Puppala, Singh, and Potnuru: Investigating the feasibility of a renewable energy-based standalone microgrid for remote area applications: An opto-techno-economic and environmental perspective
Research
Environmental Engineering Research 2025; 30(3): 240340.
Investigating the feasibility of a renewable energy-based standalone microgrid for remote area applications: An opto-techno-economic and environmental perspective
1Department of Electrical Engineering, National Institute of Technology, Meghalaya,
India-793003
2Department of Electrical and Electronics Engineering, Gayatri Vidya Parishad College of Engineering for Women (Autonomous), Visakhapatnam,
India, 530048
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Energy poverty is a significant barrier to development for millions of people globally in remote areas; developing nations like India still use conventional fuels to meet their energy needs. Microgrids can be a feasible solution for remote electrification by integrating distributed energy resources. The present work investigates the feasibility, planning, and optimal sizing of a standalone microgrid system from a socio-techno-economic and environmental perspective for the electrification of a remote area in an Indian scenario. For the feasibility analysis, a remote village, Dayarthi, in Andhra Pradesh, India, was investigated by considering the daily load profile, which includes domestic loads, community loads, and agriculture loads. The total load demand is 333.53 kWh/day, with a peak load of 45.75 kW. Four potential microgrid configurations are investigated, with various combinations of diesel generator, wind turbine, photovoltaic, and battery storage. A sociotechnical-economic-environmental analysis identifies the best configuration by looking at the different microgrid scenarios that are possible and suggesting the best one with the highest percentage of renewable energy at the lowest net present cost and levelized cost of energy with minimum unmet loads. Furthermore, the optimal scenario cost of energy is compared with the most recent study in the literature.
Keywords: Levelized cost of energy, Microgrids, Net present cost, Remote electrification, Renewable energy resources, Techno-economic analysis
Graphical Abstract
Keywords: Levelized cost of energy, Microgrids, Net present cost, Remote electrification, Renewable energy resources, Techno-economic analysis
1. Introduction
Remote electrification plays a pivotal role in achieving the goal of universal access to affordable, reliable, and modern energy services in the Sustainable Development Goals (SDGs) by 2030 [1]. Geographically, rural and remote villages may be far from the urban landscape, but with electricity, they bridge the gap, connecting to the world and contributing to the growth of a nation [2]. Rural electrification contributes to the overall progress of a country in terms of economic development, education and healthcare, quality of life, agricultural transformation, and social empowerment [3]. Electricity offers numerous benefits in remote villages, including increased work opportunities, agricultural productivity, communication and connectivity, increased security, environmental contamination of public goods, and reduced migration from rural to urban areas [4]. The main challenges for the extinction of grids in remote areas are geographical location, low population density, limited resources and expertise, and environmental concerns. To address these challenges, microgrids are a feasible solution for providing electricity to remote areas [5].
Microgrids provide localized power generation and distribution within specific areas or communities and can operate in on-grid or off-grid modes, as shown in Fig. 1. Microgrids offer a wide range of benefits, including the integration of renewable energy, enhanced energy reliability, increased energy efficiency, local economic development, demand response capabilities, grid independence, grid optimization and flexibility, environmental sustainability, and scalability. The most significant use of a microgrid is to distribute power to off-grid areas of the world, where many people do not have consistent access to electrical energy, and to keep the lights on during emergencies for crucial applications [6]. Microgrid configurations vary based on application, technological complexity, and ownership structure. They can be classified into alternating current (AC), direct current (DC) and hybrid AC/DC types with variations in distribution systems and geographical locations such as urban, remote, rural, and island settings, as shown in Fig. 2. AC microgrids consist of distributed energy resources, AC buses, and converters. In contrast, DC microgrids use DC sources like solar panels and batteries [7]. AC microgrids offer advantages like higher power transfer capability and grid stability but face synchronization and voltage regulation limitations. DC microgrids integrate well with renewable energy systems and provide continuous power during outages, although they have limitations in voltage range and market adoption. Remote microgrids are deployed where grid utilities are unavailable due to geographical constraints [8].
Distributed Energy Resources (DERs), including solar photovoltaics (PV), wind turbines (WT), hydroelectric generators (HEG), and biomass systems, play a crucial role in standalone microgrids, offering clean and sustainable alternatives to traditional grid-connected systems [9]. Life cycle and opto-techno-economic analyses are essential to investigating microgrid systems' feasibility, sustainability, and economic viability. Technically, life cycle analysis evaluates the environmental impacts of microgrid components and operations throughout their entire lifespan, while opto-techno-economic analysis integrates technical, economic, and optical considerations to assess the performance, cost-effectiveness, and social acceptance of microgrid projects [10]. These analyses help identify opportunities to minimize resource consumption, emissions, and waste generation, optimize microgrid designs, identify revenue streams, and evaluate return on investment. They provide valuable insights into microgrid systems' holistic sustainability and economic viability, facilitating informed decision-making and ensuring their long-term success in providing reliable, clean energy solutions [11].
For investigating feasibility and opto-techno-economics, sophisticated techniques are available, including classical, artificial intelligence-based, hybrid, and software-based tools [12]. Classical techniques, artificial intelligence-based techniques, hybrid techniques include various algorithms like Genetic algorithm(GA), adaptive local attractor-based quantum behaved particle swarm optimization (ALA-QPSO), Generalized reduced gradient method (GRG), Firefly algorithm (FA), dragonfly algorithm (DA), differential evolutionary (DE) algorithm; grasshopper optimization (GOA) algorithm; Gray wolf optimization (GWO), moth flame optimization (MFO), particle swarm optimization (PSO), salp swarm algorithm (SSA), multi-criteria decision making approach (MCDM), water cycle algorithm (WCA), teaching-learning-based optimization (TLBO), butterfly particle swarm optimization (BSPSO), parallel hybrid genetic algorithm-particle swarm optimization (P-GA-PSO), hybrid Gray wolf optimizer-sine cosine algorithm(HGWOSCA), sine cosine algorithm (SCA), slime mold algorithm (SMA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), improved harmony search (IHS), simulated annealing (SA), wild horse optimizer (WHO) [13].
Software tools based on opportunistic analysis offer advanced optimization algorithms, comprehensive databases, dynamic scenario analysis capabilities, intuitive interfaces, and visualization capabilities. These tools empower users to make informed decisions by leveraging sophisticated analytical techniques and complete data integration, optimizing resource allocation and maximizing economic efficiency. These include HOMER, iHOGA, HybSim, SOLSIM, HySys, APSIM, ARES, Dymola/Modelica, HySim, IPSYS, SOMES, INSEL, iGRHYSO, TRNSYS, RETScreen, and HYBRIDS [14]. HOMER is a superior tool for hybrid power system optimization due to its comprehensive and versatile nature. It considers multiple renewable energy sources, storage technologies, and conventional generators and conducts techno-economic optimization for off-grid and grid-connected systems. Its user-friendly interface, extensive database, and sensitivity analysis capabilities make it a preferred choice for engineers, planners, and researchers [15].
Over the past few years, numerous studies have explored microgrid structures' feasibility, planning, design, and opto-techno-economics for various global loads. Some of the studies [16–31] are reviewed, as shown in Table 1. The literature focuses on the design of distributed hybrid renewable energy system (DHRES)-based microgrids, considering technical and economic factors. Some attention is given to environmental and social factors such as carbon footprint, employment, and standard of living. However, the design process must consider land costs, local employment generation, and emission costs. It is emphasized that considering all factors is crucial for the sustainable design of microgrids for rural development.
This paper presents a feasibility study, planning, design, techno-economic simulation, and sizing optimization of a proposed standalone microgrid with pure integrated conventional energy sources like PV, WT, and battery systems for a case study of a remote village in Dayarthi, India, with residential, community, irrigation, and agricultural loads. The villagers of the remote community have lived without electricity until 2023 [32]. Limited grid-connected electricity access became available starting in 2023 [33], but it is intermittent due to the geological location. During festivals, they had to bring diesel generators from nearby urban areas [32]. The main motivation of this study is to provide clean, affordable, and uninterrupted electricity to remote communities with minimum NPC and COE compared to the existing studies in India and outside India from the existing literature [16–31]. Access to uninterrupted electricity will also have a positive impact on education, healthcare, and economic development in the area. This will not only improve the quality of life for residents but also contribute to global efforts to combat climate change. A feasibility analysis is critical in assessing the potential of such remote communities. Motivated by the above literature and critical issues, the following objectives are posed.
To investigate the feasibility and planning of a standalone system in terms of technical, economic, optimal, reliable, and environmental perspectives to be installed in remote areas.
To evaluate community energy needs, renewable resource availability, installation costs, environmental impacts, stakeholder engagement, and implementation approach to ensure a sustainable, cost-effective, and long-term community benefit.
To address the complex interplay of factors in designing micro-grids, various optimization techniques are proposed [34] for the optimum sizing of DHRES-based microgrids. These techniques take into account various socio-techno-economic-environmental factors to design microgrids that are both efficient and sustainable. These factors encompass social, technical, and economic aspects such as the unmet load, the renewable energy portion, the annualized and levelized cost of electricity, the human progress index, and the employment generation factor. The study investigates four different microgrid scenarios and selects the optimal configuration based on a performance analysis. Furthermore, the optimal scenario cost of energy is compared with the most recent study. The study highlights the importance of effectively identifying suitable renewable energy sources to incorporate them into the power sector. This is crucial for addressing climate change, achieving the United Nations’ goal of accessible and clean energy (SDG7), and meeting India’s panchamrit goals.
The paper is structured as follows: Section 2 describes the resources and load assessment of the region. Section 3 presents the mathematical modeling of microgrid components. Section 4 presents the methodology and design optimization criteria. Finally, Section 5 analyzes and compares the four scenarios regarding optimal, technical, and socioeconomic factors. The paper's findings and synopsis are presented in Section 6.
2. Study Area and Load Profile
The remote village of Dayarti (18.0391° N and 82.9159° E), a medium-sized remote village located in India's Andhra Pradesh state, common Visakhapatnam district, within Ananthagiri Mandal, is proposed as the location for the case study of the suggested methodology. The village covers a total area of 49 hectares in its entirety. The total population is 239, with 108 male and 131 female residents. Dayarti village has 21.76% literacy, with 28.70% males and 16.03% females [35]. The remote study village has approximately 45 houses.
2.1 Load Profile
Electricity is solely consumed by the entire village for residential, community, and agricultural needs. The total loads of the study area are classified into three categories: domestic, community, and agriculture [36]. The load-sharing pattern is shown in Table S1. The load profile for the remote village was meticulously prepared using a multi-faceted approach [37]. Data on energy consumption patterns were collected through a field survey [38] and historical electricity bills [39]. Energy calculators [40] were used to ensure accuracy, considering seasonal variations and specific energy needs. The load profile data and sample survey tables can be found in Table S5 and Table S6. It shows that most of the load is caused by household appliances during the day, while at night, only a few small-scale industries are tied to agricultural loads. Two sessions are considered for analyzing the load profile: summer (May-Sep) and winter (Oct–April). The load profile in the summer is higher than in the winter, and Fig. S1. illustrates the winter and summer day-to-day load profiles in kW. The area's total load demand is to be 333.53 kWh per day, while the peak load is 45.75 kW.
2.2 Analysis of Alternative Energy Sources
The Dayarti village (study area) has good wind speed and solar irradiance conditions, with moderate temperatures throughout the year. HOMER Pro optimizes the integrated renewable energy system by calculating solar radiation's global horizontal index (GHI) using geographical location data. The software retrieves data from NASA, the National Renewable Energy Database, and the National Solar Radiation Database. The practical abilities of renewable energy sources and available components are calculated and mathematically modelled, with available components crucial for the optimized integrated system.
Fig. S2, Fig. S3 and Fig. S4 show Dayarti village's monthly per-day solar global horizontal irradiation (GHI) data, average temperature, and the annual monthly wind speed data. The current that solar PV panels produce is directly proportional to solar irradiance. The clearness index, which measures atmospheric clarity by dividing surface radiation by extraterrestrial radiation, has a dimensionless value between 0 and 1. The average solar irradiance per day is 5.14 kWh/m2/day. Although the maximum solar irradiance of 6.612 kWh/m2/day and clearness index of 0.627 was recorded in April, the minimum of 4.45 kWh/m2/day and clearness index of 0.412 were recorded in July. The annual average temperature recorded is 24.16°C. The highest temperature recorded was 29.97°C in May. The lowest temperature recorded was 18.35°C in December. The monthly average wind speed of Dayarti village is 5.36 m/s. Whereas the highest wind speed measured was 7.8 m/s in July, the lowest was 3.9 m/s in October.
The battery system is essential for a microgrid system, as it can store surplus power and provide electricity during shortages. Lithium-ion batteries are commonly used for this purpose. The use of lithium-ion batteries in microgrids for remote areas has numerous advantages compared to the other batteries shown in Table S6. These batteries have a high energy density, making them compact and lightweight, which is crucial in areas where space and weight are limited. They also have a long cycle life and high efficiency, making them reliable and cost-effective [41]. In remote areas, lithium-ion batteries play a vital role in storing energy from renewable sources like solar and wind power, ensuring a continuous power supply when renewable generation is low. They can deliver high power output and fast response times, making them suitable for various load demands [42–43]. The environmental impact of their production and disposal raises concerns about resource depletion and pollution. To address these concerns, a circular economy approach should be implemented. This includes robust recycling processes to recover valuable materials and exploring alternative battery technologies with lower environmental impacts [44–46]. By integrating these strategies, microgrid designs can achieve a balance between technical performance and environmental sustainability, harnessing the benefits of lithium-ion batteries while mitigating their drawbacks.
3. Mathematical Modeling
The suggested microgrid system shown in Fig. 4 has PV, WT, BAT, a system converter, and DG. It is shown in nomenclature, with variable notations in Eq. (1) to Eq. (8).
3.1. Diesel Generator
The study focuses on the importance of reliability in launching a standalone system. To address the intermittency of solar and wind energies, a diesel generator set is used to compensate for any energy shortage. Table S3 and Table S4 contain technical parameters and cost details for the diesel generator. Diesel is chosen for its flexibility and cost-effectiveness compared to energy storage [47]. From Eq. (1) and Eq. (2) the diesel generator fuel consumption and electrical efficiency can be calculated.
(1)
(2)
3.2. Solar Photovoltaic
Solar PV modules are widely used to generate electricity from sunlight. Solar radiation and ambient temperature influence the amount of electricity a solar PV system produces [48]. Table S3 and Table S4 provide PV model technical and cost data and technical parameters for the PV model. The Eq. (3) can be used to evaluate the power output.
(3)
3.3. Wind Turbine
Wind turbine technology converts wind energy into electricity, generating power based on wind speed and turbine specifications. Anemometers measure wind speed at specific sites, and output power is calculated considering air density. The hybrid microgrid includes a small wind turbine as one of its components. Weibull's probability density function (Eq. (4).) can be used to estimate wind power [49]. The wind turbine's annual energy generation is as follows: Eq. (5). Table S3 and Table S4 contain technical parameters for the wind turbine.
(4)
(5)
3.4. Battery Storage
The primary purpose of incorporating a battery energy storage system into a hybrid microgrid is to facilitate the ability to maintain reliable service in the case of a power shortage caused by a gap between consumption and generation. Using Eq. (6), determine the battery's watt-hour capacity (Cwh), [48]. Table S3 and Table S4 contain technical parameters for the battery storage.
(6)
3.5. System Converter
The hybrid microgrid comprises AC and DC power-generating resources. All these sources are connected to the common bus, either AC or DC; similarly, loads may be AC or DC. So, a power converter is necessary to convert AC to DC and vice versa. As follows, the converter's capacity will exceed the power evacuated from the AC or DC sides [49]. Table S3 and Table S4 contain technical parameters for the system converter.
Table S7 illustrates the technical aspect comparisons of various types of batteries, focusing on key parameters that influence their suitability for microgrid applications. The nomenclatures and abbreviations are expressed in Table S8 and Table S9.
(7)
(8)
4. Methodology and Design Optimization Criteria
This section explains the methodology, design, and optimization criteria for the proposed microgrid scenarios.
4.1. Methodology
Optimization techniques play a critical role in thoroughly analyzing the performance of HRES-based microgrids from an optimal, technical, economical, sensitive, and environmental perspective [50]. Optimization refers to determining the most effective method for minimizing or maximizing the objective function of a problem [51]. The primary goal of the optimization process in HRES is to minimize annual costs, net present costs, electricity costs, land requirements, emissions, and power system losses while simultaneously maximizing power generation, renewable fraction, system reliability, profits, life span, and overall review [52]. Some methods for optimizing HRES-based microgrids are classical optimization methods, artificial intelligence-based optimization techniques, hybrid optimization techniques, and software-based optimization tools [53–65].
Classical optimization methods [53–55] utilize mathematical equations and differential calculus-based analysis to find optimal solutions, but they are limited when objective functions are not continuous or differentiable. Artificial intelligence-based optimization techniques [56–60] use AI algorithms to solve complex problems. Some common approaches use AI to find useful solutions. These include particle swarm optimization (PSO), NSGA II, pigeon-inspired optimization (PIO), multimodal delayed PSO algorithm (MDPSO), improved ant colony optimization (IACA), and artificial bee colony (ABC). In hybrid renewable-based microgrid configuration systems analysis, hybrid optimization techniques [61–62] integrate various performance improvements into a single process, enhancing the efficiency of individual algorithms and solving complex optimization problems more effectively. Modeling and analyzing energy infrastructure for renewable system integration involves the use of software-based optimization tools [63–66] such as PVSYST, RETScreen, SAM, PVSYST, TRNSYS, iHOGA, and HOMER. These tools aid in environmental analysis, performance simulation, energy and economic analysis, and renewable energy system optimization. The software tools can help in decision-making and pre-feasibility review for energy projects. The most widely used academic tools are RETScreen and HOMER. Compared to RETScreen, HOMER is more user-friendly [67]. Fig. S5 illustrates the key features and potentials of HOMER Pro software. Due to these features, HOMER Pro was used to optimize the microgrid structure for this study. HOMER Pro software version 3.16.2 on a laptop with the following specifications was used: 11th Gen Intel (R) Core (TM) i5-11300H @3.10GHz and 16.0 GB of RAM to model DHRES-based microgrids using PV, WT, DG, and BAT to electrify the remote off-grid area. The software was chosen for its ability to design and optimize DHRES-based microgrids, determining the most cost-effective and efficient resource integration, system size, and storage capacity. Fig. 3 illustrates the applied flowchart for microgrid optimization and feasibility.
4.2. Design Optimization Criteria
4.2.1. Net present cost (NPC)
The value of the system's lifetime costs minus its lifetime revenue. NPC depends on the capital cost, operating and maintenance (O&M) cost, salvage value, and discounted annual cash flow. It can be calculated by adding each project year's discounted cash flows [68]. NPC is to be optimized by Eq. (9) and Eq. (10).
(9)
(10)
4.2.2. Cost of energy (COE)
The ratio of the system's total annualized cost to its annual electricity delivery [69] can be expressed as follows: Eq. (11) and Eq. (12).
(11)
(12)
4.2.3. Capacity shortage fraction (CSF)
The possibility of an electrical energy shortage in the system is called the capacity shortage fraction (CSF). It mathematically represents the unmet demand or capacity shortage ratio to the total capacity required during a year in Eq. (13) [70]. The range varies from zero to one. The value zero represents the full-filled demand in all conditions, and one means zero-Pg loads will never be served.
(13)
4.2.4. Total carbon emission
Calculating the annual total CO2 emissions from the diesel generator is an essential metric for assessing the microgrid system's emission performance [71].
(14)
4.2.5. Optimization constants
The hybrid microgrid system has design optimization constants like load balance constraint, i.e., net electricity generation must always equal the load demand in Eq. (15), battery constraint, i.e., SOC and power availability, must be within safe boundaries in Eq. (16) and Eq. (17); and generation capacity constraint, i.e., diesel dispatchable unit output power must meet upper and lower limits, i.e., in Eq. (18).
(15)
(16)
(17)
(18)
5. Results and Analysis
This section presents the proposed microgrid optimal components’ capacities, microgrid parametric analyses, and techno-economic feasibility. The microgrid operates autonomously and relies on renewable energy sources like solar, wind, and battery banks. Renewable sources are vastly available in that location. The feasible microgrid scenarios are shown in Table S2, and designs of microgrids have been simulated and optimized, as shown in Fig. 4, using the flowchart shown in Fig. 3.
This design includes two separate buses in the microgrid: DC and AC buses. The solar PV and batteries are connected to the DC bus, the diesel generator (DG), wind (WT), and loads are connected to the AC bus. A converter is connected between the AC and DC buses to convert the generated voltage from DC to AC and AC to DC. Table 2 presents design optimization results for a 25-year project with an 8.25% real interest rate and a 5.70% inflation rate. It identifies optimal component sizes and energy system scenarios based on economic, technical, and energy data. The study evaluates the economic feasibility of each configuration using NPC and COE, renewable friction, capacity shortage friction, surplus energy, and unmet load. This study examines energy scenarios integrating diesel generators, focusing on their technical, economic, and ecological performance. The results are derived from hourly simulations using HOMER Pro to evaluate the feasibility of different microgrid configurations and cost analyses of these systems.
5.1. Standalone Diesel Generator (Scenario-1)
A single 48-kW diesel generator, as shown in the simulation diagram shown in Fig. S6, produces a total of 121,735 kWh annually; the annual energy production heat map is shown in Fig. S7, sufficient to meet the entire electrical load. The total cost summary of scenario 1 is shown in Fig. S8. It explains the detailed overview of the costs associated with operating a diesel generator. The NPC's costs by cost type and component are shown in Fig. S9 and Fig. S10. It explains the cost distribution among different components of a stand-alone diesel generator, which helps to understand system maintenance and operation expenses better. An observation that the resource cost is higher than the initial cost. Also, the diesel fuel price is the single most significant contributor to NPC, making up 83.33 percent of all NPC in the system. As shown in Fig. S11, understanding the fuel usage patterns of a stand-alone diesel generator is crucial for budgeting, optimizing operations, and planning fuel supply logistics. The total amount of diesel used in a year is 48,884 L, with an average of 134 L of fuel used daily and 5.58 L per hour. The monthly average electricity output from a diesel-only system is depicted in Fig. S12. It helps analyze the performance of a diesel-only electricity system over the year, identifying periods of high or low output and enabling planning for adjustments to meet energy demands. Technically, the system meets all load demands in 8,760 hours with a zero-capacity shortage and 12.4% surplus energy for future loads. It has negative economic features due to its high NPC and COE of 955,173$ and 0.607$/kWh, respectively. The environmental impact of this scenario is high, as shown in Table 2. Its annual emissions of 127,929 kg make it a poor choice and not eco-friendly.
5.2. Solar PV, Battery, and Converter (Scenario-2)
The input describes a system where solar photovoltaic panels and batteries work together to supply power to a load. In the simulation diagram shown in Fig. S13, PV panels contribute during the day, while batteries provide power at night and in cloudy conditions. Excess solar energy can be stored in the batteries. Hourly simulations and optimization determine the best component sizes to minimize costs while meeting the load requirements. The optimal system includes solar panels with a 299-kW output, 350 batteries connected in 7 parallel strings of 50 cells each, and a 47.4 kW power inverter with 339,406 $ is the NPC, while 0.216 $ per kilowatt-hour is the COE. Fig. S14 displays scenario 2's cost summary. It represents the costs of a hybrid microgrid system with PV panels, battery storage, and converters. It provides a breakdown of the total costs associated with each component. Fig. S15 and Fig. S16 show the NPC's costs by cost type and component. It explains the breakdown of costs by type, including capital, operational, and replacement costs, as well as by specific components like PV, batteries, and converters. This aids in pinpointing the system's most significant expenses, which show that the most common NPC is batteries, with capital costs of $234,079 and operational costs of $ 0.081 million. PV panel capital and operating costs. The system converter uses the NPC's rest, as shown in Table 2.
The PV panel's annual production of energy and battery state of charge is shown in Fig. S17 and Fig. S18. It explains the total amount of energy generated by the PV panels over a year, as well as the battery's state of charge throughout the year, indicating how effectively the system stores and utilizes the generated energy. It has been observed that PV is used most extensively and contributes the most power in the middle of the day during peak sun hours, especially in the summer. However, to meet the constant load requirement, the battery sharing or discharge occurs primarily in the winter months (January, November, and December). Fig. S19 depicts the monthly average electricity output from the solar photovoltaic, battery, and converter systems. Technically, the system meets the annual load demand with the slightest capacity shortage and produces 72.1% surplus energy for future loads.
5.3. Wind Turbine, Battery and Converter (Scenario-3)
In this scenario, wind turbines, batteries, and converters meet the load demand. The simulation diagram of scenario 3, shown in Fig. S20, has 30 wind turbines, 750 batteries (15 strings in parallel with the string size of the batteries 50), and a 99.1-kW power converter, all of which have been optimized from a technological and economic perspective. This system has an NPC of 711,412 $ and a COE of 0.452 $/kWh. Fig. S21 displays the cost summary of scenario 3. It represents the costs of a hybrid microgrid system with wind turbines, battery storage, and converters. It provides a breakdown of the total costs associated with each component. Fig. S22 and Fig. S23 show the NPC's costs by type and component. It explains the breakdown of costs by type, including capital, operational, and replacement costs, as well as specific components like wind turbines, batteries, and converters. This aids in pinpointing the system's most significant expenses. This system includes the battery at 41.7% of the total cost, the wind turbine at 54.47%, and the system converter at 3.74% of the total price, the highest contribution of the cost of the wind turbines shown in Table 2.
Fig. S24 and Fig. S25 show the annual energy production summary of the wind turbines and the battery's state of charge. It explains the total amount of energy generated by wind turbines over a year, as well as the battery's state of charge throughout the year, indicating how effectively the system stores and utilizes the generated energy. Based on the wind speed profile, the wind system's output power fluctuates at random throughout the year and every day, as shown in Fig. S26. High wind speeds from May to August result in excellent battery backup, while low production and state of charge occur in January, February, and October. The battery also meets the unmet load during unwind times to improve the system's supply reliability. Technically, the system meets the annual load demand with the slightest capacity shortage and produces 77.3% surplus energy for loads.
5.4. Solar PV, Wind Turbine, Battery, and Converter (Scenario-4)
In this scenario, the hybrid PV and wind turbine system is designed to supply electricity to the study area, addressing the needs of residential, community, and agricultural loads. The analysis demonstrates how the combination of photovoltaic panels and wind turbines meets the energy demand effectively, ensuring a reliable and sustainable power supply. Additionally, battery banks meet the need for energy storage during periods of excess energy. When renewable energy sources are unavailable, the battery bank supplies the load, the simulation diagram shown in Fig. S27. The optimal system includes solar panels with a 125-kW output, three 10-kW wind turbines, 300 batteries connected in six parallel strings of 50 cells each, and a 41.6 kW power converter with a total energy production of 253,034 kWh/yr.
Fig. S28 displays the cost summary of scenario 4, which includes initial, operational, and maintenance costs. Fig. S29 and Fig. S30 display the costs of the NPC by type and component. It explains the breakdown of the expenses by type, including capital, operational, and replacement costs, as well as specific component costs like solar PV panels, wind turbines, batteries, and converters. Ultimately, the system has an NPC of 259,301$ and a COE of 0.165$/kWh. The PV panels contribute to approximately 78.2% of the system's electrical output, while the wind turbine provides the remaining 22.8%. It indicates that the region under study has an abundance of renewable resources.
Fig. S31 illustrates the combined monthly average electricity output from solar photovoltaic (PV) panels and wind turbines (WT), showing how these renewable sources contribute to the overall energy generation throughout the year. Fig. S32, Fig. S33, and Fig. S34 summarize the total amount of energy generated by solar PV and wind turbines over a year, as well as the battery's state of charge throughout the year, indicating how effectively the system stores and utilizes the generated energy. The battery's State of Charge (SOC) reveals its behavior, demonstrating its transfer of power from itself to the load (discharging mode) when production from PV and WT systems was insufficient. The results indicate that the proposed hybrid microgrid system can successfully meet the hourly load requirement while maintaining a suitable level of service reliability.
Fig. S35 depicts the microgrid's energy dispatch on three consecutive days (December 27th, 28th, and 29th), illustrating how each component helped meet the load demand throughout that time frame. Technically, the system meets the total load demand annually with a minor capacity shortfall and productive surplus energy of 49.2% for loads.
5.5. Comparing the Microgrid scenarios
Comparing the optimal, technical, economic, and environmental profiles of different microgrids is essential for selecting the most suitable and sustainable energy solutions. Optimization ensures performance requirements are met and energy production efficiency is maximized. Technical comparisons assess component compatibility and reliability. Economic analysis evaluates financial viability and cost-effectiveness. Environmental profile comparisons help choose eco-friendly designs that reduce carbon footprints and resource depletion. Integrating these comparisons allows stakeholders to make informed decisions balancing technical feasibility, economic viability, and environmental sustainability for successful, long-lasting microgrid projects. The optimal design capabilities of the four scenarios are shown in Fig. S36. This explains that scenario 1 only used a diesel generator with a 48-kW power rating. Scenarios 2, 3, and 4 use PV panels, WT, batteries, and converters. The highest battery backup is used for Scenario 3. The lowest battery backup is required for Scenario 4. Comparing the PV panel capacities, WT power ratings, and converter power ratings, scenario four is best from a system optimization point of view. The technical design capabilities of the four scenarios are shown in Fig. S37 and Fig. S38 in terms of electricity production and consumption, capacity shortage factor, and surplus energy. Compared to all systems, the diesel-only system (Scenario 1) has no capacity shortage but needs to be more eco-friendly. Compared to the remaining three, scenario 4 has a lower capacity shortage factor and surplus energy.
The optimal capabilities of the four scenarios are shown in Table 2. The diesel-only system has the lowest initial cost of $19,200 but the highest operating price of $798,523 because of its excessive fuel consumption of 48,884 L/year and the NPC of 955,173$. With capital, operating, replacement, and salvage costs of 574,817$, 60,113$, 153,824$, and -77,343$, respectively, and an NPC of 711,412$, wind turbine-battery-converter best results are achieved with a hybrid microgrid (except for the operating cost of DG). With capital costs at 234,079$, operational costs at 41,950$, replacement costs at 73,613$, and salvage costs at −10,236$, with an NPC of 339,406$, the PV/BAT/CONV hybrid microgrid is superior to the WT/BAT/CONV components. The PV/WT/BAT/CONV hybrid micro-grid is the most cost-effective solution to meet the load requirements of the residential, communal, and irrigation systems under study. At 181,321$, 28,634$, 57,687$, and −8,341$, with the NPC of 259,301$, this system has the lowest capital expenses, operational costs, and replacement costs. Finally, the energy cost is shown in Fig. S39; the maximum value for the diesel-only system is 0.607$/kWh, while the PV-WT-BAT-CONV system achieves the lowest COE at only 0.165$/kWh. Fig. 5 shows the compassions of four microgrid system scenarios based on reliability, environmental impact, cost-effectiveness, and scalability, highlighting their advantages and disadvantages. The comparison of our findings with existing studies provides a clearer understanding of each scenario's performance, enhancing the credibility of our analysis and identifying the most viable solutions.
The proposed model for generating electricity by integrating solar and wind energy resources is stable and affordable compared to existing literature. The model demonstrates novelty and outperforms existing literature regarding minimal economy (NPC and COE). Table 3 provides a matrix of system compatibility electricity generation methods, highlighting the most promising technologies and energy sources from the latest studies.
6. Conclusions
In this research, the technical and financial viability of completely renewable energy-based microgrid systems that can operate autonomously to energies of 333.53 kWh/day with a peak load of 45.75 kW for domestic, community, and agriculture loads is examined in Dayarti village, Andhra Pradesh, India. The HOMER Pro optimizer was used to explore the ability to see a range of energy options, both technically and economically. There are four potential microgrid configurations simulated in this study: stand-alone diesel generator (Scenario-1), solar PV, battery, and converter (Scenario-2), wind turbines, battery, and converter (Scenario-3), solar PV, wind turbine, battery, and converter (Scenario-4), for fair comparisons. In the simulation and optimization processes, scenario 1 uses only a diesel generator, while scenarios 2, 3, and 4 incorporate PV panels, wind turbines, batteries, and converters. The simulation and optimization results provide each scenario's technical, economic, and environmental feasibility analysis. During the process of analyzing the available energy options, it was discovered that the integration of a 125 kW PV system, 30 kW WTs, 300 batteries, and a power converter rating of 41.6 kW has the best results in terms of its optimal, technical, and economic characteristics when compared to the other possibilities. The lowest NPC and COE were achieved by those mentioned above at $259,301 and 0.164 $/kWh, respectively, making an entirely sustainable energy solution with a 100% renewable percentage feasible. Furthermore, the optimal system has a surplus of energy (49.2%) that can be used for additional deferrable loads and potential use within the vicinity of the village.
All authors contributed to the conceptualization and design of the study. S.P. (PhD student) conducted all the surveys and simulations, analyzed the data, and drafted the manuscript. P.P.S. (Assistant Professor) supervised the overall work, provided guidance throughout the research process, and reviewed the manuscript. D.P. (Associate Professor) contributed to the conceptualization of the research, assisted in data interpretation, and provided critical revisions to the manuscript.
Conflict of Interest Statement
The authors declare that they have no conflict of interest.
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Fig. 1
Generic representation and structure of microgrid.
Fig. 2
The basic structure of (a). AC microgrid and (b). DC microgrid.
Fig. 3
Applied flowchart for microgrid optimization and feasibility of purely renewable source.
Fig. 4
Schematic representation of proposed microgrid design.
Fig. 5
Illustrates the Strengths and weaknesses of fourmicrogrid scenarios.
Table 1
Literature Review on the feasibilities of Hybrid renewable microgrids