http://localhost:8081/jspui/handle/123456789/48 2025-06-30T09:53:12Z 2025-06-30T09:53:12Z Tripathi, Mansi http://localhost:8081/jspui/handle/123456789/15773 2024-09-19T13:02:43Z 2019-08-01T00:00:00Z

Title: DEVELOPMENT OF WATER QUALITY INDEX FOR MANAGEMENT OF RIVER Authors: Tripathi, Mansi Abstract: Due to its limited availability, the quality of consumable surface water becomes very important. Water quality includes anything and everything that it might have picked up during its journey; in colloidal, dissolved or even in suspended state. The voluminous data involved in the water quality assessment and management can often be quite overwhelming for a lay-man. In an attempt to transfer the information in more precise and comprehendible way, a new approach has been used to integrate the data pool in the form of simple numbers termed as water quality indexes (WQIs or indices). This concept was originally developed in Germany by Horton (1965) and since this pioneer attempt, several other initiatives have been made to develop a suitable index for water quality in different regions and also for global usage. There are some limitations associated with WQIs in terms of providing the complete picture of sample in all forms of pollutants including micro pollutants and persistent pollutants but it benefits by far exceed its limitations. The benefits of WQIs are – (i) It provides a generic overall picture of water quality (ii) Gives its spatio- temporal variation to judge the effectiveness of a water quality program, (iii) It can be used for easy representation of the water for policy makers and stakeholders, (iv) It can be used as a decision making tool by government authorities (v) WQI helps in drawing comparison between water quality at different locations and thus reporting in an easily comprehendible manner. Building on the base of earlier literature and its comprehensive review, it has been found that most of the previous researches have been directed towards specific regions, hence specific assessment, monitoring and planning is required. Most of the commonly used WQIs are found not to be in sync with water quality standards and do not provide flexibility in terms of number of parameters. It can also be seen that the earlier WQIs are either theoretical or statistical in nature and a delicate balance of both approaches is the need of the hour. Most of the commonly used indices are high on ‘eclipsing’ i.e. a degraded parameter’s effect is often compensated by the positive effect of another parameter. Besides this, currently used indices classify a historical water quality status, where prediction of future parameters and issuing warning for future has not been observed. ii A WQI forms basis of a systematic study and helps to identify the effects of various Government interventions. Keeping the above requirements in view, this study makes an attempt for development of a WQI for river Ganga flowing through Uttar Pradesh state in India with the followings objectives: 1. To procure historical data from monitoring agencies and carry out water sampling for trend analysis in the selected study area. 2. To select/reduce parameters using principal component analysis (PCA) and to provide weightage to these parameters using factor analysis (FA). 3. To generate sub- index rating curves, hybrid aggregation and sensitivity analysis of the developed Ganga WQI. 4. To conduct comparative analysis of developed GWQI along with cost-benefit analysis. 5. To develop regression and ANN model from secondary data and to validate it using primary data in order to find best predictive modelling approach. In order to achieve the above objectives, the stretch of river Ganga from Garhmukteshwar to Ghazipur (967 km.) having 15 monitoring stations has been selected as the study area. The methodology adopted for this study is as follows- 1. Selection of study area and sampling locations 2. Procurement of water quality data from Central Pollution Control Board (CPCB) and Central Water Commission (CWC). 3. Collection of a set of post and pre monsoon samples from the sampling locations and their analysis 4. Selection of WQI parameters by using PCA and estimating weights of the shortlisted parameters by applying FA to the shortlisted parameters 5. Development of rating curves using national and international water quality standards 6. Aggregation of weighted sub-index values using a combination of harmonic and arithmetic means to obtain the final GWQI 7. Sensitivity Analysis of the developed GWQI 8. Analysis of historical trends and cost- benefit analysis of the GWQI 9. Predictive modelling using regression analysis and ANN. iii For parameter reduction, PCA reiterates the data variability dispersed in several dimensions into a decreased number of uncorrelated dimensions called principal components (PCs) which can be expressed in the form of linear equations. Kaiser– Meyer–Olkin (KMO) and the Bartlett’s tests of Sphericity have been performed on the available dataset to check its suitability before carrying out PCA. Both of these tests have been carried out by using SPSS software and nine parameters were selected for further analysis. For providing weights to the parameters after PCA, the parameters contributing maximum (> 0.35; positive or negative) to the first five principal components were shortlisted. These PCs account for 90.36 % of the total variance, have individual eigen values >1 and their individual contribution to the overall variance is > 10%. These PCs were subjected to FA. The next step is rotation (commonly Varimax rotation) and post rotation, a matrix of square of factor loadings is prepared. Squaring gives the weights of individual factors while weights of individual parameters are estimated once the loadings are scaled to unity, where weights are equal to the maximum contribution of a parameter to an individual factor. For sub-index development, each parameter has been assigned a value ranging from 1 to 100. The rating curves were developed on the basis of BIS (2012), WHO (2017) and DBU (standards developed by CPCB) and plotted using MATLAB software. The final aggregation has been done using a hybrid aggregation of weighted arithmetic and weighted harmonic mean. After developing the final Ganga Water Quality Index (GWQI), its sensitivity analysis was carried out by removing individual parameters and all the reduced and increased GWQI values were found to be significantly correlated with the developed GWQI values (R2 > 0.95). It was found that the developed GWQI is not biased towards a single parameter and the procedure can be used as the results are not affected by a single parameter. For obtaining primary data for the study area, river water sampling was done at the 15 monitoring sites. The mean of the values obtained from the analysis of postmonsoon and pre- monsoon samples (2016) has been plotted along with the mean values obtained from the secondary data. The plots show the water quality trend in terms of iv individual parameters as well as GWQI values. The variability in the GWQI values is represented as Box and Whisker plots. Based on the analysis, the trend is found to be more or less similar over the years. In order to make a comparative assessment, CCMEWQI and NSFWQI were also estimated using primary data and it is found that GWQI and CCMEWQI are more sensitive than NSFWQI as their trend shows greater variation at different sites. Further, the information and analysis in the form of WQIs is translated into socioeconomic benefits and subsequently monetized considering the annual average income per house hold along the considered stretch of the river. It has been further analysed how the monetary benefit varies under different aggregation conditions. BOD appeared to be the most critical parameter as a 10 point reduction in BOD sub index value leads to lowest monetary benefits. A Correlation analysis established the relation between various water quality parameters and parameters found to be dependent on it. Out of these relations, the three strongest relations were found i.e. DO – BOD (R = -0.8738), DO- GWQI (R= 0.9063) and BOD - GWQI (R= -0.8255). Considering these relations, a linear regression model and an ANN model were developed for prediction of GWQI from DO and BOD individually. For developing ANN model, the MATLAB software is used for training, validating and testing by using the data set as the secondary data collected for years 1996- 2015 by giving one input and one output. It gives the best training function as TRAINR and adaption learning function is LEARNGD. The transfer function used is TRANSIG and number of hidden neurons are 10. The data samples were divided into training sets (60% of the total -1996 to 2015), validation sets (20% of the total- 1996 to 2015) and testing sets (20% of the total - 1996 to 2015). The results show that the proposed ANN model has better and greater potential to simulate and predict GWQI from DO and BOD individually with acceptable accuracy. From this study, it is concluded that the procedure of development of GWQI can be customised as per the specific needs and historical data availability in a region in order to develop a regional WQI to address regional problems. It can also be used to compare and express water quality universally depending on availability of historical data as the number and nature of parameters can be customised for every case which is its major v advantage over the most widely used and accepted WQIs worldwide. The conclusions drawn from this study are as follows- 1. In the first step for development of GWQI, 9 parameters - DO, BOD, TDS, pH, sulphate, conductivity, TC, magnesium and chlorides were identified using PCA. These parameters are the ones contributing > 0.35 to the variance of the retained 5PCs. 2. Weights have also been allocated to these 9 parameters individual factor loadings obtained from retained 5 PCs contributing more than 90% of the total variance. 3. Rating curves have been developed after considering national and international standards. 4. Final equation for estimating GWQI has been developed using a combination of arithmetic and harmonic means and the sensitivity analysis of this equation reveals that it is not biased towards a single parameter. The final equation obtained for estimation of GWQI is as follows- GWQI = [ ] [ ] [ ] [ ] [ ] 5. The results of analysis of the monitoring stations’ water quality data for 20 years by applying developed GWQI showed that most of the samples fall under the “average” category indicating not a very good health of the river. Besides this, no significant improvement in the river can be observed over these periods regardless several efforts by the Government probably because of the exponentially growing population pressure in this area. 6. From trend analysis of various parameters and various WQIs namely NSFWQI, CCMEWQI and the newly developed GWQI; Kanpur d/s (Jajmau) and Varanasi d/s (Malviya Bridge) have been identified as pollution hotspots. 7. The cost-benefit-analysis of the GWQI identifies BOD as the most sensitive parameter and its reduction or increment drastically affects the monetary benefits associated with GWQI. 8. The correlation analysis of the 9 parameters and GWQI shows that the most correlated factors are-  DO – BOD: Strong negative correlation (R = -0.8738)  DO- GWQI : Strong positive correlation (R= 0.9063) vi  BOD - GWQI : Strong positive correlation (R= -0.8255) 9. As GWQI shows maximum correlation with DO and BOD, therefore these parameters are used to develop linear regression and ANN models in order to predict GWQI values corresponding to a DO or BOD value. The effectiveness of both the models have been compared and ANN proves to be a better model due to lesser error in predicted GWQI values, The MSE corresponding to DO- GWQI and BOD- GWQI linear regression models and DO- GWQI and BOD- GWQI ANN models were estimated to be 17.1250 e-0, 35.7256 e-0, 7.6830 e-0 and 24.1811e-0 respectively. 10. The lowest MSE value is observed for DO-GWQI ANN model and thus it is the most accurate out of these models, while ANN proves to be better predictive model than linear regression model in any case. 11. It can be concluded that with increased cost effectiveness and reduced subjectivity, GWQI can be used effectively to evaluate status of water quality of river Ganga, India and at the same time the methodology can also be effectively utilized for any other basin/river/ water body after relevant customization. Besides this, the developed index is also flexible in terms of number of parameters. 12. The developed GWQI can also be used in case of extreme financial limitations too by using the DO- ANN model to predict associated GWQI value. From this study, it can be seen that the developed GWQI has several advantages over its contemporaries and can be effectively used to represent the water quality of river Ganga in the study area. The developed index strikes a delicate balance between subjective and objective techniques. Thus the work carried out in this thesis can form the foundation of future research by forming the basis of customized indices for other parts of the world and other types of indices. There is great scope for GWQI in Indian context as it can form the basis of identification of pollution hotspots and thus basis of evaluation of Government interventions.

2019-08-01T00:00:00Z GAUTAM, ABHISHEK http://localhost:8081/jspui/handle/123456789/15566 2023-06-30T11:52:09Z 2021-06-01T00:00:00Z

Title: PERFORMANCE INVESTIGATION OF A PACKED BED SOLAR ENERGY STORAGE SYSTEM HAVING SPHERES WITH PORES AS PACKING ELEMENTS Authors: GAUTAM, ABHISHEK Abstract: The solar energy is one of the most adopted renewable energy resource due to its abundant availability and eco-friendly behavior. However, there is a need to manage few technical obstacles like low efficiency, instability in energy supply and monetary impediment for its sustainable development. In order to eliminate instability in energy supply, the solar energy systems require an effective energy storage technology to store the energy during its availability and deliver it on its requirement. For solar thermal energy systems, many thermal energy storage (TES) techniques exist and packed bed storage system (PBSS) is the one which can be integrated with applications of all temperature range. The PBSS can store thermal energy in the form of sensible heat, latent heat and thermo-chemical energy. For low temperature applications, TES in the form of sensible heat is recommended due to its lesser storage cost. There are various modes of energy transfer involved in PBSS, however, the thermal performance is majorly dependent on the convective heat transfer between heat transfer fluid and packing elements in low temperature applications. The convective heat transfer rate between heat transfer fluid and packing elements is a function of the physical properties of heat transfer fluid and packing elements, local temperature at surface of packing element, various characteristics of packed bed such as void fraction, packing arrangement, sphericity and mass flow rate of heat transfer fluid.

2021-06-01T00:00:00Z Ramesh, M. http://localhost:8081/jspui/handle/123456789/15451 2022-08-01T06:35:12Z 2020-08-01T00:00:00Z

Title: HYBRID RENEWABLE ENERGY SYSTEM FOR A REMOTE RURAL AREA IN INDIA Authors: Ramesh, M. Abstract: The increase in population, technology and per capita energy consumption leads to an exponential increase in power demand. Due to fuel constraints, inadequate investment policies, high transmission and distribution losses, the conventional power generation alone could not meet the load demand. Increased emissions of pollutants and technological developments in solar and wind energy have paved the way for an alternative to electricity generation in the last decade. It is therefore, producing electricity from renewable energy sources become essential to meet the energy needs in the global scenario. In addition, the RERs are inexhaustible in nature and capable of addressing emission problems, thus encouraging the development of green energy-based power generation and technology. Therefore, renewable energy based power systems are acquiring more attention to provide cost effective and reliable power supply. The power generation using RERs can be utilized for grid connected/off-grid applications. Worldwide grid-independent hybrid renewable energy system (HRES) is an alternative option for powering the un-electrified villages where grid extension is not feasible. For the development of off-grid HRES, the RERs such as solar, wind, biomass and small hydropower can be used to meet the electricity needs of remote areas. Off-grid rural electrification is considered as the most acceptable and economical approach. As RERs are intermittent and not reliable in nature, the required load demand of the remote rural area may not be supplied by a single RER. For reliable and cost effective power supply, it is imperative to combine two or more RERs. Moreover, batteries (BTs) and diesel generators (DGs) can also be used to confronting the irregularity of RERs. In-order to obtain feasible and efficient HRES, demand side management (DSM) can be implemented. In this context, electricity generation from available renewable energy resources is the right choice in the proposed isolated area. Under this study, an extensive literature review has been performed and it is found that most of the studies were concentrated on single/two resources with BT/DG viz: photovoltaic (PV)/BT, wind turbine (WT)/BT, PV/WT/BT and WT/DG. However, quite limited studies on PV/WT/micro hydro (MHP)/DG/BT based HRES are reported in the literature. Most of the researchers focused on feasibility studies of HRES without considering the effects of different batteries and DGs on techno-economic and environmental parameters with and without scheduling. Several researchers carried out techno-economic analysis without taking into account the impact of dispatch strategies such as load following (LF), cycle charging (CC) and combined dispatch (CD) on the techno-economic and environmental parameters of the stand-alone HRES. Very few researchers optimized the stand-alone HRES based on the comparison of different batteries like Lead Acid (LA) and Li-Ion. Keeping this in view, the present study on PV/WT/MHP/DG/BT hybrid system was proposed with the following objectives: i. To identify a cluster of un-electrified villages having sufficient renewable energy resources and to estimate the load demand and potential of RERs. ii. To assess daily and seasonally varying renewable energy resources and load demand for the selected cluster of villages. iii. To analyze the effect of LA, Li-Ion batteries and with and without scheduling of DGs on HRES operating costs such as net present cost (NPC) and cost of energy (COE) of the HRES. iv. To investigate the effects of various dispatch strategies such as LF, CC and CD on the techno-economic and environmental performance of the proposed LA and Li-Ion battery based HRES. v. To perform sensitivity analysis for evaluating the impact of input variables on the COE and NPC of the proposed HRES under different dispatch strategies. vi. To enhance the reliability and to decrease the operating costs through implementation of different DSM methods. In order to satisfy the above mentioned objectives, a study has been carried out to provide power supply using the HRES for a remote rural area in Chikmagalur district of the Karnataka state in India. As per the data collected from Mangalore Electricity Supply Company (MESCOM), it was found that there are 49 villages still un-electrified in Chikmagalur district. A cluster of 13 un-electrified villages has been considered as the study area. Due to hilly terrain, households are scattered and about 297 households are not electrified in these villages. The conventional grid connection to these households is not feasible. Therefore, a HRES is considered the alternative option for the supply of electricity to these villages. Solar, wind and hydro are the available resources in this area. The integration of these RERs together with the DG is proposed to supply the required electrical power. In addition, to store the excess energy and to act as back-up, batteries have been proposed. The effect of use of LA, Li-Ion batteries on the performance of HRES has been assessed considering four different configurations such as PV/MHP/BT, PV/MHP/WT/BT, PV/WT/BT and PV/WT/MHP/DG/BT in the present study. Out of these configurations, PV/MHP/Li-Ion_BT HRES is found to provide the optimal feasible NPC and COE of the HRES. To find out the effect of DG scheduling, four different configurations have been considered as PV/WT/MHP/DG, PV/MHP/DG, WT/MHP/DG and MHP/DG HRESs respectively. A saving in NPC and COE of about 11% is found in all the four proposed configurations using with scheduling of DG than that of without scheduling. Further, the effect of LF, CC and CD strategies on LA and Li-Ion batteries with PV/MHP/BT and PV/WT/MHP/DG/BT configurations has been studied through HOMER Pro® simulation tool. For both LA and Li-Ion batteries based hybrid systems, the COE and NPCs are found to be minimum with CD strategy. Based on analysis, it has been found that COE was reduced by 34%, 25% and 37% under LF, CC and CD strategies in PV/MHP/BT_Li-Ion system in comparison of PV/Hydro/BT_LA hybrid system. Similarly, NPCs are also reduced by 35%, 34% and 35%. The results follow similar trends with the PV/WT/MHP/DG/BT HRES also. The effects of input parameters such as discount rate, PV system cost, battery cost, fuel cost, wind speed and design flow rate have also been investigated for sensitivity analysis. For the Li-Ion battery based system, the COE is found as 20%, 17% and 34% less than LA battery based system under LF, CC and CD strategies with ±20% variation of system input parameters, whereas, NPCs are reduced by 18.5%, 10% and 34%. To improve the performance of the proposed PV/MHP/BT, PV/WT/MHP/DG/BT based HRESs, various DSM methods viz: Load shifting, Strategic conservation and Load shifting along with Strategic conservation have been implemented. Out of these three methods Load shifting with Strategic conservation method is found to give the optimal solution. Based on the comparison, it is found that number of components was saved substantially by implementing the DSM to the proposed HRES. The operating costs such as the NPC and COE are found to be saved by $2,71,884 and 0.004$/kWh respectively and pollutant emissions are reduced by 63%. Further, DSM enabled PV/MHP/BT_Li-Ion HRES under CD strategy offers the optimal operating costs such as NPC and COE respectively as $3,14,079 and 0.103 $/kWh. The optimal capacity of PV, MHP, and converters are found as 203 kW, 15.7 kW, and 81.9 kW along with 557 numbers of batteries for the considered area. This study can be useful to provide guidance to develop HRES models for supplying power to similar off-grid rural areas.

2020-08-01T00:00:00Z Mangaldas, Patel Alpeshkumar http://localhost:8081/jspui/handle/123456789/14772 2022-05-14T09:33:31Z 2019-07-01T00:00:00Z

Title: INVESTIGATION OF OFF-GRID INTEGRATED RENEWABLE ENERGY SYSTEM Authors: Mangaldas, Patel Alpeshkumar Abstract: Energy is fifth essential need of the human being after air, water, food and shelter, thus becomes the strategic need of almost all the countries for socio-economic development. Among the various forms of energy, electrical energy which is easy to handle and transport is the most convenient form of energy for variety of end-use applications. Presently, the major contribution to the electrical power generation is from the conventional energy resources. The conventional energy sources such as crude oil, coal, gas and nuclear are depleting drastically in the world and expected to be exhausted in the coming future. Further, enormous use of these resources has increased concentration of carbon dioxide in the atmosphere significantly. On the other hand, the renewable energy (RE) sources such as sunlight, wind, biomass, etc. are naturally replenished and environmentally benign. They can generate electrical power with the negligible emission of the greenhouse gases and other pollutants. In the developing countries, two-third of the total population lives in the rural areas. Generally, the human population in urban areas have access to energy in many different forms such as electricity, cooking, heating, etc., while the isolated rural communities of many developing countries are still struggling for their energy needs. According to International Energy Agency (IEA), more than 84% people live without access to electricity in rural areas. In the un-electrified rural areas, the activities of people are usually limited to daylight hours. Providing electricity access satisfies not only the lighting and household needs but also offers opportunities to conduct activities related to the education, farming, social, health, safety, communication and employment beyond the daylight hours. The electricity access to un-electrified areas can be provided mainly by three options: (i) by grid extension, (ii) by using fossil fuel based generator, and (iii) by RE source-based system. Providing electricity access by extending nearby power grid is the best solution, if it is techno-economically feasible; however, the grid extension to remotely located hilly areas is quite difficult and economically prohibitive due to the poor load density and uneven terrain. In the remote rural areas, the fossil fuel-based generator is commonly used for electrical power generation; however, this option has certain disadvantageous as: (i) high transportation, storage and supply costs, (ii) adverse environmental impact, (iii) noisy operation, and (iv) high maintenance cost. A new pathway for off-grid rural electrification has come up due to innovation in RE source-based technologies. The use of locally available ii RE sources can be explored in two ways: (i) single RE source-based system and (ii) integrated system. Due to the stochastic nature of major RE sources, a single RE source-based system cannot provide uninterrupted supply of electricity; hence, to attain the high energy security, it is necessary to oversize the rating of the generating system, which in turn increases the overall cost of the system. On the other hand, the integrated renewable energy system (IRES), which employs potential of two or more RE sources to satisfy various energy demands, offers a better option than a single RE system in terms of efficiency, reliability and cost. Various studies show that the optimal configuration of the IRES should satisfy and compromise between two main objectives: (i) overall cost and (ii) power reliability. A reasonable compromise between the cost and power reliability of the system can be achieved by using different optimization techniques. The long-term system performance is one of the most imperative design criteria for the off-grid integrated energy system. It has been indicated in the literature that the performance of the off-grid IRES mainly depends on the performance of the individual system components accommodated in it. If the system components perform well, the final combination will automatically be the cost-effective and reliable. Therefore, in order to have the cost-effective and reliable off-grid IRES design, the system components used in building the IRES must be cost-effective and capable to contribute more to the power reliability of the IRES. Further, in the remote rural areas, the loads are generally scattered in nature and the electrical distribution network needs to be developed. In the developing countries, the distribution loss (DL) accounts around 13% of the generated energy. Many studies show that the distribution losses have a direct impact on the overall economy and efficiency of the system. Hence, it is necessary to consider the losses occurred in the distribution network while designing the off-grid IRES based power generation system. Based on the comprehensive literature review, it has been found that in most of the previous studies, the optimal configuration of the integrated system has been identified without considering the alternatives of system components. The optimal results may vary in case, various alternatives of system components are considered in the analysis. Further, the concept of the optimal component selection, by evaluating and comparing the performance of various equipment considered in the analysis has been presented by very few researchers. They used a value of the capacity factor (CF) for selection of the best components. Although iii this method gives sufficient information about energy generation by the equipment over a specific time period, it is incapable to evaluate the performance of the equipment in terms of their effect on the overall cost and power reliability of the off-grid IRES. Moreover, in the previously published works, the effect of DL on the power reliability, economy and size of the off-grid IRES has not yet been identified. The present study has been carried out to develop a methodology for investigation of the off-grid IRES for stand-alone power generation with the following objectives: (i) To identify and consider an un-electrified area for investigation of the IRES-based off-grid power generation (ii) To formulate different off-grid IRES models using various alternatives of system components, which allow performance evaluation of various equipment considered in the analysis. (iii) To develop a methodology to identify the optimal components, among a list of commercially available equipment, based on their effect on the overall cost and power reliability of the off-grid IRES, and to validate the veracity of the proposed method using the existing methodology available in the literature. (iv) To identify the most optimal configuration of the off-grid IRES for the study area and to investigate the effect of DL on the power reliability, economy and size. (v) To suggest the most economical option for providing electricity access to the study area by comparing the most optimal configuration of the off-grid IRES with the conventional grid-extension option. (vi) To perform a sensitivity analysis to investigate the effect of key variables on performance of the system. In order to fulfil the above mentioned objectives, a semi-isolated small hamlet, Khatisitara, situated in the hilly region near the border of the Gujarat and Rajasthan states in India has been considered as a study area. The grid-based electrical supply is not available in this area due to the poor load density and uneven terrain. The hamlet consists of 123 scattered households with a total population of 745. The selected study area is rich in terms of the availability of the solar, wind and bio-energy resources; hence, the IRES model has been configured using the solar photovoltaic (SPV) generator, wind turbine generator (WTG), biomass generator and biogas generator. An extensive field survey was carried out for assessment of the potential of RE resources and estimation of the electricity demand of iv the study area. The annual potential of the solar radiation, wind speed, agro-forest biomass and biogas from the cattle dung has been estimated as 1607 kWh/m2/year, 1572 kWh/m2/year, 66 tons/year and 16950 m3/year respectively. Total daily electricity demand of the study area for the summer and winter seasons have been estimated as 348 kWh/day and 211 kWh/day respectively. The annual total electricity demand has been estimated as 110523 kWh/year. To implement the proposed method of modeling the IRES-based off-grid power generation, the commercially available SPV modules of 0.1 kWp, 0.2 kWp and 0.3 kWp, WTGs of 1 kW, 3.3 kW and 5 kW, and the batteries of 360 Ah and 150 Ah have been considered for the analysis. Total 18 possible combinations of the off-grid IRES model have been configured using various alternatives of components. The mathematical modeling for all combinations of the IRES has been developed to evaluate the performance of various equipment considered in the analysis. In the mathematical modeling, the number of SPV module, number of WTG, output of biomass generator, output of biogas generator, and number of battery are considered as decision variables, and the life cycle cost (LCC) and energy index of reliability (EIR) are considered as performance indicators of the system. Initially, a random sample of decision variables has been generated and the corresponding value of the rating of bi-directional converter with charge controller, LCC and EIR has evaluated; hence, a row vector of 8 data points is produced. Finally, 1000 numbers of such random samples of decision variables have been generated, which produce a feature data matrix of size 1000row×8column. Then, the multi-variable linear regression (MVLR) with gradient descent algorithm (GDA), an error-based machine learning technique, has been used to determine the impact of individual system components on the overall cost and power reliability of the IRES. In the MVLR analysis, the decision variables have been considered as predictor variables and performance indicators have been considered as response variables. Two MVLR models as the cost model and power reliability model have been formulated and the impact of different components on the LCC and EIR have been evaluated separately. Once the impact assessment of different components on the LCC and EIR is accomplished, the best component has been selected by performing a trade-off between their impacts on the LCC and EIR of the IRES using an analytical hierarchy process (AHP) technique. In the AHP problem formulation, the impacts of system components on the LCC v and EIR are considered as basic selection criteria and the combinations of the IRES are considered as alternatives. Finally, the local priority vector and global priority vector of the alternatives for both selection criteria have been evaluated, and different equipment have been ranked using their global priority. To validate the results obtained using the above methodology, a value of the CF of the SPV modules and WTGs were determined. In the optimization problem, the minimization of the LCC has been considered as an objective function. The optimal configuration of each combination of the IRES has been identified by using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm at three different power reliability levels, i.e. 90%, 95% and 100%. The most optimal configuration of the IRES has been identified based on the lowest value of the LCC. Further, the effect of distribution losses on the power reliability, economy and sizing of the most optimal off-grid IRES has been investigated. Finally, the break-even analysis for grid-extension distance has been conducted to investigate the economic feasibility limit of the proposed off-grid IRES in the study area against the conventional grid supply option. The break-even point is estimated by comparing the LCC of the off-grid IRES and grid extension. Also, the effect of DL on break-even point has been examined. To investigate the effect of variation in the most influencing system parameters, the sensitivity analysis was conducted by changing the value of following variables: (i) the solar radiation and wind speed, (ii) capital cost of the IRES components. Further, the effect of increasing the cost of the raw fuel (agro-forest biomass and cattle dung) has also been investigated. From the MVLR analysis, the non-dominated combinations of the IRES in which the SPV module, WTG and battery have the lowest impact on the LCC and/or the highest impact on the EIR have been identified. From the AHP analysis, it has been found that the combination consisting of the SPV module of 0.2 kWp, has the highest global priority of 38.83%. Similarly, the combination consisting of the WTG of 3.3 kW and battery of 150 Ah have the highest global priority of 39.28% and 21.28% respectively. From these results, it is concluded that the SPV module of vi 0.2 kWp, WTG of 3.3 kW and battery of 150 Ah are the optimal choice for the selected site, and it will formulate the cost-effective and reliable off-grid IRES. The value of CF for the SPV modules of 0.1 kWp, 0.2 kWp and 0.3 kWp are obtained as 14.92%, 15.05% and 14.75% respectively. The value of CF for the WTGs of 1 kW, 3.3 kW and 5 kW are obtained as 26.23%, 27.75% and 26.23% respectively. The SPV module of 0.2 kWp and WTG of 3.3 kW possess the highest value of CF which indicates that these equipment are the best choice, among the available options. These results validate the optimal results obtained using the MVLR and AHP analysis. For the formulated optimization problem, the PSO algorithm converges more efficiently for all combinations of the IRES at all power reliability levels. The lowest value of the LCC has been observed in the combination consisting of the SPV module of 0.2 kWp, WTG of 3.3 kW and battery of 150 Ah. For the EIR value of 90%, 95% and 100%, the optimal value of the LCC is obtained as INR 12.32 million, INR 14.14 million and INR 16.20 million respectively. The power reliability of the most optimal off-grid IRES would be compromised by 3.13%, if it is designed without considering the DL of 10%. To compensate 10% DL, the additional 44 SPV modules of 0.2 kWp, 2 WTGs of 3.3 kW, and 9 batteries of 150 Ah are required to satisfy the estimated electricity demand compared to the DL free optimal IRES. Further at 10% DL, the system is economically oversized by 11.38% with respect to the DL free optimal IRES. From the analysis for grid-extension distance, the break-even point for the DL free optimal IRES and optimal IRES with 10% DL are obtained as 4.668 km and 5.668 km respectively. This result shows that the break-even point is affected by the DL. The analysed IRES model is found economically feasible for rural electrification in the study area against grid extension provided its LCC remains below INR 22.34 million. From the sensitivity analysis, it has been observed that the optimal results are sensitive to the increment in solar radiation. At 20% increment in the solar radiation, the most optimal configuration of the IRES is found in the combination consisting of the SPV module of 0.1 kWp, WTG of 3.3 kW and battery of 150 Ah. For the variation in capital cost of the IRES components, the optimal configuration of the IRES remains unchanged. Further, vii the LCC of the optimal IRES varies from INR 16.31 million to INR 16.74 million for the increment in biomass cost from 10% to 50% respectively. The LCC of the optimal IRES varies from INR 16.45 million to INR 17.48 million for the increment in cattle dung cost from 10% to 50% respectively. It is found that the proposed IRES model is more sensitive to variation in the cost of the cattle dung as compared to the cost of biomass. The methodology developed for investigation of the off-grid IRES in this study is generic and can be used for any other site and data. It will be beneficial to the system developers, decision and policy makers and researchers working in the area of stand-alone power generation using RE sources.

2019-07-01T00:00:00Z