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E.O. Diemuodeke

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Optimum configuration and design of a photovoltaic–diesel–battery hybrid energy system for a facility in University of Port Harcourt, Nigeria

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TAEN866906

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International Journal of Ambient Energy, 2013 http://dx.doi.org/10.1080/01430750.2013.866906

Optimum configuration and design of a photovoltaic–diesel–battery hybrid energy system for a facility in University of Port Harcourt, Nigeria E.O. Diemuodekea,b∗ and C.O.C. Okoa

Q1 a Department

of Mechanical Engineering, College of Engineering, University of Port Harcourt, Port Harcourt, Nigeria; b School of Engineering, Cranfield University, Cranfield, UK (Received 10 July 2013; accepted 14 November 2013 )

Optimum configuration, using a hybrid optimisation model for electric renewable software, and design of a photovoltaic (PV)–diesel–battery hybrid energy system has been proposed to power a facility in the University of Port Harcourt, which is located in the suburb of Port Harcourt city, Nigeria. The configuration of the optimum hybrid system is selected based on top-ranked system configuration, according to the net present cost. An optimal system design delivers the best components alongside appropriate operating strategies to provide the most efficient, reliable cost-effective system possible. The system investigated reduces CO2 emissions by 36.3%/year. This will reduce costs imposed on CO2 emissions by future environmental legislation. The system has a better potential for providing the energy needs of the facility considered in this paper compared with a stand-alone PV–battery system as capital costs are reduced by 55%. Reliability was improved as the diesel generator can provide power as and when it is needed.

Coll:

Keywords: PV–diesel–battery system; optimum configuration; system design; renewable energy

QA:

Abbreviations COE DG HOMER

CE: KRR/PB

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

Q2

NPV NREL PDB PV RES

cost of energy diesel generator hybrid optimisation model for electric renewable net present value National Renewable Energy Laboratory PV–diesel–battery photovoltaic renewable energy source

1. Introduction Nigerians are facing an acute electricity power crisis, requiring about 90% of businesses in Nigeria to own their electricity generators (The World Bank 2005). Renewable energy sources (RESs) are important as alternative or complement energy sources. Depletion of fossil energy sources and attendant concerns for global warming as a result of conventional energy conversion systems play important roles in the general acceptance of RES. Renewable energy conversion systems are typified by an absence of environmentally degradable pollution (Khatib et al. 2011). Non-continuous sources and high initial cost of investment present a good potential for providing the needed energy in hybrid form. Thus, the need for combining a RES and a convectional energy source is on the increase (Agarwala and Kumarb 2012). Since the supply of RES is transient, the search ∗ Corresponding

to store the harnessed energy is paramount. Integration of hybrid renewable energy with storage would provide better and reliable energy conversion configuration system, which, of course, makes it suitable for stand-alone application (Isherwood et al. 2000; Shiroudi et al. 2012). Many recent studies have addressed the size analysis and optimisation of photovoltaic (PV)–diesel–battery system since the awareness of the utilisation of PV energy systems in the 1980s (Yang, Lu, and Zhou 2007). It is noteworthy that Nigerians are facing acute electricity power crisis, which has resulted in constant interruption of institutions of higher learning students’ laboratory experiments, workshop practices, automated designs and simulations. These institutions mitigate the power shortage by depending heavily on electricity supply from independent diesel generators (DGs). The National Electric Power Reform Bill, in Nigeria, is aimed at encouraging sufficient electricity supply from the National grid. However, the National Integrated Power Project, which is vested with the sole status of grid electricity generation and supply, has severally been rocked with corruption allegations. It suffices to say that steady and adequate power generation from the national grid may not be realisable soon. It has been noted in the World Bank (2005) that about 90% of businesses in Nigeria own electricity generators; and that all institutes of higher learning in Nigeria have DGs. The price of diesel fuel, which is used to power DGs,

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© 2013 Taylor & Francis

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E.O. Diemuodeke and C.O.C. Oko

in Nigeria is relatively high and was US$1.02/l; and the stringent environmental concerns of DGs, therefore, call for more viable electricity-generation options. One of the most promising options is the utilisation of the PV–diesel–battery (PDB) hybrid energy system. Markvart (2000) notes that on a reasonable sunny site of insolation 20 MJ/m2 /day−1 , power produced from solar energy conversion technologies, PV and solar thermal is significantly cheaper over the extended use than that from DGs. Ojosu (1990) maintains that PV energy systems have a special role to play in Nigeria power production, because of its substantial solar energy resources with daily solar radiation, which is averaged between 3.5 and 6 kW/m2 /day. Augustine and Nnabuchi (2009) and Oko and Nnamchi (2013) confirm that the Port Harcourt city, which is located at the latitude of 04◦ 40 N and longitude of 07◦ 10 E, has adequate sunshine for PV and solar thermal technologies. Many researchers have employed several methods to determine the optimum configuration for hybrid renewable energy systems (Shaahid and El-Amin 2009; Shiroudi et al. 2012). The hybrid optimisation model for electric renewable (HOMER) simulation software, developed by the National Renewable Energy Laboratory (NREL) (Shiroudi et al. 2012), was used to determine the optimum configuration of a PDB hybrid energy system for a facility located in the University of Port Harcourt, Port Harcourt, Nigeria, to ensure energy autonomy of the facility. Once the optimum configuration was determined via simulation, CO2 emissions were minimised. 2. System description The PDB system is composed of a variety of components, which include the PV modules, DG, battery bank, and a control system, which together form the entire system capable of supplying electric power; and these system elements are described in Table 1 (Oko et al. 2012). Figure 1 shows the configuration of the hybrid PDB switched system with all the functional components. In this configuration, the load can be powered either by the DG or by the PV system through the battery pack-inverter flow line. It is expected that both the PV system and the DG would charge the battery bank. The embedded switch is used to shut down the DG when the facility load demand is lower than the supply of PV system only. 3. Data collection 3.1. Solar insolation Solar energy is a reliable source of energy that is received in forms of beam and diffuse radiations. The insolation reaching the earth’s surface depends on the cloudiness or clearness of the sky, which in turn depends on the season of the year. However, harnessing the insolation for electricity generation is the primary interest of a PV system design engineer (Oko and Nnamchi 2013). Based on the typical

Table 1.

Description of the system elements.

• PV cells represent the fundamental power conversion units of the PV module. They are made from semiconductors and convert sunlight to electricity. To increase the power output of PV cells, they are connected together to form larger units called modules. Modules, in turn, are connected in parallel and series to form a larger unit called panel • The DG is a mechanical device that converts chemical energy, diesel fuel, to mechanical energy and later to electrical energy • A battery bank (storage medium) stores the electrical energy produced by the PV cells and the DG, and makes the energy available at night or on dark days (days of autonomy or no-sun-days) • A charge controller (or voltage regulator), reverses current and prevents battery from getting overcharged and overdischarged • An inverter converts a low DC-voltage into usable ACvoltage; it may be a stand-alone installation or grid and/or diesel-connected installation • AC and DC loads, appliances and devices, which consume the power generated by the PDB system

meteorological year data of Oko and Ogoloma (2011) for Port Harcourt zone and the HOMER software, Figure 2 is obtained for the location of Port Harcourt. It can be seen from Figure 2 that more solar insolation is expected from the month of December to June, whereas from July to November the solar insolation is minimal. This observation corresponds to the dry season and the wet season, respectively. The facility to be powered by the PV system is located at Choba Park campus of the University of Port Harcourt, Nigeria, which has a geographical position of latitude and longitude of 04◦ 40 N and 07◦ 10 E, respectively, with an average solar insolation of 3.75 kWh/m2 /day (Oko and Ogoloma 2011). 3.2. Diesel generator DGs are mechanical devices that convert chemical energy to mechanical energy and finally to electrical energy. Usually, these generators require high running cost, frequent maintenance and they pollute the environment. The ratings of the DG are determined by the load. The price of diesel fuel, which is used to power DGs, in Nigeria is relatively high since it amounts to US$1.02/l. 3.3. Electrical load estimation The equipment, lightings, and other electrical devices used in the facility, with their corresponding power ratings, are given in Table 2. The estimated daily energy requirement of the facility is given in Table 2. The system is assumed to work for seven days a week. The estimate daily load profile of the facility is shown in Figure 3. From the load profile, it can be seen that the

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Figure 1.

Configuration of the PDB hybrid energy system.

4. System specification In this study, all calculations, simulation and optimisation of the PDB hybrid energy system and economic performance parameters were done by the HOMER software for a proposed project of the hybrid energy system at the University of Port Harcourt. The HOMER software design specifications used in this study are given Table 3. The PV module, DG and battery type where chosen because of their technical and availability in the Nigeria market. Specifically, the battery, Trojan T-105, has a high market rating and is readily available in the HOMER software data base.

Colour online, B/W in print

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International Journal of Ambient Energy

Figure 2. Annual solar insolation and the Clearness Index for the location.

5. Table 2.

Power rating of equipment in the facility.

k

Appliance

Units (Uk )

Wattage ˙ k) (W

Hour used/day (Hk )

1 2 3 4 5 6 7 8 9

Air conditioner Refrigerator Computer Printer Ceiling fan Fluorescent light Laptop Scanners Multimedia systems

25 4 15 4 110 200 25 4 7

750 825 500 1230 75 40 25 40 140

8 24 12 4 9 9 9 2 9

maximum demand occurs during a daytime from 10 am to 6 pm, which corresponds to the working hours in the University of Port Harcourt.

Results and discussion

In this study, the optimum configuration of a PDB hybrid energy system was undertaken using NREL’s HOMER software a computer model that simplified the task of designing renewable electricity-generation systems for either – on-grid or stand-alone applications. HOMER’s optimisation and sensitivity analysis algorithms allowed the rapid techno-economic evaluation of a large number of technology options whilst accounting for variations in technology costs and energy resource availability. The user interface is convenient and powerful (Shiroudi et al. 2012). Input information to be provided to HOMER includes: electrical loads, renewable resources (such as a typical yearly solar insolation data), component technical details/costs, constraints, controls, type of dispatch strategy, etc. HOMER uses lifecycle cost in its computational algorithms. Its user interface is powerful and friendly. The sensitivity analyses, which

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4

Figure 3.

Estimated average daily load profile for the studied facility.

are tied to key design parameters, are done automatically (Shiroudi et al. 2012).

5.1. Optimum configuration The system calculation and simulation assumed a project’s lifetime of 20 years and an annual interest rate of 9%, as used by Oko et al. (2012). The HOMER software block diagram for the considered system configuration is shown in Figure 4. Two diesel-generating sets are suggested for the analyses, denoted as Gen_1 and Gen_2, respectively, as shown in Figure 4. This system had an average AC load of 576 kWh/days, with a peak load of 86 kW. The base case for the design uses the annual average global radiation of the Port Harcourt climatic zone, 3.75 kWh/m2 /day, with an annual average clearness index of 0.376; and diesel fuel price of US$1.02/l. Results shown in Figure 5 are the displayed top-ranked system configurations, which are listed according to their net present value (NPV) for possible system configuration type. The minimum renewable fraction, considering cost and emissions, is taken as 30%. According to the results shown in Figure 5, the optimum PDB hybrid energy system comprises 60 kW PV modules, one DG 20 kW, 86 kW converter and 500 batteries with life span of eight years. The cost of energy (COE) of the optimum PDB hybrid energy system is US$0.673/kWh and corresponding initial capital required, annual operating cost and NPC are US$585,500, US$73,027 and US$1,252,130, respectively. The detailed cost distribution of the PDB hybrid energy system and summarised NPV of the proposed system components are given in Figure 6. This could be a viable choice for implementation as the contribution made by the renewable resource, the PV module, is quite significant. The proposed system compared fairly well with a similar system (PDB hybrid system) in the Malaysian’s

condition, which gives COE of US$0.796/kWh for a solar insolation and cost of fuel of 5.51 kWh/m2 /h and US$2.03/l, respectively (Lau et al. 2010). A diesel standalone system gives COE of US$0.601/kWh and an annual operating cost of US$108,895. However, it should be noted that the price of diesel fuel is unstable, always on the increase, in the Nigeria market and there is always artificial scarcity, which hikes the diesel fuel price to an unbearable price. It is envisaged that the price of diesel would soar with the on-going government reforms, which include subsidy removal on petroleum products. Figure 7 shows average monthly electrical power production of the proposed PDB hybrid energy system, which culminates to yearly electrical energy production capacity of 2.66 GWh. The PV module contributes 37% of the total energy production per year, whereas DG contributes 63% towards the hybrid system. The amount of kg of CO2 emitted per litre of diesel, which is heavily dependent on the generator load and running hours, has a degrading consequence on the environment. The CO2 emission from a wholly DG energy system is 209,811 kg/year; whereas it is 133,567 kg/year for the optimised PDB hybrid energy system, which amounts to 36.3% yearly CO2 emission reduction. This emission reduction is a significant achievement as it will reduce cost imposed on CO2 emission by environmental legislations, which is the normal practice in most developed nations (Ford 2008). Although, there are currently no such environmental legislations in Nigeria, but the 36.3% carbon reduction would make the environment friendlier.

5.2.

Optimum design analyses

Since the optimum configuration of the proposed system is sorted, it is therefore easy to obtain optimum sizing of the system as follows (Oko et al. 2012).

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International Journal of Ambient Energy Table 3. No. 1

2

3

4

Technical and economic specification for the proposed PDB system. Description

PV modules (DB F80) Nominal power Nominal load voltage Short circuit current Bus voltage Nominal efficiency Nominal operating temperature Temperature coeff. of power Capital cost Replacement cost Operating and maintenance cost Life time Slope PV module floor area DG Cost/kW Replacement Operation and maintenance Fuel cost Minimum load factor Battery bank (Trojan T-105) Capital cost Replacement Operation and maintenance Nominal voltage Nominal capacity Nominal energy capacity of each battery Minimum battery life Minimum state of charge Life time throughput Converter Maximum power Capital cost Replacement Operation and maintenance Efficiency Life time Rectifier capacity relative to inverter Rectifier efficiency

Specification Polycrystalline (Oko et al. 2012) 80 W 17.5 V 5.03 A 24 V 12% 47◦ C 0.97%/◦ C US$300 US$300 0a 20 years 8.8125◦ (Oko et al. 2012) 0.42 m2 Catterpiller US$600/kW US$600/kW US$0.15/h US$1.02/l 40% US$75 US$75 US$2/year 6V 225 Ah 1.35 kWh 5 year 30% (Shiroudi et al. 2012) 845 kWh 86 kW US$86,000 US$86,000 US$8600/year 98% 20 years 100% 95%

a The

operating and maintenance cost is taken to be zero since it is negligible for a localised distributed systems (Lau et al. 2010; Shiroudi et al. 2012).

5.2.1. PV module design The number of PV modules, NPV [−] is


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NPV =

WPV 60 = = 750, WPV,nom 0.08

(1)

where WPV,nom (kW) is module nominal power and WPV (kW) is the simulated optimum PV power. The total PV flow area, APV (m2 ), is APV = NPV ∗ APV, mod = 750∗ 0.426 = 3195 (m2 ), Figure 4.

HOMER PDB hybrid energy system block diagram.

where APV, mod (m2 ) is a module area.

(2)

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6


Figure 5.

Optimal system configuration.

Figure 6.

Summarised total net present cost of the proposed system components.


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Modules are connected in series and parallel according to the system usage as follows:

(2) The string number of modules in parallel, NPV,P [−] (each containing NPV , s), is given as

(1) The number of PV module in series, NPV , s, is obtained as NPV , s =

VDC,bus 24 ∼ = = 1. VDC,mod 17.5

(3)

NPV,P =

750 NPV = = 750. NPV , s 1

(4)

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673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728

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International Journal of Ambient Energy

Figure 7.

Monthly average electric production

5.2.2. Generator The ratings of the DG are determined by the load. The optimum simulated DG load for the proposed system is 20 kW. Hence, we select a 25 kVA generator type (catterpiller) with three-phase output AC voltage (3 × 240 V), 50 Hz with a power factor of 0.85 (Mahmoud and Ibrik 2006). 5.2.3. Battery bank With the knowledge of the number of batteries, the connection of the battery bank can then be easily obtained. The number of batteries in series, NB,S [−], is given as NB,S =

VDC,bus 24 = = 4, VDC,B 6

(5)

where VDC,B [V ] is the selected battery nominal voltage for the optimum system. The number of batteries in parallel, NB,P [−], in string of NB,S , is given as NB,P =

NB 500 = = 125, NB,S 4

(6)

where NB,S [−] is the optimum number of batteries for the proposed system. 5.2.4. Converter Once the sizing of the battery bank is known, one proceeds to the sizing of the voltage regulator. A good voltage regulator, in this case, must be able to withstand the maximum current produced as well as the maximum load (is 86 kW for the optimum system considered) in the system. The input of the inverter has to be matched with the battery block voltage while its output should fulfil the specifications of the electric appliances in used, which is specified as (3 × 240), 50 Hz. 6. Conclusion Institutions of higher learning in Nigeria mitigate power shortage by heavily depending on electricity supply from independent DGs. To reduce the emissions from DG plants and to decrease the high cost of electricity from DG, hybrid systems are of considerable importance. The Port Harcourt

city, which is located at the latitude of 04◦ 40 N and longitude of 07◦ 10 E, has adequate sunshine for PV and solar thermal technologies with an average solar insolation of 3.75 kWh/m2 /day. Therefore, an optimum configuration and design of PDB hybrid energy system have been proposed to power a facility in the University of Port Harcourt, which is located in the suburb of Port Harcourt city, Nigeria. The HOMER software computer model is used to determine the most economic system for the proposed PDB hybrid energy system at a daily load of 576 kWh. The configuration of the optimum hybrid system is selected based on top-ranked system configuration, according to NPV. The optimum system gives the best components and design with appropriate operating strategy to provide an efficient, reliable and cost-effective system. The optimum simulation results showed that the proposed PDB hybrid energy system requires of 750 PV modules (0.08 kW each), 25 kVA DG, and 500 batteries (225Ah, and 6V each) to produce electricity for the facility with an average annual insolation of 3.75 kWh/m2 /day. The COE of the proposed hybrid PDB system is US$0.673/kWh, whereas the initial capital required, annual operating cost and NPV are US$585,500, US$73,027 and US$1,252,130, respectively. A similar study, with some basic economic simplifications for the utilisation of RES in the site considered here gives COE of US$0.600/kWh for a stand-alone PV–battery system (Oko et al. 2012) as against the US$0.673/kWh obtained in the present study. However, considering the high initial cost of investment, which is about US$1,900,000 for the stand-alone PV system, the proposed PDB system has a better potential for proving the energy need. Also, the proposed system gives CO2 emission reduction, which amounts to a yearly 36.3% reduction. This emission reduction is a significant achievement as it will reduce the cost imposed on CO2 emission by environmental legislations, which is the normal practice in most developed nations (Ford 2008). Although, there are currently no such environmental legislations in Nigeria, but the 36.3% carbon Q3 reduction would make the environment friendlier.

References Agarwala, N., and A. Kumarb. 2012. “Distributed Generation Q4 & Alternative Energy Journal Sizing Analysis and Cost

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Optimization of Hybrid Solar-Diesel-Battery Based Electric Power Generation System Using Simulated Annealing Technique.” Distributed Generation and Alternative Energy Journal: 37–41. Augustine, C., and M. N. Nnabuchi. 2009. “Relationship Between Global Solar Radiation and Sunshine Hours for Calabar, Port Harcourt and Enugu, Nigeria.” International Journal of Physical Science 4 (4): 182–188. Ford, A. 2008. “Simulation Scenarios for Rapid Reduction in Carbon Dioxide Emissions in the Western Electricity System.” Energy Policy 36 (1): 443–455. doi:10.1016/j.enpol.2007.09.023. Isherwood, W., J. R. Smith, S. M. Aceves, G. Berry, W. Clark, R. Johnson, D. Das, D. Goering, and R. Seifert. 2000. “Remote Power Systems with Advanced Storage Technologies for Alaskan Villages.” Energy 25 (10): 1005–1020. doi:10.1016/S0360-5442(00)00028-1. Khatib, T., A. Mohamed, K. Sopian, and M. Mahmoud. 2011. “Optimal Sizing of Building Integrated Hybrid PV/diesel Generator System for Zero Load Rejection for Malaysia.” Energy and Buildings 43 (12): 3430–3435. doi:10.1016/j.enbuild.2011.09.008. Lau, K. Y., M. F. M. Yousof, S. N. M. Arshad, M. Anwari, and A. H. M. Yatim. 2010. “Performance Analysis of Hybrid Photovoltaic/Diesel Energy System under Malaysian Conditions.” Energy 35 (8): 3245–3255. doi:10.1016/j.energy.2010.04.008. Mahmoud, M., and I. Ibrik. 2006. “Techno-Economic Feasibility of Energy Supply of Remote Villages in Palestine by PV-Systems, Diesel Generators and Electric Grid.” Renewable and Sustainable Energy Reviews 10 (2): 128–138. doi:10.1016/j.rser.2004.09.001. Markvart, T. 2000. Solar Electricity. London: Wiley. Ojosu, J. O. 1990. “The Iso-radiation Map for Nigeria.” Journal of Solar and Wind Technology 7 (5): 563–575.

Oko, C. O. C., E. O. Diemuodeke, N. F. Omunakwe, and E. Nnamdi. 2012. “Design and Economic Analysis of a Photovoltaic System: A Case Study.” International Journal of Renewable Energy Development 1 (3): 65–73. Oko, C. O. C., and S. N. Nnamchi. 2012. “Energy and Q5 Exergy Audit of a Solar Rain Dryer.” International Journal of Renewable Energy Technology 3 (1): 79. doi:10.1504/IJRET.2012.043913 Oko, C. O. C., and S. N. Nnamchi. 2013. “Coupled Heat and Mass Transfer in a Solar Grain Dryer.” Drying Technology 31 (1): 82–90. doi:10.1080/07373937.2012.719561. Oko, C. O. C., and O. B. Ogoloma. 2011. “Generation of a Typical Meteorological Year.” Journal of Engineering Science and Technology 6 (2): 204–214. Shaahid, S. M., and I. El-Amin. 2009. “Techno-Economic Evaluation of Off-grid Hybrid Photovoltaic–Diesel–Battery Power Systems for Rural Electrification in Saudi Arabia – A Way Forward for Sustainable Development.” Renewable and Sustainable Energy Reviews 13 (3): 625–633. doi:10.1016/j.rser.2007.11.017. Shiroudi, A., R. Rashidi, G. B. Gharehpetian, S. A. Mousavifar, and A. Akbari Foroud. 2012. “Case Study: Simulation and Optimization of Photovoltaic-Wind-Battery Hybrid Energy System in Taleghan-Iran Using Homer Software.” Journal of Renewable and Sustainable Energy 4 (5): 053111. doi:10.1063/1.4754440. The World Bank. 2005. Energy Sector Management Assistance Program. Nigeria: Expanding Access to Rurual Infrastructure Issues and Options for Rural Electrification, Water Supply and Telecommunications. Yang, H., L. Lu, and W. Zhou. 2007. “A Novel Optimization Sizing Model for Hybrid Solar-Wind Power Generation System.” Solar Energy 81 (1): 76–84. doi:10.1016/j.solener.2006.06.010.

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