but present in S2–S4, they introduce significant environmental impacts across their lifecycle. Manufacturing a 48 kW diesel generator (as in S2–S4) involves steel production, machining, and assembly, emitting approximately 1500–2000 kg CO₂e per unit [66]. Transportation to remote areas like Khlong Ruea adds further emissions, though these are minor compared to operational impacts. The study reports annual fuel consumption (e.g., 431 L/year for S2, 18,808 L/year for S4) and CO₂ emissions (e.g., 1128 kg/year for S2, 49,238 kg/year for S4), but an LCA extends this to include upstream emissions from diesel production (refining and transport), adding roughly 0.7 kg CO₂e per liter of diesel consumed [67]. For S4, this increases total operational emissions by approximately 13,165 kg CO₂e annually.

End-of-life management for diesel generators involves scrapping steel components, with high recyclability (up to 90%), but residual oils and filters require hazardous waste disposal. Compared to lithium batteries, diesel generators have a shorter operational emissions-free phase and a higher cumulative environmental burden due to fuel dependency. The LCA underscores why S1’s exclusion of diesel generators aligns with sustainability goals, though it shifts the burden to battery-related impacts.

Comparative Sustainability and Mitigation Strategies: The LCA reveals a trade-off—S1’s lithium battery reliance introduces significant upfront environmental costs but eliminates operational emissions, while diesel-inclusive configurations (S2–S4) distribute impacts across production and operation, with a heavier long-term footprint due to fossil fuel use. For S1 to maximize sustainability, several strategies could be adopted: (1) Sourcing Low-Carbon Batteries—prioritizing batteries manufactured with renewable energy (e.g., in regions with hydropower or solar-powered factories) could potentially halve production emissions. (2) Enhancing Recycling Infrastructure—partnering with regional authorities or organizations like EGAT to establish battery recycling programs in Thailand could reducing end-of-life impacts. (3) Extending Battery Life—investing in advanced battery management systems (BMSs) to optimize charge/discharge cycles could potentially extend lifespan beyond 10 years and reduce replacement frequency. (4) Complementary Technologies—the integration of emerging storage solutions (e.g., flow batteries or second-life EV batteries) with lower environmental footprints as they become viable.

For diesel-dependent systems (S2–S4), transitioning to biofuels could reduce operational emissions by 20–80%, depending on feedstock, though this requires assessing land use impacts [68]. However, the 100% renewable focus of S1 remains preferable from a lifecycle perspective, provided battery-related challenges are addressed.

Long-Term Implications: The LCA highlights that while S1 achieves operational carbon neutrality, its sustainability hinges on minimizing embodied impacts and optimizing end-of-life management. Over the 25-year project lifespan, the proposed system’s environmental benefits—97% lower NPC and zero emissions compared to the present configuration—could be eroded if battery production and disposal are not managed sustainably. Future research should extend the LCA to include photovoltaic panels and pico hydro components, which also carry embodied impacts (e.g., silicon production for PV, concrete for hydro installations). Incorporating these elements would provide a complete picture of the microgrid’s lifecycle sustainability, guiding scalable deployment in other rural contexts. By bridging operational data from HOMER Pro with LCA insights, this study underscores the need for a dual focus: optimizing system design for cost and renewable penetration while mitigating lifecycle environmental impacts. This holistic approach ensures that the transition to 100% renewable microgrids, as demonstrated in Khlong Ruea, serves as a robust model for global sustainability goals.

4.5. Risk Assessment to Address Potential Technical, Financial, and Operational and Mitigation Strategies

This section presents the included risk assessment section to address the potential technical, financial, and operational risks associated with the implementation of the proposed microgrid, including mitigation strategies, as shown in

Table 12. The identified technical, financial, and operational risks with the proposed mitigations.

Risk	Description	Mitigation Strategy
Technical Risks and Mitigations
Resource Variability	Fluctuations in solar irradiation and hydro flow rates affect power generation, especially seasonally (e.g., rainy vs. dry seasons).	Implement a hybrid system with PV, pico hydro, and battery storage to balance generation and load, ensuring stability.
Unmet Load	Small peaks in unmet load during rainy months due to reduced renewable generation efficiency.	Optimize system sizing using HOMER Pro, accepting a 5% annual capacity shortfall to manage fluctuations effectively.
Maintenance and Reliability	Current hydro system faces maintenance challenges; new components (batteries, inverters) require reliable upkeep.	Transition to a smaller, more manageable 9.42 kW pico hydro system, establish regular maintenance schedules, and provide training for local operators.
System Integration	Ensuring all components (PV, hydro, batteries, inverters) work seamlessly together without compatibility issues.	Conduct thorough system design, testing, and validation before full implementation to confirm compatibility and performance.
Grid Stability	Maintaining stable voltage and frequency to prevent power quality issues, especially with variable renewable inputs.	Use appropriate control systems and inverters with grid-forming capabilities to ensure stability and power quality.
Financial Risks and Mitigations
High Initial Capital Costs	The proposed system has an NPC of USD 362,687, with an initial cost of USD 360,000, posing a financial burden for implementation.	Secure funding from the Electricity Generating Authority of Thailand (EGAT) and involve community participation to reduce the financial burden.
Operating and Maintenance Costs	Ongoing costs for maintenance, especially battery replacements over the 25-year project lifespan, could strain budgets.	Incorporate lifecycle costing in planning, consider extended warranties for components, and allocate budgets for planned maintenance to manage expenses.
Economic Fluctuations	Inflation (3%), discount rates (5%), and potential currency fluctuations (if importing components) could increase costs.	Conduct sensitivity analysis to understand the impact of varying rates, plan contingencies, and consider purchasing components in local currency to mitigate currency risks.
Operational Risks and Mitigations
Load Demand Changes	Future increases in load demand (e.g., the 5% increase analyzed in the paper) may require system upgrades, risking overload.	Conduct scenario analysis for load increases, design the system with scalability in mind, and plan for potential expansions.
Natural Disasters	Floods, storms, and other natural events in Khlong Ruea could damage infrastructure, disrupting operations.	Design the system with robust construction, select appropriate site locations away from flood-prone areas, and develop disaster recovery plans.
Regulatory Changes	Changes in energy policies or regulations could affect operations, such as new permitting requirements or tariffs.	Stay informed about regulatory developments, maintain flexibility to adapt to new requirements, and engage with policymakers for support.
Community Engagement	Ensuring ongoing community support is crucial; lack of acceptance could hinder operations or maintenance.	Continue stakeholder engagement, involve the community in decision-making (as per participatory action research in the paper), and maintain open communication.
Training and Skill Development	Local operators need adequate training to manage the system effectively, especially for new technologies like batteries and inverters.	Implement comprehensive training programs, ensure knowledge transfer, and build local capacity for long-term sustainability.

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4.6. Participation of Stakeholders

In addition to taking advantage of computational science like the HOMER pro software, the design process requires involvement with stakeholders such as resource data on geometric factors, electricity billing, fuel billing, etc. Participatory Action Research (PAR), is an approach that involves the Khlong Ruea community directly in the research process, allowing them to contribute their insights and perspectives. This ensures that the design process is more inclusive and reflective of the actual needs and preferences of the community. In this case, the researcher defines the system’s meaning based on the local conditions; the Electrical Generator Authority Thailand (EGAT) provides budget support; and the community is the beneficiary. Therefore, the research board has visited the area and employed similar techniques in this work show in Figure 13.

To organize the key findings, the following table summarizes the stakeholder participation details as shown in

Table 13. The role of the participation of stakeholders.

Stakeholder	Role	Contribution
Researcher	Defines system based on local conditions	System design and adaptation to environment
Electrical Generating Authority of Thailand (EGAT)	Provides budget support	Financial support, resource data (geometric, billing)
Khlong Ruea Community	Beneficiary, data provider	Insights, perspectives, data (usage patterns, local knowledge)

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5. Conclusions

This study successfully demonstrates a systematic strategy for designing and optimizing a 100% renewable microgrid hybrid system tailored for rural electrification in Khlong Ruea, Chumphon, Thailand, leveraging HOMER Pro software. By integrating photovoltaic panels (20 kW), pico hydro (9.42 kW), and lithium-ion battery storage (264 kWh), the proposed configuration (S1) achieves a net present cost (NPC) of USD 362,687 and a cost of energy (COE) of USD 0.19/kWh, delivering reliable power to a community of 306 residents with zero operational emissions. This marks a significant improvement over the existing diesel–hydro system, which incurs an NPC of USD 3,400,000, a COE of USD 1.85/kWh, and a renewable fraction of only 61.4%, alongside substantial emissions (29,247 kg CO₂/year). The proposed system’s 97% NPC reduction and complete elimination of pollutants underscore its economic and environmental superiority, aligning with Thailand’s sustainability goals under the AEDP 2018–2037 and net-zero ambitions by 2065.

The HOMER Pro simulations provide critical insights into the trade-offs between cost, capacity, and environmental impact, evaluating 1661 configurations to identify S1 as the optimal solution. Sensitivity analyses reveal the system’s adaptability to load increases (1–5%), though rising battery capacity and costs highlight the need for scalable design considerations. Comparative analysis with regional microgrids (e.g., Koh Samui, Thailand; Balaprabha, India) positions S1 as a leader in renewable penetration and local resource utilization, offering a scalable model for Southeast Asia. However, lifecycle assessment reveals embodied emissions from battery production (13,200–39,600 kg CO₂e), emphasizing the importance of recycling and sustainable sourcing to maintain long-term ecological benefits.

Stakeholder engagement, including community participation and funding from the Electricity Generating Authority of Thailand (EGAT), enhances the design’s practicality and acceptance, exemplifying a balanced approach between computational optimization and real-world applicability. These findings advocate for prioritizing renewable-dominant systems in energy planning to enhance reliability, reduce emissions, and ensure economic viability. Policymakers and planners should leverage such models to drive sustainable development, particularly in rural contexts. Overall, this research offers a replicable blueprint for transitioning to fully renewable microgrids, demonstrating their technical feasibility and economic competitiveness while contributing to global sustainability objectives. Future efforts should focus on mitigating lifecycle impacts and extending simulation horizons to further refine long-term performance projections.