1. Introduction
Rural electrification is essential for poverty alleviation [1]. Unfortunately, electrification is challenging due to the dispersed nature of many settlements. According to Energy Outlook 2017, around 14% of the world’s population, mostly living in remote areas, lacks electricity, while 30% lack access to clean cooking fuels [2]. The role of energy is crucial in human activities, from preparing food to keeping warm or cool and many more. The economic development of a society is directly linked with energy demand [3]. The provision of electricity has been recognized as a crucial parameter for the development and alleviation of poverty by the United Nations in their Sustainable Development Goals (SDGs). However, it will be challenging to achieve universal access to electricity by 2030 [4].
Pakistan is a developing country facing economic, environmental, and social development challenges, which have led to increased power demand [5]. The country’s total power demand in 2017 was 25 Gigawatts (GW), which is estimated to grow to 40 GW by 2030 [6]. Today’s electricity supply hovers around 17 GW, which represents a shortfall of around 8 GW [7]. Under these circumstances, a large population of Pakistan living in rural areas (around 64% of the total population [8]) is severely affected by an electricity blackouts of 12–18 h a day [9]. Policymakers and planners are working on adopting various alternative sources of energy that are economically viable. The country has an untapped renewable energy resource potential that can help to meet the country’s growing energy demand [10]. Following are renewable energy sources and their estimated potential for electricity generation: solar energy (2900 Gigawatts (GW)), wind energy (346 GW), hydropower (6 GW), and biomass energy (5 GW) [11,12,13]. However, stakeholders are unwilling to invest and participate in renewable energy (RE) technologies, due to high investment costs, high-discount rates, short-back period requirements, lack of infrastructural conditions, region remoteness, and each specific region’s renewable energies potential data unavailability [14]. Moreover, due to the economic conditions of the country, many renewable energy projects have been shut down, including those in Khyber Pakhtunkhwa and Sindh, negatively affecting the development of RE sources and over USD 3 billion in investments as foreign investors who have signed memorandum of understandings with government refused to invest in projects [11].
Pakistan is an agriculture-based economy, contributing around 18.9% of the GDP, with around 43% of the population directly or indirectly involved in agricultural activities [15]. Further, as per an economic survey, the country has 19.5 million (M) livestock, producing 1.3 M tons of dung annually [15]. If not treated, these wastes are responsible for greenhouse emissions, and the most feasible treatment method is anaerobic digestion for converting wastes to biogas [16]. The abundance of RE resources such as solar energy [13,14,15,16,17,18,19] and biomass can help meet the energy demand for various purposes, including cooking, heating, and power generation, throughout rural areas [20].
Due to rising demand, the unreliability of central grid connected rural power systems, and the high cost of extending the grid to rural areas, renewable energy-based microgrids have gained traction [21]. Microgrids are small networks of electricity users with a local source of power generation that can be operated in grid connected mode or isolated mode, providing a potential opportunity for access to reliable power [14], irrespective of a community’s location.
Solar-powered stand-alone systems are the most viable solution for generating electricity in remote regions [22,23] and have the potential for future energy applications [24,25]. However, these systems have certain drawbacks, including that they only generate power during the day and require additional investment in batteries for backup power, and also that a single energy source microgrid results in oversizing of the system [26].
Numerous studies suggest the deployment of hybrid systems for rural communities [27,28,29,30,31]. Shahzad et al. [27] reported the design of a solar–biogas microgrid, using farm animals manure as a source for biogas, for an agricultural tube-well and six homes. The system consisted of a 10 kilowatt (kW) photovoltaic system and an 8-kW biogas generator, producing electricity at the cost of Pakistani Rupee (PKR) 5.51 per kilowatt-hour (kWh). Pradhan et al. [32] reported a cost analysis study of a 3.6 kW peak load of a small house in a remote area based on the solar–biomass hybrid system using livestock manure as source. The system’s ability to produce 10.13 MWh annually over 25 years has been verified through HOMER simulations. Without government subsidies, the system will take 12 years to pay for itself; however, the system will take only 6 years if government subsidies are also taken into account. However, this study did not address the community-level impact.
Olatomiwa et al. [30] used HOMER to investigate various feasible electricity generation schemes including solar, wind, and diesel generators for six rural communities in six geo-political zones of Nigeria. A PV-diesel system with and without a battery, a wind–diesel system with and without a battery, and a PV–wind–diesel system with and without a battery are the several combinations that were studied. The study concluded that a hybrid renewable configuration comprising solar, wind, diesel, and batteries is most feasible with the lowest carbon emissions. This study focused only on basic rural community needs and did not address productive energy usage. Bhatt et al. [33] reported optimal planning and feasibility analysis for the rural community by considering photovoltaic, biomass (forest and agriculture residues), biogas (animal manure) hydro, and diesel as resources to generate electricity for the peak load of 55.49 kW using HOMER software.
Agyenim et al. [34] analyzed three different scenarios consisting of solar–biogas micro-grid systems, using municipal waste and dry fruit industry waste as biogas source, for four sites in Ghana. The scenarios examined included feeding the grid, prosumer self-consumption, and irrigation. They found that the system is feasible for power feeding to the grid. Furthermore, at 24.64 to 26.18 PKR per kWh, the system can meet load demand throughout the year. The analysis reveals that self-consumption and feeding to the grid scenarios are beneficial for boosting model efficacy. A study in Bangladesh found that without subsidies and incentives, the solar–biogas hybrid model using Kitchen waste for biogas production for a 30-building urban housing society with five housing units per building is unworkable. Due to the tiny size of the project and the high cost of land (approximately 45.9% of the project cost), the system is not financially feasible [35]. In another study [36], 2.8 kilo watt (kW) PV, 10 kW biomass and 10 kW biogas rural electrification system for meeting the basic needs of a remote community in Bangladesh was proposed. Cattle manure and night soil are employed in this study as a supply for a biogas plant. This study confirmed the system’s technical viability as it will provide continuous power supply for rural communities and concluded that it has a potential role in developing rural communities.
As evident from the literature review, hybrid energy systems based on cost-effective and reliable energy generation sources are widely studied, particularly for the development of rural communities. As an agriculture-based economy with abundant solar resources, a solar–biomass hybrid power generation system is more feasible in rural areas because of the local availability of animal manure and high solar resource potential [37,38,39]. The studies presented above are primarily concerned with rural electrification, ignoring other aspects of rural life such as cooking and sanitation. Further, the literature lacks discussion of the main byproduct of biogas plant, that is, fertilizer (slurry), which can be converted to organic fertilizer, making the system more feasible. To our knowledge, there has been no comprehensive study reporting solar–biomass hybrid systems in the literature. This paper provides detailed techno-economic modeling of a solar–biomass hybrid system that generates electricity and provides biogas for cooking purposes and organic fertilizer for agriculture.
2. Methodology
The proposed hybrid energy system is shown in Figure 1. The microgrid consists of a biogas plant, a photovoltaic (PV) system, an electricity distribution system, a gas supply system, and a fertilizer facility. The PV system will provide electricity for a variety of uses, and biogas will be used to generate electricity, heat water, and cook food, and fertilizer will be used for agricultural purposes.
2.1. Description of Area
We established the following criteria for area selection, which the chosen area must meet:
Few gas connections.
Electricity supply must not be reliable.
Open area around households in most of villages should be available.
Livestock and agriculture should be main source of income for most of families.
The Khyber Pakhtunkhwa province’s District Lakki Marwat, which is located in the southern part of the region, meets this criteria because there is a large amount of open land around most of the villages. Agriculture and livestock are the main source of income in rural areas. According to Pakistan’s 2017 census, the selected district’s total population was around 876,182 [40], with rural and urban distribution listed in Table 1. According to the Pakistan Bureau of Statistics, only 356 households out of 98,051 have gas connections. The remainder cook with wood, kerosene oil, gas cylinders, or dung cakes prepared from cow and goat manure. Although the area is around 95% electrified, the power supply is unreliable, and there are frequent power outages of 12–18 h every day [41]. Shown in Table 1, area has 98,051 houses in which 876,182 people are residing, which means on average about 9 persons are living in each house in rural areas.
The primary source of income is the agriculture and livestock sector. Most households in the rural area have a significant amount of agricultural land and 2–3 livestock assets, such as cows, camels, goats, and sheep. The presence of livestock in large numbers allows the development of biogas plants for power generation and as an alternative for presently used cooking fuels. Micro-grids based on biogas can change people’s cooking habits while also contributing to the area’s economic development.
2.2. Load Assessment
The solar–biomass hybrid system was optimized using HOMER software developed by NREL (National Renewable Energy Laboratory). HOMER is used to optimize the system configurations by making energy balance for each hour and taking the electric or thermal loads per hour that a system can supply [27,28].
Before performing simulations, pre-HOMER analysis was used for load assessment as domestic, community, and productive use requirements. Three types of loads were considered for rural communities, based on the technique identified by Mandeli et al. [42], and they are discussed below.
2.2.1. Domestic Use
Industrial and economic activities are limited in rural areas; thus, households consume the most energy. Energy is primarily used for cooking, water heating, and lighting. Traditional biomass is used for cooking and water heating, while solar lamps or kerosene oil are used in lanterns for lighting. Electricity consumption in rural areas is only a few hundred watts per day per household. In this study, household electric loads such as fans, lights, mobile chargers, refrigerators, televisions, and irons are taken into account. While the supply of biogas will take care of the thermal loads, which include cooking and water heating. Cooking is classified as a thermal load because using power for cooking would raise the system’s capital and operating costs and possibly make it unsustainable.
2.2.2. Community Use
The provision of electricity to various community buildings such as schools, dispensaries, and mosques improves the services provided by these buildings. Primary schools without boarding, dispensaries with 2–3 paramedics, and small mosques are the most common community buildings in rural areas. Loads of these community-buildings range from a few hundred watts to a few kW.
2.2.3. Productive Use
The use of energy for income generation falls under this category. It includes energy used in commercial, industrial, and agricultural activities. There are typically only a few shops, a small flour mill, and irrigation water pumps in a rural area. These facilities’ loads range from a few watts to a few kW [42]. RET Screen software was used for an economic analysis of the entire system, including the power system, biogas supply system, and fertilizer production system.
2.3. Load Assessment Survey
A load assessment survey has been carried out. The survey inquires about the size of the household, the variety of electric appliances, their ratings, and the frequency of use. A total of 176 people from different villages were interviewed. Interviewees included school teachers, farmers, shop owners, religious leaders, counsellors, and chairmen of village councils.
2.4. Biomass Resource Assessment
According to the 2006 livestock census, cattle are prevalent in the area. The average household has 2.3 cattle. Buffaloes are rare in this region. Camel ownership is also common in the Kacha region. In the district, there were 98,550 cattle, 3827 buffaloes, 291,711 goats, and a minor number of other animals [43]. Although cows are favored in rural regions, buffaloes are common in urban places such as Serai Naurang and Lakki Marwat. The nation’s cattle growth rate is 4.1 percent. Using the formula for compound growth rate, which is stated in Equation (1), we can determine the current number of livestock per home in an area.
(1)
where RV stands for “recent value”, OV for “livestock census value from 2006”, GR for “growth rate”, and n for “number of years” [43]. The amount of manure produced by each animal is measured in the final phase, and the overall amount produced is calculated. As was made evident from the discussion above, people in rural areas prefer cattle and goats; thus, for the sake of this study, we only took their manure and household kitchen trash into consideration. The majority of agricultural residues are used to feed animals, and people sell forest residues on the market to earn some extra cash. However, a small proportion of these residues that cannot be used for animal feeding or for market sale can be used in this project, but it is excluded from the calculation since it could incorporate manure that has not been collected.2.5. System Design and Financial Analysis
HOMER was used in the technical design of the microgrid’s electrical system. The power system of the microgrid is composed of the PV system, biogas power plant, inverter, and batteries. The HOMER software requires information on energy resource availability, load profiles, and cost parameters for various components. Within the constraints of the sizes of each component given to HOMER, the software simulates a wide range of scenarios. The HOMER software selects the best model based on criteria for load loss and economic viability. The system design process in HOMER is shown in Figure 2. How much people can afford to spend each month on gas and electricity was a question in the load survey. We determined the gas and electricity rates based on the responses of the local population. The fertilizer tariff is chosen from an Internet retailer offering compost at PKR 40/kg [44]. In order to promote agricultural operations in the area, this research aims to deliver fertilizer at a reduced rate.
HOMER is capable of developing biogas and electricity systems for thermal and electric loads supply; however, when fertilizer is included, HOMER is unable to perform an economic analysis of the entire system. We built a power system in HOMER for this purpose and then used RET-Screen to run the model’s financial analysis. In this study, to ascertain the model’s sustainability, power and gas are provided at a cheap cost, and the revenue is mostly obtained from the sale of fertilizer. However, HOMER calculates the cost of energy using associated expenses. HOMER is unable to perform the project’s required financial analysis.
2.6. Financial Evaluation Criteria
Internal rate of return and payback period have been utilized to evaluate the financial aspects of the project. The definition of a payback period is the amount of time required for annual cash flow to become positive, meaning that the system investment and operational costs incurred up until that point have been compensated by the revenues received. The interest rate at which cash inflows and outflows are equal is known as the internal rate of return (IRR) [45].
3. Results and Discussions
3.1. Electricity Demand Assessment
For load assessment, a survey was conducted, with positive responses from 176 personnel from different villages. The survey questions included household size, family members, rating, and usage duration of electric appliances. Further, queries related to the day-to-day routine of the local community were included to explore the operational time of electric devices, as listed in Table 2. The typical electric load of community services units, including schools and health, is shown in Table 3, while the typical electric load for commercial units is shown in Table 4.
3.2. Load Profiling
According to the survey data, the system’s peak demand (35.1 kW) occurs at 09:00 a.m., with load ranging from 15 kW to 30.1 kW at other times. Deferrable loads, such as iron and mill motors, were shifted to low-demand time slots, where the time slot is not essential. The load valley filling and peak clipping can help reduce peak demand [46]. We have designated slots of 5, 10, and 15 houses for ironing. Additionally, the homes occupying that particular time slot are permitted to use iron. The only times the mill is prohibited from operating are during periods of high demand. The mill owner will have complete freedom to run business at night. The coincidence factor method is used to extract hourly load profiles [47]. The fact that the behavioral patterns among several customer types coincide is the foundation of the coincidence factor theory. Since it is improbable that all users would be using their full capacity at once, installations or networks can be designed with lower load capacities than the simple sum of the expected peak loads. The coincidence factor’s value is influenced by the quantity of loads (consumers) and declines as this quantity rises [48]. A single load has a coincidence factor of one, while a residential load with a large number of houses has a coincidence factor of 0.2 to 0.3 [49]. The micro-grid load profiles for both the summer and winter seasons are shown in Figure 3. Summer peak demand is 30.9 kW and occurs at 9:00 a.m., while winter peak demand is 27.6 kW and occurs at 11:00 a.m. These load profiles are consistent with those in a study by Kazmi et al. [50]. The summer demand is always high compared to winter. In summers, grids are at their highest level, as transformers are mostly overloaded, thereby worsening the situation for customers. On average, summer energy demand is almost twice the winter energy demand. Households have been reported consuming 5–10 times higher electricity during summer than winter [50]. The high summer load is caused by the region’s extreme summer heat, which causes people to use fans almost constantly throughout the day rather than during the winter. In our case, the system optimization required the maximum factors to be considered, i.e., summer load, to provide an uninterrupted power supply over the entire year. The model can accommodate up to 100 households because it is customary in the region for residents to build new homes on their privately held farmlands that are farther from the recent populated areas. We therefore assumed that the load would remain constant over the course of the projects. Additionally, as the project generates income, the organizing committee may increase its capacity to take on additional loads.
3.3. Gas Demand Assessment
As per Omer et al. [51] and Werner et al. [52], approx. 0.15 m3 of biogas is required for a single person to cook. According to Table 1, each rural dwelling inhibits nine people, and projects serve one hundred houses, thus the total number of people served by the project is 900 and the daily gas demand is calculated to be 0.15 × 900 = 135 m3.
3.4. Solar Resource Assessment
From 1981 to 2019, utilizing NASA’s data, the monthly average solar radiation value was determined using Excel. The yearly average daily radiation was 5.02 kWh/m2/day, with a maximum of 6.8 kWh in June and a minimum of 3.34 kWh in December [53], also confirmed by a recent study [54], as shown in Figure 4. Solar radiation data for Lakki Marwat was obtained from the NASA Power Project.
3.5. Biomass Resource Assessment
According to Equation (1), the district’s current cattle (cows and ox) population is around 172,100. While the growth rate of goats in Khyber Pakhtunkhwa province is 6.45 percent, the district currently has about 0.7 million goats [6], and this equates to 7 goats and 1.75 cows per household on average. According to statistics, a 100-household community owns 175 cattle and 700 goats. On average, nine people per house are living in the area. About 200 g of degradable (kitchen) waste is generated per person each day [55]. Table 5 shows animal manure rates and biogas production per kilogram (kg). However, since we have not included the agricultural and plant residues, we assume that total manure is collected to compensate for the agricultural and forest residues. As reported in Table 5 [27,55], 1 m3 biogas can generate 2.5 kWh of electricity. According to Table 5, there is enough biomass to generate 508 kWh of electricity and satisfy the cooking needs of 900 persons.
3.6. Initial, Operational and Periodic Costs
The capital or initial cost includes equipment purchases, such as PV modules, biogas generator, biogas plant, AC/DC hybrid converter, batteries, and different engineering and developmental works. We consulted the local market, online stores, and recent literature [59,60,61] to determine the costs. Initial costs are listed in Table 6, while Table 7 shows an optimized hybrid system’s annual operation and maintenance costs, including salaries, generator maintenance, PV system, batteries, biogas plant, and fertilizer unit maintenance. Table 8 shows the periodic costs of the system, which occur at the end of the useful life of the equipment.
Multiple online shops, markets, and literature are reviewed for pricing confirmation. Prices for PV and batteries are chosen using the solar calculator on the online On grid Solar shop website [62]. For the invertor price, the same online store’s invertor price webpage was consulted [63]. For batteries, we consulted the Solar Shop online store [64]. Prices for the anaerobic digestor and biogas generator are taken from Shahzad et al. [27] with a 10% price increase to account for inflation. We spoke with a local manufacturer to obtain a pricing quote for the fertilizer unit and estimated the cost to build the unit. Officials from the Peshawar Electric Supply Company (PESCO) have confirmed the costs of the transmission lines and substations. Table 6 provides the system’s initial cost.
The salary and biomass fuel prices are established in accordance with the local market, with 10% higher salaries and 10% higher market costs. The local service provider has evaluated the cost of maintenance. The periodic costs in Table 8 have been also verified by a local service provider.
There will be no gas or electricity connection fees collected from consumers. No meters are necessary on the consumer side because this study suggests a fixed tariff for all houses, communal structures, and commercial buildings. They simply need to draw a connection to their homes, which is outside the scope of this study and is not covered in it.
3.7. System Design and Analysis
The minimum unmet load percentage is the primary selection criterion for the model. Table 9 displays the top 5 models based on the minimum load conditions that had not been met. In the HOMER simulations, we included the cost of biomass, the capital cost of each component, as well as operational, maintenance, and periodic costs. Revenues are not included in the HOMER analysis. The HOMER simulations will provide the cost of energy per kWh as well as excess energy and unmet load percentages. On the basis of unmet load criteria, as per HOMER simulations and based on the ground demand data, we selected a 67 kW system to fulfil the community’s electricity demand. A 30 kW solar power system, a 37 kW biogas generator, an 18.5 kW hybrid converter, and 64 kWh tubular batteries made up the proposed model. As an 18.5 kW invertor is not present on the market, a 20 kW invertor will be selected for this project. According to Table 6, the selected model has the lowest cost of energy and lowest unmet load percentage.
3.8. Technical Analysis
Table 10 lists the electrical power system analysis. According to the optimization results, maximum daily power generation (around 657.3 kW) occurs in July, while the minimum daily (290.33 kW) occurs in January. Figure 5 shows the monthly power generation by each source. The PV system produces one-fourth of the total power generated, with the rest coming from the biogas generator listed in Table 5. The share of power generated by the optimized system is consistent with the literature [10,27]. The system’s unmet load is 0.01 percent, or 16.62 kWh, while the extra energy it produces is 6813 kWh (3.62 percent). The system’s capacity shortage is 0.1 percent, or 177 kWh.
In summer, the daily average biogas demand for power generation is around 188.586 m3, while in the winter, it is around 111.84 m3. Total biogas consumption for power generation and cooking is around 188.586 + 135 = 323.586 m3 in the summer and 111.84 + 135 = 246.84 m3 in the winter. Annual average of daily biogas consumption is 285.213 m3, much less than the potential. The extra gas can be sold to other people using cylinders. According to the data, 10% of the biogas produced is surplus, contributing to differences in the amount of waste collected on some days. If the entire collection cannot be accomplished on a given day, it can be made up for by including agricultural and forestry residues that can be stocked.
Approximately 10% of waste is converted into biogas, while 90% is discarded as wet slurry. Because slurry contains 75% moisture, one kg of waste can produce 1 × 0.90 × 0.25 = 0.225 kg of organic fertilizer [60]. The digester receives 5780 kg of waste per day and generates around 1300 kg of fertilizer.
3.9. Financial Analysis
According to Table 6, the initial cost of project is PKR 12.584 million. Annual operational cost shown in Table 7 is PKR 7.85 million. Periodic costs of project mentioned in Table 8 total PKR 12.58 million for the whole life of project.
Each household would pay a fixed monthly rate of PKR 700 for gas and electricity. The monthly electricity fee for each shop will be PKR 1000, while the monthly electricity fee for the flour mill would be PKR 10,000. An amount of 1300 kg of fertilizer is produced on average per day, as shown Table 11. Farmers will purchase the fertilizer at PKR 20 per kg. Electricity and gas charges of PKR 1.02 million will be collected yearly basis for the project, while the annual revenue from fertilizer sales is PKR 8.5 million.
Based on the RET-Screen analysis, the system will pay back in seven years and take 2.5 years to pay back equity. On an equity basis, the internal rate of return is 42.7%, while on an asset basis, it is 7.2%. The designed system payback period is consistent with the literature [53]. Despite this, our designed system has a longer payback period than Sarkar et al.’s study [54], as he accounted for biomass without cost. Besides revenue for the project, each house will earn PKR 4375 per month by selling wastes to the project.
At the same time, we proposed a high price for the manure to encourage local people’s interest in the project, which is essential to its sustainability. The average annual savings of the system is PKR 1.51 million. The previous model reported by Shahzad et al. [27] did not consider fertilizer selling, and revenue was mainly collected by selling electricity. Our model has been designed to benefit and develop underprivileged communities unable to afford high electricity and gas tariffs.
4. Conclusions
The study examines the solar–biomass hybrid power generation system used in rural community development from a techno-economic perspective. Several configurations of solar–biomass hybrid systems were analyzed using HOMER and RET Screen. All possible configurations were assessed based on critical parameters such as solar irradiation and the availability of biomass resources. The optimization results show that a system with 30 kW PV, 37 kW biomass, a 64 kWh battery storage capacity, and a 20 kW converter is the most cost-effective and technically feasible and can provide 515 kWh of energy and 338.50 m3 of biogas daily. The system will provide biogas for the power and cooking needs of 900 individuals living in 100 homes. The system will also provide power at a fixed rate for productive buildings and free electricity to community buildings in addition to residential applications. The system will generate 1300 kg of organic fertilizer each day, which will be sold to local farmers for 50% less than what it would cost on the open market.
The average annual savings is PKR 1.51 million. The system’s payback period is seven years, and the equity-based payback period is 2.5 years. Besides revenue for the project, each household will earn PKR 4375 monthly by selling wastes to project. Optimized hybrid system analysis shows that it is the optimum energy generation system for improving the lifestyles of rural communities in Pakistan.
Future research on how the connection between the grid and microgrids affects the financial cash flow of hybrid solar biogas microgrids. As a result of the interconnection, the biogas and power systems’ capacities may be lowered, resulting lower initial operational and maintenance costs.
Conceptualization, F.N. and M.A.; methodology, F.N. and A.N.; software, F.N. and M.S.; validation, M.A., A.N. and M.S.; formal analysis, F.N.; investigation, A.S.A.H.; resources, F.N.; data curation, F.N. and M.A.; writing—original draft preparation, F.N., A.N. and M.A.; writing—review and editing, A.S.A.H., T.A.K., S.I.U.d. and A.I.; visualization, M.A., A.N. and M.S.; supervision, A.I.; project administration, A.S.A.H. and A.I.; funding acquisition, A.S.A.H. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Not applicable.
The authors would like to thank the Solar Energy Research Institute, Universiti Kebangsaan Malaysia (UKM), and the Faculty of Science and Natural Resources, Universiti Malaysia Sabah (UMS). This research was supported by MRUN Rakan-RU-2019-001/4 (UKM) and SPBK-UMS phase 1/2022 (SBK0518-2022) (UMS) research grants. The authors also thanks: 1. Department of Renewable Energy, Khushal Khan Khattak University, Karak, Pakistan; 2. USAID Pakistan.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Population of Lakki Marwat [
| Tehsil | Region | Population | No. of Household | Persons per Households |
|---|---|---|---|---|
| Lakki Marwat | Rural | 519,809 | 60,215 | 8.63 |
| Urban | 59,465 | 7923 | 7.50 | |
| Serai Naurang | Rural | 266,953 | 26,794 | 9.96 |
| Urban | 29,995 | 3119 | 9.61 | |
| Total | 876,182 | 98,051 | 8.93 |
Domestic Load.
| Appliances | Power Rating [W] | No. [No]. | Connected Load [W] |
|---|---|---|---|
| Indoor Lights | 18 | 8 | 144 |
| Outdoor Lights | 18 | 2 | 36 |
| Fans | 50 | 4 | 200 |
| Pedestal Fans | 50 | 2 | 100 |
| Iron | 1000 | 1 | 1000 |
| Mobile Charger | 20 | 2 | 40 |
| Refrigerator | 200 | 1 | 200 |
| Single Household Load | 1720 | ||
| 100 Household Load | 172,000 | ||
Community load.
| Appliances | Power [W] | Mosque [No.] | Health Unit [No.] | School [No.] | Total [No.] | Connected Load [W] |
|---|---|---|---|---|---|---|
| Indoor Lights | 18 | 8 | 6 | 12 | 26 | 468 |
| Outdoor Lights | 18 | 2 | 2 | 2 | 6 | 108 |
| Fans | 50 | 10 | 6 | 12 | 28 | 1400 |
| Computers | 200 | 0 | 1 | 1 | 2 | 400 |
| Total Load | 2376 | |||||
Productive load.
| Appliances | Power [W] | Shops [No.] | Flour Mill [No.] | Total [No.] | Connected Load [W] |
|---|---|---|---|---|---|
| Indoor Lights | 18 | 5 | 1 | 6 | 108 |
| Outdoor Lights | 18 | 5 | 1 | 6 | 108 |
| Fans | 50 | 5 | 1 | 6 | 300 |
| Refrigerator | 200 | 2 | 0 | 2 | 400 |
| Mill Motor | 1000 | 0 | 1 | 1 | 1000 |
| Total Load | 1916 | ||||
Power and cooking requirement estimation from biomass resource.
| Waste Type |
No. of
|
Daily Manure
|
Daily Total
|
Biogas m 3 per Kg Manure | Total Biogas |
Biogas for Cooking of 900 People (@0.15 m3/Person) | Biogas for Electricity Generation |
Electricity
|
Daily Electricity
|
|---|---|---|---|---|---|---|---|---|---|
| Manure | Cows (175) | 23 [ |
4025 | 0.05 [ |
201.25 | 135 | 66 | 2.5 [ |
165 |
| Manure | Goats (700) | 2.25 [ |
1575 | 0.07 [ |
110.25 | X | 110.25 | 2.5 | 275.62 |
| Kitchen Waste | Human (900) | 0.20 [ |
180 | 0.15 [ |
27 | X | 27 | 2.5 | 67.5 |
| Total | X | X | 5780 | X | 338.50 | 135 | 203.50 | 2.5 | 508 |
Initial cost.
| Component | Specification | Life (Years) | Price (Million PKR) * |
|---|---|---|---|
| PV Modules including Installation | 30 KW | 25 [ |
2.25 |
| Invertor | 20 KW | 10 [ |
0.435 |
| Batteries | 64 kWh | 10 [ |
1.32 |
| Biogas Generator | 37 KVA | 8 [ |
1.0 |
| Anaerobic Digestor | 480 m3 | 25 [ |
4.0 |
| Fertilizer Unit | - | 25 | 2.5 |
| Transmission Line | 0.5 km | 25 | 4.0 |
| Substation | - | 10 | 0.03 |
| Training | 4 persons | - | 0.2 |
| Feasibility Study | - | - | 0.1 |
| Contingencies | 5% | - | 0.6 |
| Total | 12.584 |
* On 23 April 2021, USD 1 = PKR 154.
Annual O&M costs.
| Type | Specification | Cost (Mill. PKR) |
|---|---|---|
| Salaries | 6 persons | 1.56 |
| Maintenance | - | 2.35 |
| Fuel Price | 2100 tons (@ PKR 2500/ton) | 5.25 |
| Fertilizer Preparation | 425 ton (PKR 800/ton) | 0.34 |
| Land Rent | PKR 20,000/month | 0.24 |
| Contingencies | 3% | 0.228 |
| Total | 7.855 |
Periodic costs.
| Equipment |
Replacement Cost
|
Duration | Cost (Million PKR) |
|---|---|---|---|
| Battery | 60 | 10 | 0.792 |
| Biogas Generator | 70 | 8 | 0.7 |
| Inverter | 70 | 7 | 0.3 |
| Fertilizer Unit (Machinery) | 60 | 10 | 0.2 |
| Total | 12.584 | ||
HOMER top 5 models.
|
PV
|
Biogas
|
Invertor
|
Battery
|
COE
|
Excess Energy (%) | Unmet Load (%) |
|---|---|---|---|---|---|---|
| 30 | 37 | 18.5 | 64 | 15.2 | 3.62 | 0.01 |
| 30 | 37 | 17 | 56 | 16.38 | 1.2 | 2.3 |
| 20 | 33 | 37 | 43 | 19.58 | 0.01 | 6.73 |
| 15 | 28 | 15 | 64 | 18.2 | 0.45 | 10.21 |
| 15 | 38 | 20 | 64 | 17.51 | 0.12 | 10.38 |
Power system analysis.
| System | kWh/Year | % | Analysis | kWh/Year | % |
|---|---|---|---|---|---|
| Biogas Generator | 137,192 | 73 | Excess Power | 6813 | 3.62 |
| PV | 50,795 | 27 | Unmet Load | 16.6 | 0.01 |
| Total | 187,987 | 100 | Capacity Shortage | 177 | 0.1 |
Total revenue.
| Income Source | Amount/Month | No. | Total Revenue (Million PKR) |
|---|---|---|---|
| Electricity and Gas | PKR 700 per |
100 households | 0.84 |
| PKR 1000 per |
5 Shops | 0.060 | |
| PKR 10,000 per |
1 Flour Mill | 0.12 | |
| Fertilizer | PKR 20,000/ton | 425 tons | 8.5 |
| Total | 9.52 | ||
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Details
; Ag Sufiyan Abd Hamid 2
; Arif, Muhammad 3
; Khan, Tufial A 4
; Amir Naveed 3
; Sadiq, Muhammad 5 ; Sahibzada Imad Ud din 6 ; Ibrahim, Adnan 6
1 Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; Department of Renewable Energy, Khushal Khan Khattak University, Karak 27200, Pakistan
2 Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
3 Center for Advanced Studies in Energy, University of Engineering and Technology, Peshawar 25120, Pakistan
4 Department of Basic Sciences, University of Engineering and Technology, Peshawar 25120, Pakistan
5 Department of Mechanical Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
6 Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia