Techno-economic feasibility study on Integrated Renewable Energy System for an isolated community of India

Renewable and Sustainable Energy Reviews 59 (2016) 388–405

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Techno-economic feasibility study on Integrated Renewable Energy System for an isolated community of India Anurag Chauhan n, R.P. Saini Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Uttarakhand 247667, India

art ic l e i nf o

a b s t r a c t

Article history: Received 17 March 2015 Received in revised form 18 December 2015 Accepted 27 December 2015

In the recent years, small scale power generation has been recognized as a suitable option for energy access in isolated rural areas due to uneconomical grid extension. A techno-economic feasibility study on the development of an Integrated Renewable Energy System (IRES) is carried out in the paper in order to meet the electrical and cooking energy demands of cluster of village hamlets of Chamoli district of Uttarakhand state (India). An attempt has been made to estimate the potential of locally available renewable energy resources and demands of the study area. The selection of small wind turbine model for the site specified is performed among the various models available in market. Further, nine different combinations of renewable energy resources have been investigated by considering economic, technical and social aspect criteria. Finally, a sensitivity analysis has also been carried out in order to determine the most sensitive parameter of the system. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Renewable energy Isolated Levelized cost of energy (LCOE) Net present cost (NPC) HOMER

Contents 1. 2.

3.

4.

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6.

n

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Modelling method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 2.1. Step I – Identification of study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 2.2. Step II – Estimation of electrical and cooking energy demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 2.3. Step III – Potential assessment of available resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 2.4. Step IV – Estimation of mean annual wind power density of a specific site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 2.5. Step V – Selection of small wind turbine model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 2.6. Step VI – Selection of integration configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 2.7. Step VII – Development of optimization model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Estimation of mean annual wind power density of a specific site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 3.1. Frequency distribution of wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 3.2. Annual wind energy density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 3.3. Mean annual wind power density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Selection of small wind turbine model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 4.1. Technical specifications of small wind turbine models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 4.2. Comparison of small wind turbine models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 4.2.1. Annual energy production of wind turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 4.2.2. Capacity factor of small wind turbines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 4.2.3. Comparison of capacity factor of small wind turbine models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Energy management strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 5.1. Charging of battery bank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 5.2. Discharging of battery bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Optimization method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 6.1. Economic assessment criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398

Corresponding author. þ 91 8171015620. E-mail address: [email protected] (A. Chauhan).

http://dx.doi.org/10.1016/j.rser.2015.12.290 1364-0321/& 2016 Elsevier Ltd. All rights reserved.

A. Chauhan, R.P. Saini / Renewable and Sustainable Energy Reviews 59 (2016) 388–405

389

6.1.1. Net present cost. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 6.1.2. Total annualized cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 6.1.3. Capital recovery factor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 6.1.4. Levelized cost of energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 6.2. Technical assessment criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 6.3. Social aspect assessment criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7. Database needed for simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7.1. Hourly electrical energy demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7.2. Monthly average daily solar radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7.3. Monthly average discharge availability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7.4. Monthly average wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7.5. Monthly average biomass availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 7.6. Economical data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8. Simulation results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1. Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1.1. Combination 1: MHP-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1.2. Combination 2: Biogas-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1.3. Combination 3: Biomass-Battery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1.4. Combination 4: MHP-Biogas-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1.5. Combination 5: MHP-Biomass-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 8.1.6. Combination 6: MHP-Biogas-Biomass-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.1.7. Combination 7: MHP-Biogas-Biomass-Wind-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.1.8. Combination 8: MHP-Biogas-Biomass-PV array-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.1.9. Combination 9: MHP-Biogas-Biomass-Wind-PV array-Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.2. Comparison of simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.2.1. Achievement of 0% capacity shortage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.2.2. NPC and LCOE for different combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.2.3. Battery storage requirement for different combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.2.4. Employment generation for different combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 8.3. Selection of a suitable combination based on the results obtained considering total NPC, LCOE, battery storage, capacity shortage and employment as important criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 8.4. Cost breakdown of the total NPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 8.5. Annual generation of renewable energy sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 8.6. Frequency histogram for state of charge of battery bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 9. Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 9.1. Effect of the electrical energy demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 9.2. Effect of mean annual wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 9.3. Effect of maximum annual capacity shortage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 9.4. Effect of biomass price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 10. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

1. Introduction In developing countries like India, most of the rural areas at remote location have no access of electricity due to cost and complexity associated with the grid extension. However, these remote areas are rich in the availability of locally available renewable energy resources such as cattle dung, waste from agricultural field, forest foliage, water streams, solar intensity, wind etc. Therefore, utilization of renewable energy resources in decentralized mode would be the most economical and sustainable option for electricity supply in rural areas. As renewable energy resources (solar, wind etc.) are intermittent in nature, therefore it is essential to integrate such resources to provide a reliable and economical power supply at user end. Electrification of rural areas has the potential to improve the living standard, health conditions, standard of education and empowering the youth of the nearby population [1–8]. The concept of Integrated Renewable Energy System (IRES) has been proposed various researchers for energy access in rural households [9–15]. In IRES, electrical and cooking energy demands of an isolated area, far away from the utility grid, match with the potential of locally available renewable energy resources. In IRES, resources are utilized in appropriate and cost effective manner

based on the resource availability and energy demand of the area. Therefore, IRES offers energy conservation and high energy efficiency resulting from the combination of renewable energy resources. In order to design IRES, a careful and strategic planning is essential for matching needs and available resources to maximize benefits and efficiency of end uses. Patil et al. [9] considered four scenarios during modelling and optimization of IRES. From four different scenarios, they found that microhydro-biomass-biogas-energy plantation-wind-solar based integrated energy system offered the lowest cost of energy and recognized as a suitable option to supply required energy for cooking and electrical appliances. Rohani et al. [10] performed a techno-economical analysis of hybrid renewable power system comprised of photovoltaic array, wind turbines, batteries and diesel generators. Fazelpour et al. [16] carried out a feasibility analysis of solar photovoltaic (SPV)-wind-diesel generator-battery based hybrid system to satisfy electrical energy needs of medium-size hotel. Gupta et al. [17] optimized the cost of hybrid system in such a way that resources with lesser unit cost would share the greater of the total energy demand based on mixed integer linear mathematical programming. Moura et al. [18] optimized the cost of solar-windhydrobased integrated energy system and maximized the

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Nomenclature c scale parameter of Weibull distribution function Cannz-cap annualized capital cost of system ($) Cannz-maint annualized maintenance cost of system ($) Cannz-rep annualized replacement cost of system ($) Cannz-total total annualized cost of system Cf capacity factor of a wind turbine CRF Capital recovery factor d discount rate (6%) ED annual wind energy density (kW h/m2/yr) Epri,AC AC primary load served (kW h/yr) EWT annual energy production of a wind turbine (kW h/yr) f(V,k,c) Weibull distribution function for wind speed (V)

contribution of renewable energy sources during peak load hours. Li et al. [19] proposed an algorithm to decide the minimal system cost configuration based on energy balance. Kaldellis et al. [20] minimized the cost of electricity generation of solar-battery storage based hybrid system by enhancing the contribution of solar in the south-east Mediterranean Sea islands. They analyzed that the considered hybrid system is more reliable and economical as compared to existing thermal power stations. Yang et al. [21] proposed power reliability model based on loss of power supply probability (LPSP) and economic model based on annualized system cost. Kenfack et al. [22] performed the optimal sizing of small hydroPV-diesel generator-battery based hybrid system using HOMER tool for a remote village in Cameroon. Nema et al. [23] suggested a PV-wind-diesel generator based hybrid energy system for cellular mobile telephony base station site in isolated areas. It was found that approximately 70–80% fuel cost over conventional generator and the emission of CO2 and other harmful gases were reduced. Kumaravel and Ashok [24] performed the size optimization of PVbiomass-pico-hydel based hybrid energy system using HOMER tool for the rural areas of Kerala, India. They determined the cost of energy of generation as INR 7.274 per kW h for the annual average load of 56 kW h/d from the available resources of solar radiation of 3.89 kW h/m2/day, stream flow of 51.7 L/s and biomass of 0.692 ton/day. Balamurugan et al. [25] developed an optimization model for biomass gasifier-wind-PV-battery hybrid system using HOMER tool in order to maximize the supply of energy to the loads with the minimization of dumped energy. They obtained the value of capacity shortage and dumped energy as 10% and 1% of total energy supplied to the demand respectively at the minimum cost of energy of INR 4.20 per kW h. They found that the capacity shortage was decreased with the increase of wind speed and solar radiation. Koutroulis et al. [26] optimized the cost of PV-windbattery based hybrid system using genetic algorithm (GA) for zero load rejection. From the previous works, it has been found that many studies have been carried out for electrification of small locality; study on cluster of villages is quite limited in literature. Also, most of the researchers considered solar/battery, wind/battery and solar/ wind/battery based integrated energy system. However, solar/ wind/battery based integrated energy system along with microhydropower/biomass/biogas systems is still quite limited in literature. Further, in most of the studies, electrical energy demand of rural area is normally kept fixed for year round application during the design of IRES. The seasonal variations in electrical energy demand were not accounted while calculating the optimum size of renewable energy systems.

k LCOE MAWS N NPC PD SD Vm ηC ηCHG ηDCHG σ ρ

shape parameter of Weibull distribution function levelized cost of energy ($/kW h) mean annual wind speed (m/s) project lifetime (25 yr) nett present cost ($) Mean annual wind power density (W/m2) standard deviation of wind speeds mean of wind speeds converter efficiency charging efficiency of battery discharging efficiency of battery hourly self discharge of battery air density (1.2 kg/m3)

Based on the literature review carried out and research gaps identified as discussed above, the present paper deals with the techno-economic feasibility study on MHP-biogas-biomass-solarwind-battery based IRES in order to meet the seasonally varying electrical and cooking energy demands of cluster of village hamlets of Chamoli district of Uttarakhand state (India). A modelling method has been established for the present study to avail the energy demands and resource assessment of the study area. Further, selection of small wind turbine model for the considered IRES has been investigated. Different combinations of renewable energy resources have been considered and compared using HOMER software. Finally, a sensitivity analysis has also been conducted to analyze the effect of different parameters on the considered system.

2. Modelling method A systematic modelling method is essential for the development of optimal integrated renewable energy system model for a remote area as it that ensures continuous power supply to remote rural households. A flow chart of modelling method for the present study has been prepared as shown in Fig. 1. Important steps of the modelling method for the present study are discussed as: 2.1. Step I – Identification of study area A cluster of 48 numbers of un-electrified village hamlets of Chamoli district of Uttarakhand state, India has been selected as the study area. These hamlets are present in four blocks of the Chamoli district viz. 13 in Ghat block (zone 1), 11 hamlets in Narayanbagar block (zone 2), 12 hamlets in Tharali block (zone 3), and 12 hamlets in Dewal block (zone 4). These hamlets have total households of 723 with a total population of 3031 [27]. Grid extension is not a feasible option for energy supply in these hamlets as most of the hamlets are located at remote hilly terrain. However, these areas are rich in the availability of renewable energy resources such as micro-hydropower (MHP), solar, wind, biomass, biogas etc. The geographical location of the study area on map is shown in Fig. 2 and Table 1 provides the general information of the study area [28]. 2.2. Step II – Estimation of electrical and cooking energy demand The study area has presently low electrical energy demand, but as the electrical power will be generated near to the villages, the

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Start

Get the list of un-electrified villages/ hamlets from state nodal agency

Select un-electrified rural areas

No

Is a area Cluster of villages ?

Resource Assessment (Considering availability in all seasons) A. Solar insolation B. Wind speed C. Hydro head & discharge D. Biomass kg/day E. Any other resource if available

YES Load Assessment (Get hourly load profile in all seasons)

Collect data from site

Select resources that can meet load of the area

No Generation ≥ Demand

Enhance capacity of biomass, biogas based generation and/or increase battery storage capacity

YES Selection of small wind turbine model

Development of optimization model

Perform techno-economic optimization and obtain results Stop Fig. 1. Flow chart of modelling method for the present study.

energy consumption is expected to increase with time. Therefore, electrical energy demand of the study area has been estimated considering the future requirements of the cluster of village hamlets. At village level, primary data were collected by taking the interview of local people and revenue village officers about energy

requirement of various consumption sectors viz. domestic, agricultural, community and commercial sectors. The domestic sector requires electricity for compact fluorescent lamp (CFL), fan, TV, radio, water pump etc. for each house. The agricultural loads include electricity requirement for water lifting pump and crop threshing machine/fodder cutting machine. Community sector

392

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Fig. 2. Geographical location of the study area [28].

requires electricity for hospitals, schools, community hall and street lights. Commercial loads comprise small shops of villages, flour mill and mini dairy plants. Rating and requirement of appliances for different energy consumption sectors are considered as given in Table 2. The monthly average temperature of the area varies from 10 °C to 45 °C throughout the year. This affects the energy consumption pattern of different appliances. In order to analyze, two seasons of six months each are considered for the present study as: summer season (April–September) and winter season (October–March).

Based on energy requirement at various end uses and data of Table 2, hourly electrical energy demand of the study area has been worked out and given in Table 3. The energy consumption of the study area during summer and winter seasons has been estimated as 2272 kW h/d and 1569 kW h/d, respectively. Annual electricity consumption of the study area is estimated as 701,263 kW h/yr. Further, cooking energy demand of the study area has been estimated based on population and specific biogas consumption. The specific biogas consumption of 0.24 m3/day/person for cooking

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Table 1 General information of the study area [27,28]. Country State/Province Name of district Name of block Latitude Longitude Region Elevation from mean sea level (m) No. of unelectrified village hamlets/toks Name of village hamlets/toks

Total population of village hamlets/toks No. of households in village hamlets/toks

India Uttarakhand Chamoli Ghat 30°150 N 79°260 E Hills 1135

Narayanbagar 30°70 N 79°230 E Hills 1116

Tharali 30°40 N 79°300 E Hills 1211

Dewal 30°80 N 79°370 E Remote hills 1611

13

11

12

12

Patalgetha, Maalgetha, Gwalgher, Rudeno, Dhardar, Dhumandera, Sugaitalla, Simla H.B., Kairkot, Lathmaria, Khudaro, Bhirtoli, Kafrali 702

Jiroti, Aanroti, Tulyo, Kusdev, Latubagad, Dharkot, Gwad, Gyurilo, Durgapuri, Massadgad, Nandadharmi 722

Manartok, Rithartok, Gwadtok, Jalpani, Jalchaura, Belpuri, Bulla, jadamaar, Gasiyakhil, Talkot, Rajkundi, Dharkot 833

Bagotiya, Dharkot, Chipnagwad, Paiyoli, Rikhwani, Vinayak, Baankhadi, Thalatok, Patil, Ghatgaad, Jawar, Aagar Talla 774

167

173

194

189

Table 2 Rating and requirement of appliances for different energy consumption sectors. Energy consumption sector

Appliances

Rating (W) Quantity/HH or room

Total quantity (Nos.)

1. Domestic: CFL TV Fan Radio Water pump 2. Commercial: Shop Flourmill Mini dairy plant 3. Agricultural: Water lifting pump Crop threshing machine 4. Community: Community hall School Hospital

Street lights

CFL Fan

18 70 45 15 500

2 1 2 1 –

Points Point Points Point

18 45 5000 3750

1 Point 1 Point – –

2500 5000

– –

CFL 18 Fan 45 CFL 18 Fan 45 CFL 18 Fan 45 Refrigerator 475 Water heater 1000 Room heater 1000 CFL 150

1 1 6 6 1 1 – – –

1446 723 1446 723 40 94 94 4 4 8 4

Point Point Points Points Point Point

8 8 42 42 19 19 4 2 3 299

has been considered for the present study [29] and accordingly, the daily cooking energy demand of study area is estimated to be as 728 m3/day. Finally, an attempt has been made to verify the predicted electrical and cooking energy demands through primary data collected from the local people (end-users). 2.3. Step III – Potential assessment of available resources For the potential assessment of renewable energy resources in the study area, an extensive field survey was conducted for collecting the information regarding availability of discharge (m3/s) in water streams, availability of biomass (ton/yr) from forest and crop field, availability of cattle dung (kg/day), solar radiation (kW h/m2/day) and wind speed (m/s). Based on the head and discharge available in water streams, one MHP system of rating 25 kW capacity along with 25 numbers of MHP systems each rating of 1 kW capacity has been identified in the study area. Discharge in water streams has been measured

using salt dilution method. Also, the selected area receives daily average solar radiation of 5.371 kW h/m2/day [30]. The study area has low wind speed (2–18 m/s) and is suitable for small wind turbines/aerogenerators (1–25 kW). In the study area, only one site ‘Harmal’ of Dewal block is found to be feasible for small wind turbine installation as mean annual wind speed (MAWS) of this location is 5.11 m/s at the height of 20 m [30]. Forest foliage and crop residue are the major sources of biomass in the selected area. Based on the availability of biomass in the area and daily operating hours (10 h/day), a 40 kW rated biomass gasifier system has been suggested for the area. Also, considered hamlets have large number of indigenous cattle, buffaloes, goats and sheep whose dung can be used for biogas production and in turn utilized for cooking and to run the biogas generator [28]. Total cattle dung is estimated as 26,521.8 kg/day in the study area on 60% collection efficiency and this dung produces 1100 m3/ day biogas daily. Out of 1100 m3/day, 728 m3/day biogas is daily required to meet cooking needs of the considered area. Remaining biogas of 372 m3/day is available for electricity production in the selected area. Based on the available biogas for electricity generation and daily operating hours (10 h/day), a biogas generator of 50 kW rated capacity is proposed for the study area. Potential of renewable energy resources in the study area has been presented in Table 4. 2.4. Step IV – Estimation of mean annual wind power density of a specific site Mean annual wind power density of Harmal site of the study area has been estimated for the justification of small wind turbine installation at the selected site. In order to investigate, frequency distribution of wind speed at the selected site was collected. Further, annual wind energy density and mean annual wind power density have been estimated using the availability of wind speeds and air density. Based on the calculations, annual wind energy density of 1348.80 kW h/m2/yr and mean annual wind power density of 154 W/m2 were estimated for the selected site. 2.5. Step V – Selection of small wind turbine model An analysis has been carried out in order to recommend a suitable small wind turbine model for the selected site. In order to investigate, technical specifications of small wind turbine models have been obtained from the different manufacturers. Further, the capacity factor of wind turbine models was estimated using the calculations of annual energy production and rated power output

394

Table 3 Hourly electrical energy demand of the study area during summer and winter seasons.

CFL T.V. Fan (18 W) (70 W) (45 W)

Commercial load (kW)

Radio (15 W)

Water Fan CFL pump (18 W) (45 W) (0.5 kW)

Summer/ Winter 1:0 2:0 3:0 4:0 5:0 6:0 7:0 8:0 9:0 10:0 11:0 12:0 13:0 14:0 15:0 16:0 17:0 18:0 19:0 20:0 21:0 22:0 23:0 0:0

Flour Mini mill dairy (5 kW) Plant (3.75 kW)

Agricultural load (kW)

Community load (kW)

Water lifting Pump (2.5 kW)

Community Hall

Crop thrashing machine (5 kW)

Summer/ Winter

Electrical energy demand (kW)

School

Hospital

CFL Fan CFL Fan CFL Fan Water hea(18 W) (45 W) (18 W) (45 W) (18 W) (45 W) ter (1 kW) Summer/ Summer/ Summer/ Summer/ Winter Winter Winter Winter 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34

26.03 26.03 50.61 50.61 50.61

26.03 26.03 26.03 26.03 26.03

25.31 25.31 25.31 25.31 25.31 50.61 50.61 50.61 50.61 50.61

10.85 10.85 65.07/0 65.07/0 65.07/0 65.07/0 65.07/0 65.07/0 65.07/0 65.07/0 65.07/0

15 15 15

10.85 10.85 10.85 10.85 10.85 20 20 20 20

1.69 1.69 1.69 1.69

4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0 4.23/0

Room heater (1 kW) Summer/ Winter

20 20 20 20

0.14 0.14 0.14 0.14 0.14 0.14 0.14

0.36/0 0.36/0 0.36/0 0.36/0 0.36/0 0.36/0 0.36/0

0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76

1.89/0 1.89/0 1.89/0 1.89/0 1.89/0 1.89/0 1.89/0 1.89/0 1.89/0 0.34 0.34 0.34 0.34 0.34 0.34

0.86/0 0.86/0 0.86/0 0.86/0 0.86/0 0.86/0 0.86/0 0.86/0 0.86/0

2/2 2/2 2/2 0/2

0/3 0/3 0/3

Refrigerator (475 W)

Street Light (150 W) Summer/ Winter

1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0 1.9/0

44.85 44.85 44.85 44.85 44.85 44.85

44.85 44.85 44.85 44.85 44.85 44.85

47.09/45.19 47.09/45.19 47.09/45.19 47.09/45.19 73.12/71.22 73.12/71.22 78.70/76.80 78.70/76.80 72.16/68.37 75.85/2.76 77.21/2.90 75.21/2.90 131.36/57.05 131.36/57.05 131.36/57.05 131.36/57.05 111.36/37.05 148.69/76.64 150.51/143.52 149.65/143.52 149.65/143.52 149.65/146.52 47.09/48.19 47.09/48.19

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Hour Domestic load (kW) of day

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Table 4 Potential assessment of renewable energy resources in the study area [28,30]. Zones

Small hydro (MHP)

Solar (Daily averBiomass (Forest age solar radiation) foliage, crop residue)

Zone 1: Ghat block

 1 kW MHP systems : 8 nos. Availability: Seasonal 5.371 kW h/m2/d

Zone 2: Narayanbagar block

–

Zone 3: Tharali block

 1 kW MHP systems : 3 nos. Availability: Seasonal 5.371 kW h/m2/d

Zone 4: Dewal block

 1 kW MHP systems : 14 nos. Availability: Seasonal 5.371 kW h/m2/d

 Forest

 Indigenous

 Forest

   

foliage: 21.12 ton/yr

(April–September)

5.371 kW h/m2/d



(April–September)

(April–September)

 25 kW MHP systems : 1 no. Stream name: Wan Discharge: 145 l/s Head: 25 m Availability: Throughout the year

Annual energy potential

329,144 kW h/yr

1960.42 kW h/m2/ yr

Biogas (Animals in nos.)



foliage:19.38 ton/yr Crop residue:41.40 ton/yr Forest foliage: 16.32 ton/yr

  

Wind (Wind speed and wind power density)

– cattle: 840 Buffalo: 322 Sheep: 972 Goat: 484 – Indigenous cattle: 237 Buffalo: 119 Sheep: 0 Goat: 70

 Indigenous

– cattle: 571  Buffalo: 400  Sheep: 36  Goat: 227  Forest  MAWS: 5.11 m/s foliage:  Indigenous  MAWPD: 154 W/m2 61.82 ton/yr cattle: 447  Buffalo: 284  Sheep: 62  Goat: 275 131,440 kW h/yr 182,500 kW h/ 1348.8 kW h/m2/yr yr

year, an optimal model of IRES has been suggested for the study area. In the present study, different combinations of renewable energy resources were investigated. Further, a suitable combination for the study area is recommended considering economic, technical and social aspect as important criteria. The maximum annual capacity shortage of 0% at user-end was considered under the present study in order to supply a reliable power to rural households.

3. Estimation of mean annual wind power density of a specific site Fig. 3. Hybrid DC–AC coupled configuration of IRES.

of wind turbine. Finally, a small wind turbine model is recommended for the site based on the highest value of capacity factor.

This section presents the estimation of mean annual wind power density for Harmal site of the study area for the justification of small wind turbine installation.

2.6. Step VI – Selection of integration configuration

3.1. Frequency distribution of wind speed

Renewable energy resources in IRES can be integrated through three possible configurations, viz. DC coupled configuration, AC coupled configuration and hybrid DC–AC coupled configuration. However, hybrid DC–AC coupled configuration is the cost effective and efficient compared to DC coupled and AC coupled schemes, therefore, hybrid DC–AC coupled configuration is considered for the present study as shown in Fig. 3. In this configuration, PV array and wind turbines are connected to DC bus while MHP system, biomass gasifier and biogas are directly coupled to AC bus without any interfacing circuits. A set of batteries (12 V, 200 A h) are also included in IRES as storage subsystem.

Frequency distribution of wind speed for Harmal site is shown in Fig. 4. At the height of 20 m, the considered site has maximum frequency of 14.63% in a year for the wind speed in the range of 4– 5 m/s, 14.37% for the range of 3–4 m/s, 13.34% for the range of 5– 6 m/s followed by 12.51% for the range of 2–3 m/s. Wind speed remains more than 3 m/s for 75% time in a year. As most of the small wind turbine models have low cut-in speed in the range of 2.5–3.5 m/s, these models can easily produce power for 75% time in year. Mean annual wind speed at the site has been estimated as 5.11 m/s [30].

2.7. Step VII – Development of optimization model

3.2. Annual wind energy density

Finally, techno-economic optimization of IRES comprised of MHP, biogas, biomass, wind, PV array and battery bank has been performed using HOMER software. After hourly simulation for a

Annual wind energy density at the selected site depends on wind speed, air density and number of hours at which wind speed is available. Annual wind energy density (ED) in kW h/m2/yr at site

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16 14 Frequency (%)

12 10 8 6 4 2 0

Wind speed (m/s) Fig. 4. Frequency distribution of wind speed at the height of 20 m for Harmal site [30].

can be estimated by Eq. (1) as:  Pn  3 1 i ¼ 1 V i  hi 2ρ ED ¼ 1000

ð1Þ

where Vi is wind speed in m/s, hi is number of hours in year at which wind speed Vi is available and ρ is the air density (1.2 kg/ m3). 3.3. Mean annual wind power density Mean annual wind power density (PD) at site depends on annual wind energy density and total number of annual hours. PD (in W/m2) can be calculated by Eq. (2) as:   ED ð2Þ PD ¼ 365  24 Annual wind energy density and mean annual wind power density have been estimated using Eqs. (1-2) and presented in Table 5. From the calculation it has been found that the selected site has annual wind energy density of 1348.80 kW h/m2/yr and mean annual wind power density of 154 W/m2. Therefore, this site is suitable for small wind turbine installation as annual wind energy density at site is more than 1000 kW h/m2 at the height of 20 m.

Table 5 Estimation of annual wind energy density and mean annual power density of Harmal site. S. No. Wind speed (m/s)

Mean wind speed (m/s)

Frequency (%) Wind energy density (kW h/m2)

1 0–1 0.5 2.95 2 1–2 1.5 8.33 3 2–3 2.5 12.51 4 3–4 3.5 14.37 5 4–5 4.5 14.63 6 5–6 5.5 13.34 7 6–7 6.5 10.97 8 7–8 7.5 8.28 9 8–9 8.5 5.87 10 9–10 9.5 3.77 11 10–11 10.5 2.32 12 11–12 11.5 1.31 13 12–13 12.5 0.68 14 13–14 13.5 0.40 15 14–15 14.5 0.15 16 15–16 15.5 0.06 17 16–17 16.5 0.05 18 17–18 17.5 0.01 2 Annual wind energy density (kW h/m /yr) Mean annual wind power density (W/m2)

0.02 1.48 10.27 32.39 70.09 116.70 158.35 183.52 189.40 169.76 141.00 104.94 70.31 51.66 23.78 11.16 10.77 3.21 1348.80 154

manufacturers and is given in Table 6 [32–35]. Power curves of considered models are shown in Figs. 5–8. 4. Selection of small wind turbine model Small wind turbines are generally characterized by low rated output in the range of 1–25 kW. These turbines easily operate at low wind speeds in the range of 2.5–3.5 m/s. In this section, nine numbers of small wind turbine models of different manufacturers have been considered and compared based on the value of capacity factor.

4.2. Comparison of small wind turbine models

4.1. Technical specifications of small wind turbine models

4.2.1. Annual energy production of wind turbine Annual energy production of a wind turbine depends on power curve of selected model and the value of Weibull distribution function. The Weibull probability density function of wind speed (V) can be expressed as [36]:

In the present study, nine number of small wind turbine models are considered which are recommended by National Institute of Wind Energy (NIWE), Government of India. List of small wind turbine models include UE-33, UE-42, UE-42 þ models of Unitron Energy Systems, Genie 5000 model of Auroville Wind Systems, Whisper 200 and Whisper 500 models of Luminous Renewable Energy Solutions Pvt. Ltd., Altem 10, Altem 15, Altem 25 models of Altem Power Ltd. [31]. Information regarding technical specifications of small wind turbine models has been collected from the concerned

The considered small wind turbine models have been compared based on the value of capacity factor (Cf). The capacity factor of wind turbine at a site depends on annual energy production and rated power output.

"   #   k  1 k V V k f ðV ; k; cÞ ¼ exp  c c c

ð3Þ

where f(V,k,c) is the probability of wind speed (V), c is scale parameter, k is shape parameter and V Z 0; k 41, c 40.

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Table 6 Technical specification of small wind turbines [32–35]. S. No. Small wind turbine models

Manufacturer

1 2 3 4 5

UE-33 UE-42 UE-42 þ Genie 5000 Whisper 200

6

Whisper 500

7 8 9

Altem 10 Altem 15 Altem 25

Unitron Energy Systems Pvt. Ltd. Unitron Energy Systems Pvt. Ltd. Unitron Energy Systems Pvt. Ltd. Auroville Wind Systems Luminous Renewable Energy Solutions Pvt. Ltd. Luminous Renewable Energy Solutions Pvt. Ltd. Altem Power Limited Altem Power Limited Altem Power Limited

Rated output (kW)

Cut-in speed (m/s)

Rated speed (m/s)

Cut-out speed (m/s)

Rotor diameter (m)

Swept area (m2)

3.3 4.2 5.1 5 1

2.7 2.7 2.7 3 3.1

10.5 11 11 10.5 11.6

20 20 20 20 24

4.65 4.9 5.24 5 2.72

16.4 19 21.4

3

3.1

12

24

4.5

15.9

10 15 25

3.5 3.5 3.5

12 12 12

25 25 25

7.5 9.5 12

5.8

38 64 108

1.2 Whisper 200

UE-33 UE-42 UE-42+

5

Wind turbine output (kW)

Wind turbine output (kW)

6

4 3 2

1 0.8 0.6 0.4 0.2

1

0 1

0 1

2

3

4

5

6

7

8

2

3

4

5

6

9 10 11 12 13 14 15 16 17 18 19 20 21 Wind speed (m/s)

7

8

9

10 11 12 13 14 15 16 17 18 19

Wind speed (m/s) Fig. 8. Power curve of Whisper 200 wind turbine model [35].

Fig. 5. Power curves of UE-33, UE-42 and UE-42þ wind turbine models [32].

The shape and scale parameters of the Weibull distribution function can be estimated by Eqs. (4)–(6) as:    1:086 SD ð4Þ k¼ Vm

Wind turbine output (kW)

30 Altem 10 Altem 15 Altem 25

25

Vm  c¼  Γ 1 þ 1k

20 15

ð5Þ

where SD is the standard deviation, Vm is the mean wind speed and ΓðÞ is the gamma function. Gamma function can be expressed as: Z 1 Γ ðx Þ ¼ t x  1 e  t dt ð6Þ

10 5 0

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19

Wind speed (m/s) Fig. 6. Power curves of Altem 10, Altem 15 and Altem 25 wind turbine models [33].

Annual energy production (EWT) of a wind turbine at a specific site can be estimated by Eq. (7) as: " # V cut  out X EWT ¼ 365  24  P WT ðV Þ  f ðV; k; cÞ ð7Þ V ¼0

where, PWT(V) is power output of wind turbine for wind speed (V), Vcut-out is cut out speed of wind turbine.

6

Wind turbine ouput (kW)

Whisper 500 Genie 5000

5

4.2.2. Capacity factor of small wind turbines Capacity factor (Cf) is the ratio of annual energy production of a wind turbine to rated energy production in a year and can be estimated by Eq. (8) as [36]:    EWT kW h=yr Cf ¼ Rated output of wind turbine model ðkWÞ  8760 ðhÞ ð8Þ

4 3 2 1 0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19

Wind speed (m/s) Fig. 7. Power curves of Genie 5000 and Whisper 500 wind turbine models [34,35].

4.2.3. Comparison of capacity factor of small wind turbine models Annual energy production and capacity factor of small wind turbine models have been worked out and are shown in Figs. 9 and

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where EEE(t) is excess energy from renewable energy resources after serving demand, EBatt(t 1) is battery capacity in previous state, EDem(t) is hourly electrical energy demand, EAC(t) is sum of power generation of MHP system, biomass generator and biogas generator, EDC(t) is sum of power generation of PV array and wind turbines, ηCHG is charging efficiency of battery, ηC is converter efficiency and Δt is time step (1 h).

Annual energy production (kWh/yr)

30000 25000 20000 15000 10000

5.2. Discharging of battery bank

5000 0 UE-33

UE-42 UE-42+ Genie Whisper Whisper Altem 10Altem 15Altem 25 5000 200 500

Small wind turbine models Fig. 9. Annual energy production of small wind turbine models for Harmal site.

30

 ENetload ðt Þ ¼ EDem ðt Þ  EAC ðt Þ þEDC ðt Þ  ηC

ð14Þ

where ENetDem(t) is net deficit demand, σ is hourly self discharge rate, ηDCHG is discharging efficiency of battery.

25 Capacity factor (%)

If electrical energy demand exceeds power generation, battery bank is used to assure the net deficit demand and battery bank storage capacity can be calculated by Eqs. (13) and (14) as:  ENetDem ðt Þ EBatt ðt Þ ¼ ð1 σ Þ  EBatt ðt  1Þ  ð13Þ ηC  ηDCHG

20

6. Optimization method

15 10 5 0 UE-33

UE-42 UE-42+

Genie Whisper Whisper Altem 10 Altem 15 Altem 25 5000 200 500

Small wind turbine models Fig. 10. Capacity factor of small wind turbine models for Harmal site.

10. The wind turbine models UE-33, UE-42 and UE-42 þ produce 7409 kW h (Cf ¼25.63%), 8831 kW h (Cf ¼24%) and 10,543 kW h (Cf ¼23.60%) electrical energy in a year respectively. Whisper 200 model generate 2194 kW h electrical energy in a year at the capacity factor of 25.40%. Compared to all other small wind turbine models, Whisper 500 model produces 6779 kW h energy at the highest value of capacity factor of 25.79%. Based on the results obtained, Whisper 500 model has been recommended for the site specified and considered in the optimization of proposed IRES.

5. Energy management strategy Battery bank storage system has been incorporated in the proposed IRES in order to counteract the intermittent nature of renewable energy resources. Energy management strategy for the considered system is summarized as:

The Hybrid Optimization Model for Electric Renewables (HOMER) software developed by National Renewable Energy Laboratory (NREL), USA is used for the optimization of the proposed IRES. The HOMER software performs simulation, optimization and sensitivity analysis of integrated energy system. During simulation process, it simulates various possible configurations of considered IRES based on energy balance for each hour of the year. It also considers the feasibility of configurations by satisfying all the constraints imposed by the modeller [37–40]. Further, economic, technical and social aspects assessment are considered to be the important criteria for the evaluation of energy supply systems which are discussed as: 6.1. Economic assessment criteria In the present work, the total net present cost (NPC) of IRES is considered as the economic criteria to evaluate the financial viability of energy production. 6.1.1. Net present cost The total NPC of IRES condenses all costs and revenues that occur within the project lifetime. It includes initial capital costs, replacement costs, maintenance costs, fuel costs, emission penalties and costs of purchasing power from the grid. System's revenues include salvage values of batteries, biomass generator, biogas generator, diesel generator and selling of electricity to grid. The total net present cost of an integrated energy system is expressed as [41]: C annz  total CRFðd; NÞ

5.1. Charging of battery bank

NPC ¼

When the total power generation is greater than the hourly electrical energy demand, the excess energy is stored in the battery bank and the available battery bank capacity at hour t can be estimated by Eqs. (9)–(12) as:

where Cannz-total ¼ total annualized cost of system, CRF ¼ Capital recovery factor, d ¼discount rate, N ¼ project lifetime.

ð15Þ

EBatt ðt Þ ¼ EBatt ðt  1Þ þ EEE ðt Þ  ηCHG  ηC

ð9Þ

 EEE ðt Þ ¼ EAC ðt Þ þ EDC ðt Þ  ηC  EDem ðt Þ

ð10Þ

EAC ðt Þ ¼ ½P MHPS ðt Þ þ P BMGS ðt Þ þ P BGGS ðt Þ  Δt

ð11Þ

6.1.2. Total annualized cost The total annualized cost of system (Cannz-total) is the sum of the annualized capital cost (Cannz-cap), the annualized replacement cost (Cannz-rep) and the annualized maintenance cost (Cannz-maint) of all components of the system. The total annualized cost of system can be estimated by Eq. (16) as [41]

EDC ðt Þ ¼ ½P WECS ðt Þ þ P SPVS ðt Þ  Δt

ð12Þ

C annz  total ¼ C annz  cap þC annz  rep þ C annz  maint

ð16Þ

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6.1.3. Capital recovery factor A capital recovery factor (CRF) converts the present value into a series of equal annual cash flows over the project lifetime, at a specified discount rate. Capital recovery factor is calculated by Eq. (17) as:

399

jobs created by the energy supply systems. Employment potential of renewable energy supply systems is given in Table 7.

7. Database needed for simulation

N

CRF ¼

dð1 þ dÞ

ð17Þ

N

ð1 þ dÞ 1

6.1.4. Levelized cost of energy The levelized cost of energy (LCOE) is the main economic assessment output for an integrated energy system. It is the average cost to generate one kW h of electricity by the system. LCOE is the ratio of annualized system cost of producing electricity to the total useful electric energy generation by the system and can be calculated by Eq. (18) as [42]: LCOE ¼

C annz_total Epri;AC þ Epri;DC þ Edeff þEgrid;sales

ð18Þ

where Epri,DC ¼DC primary load served (kW h/yr), Epri,AC ¼AC primary load served (kW h/yr), Edeff ¼ deferrable load served (kW h/ yr), Egrid,sales ¼total grid sales (kW h/yr). 6.2. Technical assessment criteria The size of battery bank in an Integrated Renewable Energy System for remote rural areas is considered to be very important due to its frequent replacements over project lifetime. Therefore, minimum battery bank storage system for 0% capacity shortage has been considered as the technical criteria for the present work. 6.3. Social aspect assessment criteria

7.1. Hourly electrical energy demand Hourly electrical energy demand of the study area, calculated in Table 3, has been considered for the simulation. The peak electrical energy demand is calculated as 150.51 kW during summer season and 146.52 kW during winter season as shown in Fig. 11. 7.2. Monthly average daily solar radiation Monthly average daily solar radiation of the study area is shown in Fig. 12. The study area receives the highest solar radiation of 7.403 kW h/m2/day in the month of May while the lowest solar radiation of 3.589 kW h/m2/day has been recorded in the month of December [30]. 7.3. Monthly average discharge availability Monthly availability of discharge in water streams is required in order to calculate the power output of MHP system. It has been estimated by salt dilution method as shown in Fig. 13. 7.4. Monthly average wind speed

Installation of energy supply systems in the study area employs many people during the lifecycle of the project, from construction and operation until decommissioning. Therefore, a social aspect assessment criterion has been considered in order to estimate the Table 7 Employment potential of renewable energy supply systems. S. No. Renewable energy supply systems

Jobs creation (10  7 jobs/kW h/ yr)

1 2 3 4 5

1.466 0.27549 8.333 0.27549 0.27549

MHP system Biogas system Biomass gasifier system Wind energy Solar energy

A set of technical and economical database is required for simulation of the proposed IRES which are discussed as:

Mean annual wind speed for the selected site (‘Harmal’) of Dewal block is estimated to be as 5.11 m/s. Monthly average wind speed (m/s) for the selected site is given in Table 8 [30]. Power curve of the selected wind turbine model (Whisper 500) is shown in Fig. 7. 7.5. Monthly average biomass availability Based on the availability of biomass in the study area, monthly average biomass of 8900 kg/day has been taken as the biomass resource to operate both biomass gasifier and biogas digester system. Specific consumption of biomass for the present study has been considered as: 1.1 kg/kW h for biomass gasifier system and 0.7 kg/kW h for biogas digester system.

Electrical energy demand (kW)

160 140

Summer season Winter season

120 100 80 60 40 20 0

Hour of day Fig. 11. Hourly electrical energy demand of the study area.

400

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7.6. Economical data

8. Simulation results and discussions

Economical data comprises capital cost, replacement cost, O & M cost of system components and is given in Table 9 [43]. Cost of biomass of $ 15/ton, annual discount rate of 6% and project life of 25 yr are considered during the simulation of the proposed IRES.

As discussed earlier, the objective of the present work is to develop an optimal integrated renewable energy system model in order to fulfill the electrical and cooking energy demands of the selected cluster of un-electrified village hamlets. The proposed configuration of IRES has been simulated in HOMER software using the required technical and economical database.

8 Daily solar radiation (kWh/m2/day)

7

8.1. Simulation results

6

Nine combinations of renewable energy resources have been considered and simulated in HOMER software. After hourly simulation for a year, optimal configurations of different combinations of IRES are obtained as given in Table 10. The simulation results for each combination are discussed as:

5 4 3 2 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of the year

Fig. 12. Monthly average daily solar radiation of the study area [30].

Stream flow (Litre/sec)

600 500

8.1.1. Combination 1: MHP-Battery After hourly simulation for a year, the optimum size of this combination consists of 50 kW MHP system, 20 kW h battery bank storage system and 1 kW converter. The total NPC of this combination is $ 103,007 at the estimated LCOE of $ 0.026 per kW h. However, the capacity shortage at user-end from this combination has been determined as 64%. 8.1.2. Combination 2: Biogas-Battery The optimum configuration of this combination consists of 50 kW biogas system, 96 kW h battery bank storage system and 1 kW converter. The total NPC and LCOE of this combination have been estimated as $ 158,473 and $ 0.068 per kW h respectively for the capacity shortage of 84% at user-end.

400 300 200 100 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of the year Fig. 13. Average water flow in streams.

Table 8 Monthly average wind speed at Harmal site of the study area [30]. S. No.

Month

Wind speed (m/s)

1 2 3 4 5 6 7 8 9 10 11 12 Annual average

January February March April May June July August September October November December

5.01 5.03 4.94 4.98 5.44 5.37 5.12 4.98 5.01 5.19 5.09 5.18 5.11

8.1.3. Combination 3: Biomass-Battery The optimum size of this combination consists of 40 kW biomass gasifier system, 24 kW h battery bank storage system and 1 kW converter for the capacity shortage of 89% at user-end. The total NPC of this combination is $ 189,868 at the estimated LCOE of $ 0.102 per kW h. 8.1.4. Combination 4: MHP-Biogas-Battery The optimum configuration of this combination consists of 50 kW MHP system, 50 kW biogas system, 48 kW h battery bank storage system and 1 kW converter for the capacity shortage of 38% at user-end. The total NPC of this combination is $ 240,343 at the estimated LCOE of $ 0.038 per kW h. 8.1.5. Combination 5: MHP-Biomass-Battery The optimum configuration of this combination consists of 50 kW MHP system, 40 kW biomass gasifier system, 24 kW h battery bank storage system and 1 kW converter. The total NPC and LCOE of this combination have been estimated as $ 288,464 and $ 0.052 per kW h respectively for capacity shortage of 46% at user-end.

Table 9 Economical data of system components [25]. S. No.

Renewable energy sources

Size considered

Quantities considered

Capital cost

Replacement cost

O & M cost

Lifetime

1 2 3 4 5 6 7

MHP system Biogas system Biomass gasifier system Wind turbine (Whisper 500) PV array Battery Converter

50 kW 50 kW 40 kW 3 kW 1 kW 200 A h, 12 V 1 kW

1 1 1 0–200 0–200 0–600 0–200

$ $ $ $ $ $ $

$ $ $ $ $ $ $

$ $ $ $ $ $ $

25 yr 20,000 h 15,000 h 25 yr 25 yr 3.5 yr 10 yr

1333/kW 660/kW 1033/kW 5000/WT 1333/kW 284/Batt 117/kW

1333/kW 450/kW 750/kW 5000/WT 1333/kW 220/Batt 117/kW

2000/yr 0.01/kW/h 0.01/kW/h 150/WT/yr 26/kW/yr 6/Batt/yr 3/kW/yr

A. Chauhan, R.P. Saini / Renewable and Sustainable Energy Reviews 59 (2016) 388–405

0 0.1792 0.092 47 57 50

50

40

33

427.20

825,408

0 0.1790 0.096 47 80 50

50

40

–

700.80

856,946

0 0.1788 0.107 47 – 50

50

40

87

1276.80

960,234

64 84 89 38 46 20 0.0483 0.0050 0.1217 0.0533 0.1699 0.1749 0.026 0.068 0.102 0.038 0.052 0.055 1 1 1 1 1 1 – – – – – –

9

8

7

1 2 3 4 5 6

MHP-Battery Biogas-Battery Biomass-Battery MHP-Biogas-Battery MHP-Biomass-Battery MHP-Biogas-BiomassBattery MHP-Biogas-BiomassWind-Battery MHP-Biogas-BiomassSolar-Battery MHP-Biogas-BiomassWind-Solar-Battery

50 – – 50 50 50

– 50 – 50 – 50

– – 40 – 40 40

– – – – – –

48 96 24 48 24 24

103,007 158,473 189,868 240,343 288,464 427,586

Capacity shortage (%) LCOE ($/kW h) Converter (kW) PV array (kW)

Battery (kW h

Total NPC ($)

Employment (jobs/yr)

Table 11 Rank of different combinations for economic, technical and social aspects criteria for capacity shortage of 0% at user-end (I-First, II-Second, III-Third).

Wind turbines (kW) Biomass gasifier (kW) Biogas digester (kW) Microhydro (kW) Combination Configuration

Table 10 Optimal configurations of different combinations of IRES.

401

Combination

Total NPC

LCOE

Battery storage

Employment

1 2 3 4 5 6 7 8 9

III II I

III II I

III II I

III II I

8.1.6. Combination 6: MHP-Biogas-Biomass-Battery The optimum size of this combination consists of 50 kW MHP system, 50 kW biogas system, 40 kW biomass gasifier system, 24 kW h battery bank storage system and 1 kW converter for the capacity shortage of 20% at user-end. The total NPC of this combination is $ 427,586 at the estimated LCOE of $ 0.055 per kW h. 8.1.7. Combination 7: MHP-Biogas-Biomass-Wind-Battery The optimum size of this combination consists of 50 kW MHP system, 50 kW biogas system, 40 kW biomass gasifier system, 87 kW wind energy system, 1276.80 kW h battery bank storage system and 47 kW converter. The total NPC of this combination is $ 960,234 at the estimated LCOE of $ 0.107 per kW h for the capacity shortage of 0% at user-end. 8.1.8. Combination 8: MHP-Biogas-Biomass-PV array-Battery The optimum configuration of this combination consists of 50 kW MHP system, 50 kW biogas system, 40 kW biomass gasifier system, 80 kW PV array, 700.80 kW h battery bank storage system and 47 kW converter. The total NPC and LCOE of this combination have been estimated as $ 856,946 and $ 0.096 per kW h respectively for the capacity shortage of 0% at user-end. 8.1.9. Combination 9: MHP-Biogas-Biomass-Wind-PV array-Battery The optimum size of this combination consists of 50 kW MHP system, 50 kW biogas system, 40 kW biomass gasifier system, 33 kW wind energy system, 57 kW PV array, 427.20 kW h battery bank storage system and 47 kW converter. The total NPC and LCOE of this combination have been estimated as $ 825,408 and $ 0.092 per kW h respectively for the capacity shortage of 0% at user-end. 8.2. Comparison of simulation results 8.2.1. Achievement of 0% capacity shortage Among all the considered combinations of IRES, capacity shortage of 0% at end-user was obtained for combinations 7, 8 and 9. 8.2.2. NPC and LCOE for different combinations In order to achieve 0% capacity shortage at user-end, optimal configuration of combination 9 offers the lowest NPC of $ 825,408 at the estimated LCOE of $ 0.092/kW h among combinations 1–9. 8.2.3. Battery storage requirement for different combinations The combination 9 requires the lowest battery bank storage of 427.20 kW h among combinations 1–9 for 0% capacity shortage. 8.2.4. Employment generation for different combinations Among combinations 1–9, the optimal configuration of combinations 9 generates the highest jobs of 0.1792 per year in the study area.

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Table 12 Cost breakdown of the total NPC of combination 9. Component

Capital cost ($)

Replacement cost ($)

O & M cost ($)

Fuel cost ($)

Salvage cost ($)

Total ($)

MHP system Biogas system Biomass gasifier system Wind turbine (Whisper 500) PV array Battery Converter Total NPC

66,667 33,000 41,320 55,000 75,981 50,552 5499

0 43,139 84,524 0 0 172,747 4785

25,567 23,330 18,664 21,093 18,945 13,653 1802

0 39,994 46,260 0 0 0 0

0  2294  6407 0 0  7769  641

92,233 137,168 184,360 76,093 94,926 229,182 11,446 825,408

Table 13 Annual generation of renewable energy sources for combination 9. S. No. Components

Production (kW h/yr)

Percentage contribution

1 2 3 4 5 Total

329,144 182,500 146,000 54,327 101,478 813,449

40 22 18 7 12 100

MHP system Biogas generator Biomass generator Wind turbines PV array

8.6. Frequency histogram for state of charge of battery bank Frequency histogram for state of charge (SOC) of battery bank for combination 9 is shown in Fig. 14. It has been found that SOC of battery bank remains 100% for more than 40% of annual duration.

9. Sensitivity analysis Sensitivity analysis of IRES is essential as it provides the information about the system's behavior under the random changes in the system's parameters. Sensitivity analysis of combination 9 has been performed for variation in electrical energy demand, mean annual wind speed, maximum annual capacity shortage and biomass price. 9.1. Effect of the electrical energy demand

Fig. 14. Frequency histogram for state of charge of battery bank.

8.3. Selection of a suitable combination based on the results obtained considering total NPC, LCOE, battery storage, capacity shortage and employment as important criteria Based on the results obtained, rank of different combinations has been made for 0% capacity shortage as given in Table 11. It has been found that configuration of combination 9 offers minimum NPC, LCOE and battery storage along with the highest jobs generation among combinations 1–9. Therefore, configuration of combination 9 (MHP-Biogas-Biomass-Wind-PV array-Battery) is recommended as the best option for the study area. 8.4. Cost breakdown of the total NPC Cost breakdown of the total NPC of combination 9 is given in Table 12. Among various components, PV array has the highest capital costs of $ 75,981 while battery storage system has the highest replacement cost of $ 172,747.

The mean electrical energy demand of 1963 kW h/d was considered for the present study. The system must be designed in order to compensate the fluctuation of the demand of the study area. Therefore, the demand of study area has been varied from 1900 kW h/d to 1980 kW h/d and accordingly, LCOE and total NPC have been evaluated. The LCOE of considered system has been deviated from $ 0.094 per kW h to $ 0.097 per kW h for energy consumption varying from 1900 kW h/d to 1980 kW h/d. The effect of the electrical energy demand on LCOE and total NPC is shown in Fig. 15. 9.2. Effect of mean annual wind speed Mean annual wind speed of the study area has been changed from 4.8 m/s to 5/6 m/s and accordingly, its effect on LCOE and total NPC has been analyzed. It has been found that the total NPC has been decreased from $ 833,607 to $ 806,458 for wind speed varying from 4.8 m/s to 5.6 m/s. The effect of mean annual wind speed on LCOE and NPC is shown in Fig. 16. 9.3. Effect of maximum annual capacity shortage Maximum annual capacity shortage is a reliability indicator of energy supply at the user-end. The variation of the LCOE with respect to maximum annual capacity shortage for combination 9 has been estimated as shown in Fig. 17. It has been found that LCOE deviates from $ 0.092 per kW h to $ 0.087 per kW h for 0% to 5% variation in maximum annual capacity shortage.

8.5. Annual generation of renewable energy sources 9.4. Effect of biomass price Annual generation of renewable energy sources for combination 9 is given in Table 13. The percentage contribution of MHP system, biogas generator, biomass generator, wind turbines and PV array in annual electricity generation is found to be as 40%, 22%, 18%, 7% and 12% respectively.

Biomass price includes the various charges for drying, collection, transportation and labor. Price of biomass feedstock has been varied from $ 15/ton to $ 50/ton and accordingly, LCOE and NPC have been evaluated in HOMER software. With the biomass price

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403

Fig. 15. Effect of the electrical energy demand on total LCOE and NPC.

Fig. 16. Effect of mean annual wind speed on LCOE and NPC.

Fig. 17. Effect of maximum annual capacity shortage on LCOE and total annualized cost.

fluctuation from $ 15/ton to $ 50/ton, LCOE of combination 9 varies from $ 0.092 per kW h to $ 0.115/kW h. The effect of biomass price on LCOE and NPC is shown in Fig. 18.

10. Conclusion In the present paper, a techno-economic feasibility study on Integrated Renewable Energy System has been carried out for 48 number of un-electrified village hamlets of the Chamoli district of Uttarakhand state (India). The data collected from the study area were used for the demand assessment and resource assessment. Small wind turbine models of different manufacturers were considered and compared based on the capacity factor. The

Whisper 500 wind turbine model was selected as this model offers the highest capacity factor of 25.79% at Harmal site of the study area. Further, nine different combinations of renewable energy resources have been investigated using HOMER software. Among different combinations, MHP-Biogas-Biomass-Wind-PV arrayBattery based configuration is found to be the most suitable option for the study area as it offers the minimum NPC of $ 825,408 LCOE at the estimated LCOE of $ 0.092 per kW h along with the highest jobs generation of 0.1792 per year. Optimum configuration of this combination consists of 50 kW MHP system, 50 kW biogas system, 40 kW biomass gasifier system, 33 kW wind energy system, 57 kW PV array, 427.20 kW h battery bank storage system and 47 kW converter.

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Fig. 18. Effect of biomass price on LCOE and total NPC.

A sensitivity analysis has also been performed for variation in electrical energy demand, mean annual wind speed, maximum annual capacity shortage and biomass prices. It has been found that the considered system is very sensitive towards the fluctuation of biomass prices. With the biomass price variation from $ 15/ ton to $ 50/ton, the LCOE has been deviated from $ 0.092 per kW h to $ 0.115 per kW h. The study carried out may be useful for the development of IRES in stand-alone mode for the electrification of other similar remote rural areas.

Acknowledgment One of the authors (Anurag Chauhan) acknowledges the financial support of Ministry of Human Resource Development (MHRD), Government of India, in the form of fellowship.

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