In recent years, energy consumption has been significantly affected by globalization and the rapid pace of industrialization. The growing world population is another factor that contributes to increased energy consumption. Traditionally, fossil fuels and other non-renewable energy sources were used to meet the majority of the energy demand of the population. However, the dependency on fossil fuels to meet energy demand grows with two serious issues i.e., their depletion and increasing levels of carbon dioxide into the atmosphere which adds to the global warming potential. The traditional power sources, such as coal, natural gas, and nuclear, and load demand, are connected to the grid across India. Although the aforementioned structure is convenient to meet energy demands at various places, rural areas face power availability challenges. Some of those rural areas only have power for limited hours and others have no power at all. Also, fossil fuels are not available near those rural areas. Therefore, building electrical infrastructure in these rural areas would be expensive and the low demands for energy generate other concerns [1
Renewable energy sources (RESs) of electricity, therefore play a quintessential role in such areas because of the associated advantages such as their availability, low maintenance cost and negligible pollution to the environment. It was also estimated by the International Energy Agency (IEA) [2
] that around 30% of the power would be available from the different RESs. India has an agrarian-based economy and rural areas are its backbone. In these areas, most of the populace is engaged in the agriculture sector. The electricity supply is necessary at the time of different agriculture activities such as water pumping, crop cutting, etc. This demand is in addition to the regular household demand. It was analyzed in one of the studies that around 18% of the rural population in India does not have access to the electricity that is regular and reliable at the same time [3
]. Power cuts are one of the major issues in rural areas, although the aforementioned challenges can be met through the availability of RESs. The integration of renewable sources of energy is one such solution that can provide eco-friendly, cost-effective and continuous power supply to the rural areas [4
]. However, they have inherent intermittent power generation and as such the desired reliability cannot be guaranteed. To cope up with this problem associated with RESs, integrated RES (IRES) microgrid is suggested. This system is modelled using a battery energy storage system (BESS), RESs and time-constrained grid. Some other relevant studies aiming to find the optimal sizing of the microgrid systems using hybrid optimization model for electric renewable (HOMER) software and metaheuristic techniques are presented in [6
]. Other parameters such as Levelized cost of electricity (LCOE), renewable fraction (RF) and total net present cost (TNPC) were taken into consideration while carrying out the aforementioned studies.
Optimal sizing of different components in IRES microgrid systems and cost-effective solutions are obtained where grid extension is critical to manage [9
]. Load profiles for the off-grid system are developed in [11
]. HOMER software was adopted by many researchers to obtain reliable and cost-effective secure energy systems [12
]. A study on microgrids for rural electrification is done in [16
] and investigated the possibility of reducing the overall cost of electricity through the employability of RESs within a stand-alone microgrids. A hybrid energy system is proposed in [17
] and optimal sizing of the components within the proposed hybrid energy systems is obtained for the non-electrified rural areas located in Uttarakhand, India. In addition, to finding out the optimal sizing of the different components, the investigation is also carried out to provide uninterrupted power supply at the lowest possible cost. A hybrid system for supplying electrical power in Namin, Ardabil, Iran is suggested in [18
] considering fuel cell as one of its critical components. A genetic algorithm-based multi-objective optimization algorithm is proposed in [19
] for analysis of the grid-independent hybrid energy system. Ramli et al. [20
] carried out an economic analysis of a hybrid electrification system comprising solar photovoltaic (SPV) and diesel generator (DG). The proposed electrification hybrid system is analyzed with the aid of a tri-objective-based algorithm. Different objectives considered for this study are to reduce the life-cycle cost of the proposed hybrid system, the emissions from the hybrid system and also the total dump energy associated with the hybrid system, if any. Similarly, a study for hybrid system comprising of 30 kW photovoltaic system, 25 kW DG set, a 40 kW wind system and a storage system is carried out for one of the rural village areas situated in Sri Lanka [21
Singh and Fernandez [22
] employed a cuckoo search algorithm to find out the optimal sizing of the different components of a proposed hybrid system. In another study [23
], the optimal sizing associated with the proposed off-grid system is determined using a linear programming model. This study developed an approach that consists of the monitoring of the battery dilapidation process. The electrical storage system is introduced in [24
] with the objective to provide flexibility to the off-grid system. Various researchers proposed and developed different size optimization algorithms and also carried out the economic analysis for hybrid energy systems [25
]. A review of size optimization techniques is presented for the microgrid systems in [28
]. A grid-connected photovoltaic energy system for a dairy farm in Algeria was investigated for economic and technical requirements [29
In India, rural electrification is plagued by a limited supply of electricity for a few hours of the day. Hence the major challenge lies into optimal designing of the microgrid system with respect to the annualized system cost that can meet the load demand for the area under consideration 24 × 7. Therefore, the main goal of the proposed work is to investigate the feasibility of an integrated renewable energy system to meet the electricity demand of a village. The proposed system is optimized to meet the electricity demand 24 × 7 and to find the lowest possible LCOE. Moreover, the present study also attempts to investigate an improvised version of the MFABC algorithm that may lead to a more precise result in fewer solution cycles. The reason for employing the new version of artificial bee colony optimization algorithm over the other algorithms such as particle swarm optimization, genetic algorithm, etc., is because of the involvement of lesser number of control variables. The main contributions of this study are listed below:
Hybrid system design is proposed for rural electrification in a rural village that comprises of SPV system with limited grid power supply and BESS.
The modified multi-strategy fusion artificial bee colony (MFABC+) algorithm is proposed and its feasibility and superiority demonstrated in comparison to MFABC and other optimization method.
The optimal capacity of the system components is determined using the proposed MFABC+ algorithm.
An integrated renewable energy-based microgrid system is proposed for lowest possible LCOE.
The work is structured as follows: Section 1
presents the background as well as the introduction. The targeted area of investigation is described in Section 2
. This section provides an overview of the area to be investigated with various statistical as well as the geographical parameters. The load assessment as well as the resource assessment are presented in this section. Section 3
depicts the mathematical modelling of different components used in proposed microgrid. The problem formulation as well as associated constraints are explained in Section 4
. The overview of the used optimization methodology was also discussed in this section. Section 5
presents the results derived from the study. The work finally terminates with the concluding remarks in Section 6
5. Results and Discussion
The methodology depicted in the present work is designed to fulfill the optimal electricity demand for a rural village. The electricity demand was met through the design of small stand-alone PV-battery hybrid system. This work considers peak load demand for 45 households in a small microgrid. The evaluated peak load demand is 100.02 kW and the associated load factor is 0.415. Four different load profiles were considered for four distinct seasons. The summer, autumn, winter and spring seasons are assumed to be presented from May to August, September to October, November to February and February to April, respectively, as shown in Figure 1
. The availability of solar radiation is throughout the year and the maximum radiation occurs in the month of May. The estimated data reveals an average solar radiation of 3.45 kWh/m
/day, as given in Figure 3
. The detailed load demand of the small community with forty five households was delineated in Table 1
. Table 2
depicts the technical and economical parameters associated with the different components employed in the present study. The life time of 20 years and interest rate of 6% are assumed for the SPV renewable system which were assumed for the considered area.
5.1. Analysis of Results
The simulations were performed for a time step equivalent to one hour using MATLAB 2016a and were run for the data that are recorded for one year duration. The objective function is solved using PSO, MFABC and MFABC+ algorithms. All algorithm parameters used for simulation are presented in Table 3
. The number of solar PV units and the number of batteries are the variables that are required to be found so that the ASC can be minimized. The size of the inverter was not included as the decision variable as this was selected on the basis of the peak demand. These algorithms run by considering the similar maximum number of solar panels and batteries i.e., 300 and 1000, respectively. The rating of the inverter is considered to be 110 kW and obtained using (2
). The results have also been obtained using HOMER software and used as reference for comparing the solutions obtained using aforementioned algorithms. The results obtained are compared and presented in Table 4
It presents total number of PV and battery units. It can be observed that results obtained using MFABC+ is better or comparable than other techniques and HOMER software in terms of optimized value of solar and battery units. Table 4
also presents ASC, TNPC and LCOE costs and all these costs are found to be better in case of MFABC+ algorithm. Total 230 number of PV units, 800 battery units with the ASC of 56,002 $
/yr, TNPC of 700,000$
, with LCOE of 0.195 $
/kWh are obtained using MFABC+ algorithm. These parameter values are minimum than values obtained by other methods.
The performance of the MFABC+ algorithm was observed to be much better in comparison to other meta-heuristics i.e., MFABC and PSO in terms of results as well as the computational time. Moreover, it is also observed that the solutions provided by the considered meta-heuristics are better in comparison to that obtained using HOMER software. The results obtained and associated LCOE reveals that the proposed system fulfills the energy requirement of the community with an acceptable cost. The least LCOE is provided by the MFABC+ algorithm and hence MFABC+ is found to be better among all methods.
Results obtained using all solution methods are further extended in Table 5
to present solar power generations, battery in and out, total served demand and excess electricity. The excess generated energy was revealed to be 6897 kWh/yr i.e., 2.1% of the total load served in the case of MFABC algorithm. The excess energy generated with implementation of MFABC+ is 5320 kWh/yr i.e., 1.45% of total energy served. The results obtained using HOMER reveals an excess energy of 128,087 kWh/yr and amounts to 43.92% of the total energy served.
The detailed analysis with respect to the annualized cost of the proposed design obtained using MFABC+ algorithm was demonstrated in Table 6
. The capital cost, replacement cost, maintenance cost and salvage cost of each component connected in the proposed system are presented in the table. The cost recovery factor, depicted in (16
), is employed to arrive at the ASCs. No replacement cost is required in case of the PV panels owing to the fact that the lifetime of PV panels is similar to that of the lifetime associated with the project. The replacement is required for the battery bank and it was assumed that the employed battery bank needs to be replaced in every five years. Hence total number of replacements associated with the batteries is three. The cost contribution associated with the batteries is approximately 33% of the total cost of the designed system while the SPV system contribute 43% of the total cost of the project.
The convergence graphs for ASC using PSO, MFABC, and MFABC+ are shown in Figure 5
. The cost function is formulated for minimization, so better performing algorithm will provide minimum cost value than other algorithms. It can be observed from figure that convergence obtained using MFABC+ is better than other methods. All three meta-heuristics take 20–30 min to converge. HOMER software on the other hand take more time to converge. The total energy demand was fulfilled through the employability of PV panels and batteries. The monthly average energy balance for one year was delineated in Figure 6
. It can be observed that, during summers, more power is drawn from the batteries. For other seasons, the energy requirement was met through the generated solar power. The excess energy is available only for three months. The proposed system was optimized to minimize or dump the excess generated energy and utilize maximum RESs with the help of efficient energy storage system.
Three weeks were opted for in order to judge the validity of the optimal operation between load and demand of the community that is obtained through the implementation of the system under consideration for a period of one year. The first week selected is in month of April, second week is for the month of July and third week is for the month of December. These months are selected as in the month of April the load demand is moderate, the demand is at peak in the month of July and is at minimum for the month of December. The complete power exchange associated with the different components of the proposed integrated renewable energy system for the duration of one week being considered for the month of April is depicted in Figure 7
. It can be observed from the figure that the generation from the installed solar power plant is low for a few hours and also the SOC of the BESS is low. Therefore during these hours, the power is provided by the DG. The management of the energy during the last week for the month of July is depicted in Figure 8
and for the month of December in Figure 9
. It is revealed from Figure 9
that the solar power generation is sufficient and hence there is no requirement to run the DG set. The total power demand of the considered area can therefore be met through the SPV and the BESS.
The SOC of the battery Trojan SAGM 06 315 storage system is delineated in Figure 10
. The monthly average SOC of the battery varies between 30–80% of the total load consumption. It can be observed that the SOC for the employed BESS always remains well within the defined limits. The initial SOC is considered to be 100% in January i.e., the SOC is 2984 kWh. However, the SOC is observed to be at 30% during certain hours of the year. It is also deducible that for most of the time around the year, the SOC of the BESS remains good. However, during a few instances such as during the month of April and July, the SOC is poor due to low availability of natural resources and high demand respectively for the month of January and June.
5.2. Effectiveness of MFABC+ Algorithm
The effectiveness of the MFABC+ algorithm over MFABC and PSO algorithms was verified using a total of thirty iterations for each of the aforementioned algorithm. Minimum deviations is revealed for the MFABC+ algorithm in comparison to the MFABC, and PSO algorithm and therefore suggests that MFABC+ algorithm is better than MFABC, and PSO algorithms. The validity of the effectiveness was established through the paired student’s t-test. The validity associated with the effectiveness of the solution was accomplished at confidence level assumed to be 5%. The value of ASC that was achieved using the implementation of the MFABC+ algorithm is observed to be significantly less in comparison to that obtained using the MFABC and PSO algorithms.
5.3. Battery’s Efficiency and LCOE
The effect on the LCOE of the proposed system was investigated with the different round trip efficiencies associated with the battery system considered. The results achieved so far were for the 95% round trip efficiency of the battery system. The efficiencies related to battery charging and discharging are considered to be 100% and 80%, respectively. In order to obtain more practical solution, the efficiencies associated with charging and discharging were considered to be the same. Figure 11
depicts the results obtained for different round trip efficiencies of the battery while keeping the charging and discharging efficiencies equal. The LCOE decreases with the increase in the round trip efficiency of the battery.