Optimal operation of an integrated hybrid renewable energy system with demand-side management in a rural context

Citation: Kumar, P.P.; Nuvvula, R.S.S.; Hossain, M.A.; Shezan, S.A.; Suresh, V.; Jasinski, M.; Gono, R.; Leonowicz, Z. Optimal Operation of an Integrated Hybrid Renewable Energy System with Demand-Side Management in a Rural Context. Energies 2022,15, 5176. https:// doi.org/10.3390/en15145176 Academic Editor: Albana Ilo Received: 7 May 2022 Accepted: 7 July 2022 Published: 17 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Optimal Operation of an Integrated Hybrid Renewable Energy System with Demand-Side Management in a Rural Context Polamarasetty P Kumar 1,*, Ramakrishna S. S. Nuvvula 1, Md. Alamgir Hossain 2, SK. A. Shezan 3, Vishnu Suresh 4,* , Michal Jasinski 4,* , Radomir Gono 5and Zbigniew Leonowicz 4 1Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam 532127, India; [email protected] 2Queensland Micro and Nano-Technology Centre, Griffith University, Nathan, QLD 4113, Australia; [email protected] 3Department of Electrical Engineering, Engineering Institute of Technology, Melbourne, VIC 3001, Australia; [email protected] 4Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland; [email protected] 5Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic; radomir[email protected] *Correspondence: praveenkumar[email protected] (P.P.K.); [email protected] (V.S.); [email protected] (M.J.) Abstract: A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or costeffective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB © environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiencybased scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system’s performance. Keywords: off-grid; integrated renewable energy; demand-side management; optimization techniques; different batteries 1. Introduction 1.1. Need for Energy Management Systems Energy and freshwater are essential to humankind, but the planet is suffering greatly from future and current energy demands as well as freshwater requirements due to the Energies 2022,15, 5176. https://doi.org/10.3390/en15145176 https://www.mdpi.com/journal/energies Energies 2022,15, 5176 2 of 50 rapid climate change and population growth [ 1 ]. To resolve this power shortage, a backup power system is needed. Diesel generators (DGs) have been employed as a backup mechanism for a range of off-grid applications, but they face a number of key issues, including fuel price volatility and high operating and maintenance costs. However, the optimum combination of RE resources and DGs results in a cost-effective, efficient, and clean energy system that reduces the uncertainties, energy prices, and CO 2 emissions. At the same time, an off-grid RE-based power system comprised of one or two RE resources in conjunction with the battery storage system and DG is an ideal combination for electrifying the off-grid rural areas. In the context of microgrid sizing, microgrids are typically either undersized or oversized to meet the energy demands. An undersized microgrid would result in a loss of power supply while an oversized microgrid would result in high system costs and excess electricity production. Hence, to resolve these issues and reap the benefits of the RE-based microgrid, a strong energy management strategy (EMS) is required [2]. 1.2. The Importance of a Reverse Osmosis Desalination Plant for Remote Villagers In India, safe drinking water is exceedingly limited, particularly in remote rural villages. Although some villages continue to receive government water supply, almost 73% of Indian villages still rely on groundwater supply. Unfortunately, none of these resources are unsuitable for providing a safe drinking water supply. The usage of fertilizers in such areas, as well as other activities such as mining, has polluted the groundwater supply. Streams surrounding human areas, such as villages, are also heavily polluted. Numerous Indian soils have brackish groundwater with total dissolved solid (TDS) concentrations of more than 500 mg/L. It is greater than the Bureau of Indian Drinking Water Standards’ recommendation. Children of all ages are affected by this contaminated drinking water. Children under the age of five are especially vulnerable since it frequently kills them and creates serious health-related problems. As a result, the usage of reverse osmosis Desalination (ROD) plants is essential for the health and well-being of rural village dwellers. Membranes and chemicals are now widely available in the market as replacement components of the ROD units, and nowadays, ROD units can be powered by locally available RE resources such as solar, wind, and biomass. As a result, deploying ROD units in isolated rural communities has become both simple and cost-effective, as well as necessary. 1.3. Overview of the Optimization Techniques Several studies on microgrid size issues have been reported in the literature. The preceding approaches can be divided into three categories: (i) software tools such as RETScreen, HOMER, IHOGA, HOGA [ 3 ], etc., (ii) deterministic approaches such as graphical construction, probabilistic, iterative, linear programming, and analytical and numerical methods [ 4 ], and (iii) metaheuristic algorithms such as grasshopper optimization algorithm (GOA) [ 5 ], grey wolf optimization (GWO) [ 6 ], particle swarm optimization (PSO) [ 7 ], genetic algorithm (GA) [ 8 ], etc. Although the software tools are simple to use, users cannot select the necessary components in it and have no access or control over the algorithms and calculations contained within them. Using software tools, several assumptions and sequences can limit the microgrid size issues. Additionally, the deterministic approaches outperform the software tools [ 9 ]. However, because of the complexities of microgrid sizing, at the local optima, the optimal solution is extremely entrapped. In these circumstances, they are unable to converge to the global best optimum solution. As a result, the algorithm must be repeated numerous times with the initial condition chosen at random to avoid this local optima entanglement. Hence, the solution is unlikely to be the global best optimal solution, and the algorithm has to try several times to discover it. Therefore, metaheuristic algorithms have become one of the most promising and extensively used methods [4]. Since the last decade, a number of metaheuristic algorithms have been developed and paved the way for concerns such as microgrid sizing. Interestingly, few of these methods, such as particle swarm optimization (PSO) and genetic algorithm (GA), are well-known not only among the computer scientists but also among a large number of scientists from Energies 2022,15, 5176 3 of 50 other fields. They are adaptive approaches that outperform deterministic methods because their solutions are not substantially entangled at local optima. All of these algorithms have various benefits, including the ability to handle any type of optimization problem [ 9 ]. In contrast, the no-free-lunch theorem states that a particular metaheuristic algorithm can achieve the global best optimal solution for a specific objective function but it may produce ineffective outcomes for other objective functions [ 10 ]. This has prompted microgrid size researchers to look at the maiden metaheuristic algorithms [4]. 1.4. Literature Review on Optimization Techniques and Different Battery Technologies Rechargeable batteries, soil physics, and chemical engineering are just a few of the many fields that make use of electrolyte diffusion in electrolyte solutions [ 11 , 12 ]. For the standalone un-electrified villages in the Chikmagalur district of Karnataka, Ramesh and Saini [ 13 ] used the HOMER Pro to conduct a feasibility analysis for the PV/diesel generator (DG)/micro hydro power (MHP)/WT/BAT configuration with LA and Li-Ion battery technologies and three dispatch strategies such as cycle charging (CC), combined dispatch (CD), and load following (LF) and it was revealed that the Li-Ion battery-based IHRES with CD strategy had the lowest net present cost (NPC) and cost of energies (COEs) when compared to CC and LF strategies. Alpesh and Sunil [ 14 ] used a PV/biogas generator (BGG)/biomass generator (BMG)/WT/ LA battery configuration to power a simple off-grid village of 123 hamlets near the GujaratRajasthan state border in India and conducted an assessment using the technique of optimum component selection with widely available types of equipment using a multi-variable linear regression algorithm (MVLRA) and PSO to obtain the optimal results with the MVLRA. Rajanna and Saini [ 15 ] employed a genetic algorithm to electrify five independent un-electrified hamlets in India’s Chamarajanagar district of Karnataka state using a configuration of PV/BMG/BGG/WT/MHP/LA battery technology. Ankit et al. [ 16 ] used the HOMER Pro ® software tool to electrify five independent un-electrified hamlets in the Almora district of Uttarakhand state in India using a PV/BGG/DG/MHP/BMG/LA battery configuration to minimize the system’s NPC. Upadhyay and Sharma [ 17 ] used CC and LF strategies with GA, biogeography-based optimization (BBO), and PSO algorithms to power seven standalone villages in the Indian state of Uttarakhand with a configuration of PV/BMG/DG/BGG/MHP/LA battery technology. From the results, it was observed that the BBO algorithm produced the optimal results. Chong Li et al. [ 18 ] used the HOMER pro ® software tool to conduct a study for 280 single-family homes in Gansu Province, China, employing a WT/DG/BAT configuration with Li-Ion, LA, and zinc-bromine (ZB) battery technologies. According to the findings, the ZB battery technology produced the optimal results. Bart et al. [ 19 ] used a Simapro software tool to conduct a life cycle assessment of PV lighting products in a solitary rural area in South-East Asia and found that solar PV lighting has a lower environmental effect than traditional lighting options. Shezan et al. [ 20 ] used the HOMER Pro ® software tool to conduct a study in a solitary rural region of KLIA Sepang Station in the Malaysian state of Selangor, employing a configuration of PV/DG/WT/LA battery technology to lower the system’s NPC. Carlos et al. [ 21 ] used GA-based algorithms to analyze how to power an Indonesian island with PV/DG/Li-Ion battery technologies. Chhunheng and Supachart [22] used the HOMER Pro®software tool to analyze how to electrify a solitary rural region in Cambodia utilizing PV/DG/LA battery technology to lower the system’s NPC. Sompol et al. [ 23 ] used the LABVIEW software tool to conduct a study for off-grid applications in Thailand with a BMG/PV/Li-Ion battery configuration. Haein and Tae [ 24 ] used the HOMER Pro ® software tool to conduct an analysis to power a freestanding region in Myanmar utilizing a PV/DG/BAT configuration with LA and Li-Ion battery technologies to lower the system’s NPC, and from the results, it was identified that the LA battery technology provided the optimal results. Lorafe et al. [ 25 ] used the HOMER Pro ® software tool to analyze how PV/LA battery technology could be used to power South-East Asian islands: Philippines, Gilutongan, Energies 2022,15, 5176 4 of 50 Cebu, and Cordova with minimum NPC. Sarah et al. [ 26 ] conducted an analysis in Dodoma and Tanzania utilizing the PV/DG/WT/BAT configuration with LA and Li-Ion battery technologies and found that the Li-Ion battery-based configuration with GA had the lowest COE. Kaabeche and Bakelli [ 27 ] conducted an assessment using a WT/PV/BAT configuration with Li-Ion, LA, and nickel-cadmium battery technologies. The ALO, GWO, JAYA, and Krill Herd algorithms were used to examine the system’s unit electricity cost and it was discovered that the JAYA algorithm provided a viable solution with an LA battery-based system, followed by Li-Ion and Ni-cd batteries. 1.5. Demand-Side Management In general, uncoordinated peak and valley load demands increase energy costs by expanding the generation and distribution networks, as well as forcing generators to run out of their rated capacity during peak load periods [ 11 ]. Hence, it would be advantageous to lower some of these demands in order to avoid the need for costly extra installations [ 12 ]. Furthermore, the energy demand curves must be as smooth as possible for several reasons, including minimizing the strain on power generation equipment and other protective components of the microgrid, as well as lowering energy costs and deferring or avoiding future equipment investment. In this context, several demand-side management (DSM) strategies in the power system industry have been applied using a variety of methods such as “peak clipping, valley filling, load shifting, energy conservation, load building and flexible load shape” [13], which are illustrated in Figure 1and described as follows [28]: (a) Peak clipping: peak clipping is a technique for reducing load demand during peak hours. It is frequently accomplished by either limiting the use of appliances during peak hours or motivating customers to modify their demand behavior by offering attractive price signals. (b) Valley filling: the purpose of valley filling is to stimulate energy use during offpeak hours in order to increase average energy utilization. It can be done by encouraging customers to do things such as loading and charging during off-peak hours when utilities prefer to use less energy to meet the load demand. (c) Load shifting: this is intended to shift the loads from on-peak to off-peak hours without altering the energy use pattern. For example, during off-peak hours, customers can store thermal heat and use it to keep the room warm all day. Similarly, other household activities such as washing clothes and washing dishes can be done at night to prevent peak loading. (d) Energy conservation: the goal of energy conservation is to reduce the energy demand by using energy-efficient devices. Changing to efficient devices can reduce the load demand as well as change the load shape. (e) Load building: load building and flexible loads are connected to the network supported under the principle of smart grids. Load building improves load sharing as well as energy storage systems to improve grid responsiveness. (f) Flexible load shape: flexible loads can be handled in return for the benefits. This implies that the load shape is responsible for the reliability conditions which means that the loads can be modified according to the reliability of the system. Energies 2022,15, 5176 5 of 50 Figure 1. All types of demand-side management. 1.6. Literature Review of Demand-Side Management The concept of DSM has inspired the attention of researchers working on autonomous IHRESs. Rajanna and Saini [ 29 ] used GA and PSO algorithms to analyze the performance of Energies 2022,15, 5176 6 of 50 four un-electrified village zones in India with a DSM strategy using three investment-based scenarios, such as low investment with high rating appliances, medium investment with moderate rating appliances, and high investment with low rating appliances and found that the systems with DSM strategy had the lowest costs using PSO. Upadhyay and Sharma [ 17 ] proposed three energy management schemes based on the HOMER pro ® software tool and GA and PSO algorithms, claiming that peak shaving with the CC strategy utilizing the PSO method would be more cost-effective than other methods. Chauhan and Saini [ 30 ] investigated the techno-economic aspects of an IHRES using an energy management approach by considering a load-shifting strategy based on DSM to meet the energy demands of the population of Uttarakhand state villages in India, finding that the DSM strategy was a more cost-effective solution than the NON-DSM strategy. Zheng et al. [ 31 ] used linear economic programming to design a tariff-based loadshifting algorithm to lower the operational costs of a biomass-based microgrid with combined heat and power. Wang et al. [ 32 ] combined the receding horizon optimization technique with DSM to lower the maximum operating and environmental expenses of a standalone PV/WT network-based single-family dwelling. To obtain the best performance in standalone systems, Marzband et al. [ 33 ] presented a stochastic optimization technique that takes into account fluctuations in the design of load utilization. Matallanas et al. [ 34 ] suggested a DSM control technique for enhancing business planning in PV systems using neural networks with the goal of increasing energy efficiency. Gudi et al. [ 35 ] used a binary particle swarm optimization to apply the DSM strategy in the home sector for cost savings of the suggested system. Kyriakarakos et al. [ 36 ] proposed a smart DSM solution based on the grey prediction algorithm to meet system architectural principles and ensure the effectiveness of a freestanding multi-generated microgrid operating in remote places. Randa Kallel et al. [ 37 ] investigated the benefits of the proposed integrated system strategic plan under various scenarios and conducted a comparison between the DSM and NON-DSM energy management strategies. 1.7. Motivation for the Article to Consider Energy Conservation-Based DSM An energy conservation-based electrification is highly recommended in India. On 5 January 2015, the Government of India launched Unnat Jeevan by Affordable LEDs and Appliances for All (UJALA) scheme, which will provide people with cost-effective energyefficient LED bulbs compared to market prices through Energy Efficiency Services Ltd. (EESL) in a joint venture managed by the Indian Ministry of Power providing widespread distribution of LED bulbs and energy-efficient electrical appliances. It has been distributed more than 21.7 crores of energy-efficient LED bulbs with its network spread over 24 states in India, resulting in energy and electrical bill savings as well as the reduction in both the CO 2 emissions and peak load demands. The National Energy Efficient Fan Program (NEEFP) was also introduced by the EESL to promote energy conservation through increased residential use of energy-efficient fans and EESL also developed a service model such as the Street Lighting National Program (SLNP) scheme that allows municipalities to replace conventional lights with LEDs without any upfront costs, where the balance of costs is recovered by monetizing the energy savings through local municipalities [ 38 ]. These are all schemes of the Government of India that inspired us to write this article about energy conservation-based DSM. 1.8. Novelty and Overview of the Article It was identified that there has been no research on the supply of freshwater to the isolated regions in the Indian scenario using a configuration of PV/BMG/DG/BAT with Ni-Fe, LA, and Li-Ion battery technologies using LF and CC strategies. Furthermore, several researchers conducted a techno-economic feasibility study to provide an uninterruptable power supply using only one or two types of battery technologies, such as Li-Ion and LA. Moreover, various researchers have conducted studies on different types of DSM approaches. However, no studies have been attempted to consider the efficiency-based Energies 2022,15, 5176 7 of 50 scenarios (energy conservation-based DSM) such as high power rated appliances of low cost (HPRALC), medium power rated appliances of moderate cost (MPRAMC), and low power rated appliances of high cost (LPRAHC) with different dispatch strategies using different battery technologies for off-grid rural areas, which was identified as a significant gap in the existing literature. Finally, several researchers have compared the proposed algorithm’s convergence and robustness efficiency to those of only one or two other algorithms. For a realistic analysis of an off-grid IHRES, the aforementioned gaps and limitations must be resolved. To keep in this view, five un-electrified off-grid villages in the Odisha state of Rayagada district were identified as a study area in order to provide power and freshwater availability using accessible RE resources in the study area, such as biomass and solar. Owing to the intermittent nature of these RE resources, the power supply is not continuous. To ensure a continuous power supply, the study conducted feasibility studies with three different types of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA), as well as a diesel generator (DG), by taking into account LF and CC strategies. In general, the rural people’s load usage pattern is almost the same throughout the day. Regularly, the peak loads occur in the evening due to the priority loads such as lamps, fans, and TVs; these loads cannot be altered by their habitual pattern of use. Therefore, peak clipping and load shifting are not possible for off-grid rural villagers, especially in the evening time. However, with proper energy conservation management, these peak loads can be reduced without peak clipping and load shifting. It is one of the most successful and favored demand response programs for off-grid rural villagers due to its easy-to-adopt benefits, no maintenance, and no need of shifting priority loads. Hence, the study considered energy conservation-based DSM using consumers’ loads usage patterns such as high power rated appliances of low cost (HPRALC), medium power rated appliances of moderate cost (MPRAMC), and low power rated appliances of high cost (LPRAHC) with different dispatch strategies using different battery technologies. Six different configurations were modelled in order to determine the optimum configuration for electrifying the study area using available RE resources and the proposed battery technologies with their different depth of discharges (DODs) such as PV/BMG/DG/LA at 70% DOD, PV/BMG/DG/LA at 80% DOD, PV/BMG/DG/Li-Ion at 50% DOD, PV/BMG/ DG/Li-Ion at 70% DOD, PV/BMG/DG/Li-Ion at 80% DOD, and PV/BMG/DG/Ni-Fe at 80% DOD, which were tested with two different dispatch strategies such as LF and CC using consumers load usage patterns such as HPRALC, MPRAMC, and LPRAHC-based scenarios. To obtain an optimum configuration from these six configurations, a maiden algorithm called a Salp Swarm Algorithm from the metaheuristic family was proposed in the study [ 10 ]. To demonstrate its convergence and robustness efficiency in identifying the global best optimal values, it was compared with eight other proven and well-known algorithms, namely: particle swarm optimization (PSO) [ 7 ], differential evolutionary algorithm (DE) [ 39 ], genetic algorithm (GA) [ 8 ], ant lion optimization (ALO) [ 40 ], grasshopper optimization algorithm (GOA) [ 5 ], grey wolf optimization (GWO) [ 6 ], moth flame optimization (MFO) [ 41 ], and dragonfly algorithm (DA) [ 42 ]. Finally, the optimal configuration’s sensitivity analysis was investigated using various input parameters such as biomass foliage collection rate, interest rate, and diesel prices. 2. Development of the IRES A systematic process is essential for the implementation of an IHRES for isolated rural communities, which is outlined in the following steps: 2.1. Step 1—Study Area Identification In the Rayagada district of Odisha state in India, a group of five un-electrified villages of Muniguda block were considered as a study area. Figure 2shows its geographic location on the map. It is located at 19 ◦ 37 0 16.6944 00 N latitude and 83 ◦ 29 0 50.6688 00 E longitude, at a height of 206 m from the above mean sea level. In this area, a total of 1213 people live in 266 households, none of which have access to electricity or a safe drinking water supply. Energies 2022,15, 5176 8 of 50 Because of its remote location, it has yet to be electrified, so people still rely on solar lamps, kerosene lanterns, and candles for lighting. Figure 2. The location of the study area on the map. 2.2. Step 2—Estimation of Electrical Energy Demand and Hourly Freshwater Requirements This study contains three different types of energy-efficiency scenarios based on the household electrical appliance usage patterns such as high power rated appliances of low cost (HPRALC), medium power rated appliances of moderate cost (MPRAMC), and low power rated appliances of high cost (LPRAHC). Each scenario was classified into community, domestic, commercial, agricultural, or small-scale industrial sectors. HPRALC appliances are incandescent bulbs, LCD TVs, incandescent street light lamps, and lowefficiency ceiling fans. MPRAMC appliances are CFL lights, LCD TVs, CFL street lights, and medium-efficiency ceiling fans. LPRAHC appliances are LED TVs, LED lights, LED street lights, and high-efficiency ceiling fans. The corresponding hourly load demands for both the summer and winter seasons and their related details are given in Tables 1–3and the associated load curves are shown in Figure 3. Energies 2022,15, 5176 9 of 50 Table 1. HPRALC-based scenario load demand for both winter and summer seasons. Load Sector →Domestic Load Community Load Agricultural Load Commercial Load SIL Hourly Energy Demand (kWh) School Hospital Community Hall SL PW MCTM Shops MDP Flour Mill Saw Mills Appliance → LED Lamp Fan TV + Dish MC LED Lamp Fan Computer LED Lamp Fan Refrigerator LED Lamp Fan LED Lights Motor (2 hp) Motor (5 hp) LED Lamp Fan Motor (4 hp) Motor (5 hp) Saw Machine Rated Power → 20 W 75 W 150 W 5 W 20 W 75 W 250 W 20 W 75 W 200 W 20 W 75 W 40 W 1.5 KW 3.73 kW 20 W 75 W 2.983kW 3.73kW 1.8kW Quantity →2 1 1 1 12 12 12 5 5 1 3 3 27 6 1 5 5 3 1 1 Time (h) ↓S/W S/W S/W S/W S/W S/W 0:00–1:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 1:00–2:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 2:00–3:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 3:00–4:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 4:00–5:00 31.92 19.95/0 0.3 0.38/0 0.2 1.08 2.98 56.81/36.48 5:00–6:00 31.92 19.95/0 9.98 0.3 0.38/0 0.2 1.08 2.98 66.79/46.46 6:00–7:00 19.95/0 9.98 1.33 0.38/0 0.2 9 2.98 43.82/23.49 7:00–8:00 19.95/0 9.98 1.33 0.38/0 0.2 9 2.98 43.82/23.49 8:00–9:00 19.95/0 9.98 0.67 0.38/0 0.2 9 2.98 43.16/22.83 9:00–10:00 9.98/0 9.98 0.72 0.9/0 30.38/0 0.2 3.73 0.3 0.38/0 8.95 1.8 40.32/28.68 10:00–11:00 9.98/0 9.98 0.72 0.9/0 30.38/0 0.2 3.73 0.3 0.38/0 8.95 1.8 40.32/28.68 11:00–12:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 3.73 0.3 0.38/0 8.95 1.8 70.65/58.78 12:00–13:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 3.73 0.3 0.38/0 8.95 1.8 70.65/58.78 13:00–14:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 3.73 0.3 0.38/0 2.98 3.73 66.61/54.74 14:00–15:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 62.88/51.01 15:00–16:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 62.88/51.01 16:00–17:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 62.88/51.01 17:00–18:00 9.98/0 39.9 0.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 58.26/47.29 18:00–19:00 31.92 19.95/0 19.95 1.33 0.3 0.38/0 0.2 0.18 0.23/0 1.08 0.3 0.38/0 2.98 79.18/58.24 Energies 2022,15, 5176 16 of 50 is 26 ◦ C. The study area is surrounded by a high dense forest covering about 87 hectares, with a collection rate of 60% of forest foliage such as leaves, pine needles, and firewood. The biomass supply is projected to be 9 tons/year. The study considered in the simulation an average of 10 years (2005–2015) of hourly solar radiation and ambient temperatures, which were taken from the National Renewable Energy Laboratory (NREL) and are shown in Figures 5and 6. Figure 5. Annual global solar radiation of the study area. Figure 6. The annual ambient temperature of the study area. 3. The IHRES Component Mathematical Modelling Before optimal sizing of the IHRES, proper mathematical modelling of the components is needed. The study proposed an IHRES model that incorporates biomass and solar energy Energies 2022,15, 5176 17 of 50 resources as well as a battery bank and diesel generator as a backup power supply. Its schematic diagram is depicted in Figure 7and the corresponding mathematical models are described as follows. Figure 7. The schematic diagram of the IRES. 3.1. Solar Energy System Various models for measuring PV output power have been proposed in the literature. In this analysis, a simplified model was used to calculate the output power of a PV panel Energies 2022,15, 5176 18 of 50 (PPV(t)) by using Equation (1) based on the hourly ambient temperature (Tamb(t)) and hourly solar irradiation (G(t))in the study area [9]. PPV(t) = PVrated ×(G(t)/Gre f )×h1+KT×TC−Tre f i (1) where, Gre f is a reference condition solar radiation, its value is 1000 W/m 2 , KT is the maximum power temperature coefficient, its value is 3.7 × 10 − 3 (1/ ◦ C), Tre f is at the standard test condition PV cell temperature, its value is 25 ◦ C, and PVrated is the PV panel rated power. The cell temperature (TC)is calculated as: TC=Tamb(t) + (0.0256 ×G(t)). (2) where, Tamb(t)is the hourly ambient temperature (◦C). The PV panel energy generation (EPV) is calculated as follows: EPV (t) = NPV ×PPV(t)×∆t(3) where, ∆tis the time span and is considered as one hour. 3.2. Biomass Generator (BMG) The biomass generator is made up of four major components such as “producer gasbased engine cum generator set, gas cleaning system, gas cooling system and biomass gasifier. For the biomass gasifier, the study used a downdraft gasifier design; in this gasifier, mainly seven parts are there such as drying zone, hopper lid, combustion zone, reduction zone, pyrolysis zone, ash removal tank and smoke valve. The cleaning system consists of pan filter, a cyclone, cotton filter and sawdust filter and the cooling system consists of a chiller plant” [ 43 , 44 ]. The front and rear views of the biomass generator are shown in Figures 8and 9, respectively. Figure 8. Front view of the biomass generator [44]. Energies 2022,15, 5176 19 of 50 Figure 9. Rear view of the biomass generator [44]. The power generated by the biomass generator (PBMG) is calculated as [14]: PBMG(t) = QBM ×ηBMG ×CVBM ×1000 DOHBMG ×365 ×860 (4) where, ηBMG is the efficiency of the BMG, CVBM is the calorific value of the biomass, its value is 4015 kcal/kg , DOHBMG is the daily operative hours of the BMG, the 860 value used in the formula is a converting factor from kcal to kWh, and QBM is the availability of the quantity of biomass (tons/year). The energy generated by the BMG (EBMG) is calculated as follows: EBMG(t) = PBMG(t)×∆t(5) where, ∆tis the time period and is considered to be one hour. 3.3. Battery Bank When RE resources are unavailable or the system is experiencing peak load demand, the battery bank usually supplies the power. Whenever excess energy is generated by the RE resources, it is stored in the battery bank. The energy stored in the battery bank at any hour ‘t’ is expressed as follows [9]: EBat(t) = (1−σ)×EBat(t−1)+(EG(t)−EL(t)/ηConv)×ηCC ×ηrbat (6) where, σ is the battery hourly self-discharge rate, EG is the electrical energy generated, EL is the electrical energy demand, ηConv is the bi-directional converter efficiency, ηCC is the charge controller efficiency, ηrbat is the battery round trip efficiency, and EBat(t) and EBat(t−1)are battery bank energy levels at time ‘t0and ‘t−10, respectively. The electrical energy generated (EG)by the RE resources are calculated as follows: EG(t) = [EDC(t) + EAC(t)]×ηConv (7) where the DC energy generated (EDC) by the RE resources are calculated as follows: EDC(t) = EPV(t)(8) Energies 2022,15, 5176 20 of 50 The AC energy generated (EAC) by the RE resources are calculated as follows: EAC(t) = EBMG(t)(9) The electrical power generated by the RE resources during the discharge process is less than the load demand. Therefore, the battery bank can provide the necessary deficit load, which can be expressed as follows: EBat(t) = (1−σ)×EBat(t−1)−(EL(t)/ηConv −EG(t))/ηrbat (10) 3.4. Diesel Generator (DG) Diesel generators are beneficial in off-grid areas because they provide power when batteries fail to fulfill the load demand or when renewable energy supplies are disrupted by prolonged cloudy weather or rainy seasons. The main reason for including DG in the study is that many households, primary health care centers, and businesses are left in the dark during the blackouts caused by super storms and other unexpected occurrences. Furthermore, in the last ten years, blackout cases have doubled. Thus, incorporating a DG set into integrated RE systems improves the efficiency of a microgrid by providing a reliable power source in emergency situations and sharing peak load demands when batteries fail to meet the peak load demands [ 45 , 46 ]. The DG hourly fuel consumption ( FDG ) can be calculated using a linear law based on the required load demand as follows [9]: FDG(t) = aDG ×PDG,gen(t) + bDG ×PDG,ratl/h(11) where, aDG and bDG are the DG fuel consumption curve coefficients and their values are aDG = 0.246 (l/kWh) and bDG = 0.08145 (l/kWh). PDG,gen(t) and PDG,rat are the hourly generated power and rated power of the DG, respectively. The DG annual fuel consumption (AFC) is calculated as follows: AFC = 8760 ∑ t=1 FDG(t)(12) CO2Emissions The hourly CO 2 emissions of DG estimated with respect to the hourly fuel consumption are as follows [47]: CO2(t) = SECO2(kg/l)×FDG(t)(l/h)(13) where, SECO2is the specific CO2emissions per L of diesel and its value is 2.7 kg/L. The DG annual CO2emissions are estimated as follows: ACO2emission = 8760 ∑ t=1 CO2(t)(14) 3.5. Bi-Directional Converter with a Charge Controller (BDC-CC) In general, the BDC-CC converts electrical energy into rectifier and inverter modes of operation. In the inverter mode, it converts the direct current (DC) into an alternate current (AC) and in the rectifier mode, it converts AC into DC. The charge controller is useful for ensuring that the battery bank is not overcharged or over-discharged. The BDC-CC power rating (PBDC-CC) is calculated as follows [14]: PBDC-CC =ET,max ×1.1 (15) where, the multiplication factor 1.1 represents the converter’s 10% overloading capability and ET,max is the maximum amount of energy transferred through the converter. Energies 2022,15, 5176 21 of 50 3.6. Reverse Osmosis Desalination (ROD) Plant In relation to the specific energy consumption ( SEC ), the power ( PDEM ) required by the ROD unit to generate an hourly freshwater demand ( HVDW ) for the desalination process is expressed as follows [1]: PDEM(t) = HVDM(t)×SEC (16) In this study, the ROD unit was expected to consume 2 kWh/m 3 ( SEC ) of specific energy. The ROD unit consists of pumps, a desalination unit, membranes, and energy recovery devices. The ROD unit’s daily volumetric demand of freshwater ( DVDW ) is calculated as follows: DVDW =24 ×PDEM SEC (17) To analyze the characteristic curves of RO membranes, the ROD system was designed to operate in between the installed power ( PI ) and minimum load requirement ( PMLD ), i.e., PMLD ≤PDEM ≤PI(18) where, the ROD unit’s minimum load demand (PMLD) is used to resolve the osmotic pressure produced by the ROD unit, which was estimated to be 25% of the installed power (PI). The ROD unit autonomy assumes a two-day storage period of a freshwater tank to calculate the volumetric capacity of a freshwater tank (VCWT) , which is calculated as follows: VCWT =2×DVDW (19) 4. Economic Analysis of the IRES Several approaches have been used to investigate the economic feasibility of the IHRES such as net present cost, annual levelized cost, life cycle cost (LCC), and payback period. In these scenarios, the LCC methodology for economic analysis is extensively employed since it provides an accurate overview of project expenses over the project’s lifespan. In this study, the LCC of the IHRES was calculated using Equation (20) [ 46 ] by summing the erection costs, initial capital costs, O&M costs, fuel costs, and replacement costs of all system components. The analysis comprised the following assumptions. The erection costs of the PV, BMG, DG, BAT, BDC-CC, and ROD unit were taken as 20% [14], 5% [14], 5%, 3% [14], 3% [14], and 3% [46] of their capital costs, respectively. The replacement cost of the BMG, DG, BAT, BDC-CC, MEM, and CHEM were considered as 70% [ 14 ], 100% [ 46 ], 100% [ 14 ], 100% [ 14 ], 100% [ 14 ], and 100% [ 14 ] of their capital costs, respectively. LCC =ICC +PV,O&M+PV,REP +PV,FUEL (20) The initial capital cost (ICC) of the IRES components are calculated as follows [14]: ICC =CBMG,cap+NPV ×CPV,cap+NBAT ×CBAT,cap+ CROD,cap+CBDC-CC,cap+CMEM,cap+CWTA,cap+CCHE,cap(21) where, CBMG,cap , CPV,cap , CBAT,cap , CROD,cap , CBDC-CC,cap , CMEM,cap , CWTA,cap , and CCHE,cap are the initial capital costs of the BMG, PV, BAT, ROD unit, BDC-CC, MEM, WTA, and CHE, respectively. The erection costs (EREC) of the IRES components are calculated as follows [14]: EREC =       (NPV ×CPV,erect)+(CROD,erect)+ (NBAT ×CBAT,erect)×Nr ∑ b=1 (1+x)bNc−1 (1+y)bNc+ CBDC-CC,erect ×Nr ∑ d=1 (1+x)dNc−1 (1+y)dNc+ CBMG,erect ×Nr ∑ g=1 (1+x)gNc−1 (1+y)gNc!        (22) Energies 2022,15, 5176 22 of 50 where, CPV,erect , CROD,erect , CBAT,erect , CBDC-CC,erect , and CBMG,erect are erection costs of PV, ROD unit, BAT, BDC-CC, and BMG, respectively. The present value of annual O&M ( PV,O&M ) costs of the IRES components are calculated as follows [14]: PV,O&M=(NPV ×CPV,o&m)+(CBMG,o&m) (NBAT ×CBAT,o&m)+(CBDC-CC,o&m)+(CROD,o&m)× N ∑ i=1 (1+x)i−1 (1+y)i(23) where, CPV,o&m , CBMG,o&m , CBAT,o&m , CBDC-CC,o&m , and CROD,o&m are O&M costs of the PV, BMG, BAT, BDC-CC, and ROD unit, respectively, and y is defined as follows [14]: y=Inom −x 1+x(24) where, Inom ,y, N , and xare the nominal interest rate, discount rate, lifespan, and the inflation rate of the project, respectively. The components’ lifespan such as that of batteries, biomass generator, bi-directional converter with a charge controller, membranes, chemicals, and DG are shorter than the project lifetime. Therefore, they need to be replaced at some stage during the project’s lifetime. The present value of annual replacement cost ( PV,REP ) of the IRES is calculated as follows: PV,REP =          NBAT ×CBAT,rep ×Nr ∑ b=1 (1+x)bNc−1 (1+y)bNc+ CBMG,rep ×Nr ∑ g=1 (1+x)gNc−1 (1+y)gNc!+ CCHE,rep ×Nr ∑ c=1 (1+x)cNc−1 (1+y)cNc+CBDC-CC,rep ×Nr ∑ d=1 (1+x)dNc−1 (1+y)dNc+ NMEM ×CMEM,rep ×Nr ∑ i=1 (1+x)mNc−1 (1+y)mNc          (25) where, CBAT,rep , CBMG,rep , CCHE,rep , CBDC-CC,rep , and CMEM,rep are the replacement costs of the BAT, BMG, CHE, BDC-CC, and MEM, respectively, and the Nr is defined as follows [ 14 ]: Nr=intN−Nc Nc(26) where, Nr and Nc are the number of replacements needed for the system components and lifespan of each system component, respectively. The present value of annual fuel cost (PV,FUEL) of the IRES is calculated as [14]: PV,FUEL =[(CBM ×QBM)+(AFCDG)] × N ∑ i=1 (1+x)i−1 (1+y)i(27) where CBM and QBM are the cost and quantity of the biomass, respectively, and AFCDG is the annual fuel consumption of the DG. 5. The Objective Function and Its Constraints The system’s objective function, i.e., life cycle cost (LCC), and its constraints are discussed as follows. Energies 2022,15, 5176 23 of 50 5.1. Life Cycle Cost The objective function as expressed in Equation (28) was used to calculate the system’s life cycle cost. The objective function is primarily dependent on two integer decision variables such as the number of batteries (NBAT) and PV panels (NPV). min LCC(NPV,NBAT)= min ∑ C=PV,BMG,BAT,ROD,BDC−CC (LCC)C(28) 5.2. Upper and Lower Bounds In this study, it was presumed that the biomass generator operates as a fixed energy resource with a rated power of 5 kW and works daily for five hours during the peak load demands, i.e., from 6 P.M. to 10 P.M., to generate 4 kWh of energy per hour. Hence, it was not bound by any constraints. Furthermore, the remaining solar energy resource was subject to the following constraint. 0≤NPV ≤NPV−max (29) where, NPV is the number of PV panels. Furthermore, the battery bank was subjected to the following constraint. 0≤NBAT ≤NBAT−max (30) where, NBAT is the number of batteries. 5.3. Battery Bank Energy Storage Limits The amount of energy stored in the battery bank at any hour ‘t’ is determined by the following constraint [45]: EBat_min ≤EBat(t)≤EBat_max (31) The maximum and minimum energy storage levels of the battery bank is calculated as follows: EBat_max =NBAT ×VBAT ×SBAT 1000 ×SOCmax−bat (32) EBat_min =NBAT ×VBAT ×SBAT 1000 ×SOCmin−bat (33) where, VBAT and SBAT are the voltage and rated capacity (Ah) of the battery, respectively. The minimum and maximum state of charges of the battery is calculated as follows: SOCmin−bat =1−DOD SOCmax−bat =SOCmin−bat +DOD where, DOD is the depth of discharge of the battery. 5.4. Diesel Generator Operating Limits At higher loads, the diesel generator is much more efficient. As a result, the minimum load required for the DG operation is set at 40% of its rated capacity. Accordingly, the DG runs in the operating mode after adhering to the limitations mentioned below [48]: EL(t) ηconv ≥40% of Prdg ×∆t(34) where EL(t) is the hourly energy demand, ηconv is the efficiency of the converter, Prdg is the rated power of the diesel generator, and ∆tis the time period. Energies 2022,15, 5176 24 of 50 5.5. Power Reliability Index The power system’s reliability is described as its ability to supply power for a specified period of time under specific conditions. In this study, The IHRES power reliability was assessed using the loss of power supply probability (LPSP), which is calculated by summing the hours of a power outage to the sum of hourly energy demands. The loss of power supply (LPS) at any hour ‘t’ is calculated as follows [1]: LPS(t) = EL(t) ηConv −EG(t)−[(1−σ)×EBat(t−1)−EBat_min]×ηrbat (35) The LPSP is calculated as follows [1]: LPSP =∑T t=1LPS(t) ∑T t=1EL(t)(36) During the optimization process, the following constraint is useful for analyzing the maximum permissible loss of power supply probability (LPSP*). LPSP∗≥LPSP (37) 6. Methodology 6.1. Load following Strategy The main feature of the LF strategy is that the DG can satisfy the deficiency load demand when the batteries and RE resources are unable to supply the electricity demand. The key concern is that it just provides the deficit load demand only and does not charge the batteries. The overall operation of the LF strategy is outlined in the following modes [24]. 6.2. Cycle Charging Strategy The CC strategy is distinguished by the fact that the DG turns on to satisfy the deficit load demand while also storing energy in the battery bank through the charging process. The overall operation of the CC strategy is outlined in the following modes [49]: The complete EMS operation was conducted in the MATLAB © environment by simulating the input parameters such as techno-economic values of the components, load demand, ambient temperature, and solar irradiation for 8760 h, i.e., for 1 year. The system’s electrical energy demand at any hour ‘t’ is determined as follows: EL(t) = (ELoad(t) + EROD(t))/ηConv (38) The electricity provided by the RE resources (EG)is computed at any hour ‘t’ as follows: EG(t) = [EDC(t) + EAC(t)]×ηConv (39) where the generated AC energy (EAC)and DC energy (EDC) are calculated as follows: EDC(t) = EPV(t)(40) EAC(t) = EBMG(t)(41) During the peak load time from 6 P.M. to 10 P.M., the biomass generator works daily. The minimum and maximum battery bank energy storage limits are calculated as follows: EBat_min =NBAT ×VBAT ×SBAT 1000 ×SOCmin−bat (42) EBat_max =NBAT ×VBAT ×SBAT 1000 ×SOCmax−bat (43) Energies 2022,15, 5176 25 of 50 where, SBAT and VBAT are the rated capacity (Ah) and voltage of the battery, respectively. The minimum and maximum state of charge (SOC) of the battery bank are estimated as follows: SOCmax−bat =SOCmin−bat +DOD (44) SOCmin−bat =1−DOD (45) where, DOD is the depth of discharge of the batteries. At any hour ‘t’, the net energy of the system is estimated as the difference between the hourly energy generated by the RE resources and the projected load demand: Enet(t) = EG(t)−EL(t)(46) Now, the ‘for’ loop begins for 8760 h of simulation. For t= 1:8760 if Enet(t) = 0 (47) Mode 1: in this operating mode, the total net energy provided by the system is equal to 0, and the energy level of the battery bank at that time ‘t’ is equal to the energy level of the previous hour. This mode of operation is described pictorially in Figure 10a, which explains that the switches S 1 and S 3 are in the closed position and the switches S 2 , S 4 , and S 5 are in the open position. The expected load demand is met and there is no power outage, which is mathematically expressed as follows: EBat(t) = EBat(t−1)(48) LPS(t) = 0 (49) ELoad_supplied(t) = EL(t)(50) elseif Enet(t)>0 (51) Ech(t) = EG(t)−EL(t)(52) if Ech(t)≤EBat_max −EBat(t−1)(53) Mode 2: in this operating mode, the RE resources first meet the load demand and then store the produced surplus energy in the battery bank if the energy levels in the battery bank are between the minimum and maximum range, i.e., if (EBat_min ≤EBat(t)≤EBat_max) . This mode of operation is described pictorially in Figure 10b, which explains that the switches S 1 , S 2 , and S 3 are in the closed position and the switches S 4 and S 5 are in the open position. The expected load demand is met and there is no power outage, which is mathematically expressed as follows: EBat(t) = (1−σ)∗EBat(t−1)+Ech(t)∗ηCC ∗ηrbat (54) LPS(t) = 0 (55) ELoad_supplied(t) = EL(t)(56) else Mode 3: in this operating mode, energy from the RE resources initially satisfies the load demand, and if the energy level of the battery bank is at its maximum limit, i.e., if (EBat(t) = EBat_max) , then the surplus energy is used to operate the dump load. In this mode of operation, as shown in Figure 10c, S 1 , S 3 , and S 5 switches are in a closed position and S 2 and S 4 switches are in an open position. The expected load demand is met and there is no power outage, which is mathematically expressed as follows: EBat(t) = EBat_max (57) Energies 2022,15, 5176 32 of 50 Figure 15. Flowchart illustrating the procedure for evaluating the optimal IRES sizing with SSA. 8. Results and Discussion In this study, an optimal IHRES configuration was used to supply freshwater and electricity demands to five rural un-electrified villages in the Indian state of Odisha. These villages are enriched with RE resources such as biomass and solar, which can be used to electrify them. Because of the uncertainties associated with these RE sources, a reliable battery storage system in conjunction with a diesel generator is required to provide a continuous power supply. Therefore, the study focused on three different types of battery technologies, namely lithium-ion (Li-Ion), lead-acid (LA), and nickel-iron (Ni-Fe), to provide a continuous power supply. The purpose of examining various battery technologies is the LA battery technology since it is less expensive in all regions of the world than all kinds of battery technologies. As a result, developing countries such as Pakistan, India, Sri Lanka, Bangladesh, etc., are employing this battery technology to electrify the standalone remote regions without taking into account significant drawbacks such as their durability and lifespan. This battery technology has a shorter lifespan in comparison to other battery technologies and its lifespan is dependent on the ambient temperature at which the batteries are installed. Therefore, every three to five years, they must be replaced. Frequently, replacing batteries in remote regions via difficult roads causes plenty of technical, physical, and economic issues. Hence, before beginning a project, it is indeed essential to understand the technological and economic features of a battery technology such as, technically: durability, high operating Energies 2022,15, 5176 33 of 50 temperature capability, longevity, and round trip efficiency, and economically: operation and maintenance costs, replacement frequency, and capital costs [43]. 8.1. Robustness and History of the Ni-Fe Battery Technology This study proposed a Ni-Fe battery technology to address the aforementioned issues. Although the Ni-Fe batteries are still in the early stages of development, they are the most powerful and reliable battery technology available today and are an excellent option for off-grid RE and solar applications. Ni-Fe batteries have a track record of more than 100 years. Thomas Edison invented and manufactured Ni-Fe batteries in the early 1900s to make them “much stronger than batteries using lead plates and acid”. In the early 1910s, the first electric car was outfitted with Ni-Fe batteries. While they were never used for the starting batteries for internal combustion engines at the period of the automobile invention, their foothold was found in the twentieth century in many railroads, forklifts, and standby power applications. Because of their long life, robustness, and durability, Ni-Fe batteries have been reborn in the twenty-first century for use in RE applications. In comparison to many other types of batteries, the depth of discharge (DOD) of the Ni-Fe batteries has no impact on their life cycle. As a result, consumers can discharge them up to 80% of their rated capacity and have a battery life of 30+ years. It is a well-known fact that if an LA battery is over-discharged even once, its lifetime is significantly reduced. This is true for the majority of battery technologies but not for the Ni-Fe batteries because discharging them up to 80% or more does not shorten their lifetime. Furthermore, Ni-Fe batteries can be overcharged without losing their life expectancy [50]. 8.2. Technical Comparison of the Battery Technologies Used in the Study The following are the technical characteristics of the three different types of battery technologies: Li-Ion, Ni-Fe, and LA. 8.2.1. The Lifetime of the Batteries The battery’s lifetime mainly depends on its depth of discharge (DOD); the DOD simply describes the degree to which the battery has been discharged in relation to its overall capacity. If the battery is fully discharged, then its DOD is 100%. According to the manufacturers, the three batteries, Ni-Fe, Li-Ion, and LA used in the study, have different lifespans depending on their use of the allowable DODs. • The LA battery used in the study can be usable in two allowable DODs, such as 70% and 80%; if it is used at 70% DOD, its lifespan is 3 years; if it is used at 80% DOD, then its lifespan is 2.5 years. • The Li-Ion battery used in the study can be usable in three allowable DODs, such as 50%, 70%, and 80%; if it is used at 50% DOD, its lifespan is 15 years; if it is used at 70% DOD, its lifespan is 9 years; if it is used at 80% DOD, its lifespan is 7.5 years. • The Ni-Fe battery used in the study can be usable in two allowable DODs, such as 50% and 80%; if it is used at 50% DOD, its lifespan is 30+ years; if it is used at 80% DOD, its lifespan is also 30+ years. This is why it is the most robust battery technology because the DOD does not affect its lifespan and it is more suitable for off-grid rural electrification for people living in remote areas since it does not need to be replaced during the lifespan of the project. 8.2.2. Round Trip Efficiency of the Batteries In view of the round trip efficiency of the batteries, the Li-Ion battery has the highest efficiency with 92%, the second best is the LA battery which has an efficiency of 85%, and the third best is the Ni-Fe battery which has an efficiency of 80%. 8.2.3. The Self-Discharge Rate of the Batteries The Li-Ion battery has a self-discharge rate of 0.3%/day, the LA battery technology has a rate of 0.2%/day, and the Ni-Fe battery has a rate of 1%/day. However, in terms of Energies 2022,15, 5176 34 of 50 self-discharge energy losses, the Ni-Fe battery may have negligible losses. For example, if the Ni-Fe battery consumes 25 kWh of energy per day, the self-discharge energy loss is only 0.25 kWh, allowing the remaining 24.75 kWh to be used without any issue. Hence, the self-discharge losses with this battery technology are not that much higher. 8.2.4. Operating Temperature Capabilities of the Batteries With the exception of the Ni-Fe battery technology, most batteries do not have hightemperature capabilities. The Ni-Fe battery offers worry-free service in extreme cold and hot conditions with working temperatures ranging from − 30 ◦ C to +60 ◦ C. The second best is the Li-Ion battery technology with operating temperatures ranging from − 20 ◦ C to +50 ◦ C. Finally, the third best is the LA battery technology with operating temperatures ranging from −20 ◦C to +45 ◦C. 8.2.5. Replacement Frequency of the Batteries during the Lifespan of the Project The current study assumed a project life of twenty-five years, and if the LA battery was used in the study, it must be replaced 9 to 10 times at 70% and at 80% usage of DODs, respectively. If the study considered Li-Ion batteries, they would need to be replaced 2, 3, and 4 times, respectively, at 50%, at 70%, and at 80% usage of DODs. If the study considered the Ni-Fe battery, no replacement would be required during the lifespan of the project, either at 50% usage of DOD or at 80% usage of DOD, and it would operate for another five more years outside of the lifespan of the project. 8.2.6. Cycle Life of the Batteries The number of charging and discharging cycles a battery can complete before losing its capacity is referred to as its cycle life. In this study, the LA battery technology has two-cycle lives, such as 800 and 750 cycles at 70% and at 80% DODs, respectively. The Li-Ion battery has three cycle lives, such as 5000, 3000, and 2500 cycles at 50%, at 70%, and at 80% DODs, respectively. The Ni-Fe battery has two cycle lives, such as 11,000+ and 11,000+ cycles at 50% and at 80% DODs, respectively. Compared to other battery technologies, the cycle life of the Ni-Fe battery technology is much higher. 8.3. Modelling of Different Configurations Using Battery Technologies and RE Resources The Li-Ion battery can work at three different DODs, i.e., at 50%, at 70%, and at 80%; therefore, three configurations were modelled using the Li-Ion battery technology: PV/BMG/DG/LiIon at 50% DOD, PV/BMG/DG/Li-Ion at 70% DOD, and PV/BMG/DG/Li-Ion at 80% DOD. Similarly, the LA battery can work at two DODs such as at 70% and at 80%; therefore, two configurations were modelled using the LA battery technology: PV/BMG/DG/LA at 70% DOD and PV/BMG/DG/LA at 80% DOD. Similarly, the Ni-Fe battery can work at two different DODs, such as at 50% and at 80%; however, its primary strength is that it has a lifespan of more than 30 years at both the DODs. Hence, for the current study, the techno-economic analysis with Ni-Fe battery technology was accomplished with at 80% DOD only. As a result, a PV/BMG/DG/Ni-Fe at 80% DOD configuration was modelled. In order to electrify the study area with an optimum configuration, the six configurations mentioned above were evaluated at an LPSP value of 0% with LF and CC strategies using the HPRALC-based scenario, i.e., without DSM. To simplify the analysis, after determining the optimal configuration from the HPRALC-based scenario, it was further evaluated with LF and CC strategies using MPRAMC and LPRAHC-based scenarios, i.e., with DSM at an LPSP value of 0%. 8.4. Optimization Algorithms and Components Technical and Cost Values The proposed SSA algorithm’s convergence efficiency and robustness were compared to those of eight other well-known and proven algorithms, namely: PSO, GA, GWO, DE, ALO, MFO, DA, and GOA in the MATLAB © environment with a population of 100 and 100 iterations. The nine algorithms’ control parameter values are given in Table 4. All Energies 2022,15, 5176 35 of 50 components’ cost and technical values used in the study are given in Tables 5–7. The peak load demand in the HPRALC-based scenario was 79.18 kW, hence the converter-rated power was 87 kW (for safety reasons, the converter’s power rating should be 10% greater than the peak load demand). The peak load demand in the MPRAMC-based scenario was 54.44 kW. As a result, for the MPRAMC-based scenario, the converter-rated power was considered to be 60 kW. The peak load demand for the LPRAHC-based scenario was 52.02 kW. Hence, for MPRAMC-based scenarios, the converter-rated power was taken as 57 kW. Similarly, in order to meet the peak load demand of the HPRALC-based scenario, the DG-rated power was considered to be 80 kW, whereas in order to meet the peak load demand of the MPRAMC and LPRAHC-based scenarios, the DG-rated power was considered to be 60 kW because the commercially available rated power of the DG for these two scenarios is only 60 kW. Tables 8and 9give the optimal results of the six configurations described above with LF and CC strategies, respectively, using the nine metaheuristic algorithms at an LPSP value of 0% using the HPRALC-based scenario. The optimal configuration from the HPRALC-based scenario was further evaluated with LF and CC strategies at an LPSP value of 0% with MPRAMC and LPRAHC-based scenarios and their optimal results provided in Tables 10–13, respectively. Table 4. Control parameters of the algorithms. Algorithm Parameters GA Pop Itermax µCR 100 100 0.1 0.9 PSO Pop Itermax wmax wmin c1 c2 100 100 0.9 0.2 2 2 DE Pop Itermax F CR 100 100 0.5 0.9 GWO Pop Itermax a C1 C2 C3 100 100 0 to 2 2 ×rand(0,1) 2 ×rand(0,1) 2 ×rand(0,1) ALO Pop Itermax I weights 100 100 1 (1,5,3,15,8,1) DA Pop Itermax w s a c f e 100 100 0.9 to 0.2 0.1 0.1 0.7 1 1 MFO Pop Itermax a b 100 100 −1 to −2 1 GOA Pop Itermax cmax cmin 100 100 1 0.00004 SSA Pop Itermax c1 c2 c3 100 100 rand(0,1) rand(0,1) rand(0,1) Energies 2022,15, 5176 36 of 50 Table 5. Batteries’ techno-economic parameters and their values. Battery Type Lead-Acid (PbSO4) Lithium Iron Phosphate (LiFePO4) Nickel-Iron (Ni-Fe) Manufacturer Trojan [51] Victron [52] Iron Edison [50] Model SSIG 06 490 LFP-12.8/200-a TN 1000 Nominal capacity (SBAT) 490 Ah 300 Ah 1000 Ah Nominal voltage (VBAT) 6 V 12.8 V 1.2 V Round trip efficiency (ηrbat)85% 92% 80% Lifespan in years 3 years at 70% DOD 15 years at 50% DOD 30 years+ at 50% DOD 2.5 years at 80% DOD 9 years at 70% DOD 30 years+ at 80% DOD 7.5 years at 80% DOD Self-discharge rate (%/day) (σ)0.3% 0.2% 1% Capital cost (CC) in USD USD 410 USD 3317 USD 1057 Annual O&M cost in USD 2.5% of CC No maintenance 2% of CC Operating temperature −20◦C to +45 ◦C−20◦C to +50 ◦C−30 ◦C to +60 ◦C Cycle life of the batteries 800 cycles at 70% DOD 5000 cycles at 50% DOD 11,000+ cycles at 50% DOD 750 cycles at 80% DOD 3000 cycles at 70% DOD 11,000+ Cycles at 80% DOD 2500 cycles at 80% DOD Table 6. Technical and cost values of the biomass generator. Manufacturer [44] Enersol Bio Power Water Tank Capacity [44] 300 L Rated Power of BMG [44] 5 kW Frequency [44] 50 Hz Fuel Mode [44] 100% Producer Gas Based Life time of BMG [14] 15,000 h Plant Size (L * W * H) [44] 10 * 9 * 6 Feet BMG Capital cost [44] USD 4505 Number of Phases [44] Single Phase AO&M cost of BMG [14] USD 27 Rated Current [44] 26 Amperes Quantity of biomass 9 t/year Alternator Make [44] Kirloskar Manufacturers Cost of biomass [14] 15 USD/t Voltage [44] 230 V, AC Efficiency of BMG [14] 20% Table 7. Technical and cost values of the IHRES. Parameters Value Parameters Value Project lifetime 25 years No. of MEM Repl./year [1] 2 Nominal interest rate [14] 13% MEM Replacement cost [1]0.06 USD/m3 Inflation rate [53] 5% Repl. cost of chemicals [1]0.06 USD/m3 Manufacturer of PV Panel [54] Vikram solar Rated power of converter for HPRALC scenario 87 kW Model No. of PV Panel [54] Somera 385 Rated power of converter for MPRAMC scenario 60 kW Rated power of PV Panel [54] 385 Wp Rated power of converter for LPRAHC scenario 57 kW Lifetime of PV Panel [54] 25 years Lifetime of converter [14] 10 years Capital cost of PV Panel [54] USD 128 C&R of converter per kW USD 108 AO&M cost of PV Panel [14] USD 3.2 AO&M cost of converter [14] USD 15 Mechanical structure cost of PV Panel [55]USD 41 Efficiency of converter [14] 95% Energies 2022,15, 5176 37 of 50 Table 7. Cont. Parameters Value Parameters Value Life time of mechanical structure of PV panel [55]25 years DG (Company: Cummins, Model No: C100D5) for HPRALC scenario [56] 100 KVA 80 kW ROD capital cost (1 m3/day) [1]USD 532 C&R of DG for HPRALC scenario [56] USD 9144 Capital cost of Water tank [1]256 USD/m3DG (Company: Kirloskar, Model No: KEC-T75-II) for MPRAMC and LPRAHC scenarios [57] 75 KVA 60 kW Capital cost of membrane [1]0.06 USD/m3 C&R of DG for MPRAMC and LPRAHC scenarios [57]USD 6858 Capital cost of chemicals [1]0.06 USD/m3Diesel Price USD 1.08 AO&M cost of ROD [1]0.2 USD/m3AO&M cost of DG [46] 3% of TAOHDG” Table 8. Optimization results of the HPRALC-based IHRESs using LF strategy at LPSP value of 0%. Configuration Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 1282 1280 1281 1275 1280 1280 1280 1280 1275 BMG/ NBAT 917 917 879 893 917 917 917 917 893 DG/ AFC 1574 887 2192 1600 887 887 887 887 1600 Ni-Fe ACO24251 2395 5917 4320 2395 2395 2395 2395 4320 at DOD = 80% LCC (USD) 918,176 918,040 921,542 916,728 918,040 918,040 918,040 918,040 916,728 PV/ NPV 1243 1237 1237 1238 1237 1237 1237 1237 1237 BMG/ NBAT 342 340 340 340 340 340 340 340 340 DG/ AFC 7338 7499 7499 7498 7499 7499 7499 7499 7499 LA ACO219,813 20,247 20,247 20,246 20,247 20,247 20,247 20,247 20,247 at DOD = 70% LCC (USD) 1,516,213 1,511,891 1,511,891 1,512,129 1,511,891 1,511,891 1,511,891 1,511,891 1,511,891 PV/ NPV 1237 1237 1301 1237 1237 1237 1237 1237 1237 BMG/ NBAT 297 297 299 297 297 297 297 297 297 DG/ AFC 7590 7590 7123 7590 7590 7590 7590 7590 7590 LA ACO220,494 20,494 19,232 20,494 20,494 20,494 20,494 20,494 20,494 at DOD = 80% LCC (USD) 1,492,491 1,492,491 1,506,249 1,492,491 1,492,491 1,492,491 1,492,491 1,492,491 1,492,491 PV/ NPV 1136 1136 1183 1138 1136 1136 1136 1136 1136 BMG/ NBAT 339 339 338 339 339 339 339 339 339 DG/ AFC 7859 7859 7895 7858 7859 7859 7859 7859 7859 Li-Ion ACO221,220 21,220 21,315 21,216 21,220 21,220 21,220 21,220 21,220 at DOD = 50% LCC (USD) 2,397,086 2,397,086 2,403,931 2,397,563 2,397,086 2,397,086 2,397,086 2,397,086 2,397,086 PV/ NPV 1135 1135 1135 1135 1135 1150 1211 1135 1135 BMG/ NBAT 242 242 242 242 242 242 244 242 242 DG/ AFC 7891 7891 7891 7891 7891 7781 7172 7891 7891 Li-Ion ACO221,307 21,307 21,307 21,307 21,307 21,009 19,364 21,307 21,307 at DOD = 70% LCC (USD) 2,446,102 2,446,102 2,446,102 2,446,102 2,446,102 2,447,773 2,467,615 2,446,102 2,446,102 PV/ NPV 1135 1135 1135 1135 1203 1326 1135 1199 1135 BMG/ NBAT 212 212 212 212 211 210 212 213 212 DG/ AFC 7771 7771 7771 7771 7837 7723 7771 7337 7771 Li-Ion ACO220,981 20,981 20,981 20,981 21,161 20,853 20,981 19,810 20,981 at DOD = 80% LCC (USD) 2,605,751 2,605,751 2,605,751 2,605,751 2,611,090 2,624,535 2,605,751 2,621,122 2,605,751 Energies 2022,15, 5176 38 of 50 Table 9. Optimization results of the HPRALC-based IHRES using CC strategy at LPSP value of 0%. Configuration Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 1268 1265 1332 1268 1265 1265 1265 1265 1265 BMG/ NBAT 933 928 925 933 928 928 928 928 928 DG/ AFC 1045 871 821 1045 871 871 871 871 871 Ni-Fe ACO22822 2352 2217 2822 2352 2352 2352 2352 2352 at DOD = 80% LCC (USD) 927,077 926,800 934,345 927,077 926,800 926,800 926,800 926,800 926,800 PV/ NPV 1178 1202 1178 1178 1178 1178 1219 1178 1178 BMG/ NBAT 347 394 347 347 347 347 346 347 347 DG/ AFC 7839 5823 7839 7839 7839 7839 7864 7839 7839 LA ACO221,166 15,723 21,166 21,166 21,166 21,166 21,233 21,166 21,166 at DOD = 70% LCC (USD) 1,525,952 1,629,822 1,525,952 1,525,952 1,525,952 1,525,952 1,531,881 1,525,952 1,525,952 PV/ NPV 1174 1174 1226 1174 1174 1213 1174 1174 1174 BMG/ NBAT 305 305 306 305 305 304 305 305 305 DG/ AFC 7640 7640 7192 7640 7640 7615 7640 7640 7640 LA ACO220,628 20,628 19,419 20,628 20,628 20,561 20,628 20,628 20,628 at DOD = 80% LCC (USD) 1,503,917 1,503,917 1,511,744 1,503,917 1,503,917 1,509,935 1,503,917 1,503,917 1,503,917 PV/ NPV 1098 1098 1133 1098 1098 1082 1098 1098 1098 BMG/ NBAT 340 340 340 340 340 341 340 340 340 DG/ AFC 9083 9083 9009 9083 9083 9059 9083 9083 9083 Li-Ion ACO224,525 24,525 24,324 24,525 24,525 24,458 24,525 24,525 24,525 at DOD = 50% LCC (USD) 2,416,367 2,416,367 2,423,727 2,416,367 2,416,367 2,417,493 2,416,367 2,416,367 2,416,367 PV/ NPV 1084 1084 1084 1084 1195 1930 1195 1225 1084 BMG/ NBAT 243 243 243 243 241 235 241 241 243 DG/ AFC 9083 9083 9083 9083 9009 7366 9009 8735 9083 Li-Ion ACO224,525 24,525 24,525 24,525 24,324 19,889 24,324 23,585 24,525 at DOD = 70% LCC (USD) 2,463,960 2,463,960 2,463,960 2,463,960 2,474,258 2,579,041 2,474,258 2,476,585 2,463,960 PV/ NPV 1177 1116 1116 1176 1486 1516 1116 1176 1116 BMG/ NBAT 211 212 212 211 208 208 212 211 212 DG/ AFC 9083 9108 9108 9083 8138 7964 9108 9083 9108 Li-Ion ACO224,525 24,593 24,593 24,525 21,972 21,502 24,593 24,525 24,593 at DOD = 80% LCC (USD) 2,629,251 2,627,111 2,627,111 2,629,041 2,645,568 2,648,554 2,627,111 2,629,041 2,627,111 Table 10. Optimization results of the MPRAMC-based IHRES using LF strategy at LPSP value of 0%. Configuration Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 904 904 921 904 904 904 904 904 904 BMG/ NBAT 569 569 563 569 569 569 569 569 569 DG/ AFC 854 854 1010 854 854 854 854 854 854 Ni-Fe ACO22306 2306 2726 2306 2306 2306 2306 2306 2306 at DOD = 80% LCC (USD) 613,841 613,841 617,660 613,841 613,841 613,841 613,841 613,841 613,841 Table 11. Optimization results of the MPRAMC-based IHRES using CC strategy at LPSP value of 0%. Configuration Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 877 890 903 891 890 877 890 877 890 BMG/ NBAT 594 599 588 596 599 594 599 594 599 DG/ AFC 989 653 915 747 653 989 653 989 653 Ni-Fe ACO22671 1764 2469 2016 1764 2671 1764 2671 1764 at DOD = 80% LCC (USD) 623,772 623,484 625,477 623,808 623,484 623,772 623,484 623,772 623,484 Energies 2022,15, 5176 39 of 50 Table 12. Optimization results of the LPRAHC-based IHRES using LF strategy at LPSP value of 0%. Configuration Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 814 814 834 814 814 814 814 814 814 BMG/ NBAT 450 450 458 450 450 450 450 450 450 DG/ AFC 837 837 532 837 837 837 837 837 837 Ni-Fe ACO22259 2259 1438 2259 2259 2259 2259 2259 2259 at DOD = 80% LCC (USD) 522,945 522,945 526,708 522,945 522,945 522,945 522,945 522,945 522,945 Table 13. Optimization results of the LPRAHC-based IHRES using CC strategy at LPSP value of 0%. Configuration Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 791 791 794 791 791 791 801 791 791 BMG/ NBAT 496 496 495 496 496 496 487 496 496 DG/ AFC 131 131 149 131 131 131 280 131 131 Ni-Fe ACO2353 353 403 353 353 353 756 353 353 at DOD = 80% LCC (USD) 529,795 529,795 530,336 529,795 529,795 529,795 530,042 529,795 529,795 8.5. Optimal Configuration from the LA Battery-Based IHRESs The LA battery can operate at two different DODs, i.e., at 70% and at 80%. In such a way that a total of two configurations were modelled using LA battery technology and which were tested with two different dispatch strategies such as LF and CC, their corresponding results using the HPRALC-based scenario are provided in Tables 8and 9, respectively. From the results, it was observed that the LA battery-based IHRES at 80% DOD with LF strategy is economically feasible as compared to its CC strategy, as well as LA battery-based IHRES at 70% DOD using LF and CC strategies, with an LCC of USD 1,492,491. It is about 1% lower than its CC strategy’s LCC, and it is about 1% and 2% lower than the LCCs of LA battery-based IHRES at 70% DOD using LF and CC strategies, respectively. The corresponding optimum component values were N PV = 1237 and N BAT (LA) = 297. Therefore, for further comparisons with other battery-based IHRESs, the LA battery-based IHRES at 80% DOD with LF strategy was taken into account. The Effect of Dispatch Strategies on LA Battery-Based IHRESs The above discussion reveals that the LA battery-based IHRES at 80% DOD with LF strategy was identified as an optimal configuration; its annual fuel consumption (AFC) and annual carbon dioxide (ACO 2 ) emissions compared to other dispatch strategies (LF and CC) of the remaining LA battery-based IHRESs are discussed as follows: From the results given in Tables 8and 9, it is observed that the AFC of the LA batterybased IHRES at 80% DOD with LF strategy was 7590 L, which is 50 L less than its CC strategy’s AFC, as well as 91 L more and 249 L less than the LA battery-based IHRES at 70% DOD’s LF and CC strategies’ AFCs, respectively. From the results given in Tables 8and 9, it is observed that the ACO 2 emissions of the LA battery-based IHRES at 80% DOD with LF strategy was 20,494 kg, which is 134 kg less than the ACO 2 emissions of its CC strategy, as well as 316 and 247 kg more and 672 kg less than the ACO 2 emissions of the LA battery-based IHRES at 70% DOD’s LF and CC strategies, respectively. 8.6. Optimal Configuration from the Li-Ion Battery-Based IHRESs The Li-Ion battery can operate at three different DODs, i.e., at 50%, at 70%, and at 80%. In such a way that a total of three configurations were modelled using Li-Ion battery technology and which were tested with two dispatch strategies such as LF and CC, their corresponding Energies 2022,15, 5176 40 of 50 optimal results using the HPRALC-based scenario are provided in Tables 8and 9, respectively. From the results, it was observed that the Li-Ion battery-based IHRES at 50% DOD with LF strategy is economically feasible as compared to its CC strategy, as well as Li-Ion battery-based IHRESs at 70% and at 80% DOD’s LF and CC strategies, with an LCC of USD 2,397,086. It is about 1% lower than its CC strategy’s LCC, and it is about 2%, 3%, 8%, and 9% lower than the LCCs of Li-Ion battery-based IHRESs at 70% and at 80% DOD’s LF and CC strategies. The corresponding optimum component values of N PV and N BAT (Li-Ion) were 1136 and 339, respectively. Therefore, for further comparisons with other battery-based IHRESs, the Li-Ion battery-based IHRES at 50% DOD with LF strategy was taken into account. The Effect of Dispatch Strategies on Li-Ion Battery-Based IHRESs The above discussion reveals that the Li-Ion battery-based IHRES at 50% DOD with LF strategy was identified as an optimal configuration; its AFC and ACO 2 emissions compared to other dispatch strategies (LF and CC) of the remaining Li-Ion battery-based IHRESs are discussed as follows: From the results given in Tables 8and 9, it is observed that the AFC of the Li-Ion battery-based IHRES at 50% DOD with LF strategy was 7859 L, which is 1224 L less than its CC strategy’s AFC and 32 and 1224 L less than the Li-Ion battery-based IHRES at 70% DOD’s LF and CC strategies’ AFCs, respectively, as well as 88 L more and 1249 L less than the Li-Ion battery-based IHRES at 80% DOD’s LF and CC strategies’ AFCs, respectively. From the results given in Tables 8and 9, it is observed that the ACO 2 emissions of the Li-Ion battery-based IHRES at 50% DOD with LF strategy was 21,220 kg, which is 3305 kg less than its CC strategy’s ACO 2 emissions and 87 kg and 3305 kg less than the Li-Ion battery-based IHRES at 70% DOD’s LF and CC strategies’ ACO 2 emissions, respectively, as well as 239 kg more and 3373 kg less than the Li-Ion battery-based IHRES at 80% DOD’s LF and CC strategies’ ACO2emissions, respectively. 8.7. Optimal Configuration from the Ni-Fe Battery-Based IHRESs The Ni-Fe battery can operate at two different DODs, such as at 50% and at 80%, and it has a lifespan of more than 30+ years at both the DODs. Therefore, the analysis was conducted at 80% DOD only with two different dispatch strategies such as LF and CC, and its corresponding optimal results using the HPRALC-based scenario are provided in Tables 8and 9, respectively. From the results, it is observed that the Ni-Fe batterybased IHRES at 80% DOD with LF strategy was identified as an optimal configuration as compared to its CC strategy, with an LCC of USD 916,728. It is about 1% lower than its CC strategy’s LCC. The corresponding optimum component values were N PV = 1275 and N BAT (Ni-Fe) = 893. Therefore, for further comparisons with other battery-based IHRESs, the Ni-Fe battery-based IHRES at 80% DOD with LF strategy was considered. The Effect of Dispatch Strategies on Ni-Fe Battery-Based IHRESs The above discussion reveals that the Ni-Fe battery-based IHRES at 80% DOD with CC strategy was identified as an optimal configuration; its AFC and ACO 2 emissions compared to its LF strategy are discussed as follows: From the results given in Tables 8and 9, it is observed that the AFC of the Ni-Fe battery-based IHRES at 80% DOD with LF strategy was 1600 L, which is 729 L more than its CC strategy’s AFC. From the results given in Tables 8and 9, it is observed that the ACO 2 emission of the Ni-Fe battery-based IHRES at 80% DOD with LF strategy was 4320 kg, which is 1968 kg more than its CC strategy’s ACO2emissions. Energies 2022,15, 5176 41 of 50 8.8. The System Performance with Different Battery Technologies Using HPRALC, MPRAMC, and LPRAHC-Based Scenarios The following describes the impact of different battery technologies and dispatch strategies on evaluating an optimal configuration using three efficiency-based scenarios: HPRALC, MPRAMC, and LPRAHC. 8.8.1. Low-Efficiency Appliance Usage-Based Scenario (HPRALC) (without DSM) A low-efficiency appliance usage-based scenario refers to the usage of high power rated appliances of low cost (HPRALC) by the consumers, which falls under the concept without DSM. According to the results given in Tables 8and 9for the HPRALC-based scenario, it is observed that the Ni-Fe battery-based IHRES at 80% DOD with LF strategy (base case) provided an LCC of USD 916,728. It is an optimal value when compared to other battery-based IHRES LCCs with different dispatch strategies. The LA battery-based IHRES at 80% DOD with LF strategy provided an optimal LCC of USD 1,492,491, which is about 63% higher than the base case LCC. The Li-Ion battery-based IHRES at 50% DOD with LF strategy provided an optimal LCC of USD 2,397,086, which is about 162% higher than the base case LCC. The Effect of Dispatch Strategies with Different Battery Technologies Using Low-Efficiency Appliance Usage-Based Scenario (HPRALC) (without DSM) From the results given in Tables 8and 9, it is observed that the AFC of the Ni-Fe battery-based IHRES at 80% DOD with LF strategy was 1600 L, which is 5990 L less than the AFC of LA battery-based IHRES at 80% DOD’s LF strategy, and it is 6259 L less than the AFC of Li-Ion battery-based IHRES at 50% DOD’s LF strategy. From the results given in Tables 8and 9, it is observed that the ACO 2 emission of the Ni-Fe battery-based IHRES at 80% DOD with LF strategy was 4320 kg, which is 16,174 kg less than the ACO 2 emissions of LA battery-based IHRES at 80% DOD’s LF strategy, and it is 16,900 kg less than the ACO 2 emission of the Li-Ion battery-based IHRES at 50% DOD’s LF strategy. Finally, it was found that the Ni-Fe battery-based IHRES with LF strategy is more suitable for electrifying the study area. It is clear from the preceding Section 8.8.1 that Ni-Fe battery-based IHRES with LF strategy is more economically feasible as compared to its CC strategy and other battery-based IHRESs with different dispatch strategies, and it is also more eco-friendly in terms of annual fuel consumption and carbon emissions due to its lower fuel consumption and carbon emissions as compared to other battery-based IHRESs with different dispatch strategies. Therefore, IHRESs based on LA and Li-Ion batteries are not considered for further analysis in Section 8.8.2 (MPRAMC-based scenario) and Section 8.8.3 (LPRAHC-based scenario), since it is clear from the above discussion that these two battery technologies are not economically and environmentally feasible when compared to the Ni-Fe battery technology. 8.8.2. The Effect of Ni-Fe Battery-Based IHRES Using Medium-Efficiency Appliance Usage-Based Scenario (MPRAMC) (with DSM) From the results of the low-efficiency appliance usage-based scenario (HPRALC), it was identified that the Ni-Fe battery-based IHRES with LF strategy (base case) provided a minimum LCC when compared to its CC strategy, as well as other battery-based IHRESs with different dispatch strategies. Therefore, this configuration was further analyzed with the medium-efficiency appliance usage-based scenario, i.e., with DSM. The mediumefficiency appliance usage-based scenario refers to the usage of medium power rated appliances of moderate cost (MPRAMC) by the consumers and it is a part of the concept of energy conservation-based DSM. From the results given in Tables 10 and 11, it is observed that the base case LCC with the MPRAMC-based scenario with LF strategy was USD 613,841, which is about 2% lower than its CC strategy’s LCC, as well as about 33% lower than the LCC using the HPRALC-based scenario with LF strategy. The current scenario optimum component values are N PV = 904 and N BAT (Ni-Fe) = 569. If these values were Energies 2022,15, 5176 48 of 50 different types of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) was considered in order to provide a continuous power supply in conjunction with a diesel generator (DG). In order to find out an optimal configuration to electrify the study area, six different configurations were modelled using available RE resources and battery technologies. Initially, these six configurations were evaluated with load following (LF) and cycle charging (CC) strategies using the low-efficiency appliance usage-based scenario in the MATLAB © environment at an LPSP value of 0% with nine metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, genetic algorithm, ant lion optimization, differential evolutionary algorithm, moth flame optimization, dragonfly algorithm, grasshopper optimization algorithm, and Salp Swarm Algorithm. According to the results, the Salp Swarm Algorithm showed its convergence and robustness efficiencies in comparison to other algorithms in order to find the global best optimal values. From the results of the lowefficiency appliance usage-based scenario, the Ni-Fe battery-based IHRES with LF strategy was found to be an optimal configuration. This was further evaluated with medium and high-efficiency appliance usage-based scenarios. The summary of these results are listed as follows: The Ni-Fe battery-based IHRES with LF strategy using the low-efficiency appliance usage-based scenario, i.e., without DSM, obtained an LCC of USD 916,728, which is about 39% and 62% lower than the LCCs of LA (at 80% DOD) and Li-Ion (at 50% DOD) batterybased IHRES’s LF strategies, respectively. The Ni-Fe battery-based IHRES with LF strategy using the medium-efficiency appliance usage-based scenario, i.e., with DSM obtained an LCC of USD 613,841, which is about 33% lower than its LCC using low-efficiency appliance usage-based scenario. The Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usagebased scenario, i.e., with DSM, obtained an LCC of USD 522,945, which is about 43% and 15% lower than its low and medium-efficiency appliance usage-based scenarios, respectively. Finally, the sensitivity analysis was performed by varying the interest rate, biomass foliage collection rate, and diesel prices as compared to the other parameters, and the effect of the interest rate was found to have a major impact on the system performance. The current study focused on demand-side management based on energy conservation. Using this study, power consumption in rural households as well as power production components in the study area can be reduced significantly. As a result, energy bills and total investment costs in the study area can be reduced. In future studies, with proper planning, peak load demands can be reduced by shifting non-peak load demand periods using energy conservation-based management. This methodology can significantly reduce the investment cost for the specific study area. Author Contributions: Conceptualization, P.P.K. and R.S.S.N.; methodology, P.P.K. and V.S.; software, P.P.K. and S.A.S.; validation, M.A.H. and M.J.; formal analysis, P.P.K.; investigation, P.P.K. and M.J.; resources, P.P.K. and R.S.S.N.; data curation, V.S. and M.J.; writing—original draft preparation, P.P.K.; writing—review and editing, M.A.H. and S.A.S.; visualization, P.P.K. and R.S.S.N.; supervision, R.G. and Z.L.; project administration, M.J. and R.G.; funding acquisition, R.G. and Z.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by SGS Grant from VSB—Technical University of Ostrava under grant number SP2022/21. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available on request on correspondence to first author. Conflicts of Interest: The authors declare no conflict of interest. Energies 2022,15, 5176 49 of 50 References 1. 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