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Solar regime and LVOE of PV embedded generation systems in Nigeria
Renewable Energy 78 (2015) 226e235
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Solar regime and LVOE of PV embedded generation systems in Nigeria O.D. Ohijeagbon a, Oluseyi O. Ajayi b, * a b
Mechanical Engineering Department, University of Lagos, Akoka, Lagos, Nigeria Mechanical Engineering Department, Covenant University, P.M.B. 1023, Ota, Nigeria
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 February 2014 Accepted 5 January 2015 Available online
The study assessed the potential and economic viability of solar PV standalone systems for embedded generation, taking into account its benefits to small off-grid rural communities in forty meteorological sites in Nigeria. A specific electric load profile was developed to suite the communities consisting 200 homes, a school and community health centre. Data (daily mean relative humidity, maximum and minimum temperatures, and daily global solar radiation for 24 years spanning 1987-2010) obtained from the Nigeria Meteorological Agency were used. It focused on the assessment of design that will optimally meet daily load demand for the rural communities with an LOLP of 0.01. The HOMER® software optimizing tool was engaged for the feasibility study and design. A 15 MW PV distributed generation system was utilized to economically compare the different sites in terms of life cycle cost as well as levelised cost of producing energy. A profit for potential investors in the range of $ 0.01/kWh to $ 0.17/kWh was discovered for 29 of the 40 available meteorological sites, while the remaining sites were not profitable with the present tariff regime in Nigeria. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Photovoltaic power Distributed generation Cost per kWh Clean energy Nigeria
1. Introduction Photovoltaic (PV) technology has immense capacity of meeting the energy needs of rural and remote communities in developing countries [1e4]. The high capital cost of this technology though, means that innovative approaches to its execution are required with new models so as to encourage more pervasive exploitation of the technology [5]. The amount of solar energy said to reach the earth's surface annually is about 10,000 times more than the annual global energy demand. This exceeds all the earth's available energy reserves. It has thus been stated that only 0.01% of the solar energy reaching the earth is required to meet the world's energy needs [6]. However, energy produced using PV systems is presently more expensive than the price of grid electricity in many countries due to high production costs. Nevertheless, between 1976 and 2008 the capital cost of PV modules/watt has shrank from more than $58 per watt to less than $4 [6]. This is in addition to being environmentally safe with no harmful gas or noise emissions, as solar energy is known to be a clean and non-
* Corresponding author. Tel.: þ234 8036208899. E-mail address: [email protected] (O.O. Ajayi). http://dx.doi.org/10.1016/j.renene.2015.01.014 0960-1481/© 2015 Elsevier Ltd. All rights reserved.
depleting resource of energy. Also because of its advantage of a lack of moving parts, it requires very little maintenance. The momentous growth of this technology has however been principally credited to new manufacturing plants established to produce low cost PV cells [7e11]. One of the world's largest PV markets is Germany with the driving force being the favourable feed-in tariff law on solar electricity. These regulations have enabled consumers to supply additional green (PV) electrical energy to the grid network at prices above that of network electricity [6]. In Nigeria, a similar regulation exists through embedded generation [12], which can be described as a form of generation where excess renewable energy generated by a consumer above the 1 MW mark may be sold to a nearby distribution network at prices that are higher than grid electricity. This is presented in the multi-year tariff order for 2012e2017 [13]. Therefore PV technology can supply a significant amount of electricity, thus making significant contributions to improving the nations' energy deficit [14]. This study therefore made effort at presenting options that could optimize the use of PV technology in isolated remote communities with minimum environmental degradation. It also made a case for Nigeria by exposing the solar profile/regime across the different states and geopolitical zones of the country. One major attraction to investor is the ability to have first glance opportunity to know and ascertain the potential and likely cost-benefit of a project.
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2. Review of solar energy studies and opportunities for its application in Nigeria Akinboro et al. [15] stated that less than 40% of Nigeria is connected to the national electric grid and less than 60% of the energy demand of this 40% is generated and distributed. Therefore the study sought to proffer the use of solar system in Nigeria as an alternative energy source. It highlighted the problems that have been encountered so far. The paper also presented a review of the technical information on the solar energy standalone and hybrid installations, with more attention being allotted to domestic and industrial installations. Anayochukwu and Nnene [16] investigated a photovoltaic (PV)diesel hybrid power generation system suited to a Global System for Mobile communication (GSM) base station site located in Abuja (FCT), Nigeria. The Hybrid Optimization Model for Electrical Renewables (HOMER) software was engaged for the design of the proposed power system. The daily load was 318 kWh/day with annual average solar radiation being 5.45 kWh m2 d1. The study estimated the savings related with the use of a PV-diesel hybrid power system as against a standalone diesel powered system. From the simulation results, the proposed system has about 72%e87% savings in Total Net Present Cost (NPC) and amount of CO2 emission when compared with the diesel only system. Akpama et al. [17] reviewed the current investments and projects in relation to photovoltaic technology in Nigeria and proposed a photovoltaic incentive program that would enhance performance. They recommended a governmenteconsumer partnership for the installation of PV power systems to remote areas. Habib et al. [18] researched the potential for electrical energy generation through the use of either solar Photovoltaic or Concentrating Solar Power (CSP). Solar radiation potentials were stated to range between 3.5 and 7.0 kWh/m2-day, while the projected total potential for solar PV with 1% area for twenty select states was estimated at 1189321.65 MWh. The value range of direct normal irradiance (DNI) in northern Nigeria was said to meet the minimum DNI threshold required for economically feasible CSP project. The total potential for CSP within the fourteen frontline northern states was projected at 427,829 MW, with an electricity potential of 26,841 TWh/yr. Oodo et al. [19] studied the possibility of developing a component PV system to be integrated into the Nigerian rural micro distribution grid. This hybrid diesel-PV Autonomous generation system is said to be a practical way of enhancing rural electrification for use in remote areas far removed from the national grid. Melodi et al. [20] studied the solar energy potential in Akure, South-West Nigeria as compared with grid electricity. The work, cost-comparatively, investigated both sources of energy with a view to determining the timing of its evenness with grid supply and factors which may force earlier attainment. The study discovered that solar PV-grid parity is achievable only on the long term for the test case region. The work further then proposes an action plan for the attainment of this goal. Abdulkarim [21] studied the global and regional availability of solar energy and its feasibility for power generation. The technologies researched include concentration, solarethermal and solareelectric systems. The costs of solar energy systems were investigated and solar technologies were contrasted economically with conventional technologies of power generation. The terrestrial radiation on Nigeria's land area was said to be 2.079 1015 kWh/ year, while the average annual consumption of all forms of energy in Nigeria said to be 2.4026 1011 kWh. Based on the previous studies of PV systems in Nigeria, most papers were focused on investigations into the feasibility of generating energy via PV systems. Not much has been done so far on optimization for rural communities that specifically determine
227
the technical and econometric results as well as provision for distributed generation. This is part of the focus of this study. Another focus of the study was to investigate the solar regime over the different geopolitical zone and states of Nigeria. This is in a bid to characterize the PV potential of the different sites/states across the nation. The study takes advantage of the geopolitical differentiates that spans the land mass and topography of the nation. 3. Research methodology The location parameters of the selected sites are as shown in Table 1. Cumulative solar panels of 15 MW were utilized for all locations. 3.1. Data collection The twenty-four years (1987e2010) daily global solar radiations that were employed for this study were sourced from the Nigeria Meteorological agency (NIMET), Oshodi, Lagos, Nigeria. 3.2. Load calculation The load profiles of rural communities have been reported to be an average of 1 kWh/day per home [22,23]. Conversely, for the purpose of this study, the assumed energy demand requirement of the rural communities were analyzed to be 1.4 kWh/day, based on the individual power rating of the appliances utilized in each home. Table 1 Location parameter of the studied sites. Geopolitical zone
State
Latitude ( N)
Longitude ( E)
North-West
Sokoto Yelwa Gusau Kaduna Katsina Zaria Kano Maiduguri Bauchi Potiskum Nguru Yola Ibi Jos Ilorin Bida Abuja Lokoja Makurdi Minna Iseyin Ikeja Lagos Ijebu-ode Abeokuta Oshogbo Ondo Akure Shaki Ibadan Onitsha Enugu Owerri Calabar Port-Harcourt Ikom Ogoja Warri Uyo Benin City
13.0833 10.8331 12.1667 10.5167 12.9833 11.0667 12.0031 11.8333 10.5000 11.7333 12.8750 9.2300 8.1850 9.9167 8.5000 9.0833 9.0667 7.8167 7.7333 9.6167 7.9667 6.5833 6.4500 6.8208 7.1500 7.7667 7.1667 7.2500
5.2500 4.7387 6.6667 7.4333 7.6000 7.7000 8.5288 13.1500 10.0000 11.1500 10.4550 12.4600 9.7450 8.9000 4.5500 6.0167 7.4833 6.7500 8.5333 6.5500 3.6000 3.3333 3.3833 3.9208 3.3500 4.5667 5.0833 5.1950
7.3907 6.1667 6.4500 5.4833 4.9500 4.7833 6.0833 6.6667 5.5167 5.0500 6.3176
3.8923 6.7833 7.5000 7.0333 8.3250 7.000 8.6167 8.800 5.7500 7.9333 5.6145
North-East
North-Central
South-West
South-East
South-South
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Fig. 1. Daily load profile used as input in HOMER® for the design of hybrid energy systems in rural areas of Nigeria.
The primary peak load consumption per home was calculated as 46 kW. Fig. 1 presents the 24 h hourly load profile for the communities. The mode of analysis for this load profile is presented in Tables 2 and 3. The HOMER® software receives an hourly load profile and a monthly average solar radiation data for use in simulating the optimized technical and economic result. Hence, the 24 years average monthly solar radiation data were utilized in the analysis. 3.3. Modelling the photovoltaic (PV) project The study takes up two different forms: 1. Community focused, such that the optimal techno-economic results that will supply electricity at 0.01 LOLP to each community made up of 200 homes is discovered. 2. Distributed generation focused, such that 15 MW PV arrays are designed for each site so as to analyze their respective return on investment. This analysis also covers the same rural communities of 200 homes to be supplied electricity for free.
3.3.1. Description of the solar radiation algorithm The solar radiation data utilized for about 50% of the sites were derived from the model developed by Ref. [27] due to insufficient
(up-to-date) and unreliable data for some of the sites. Worthy of note is the fact that, while some of the sites have complete datasets covering the entire period of 1987e2010, others have data up to few years below 2010. Based on this, a robust model [27], developed from complete and reliable ground based datasets covering several sites across the nation was employed for the sites without up-to-data and reliable datasets. This was done in order to have uniformity of assessment period. To this end, the model [27] utilized the instantaneous value of temperature and other related meteorological variables which represents the precise environmental situation for each particular site to develop the instantaneous value of solar radiation for each precise latitudinal position. The data period covered the same 24 years of this study and obtained from NIMET. It must however be noted that this model is more effective than satellite-based solar irradiation databases. This is because satellite models give average values over a range of longitudes and latitudes. Hence for a number of yearly results they generate exactly same values for a number of sites within such widespread regions. 3.3.2. PV array model HOMER® uses the equation below to calculate the output of the PV array:
Fig. 2. 24-year monthly average solar radiation (kWh/m2-day) for sites in North-West Nigeria.
O.D. Ohijeagbon, O.O. Ajayi / Renewable Energy 78 (2015) 226e235
where: Cann,tot ¼ total annualized cost, i, the annual real interest rate (the discount rate), Rproj the project lifetime, and CRF() is the capital recovery factor, given by the equation:
Table 2 General wattage chart for some household appliances [24,25]. Power rating
Household appliance
24 W 55e90 W 150e340 W 60 W 18 W
4200 ceiling fan (low speed) 1900 CRT television Desktop Computer and 1700 CRT monitor 60-Watt light bulb (incandescent) CFL light bulb (60-Watt equivalent)
PPV ¼ YPV fPV
GT GT; STC
!
1 þ aP Tc Tc;STC
(1)
CRFði; NÞ ¼
ið1 þ iÞN
(3)
ð1 þ iÞN 1
where, i is the annual real interest rate and N is the number of years (25 years). The annualized capital cost of each component is calculated using the following equation:
Cacap ¼ Ccap $CRF i; Rproj
where: YPV ¼ rated capacity of the PV array, meaning its power under standard test conditions (kW) fPV ¼ PV derating factor (%) Gt ¼ solar radiation incident on the PV array in the current time step (kW/m2) GT; STC ¼ incident radiation at standard test conditions (1 kW/ m2 ) aP ¼ temperature coefficient of power (%/ C) Tc ¼ PV cell temperature in the current time step ( C) Tc, STC ¼ PV cell temperature under standard test conditions (25 C)
(4)
where: Ccap ¼ initial capital cost of the component The annualized replacement cost of a system component is the annualized value of all the replacement costs occurring throughout the lifetime of the project, minus the salvage value at the end of the project lifetime. It must be noted that the annualized replacement cost can be negative because it includes the annualized salvage value. The following equation is used in calculating each component's annualized replacement cost:
Carep ¼ Crep $frep $ SFF i; Rcomp S$ SFF i; Rcomp
3.4. Cost benefit analysis Economics is vital in the selection of energy resources, as renewable and non-renewable energy sources usually have very diverse cost characteristics. Renewable sources tend towards having high initial capital costs and low operating costs, while conventional non-renewable sources usually tend to have low capital and high operating costs. The life-cycle cost (or NPC) analysis comprises the costs of initial construction, component replacements, maintenance, fuel, cost of buying power from the grid, and miscellaneous costs such as fines ensuing from pollutant emissions. Revenues take into account income from selling power to the grid, plus any salvage value that occurs at the end of the project lifetime. As for the NPC estimation, costs are seen as positive and revenues are negative. A negative NPC value typifies a net present value (NPV). For each component, the capital, replacement, maintenance, and fuel costs, along with the salvage value and other costs or revenues, make up the component's annualized cost. The annualized costs are therefore totalled for each component, together with any miscellaneous costs, so as to find the total annualized cost of the system. The total net present cost is:
CNPC ¼
229
Cann;tot CRF i; Rproj
(2)
(5)
frep, is a factor arising due to the fact that the component lifetime can be different from the project lifetime, as given by:
frep ¼
CRF i; Rproj CRF i; Rrep ; 0;
Rrep > 0 Rrep ¼ 0
(6)
Rrep, the replacement cost duration, is given by:
Rrep ¼ Rcomp $INT
Rproj Rcomp
(7)
where, INT ( ) is the integer function, returning the integer portion of a real value. The integer function does not round up but only returns the integer portion of the number. The salvage value of each component at the end of the project lifetime is assumed proportional to its remaining life. Therefore the salvage value S is given by:
Rrem S ¼ Crep $ Rcomp
(8)
where Rrem, the remaining life of the component at the end of the project lifetime, and it is given by:
Table 3 Electricity consumption analysis for a rural community of 200 homes [26]. Description
AC/DC
Intermittent resourceeload correlation
Base case load/home (watt)
No. of appliance per home (watt)
Hours of use per day (hr/day)
TV Bulb Fan Water Pump Radio Clinic equipment School electronics Electricity e daily e AC (KWh)
AC AC AC AC DC AC AC
Negative Negative Zero Positive Zero Positive Positive
90 18 24 Community based 6 Community based Community based
1 6 6 7 3 8 Community based 3 1 5 Community based 5 Community based 5 Electricity e daily e AC (KWh)
Days of use per week
Base case load for community (watt)
7 7 7 3 7 5 5
18,000 21,600 14,400 20,000 1200 2000 2400 357.256571
230
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Rrem ¼ Rcomp Rproj Rrep
(9)
The levelised cost of energy (LCOE) is therefore:
LCOE ¼ Crep ¼ replacement cost of the component. SFF ( ) ¼ sinking fund factor Rcomp ¼ lifetime of the component The sinking fund factor is a ratio used to calculate the future value of a series of equal annual cash flows, (the future value being the equivalent value at some designated future date of a sequence of cash flows, taking into account the time value of money). The equation for the sinking fund factor is:
SFFði; NÞ ¼
i
(10)
ð1 þ iÞN 1
The total O&M cost is the sum of: the system fixed O&M cost, the penalty for capacity shortage and penalty for emissions (if any). The following equation is used in calculating the total annual O&M cost:
Com;total ¼ Com;fixed þ Ccs þ Cemissions
(11)
where: Com,fixed ¼ system fixed O&M cost ($/yr) Ccs ¼ the penalty for capacity shortage ($/yr) Cemissions ¼ the penalty for emissions ($/yr) The following equation is used to calculate the penalty for capacity shortage:
Ccs ¼ ccs $Ecs
Cann;tot Eprim þ Edef þ Egrid;sales
(14)
where, Cann,tot is the total annualized cost, Eprim and Edef are the total amounts of primary and deferrable load, respectively, that the system serves per year, and Egrid,sales is the amount of energy sold to the grid per year. It must be noted that when revenues from the project far supersedes other incurred costs, i.e. Com,total (the annual operating cost of the project), and the summation of (Cacap,total and Carep,total). It will result in a negative total annualized cost and by extension, a negative LCOE which could be termed levelised value of energy (LVOE); which reveals the profitability of the project from an investors' stance. Hence,
ðLCOEÞ ¼
Cann;tot ¼ LVOE Eprim þ Edef þ Egrid;sales
3.5. Specifications of solar panel used in this study The solar cell specification was that of a 1 kW SUNPOWER mono-crystalline silicon cell [26]. It has a collector area of 5.1 m2 and an efficiency of 19.6%. It also has a nominal operating cell temperature of 45 C and temperature coefficient of 0.40%/ C. Miscellaneous losses were taken as 10%. Therefore as required to meet the load demand for each site, more solar cells are added such that the size of the solar array is increased above 1 kW with other parameters remaining constant.
(12) 4. Results and discussion
where: 4.1. Solar profile/regime ccs ¼ capacity shortage penalty ($/kWh) Ecs ¼ total capacity shortage (kWh/yr) Therefore, the total annualized cost is:
Cann;tot ¼ Cacap;total þ Carep;total þ Com;total þ Rann;proj where, Rann,proj ¼ annual project revenue ($/yr)
(13)
Based on the data from NIMET and the solar radiation model discussed in Section 3.3.1, the solar regime across the country is presented per geopolitical zones in Figs. 3e8. The figures present the average monthly solar radiation profiles covering the period between 1987 and 2010. Fig. 2 shows that the 24 years monthly average solar radiation across the studied North-Western region's sites ranged between 4.0
Fig. 3. 24-year monthly average radiation (kWh/m2-day) for sites in North-East Nigeria.
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Fig. 4. 24-year monthly average radiation (kWh/m2-day) for sites in North-Central Nigeria.
Fig. 5. 24-year monthly average solar radiation (kWh/m2-day) for sites in South-West Nigeria.
Fig. 6. 24-year monthly average radiation (kWh/m2-day) for sites in South-East Nigeria.
231
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Fig. 7. 24-year yearly average radiation (kWh/m2-day) for sites in South-South Nigeria.
(kWh/m2/d) in August for Gusau and 6.7 (kWh/m2/d) in April for Yelwa. Fig. 2 also shows that the period between July and August experiences the least solar radiation across the sites/states. Kano and Katsina appear to be the sites/states with the better solar profiles and Gusau and Yelwa with the worst. Fig. 3 shows that the 24 years monthly average solar radiation profiles across the North-East sites ranged between 3.6 (kWh/m2/ d) in September for Bauchi and 6.7 (kWh/m2/d) in April for Nguru. Fig. 3 also shows that the period between June and September experiences the least solar radiation across the sites/states. Maiduguri, Nguru and Potiskum appear to be the sites/states with the better solar profiles and Ibi and Bauchi with the worst.
Fig. 4 shows that the 24 years monthly average solar radiation for sites in North-Central Nigeria ranged between 3.2 (kWh/m2/d) in August for Abuja and 5.8 (kWh/m2/d) in March for Minna. Fig. 4 also shows that the period between June and September experiences the least solar radiation across the sites/states. Minna, Ilorin, Lokoja and Bida appear to be the sites/states with the better solar profiles and Jos with the worst in terms of 24-year monthly average. Fig. 5 shows that the 24 years monthly average solar radiation ranged between 2.3 (kWh/m2/d) in august for Abeokuta and 5.8 (kWh/m2/d) in March for Ondo. Fig. 5 also shows that the period between June and September experiences the least solar radiation
Fig. 8. Solar PV power mapping for embedded generation in Nigeria.
O.D. Ohijeagbon, O.O. Ajayi / Renewable Energy 78 (2015) 226e235
across the sites/states. Ibadan, Ondo, Iseyin and Shaki appear to be the sites/states with the better solar profiles and Ikeja and Abeokuta with the worst. On the other hand, Fig. 6 demonstrates that the 24 years monthly average solar radiation over the South-Eastern sites ranged between 2.9 (kWh/m2/d) in July for Owerri and 5.4 (kWh/m2/d) in December for Enugu. The figure further shows that the period between July and August experiences the least solar radiation across the sites/states. Enugu and Onitsha appear to be the sites/states with the better solar profiles and Owerri with the worst. Fig. 7 shows that the 24 years monthly average solar radiation ranged between 3.0 (kWh/m2/d) in August for Ogoja and 5.5 (kWh/ m2/d) in March for Benin. Fig. 7 also shows that the period between May and September experiences the least solar radiation across the sites/states. Benin and Ikom appear to be the sites/states with the better solar profiles and Ogoja and Port-Harcourt with the worst. 4.2. Solar photovoltaic mapping and classification for Nigeria Table 4 reveals a techno-economic ranking based on LCOE for all forty sites in Nigeria. It reveals in descending order the most viable site in terms of providing electricity to the rural poor without considering any commercial incentive to the project proponent.
Table 4 Community focused techno-economic optimization for supplying electricity at 0.01 LOLP to each rural community made up of 200 homes. S/n
Site
Region
PV LCOE ($/kWh)
PV LCOE (NGN/kWh)
PV total NPC ($)
PV total NPC (NGN)
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
Nguru Kano Katsina Maiduguri Potiskum Yola Ibadan Zaria Yelwa Minna Sokoto Kaduna Ibi Gusau Bauchi Lokoja Ilorin Onitsha Enugu Shaki Benin Bida Abuja Makurdi Jos Calabar Akure Ikom Ondo Ogoja Owerri P/H Uyo Iseyin Warri Oshogbo Abeokuta Ikeja Lagos Ijebu-Ode
NE NW NW NE NE NE SW NW NW NC NW NW NE NW NE NC NC SE SE SW SS NC NC NC NC SS SW SS SW SS SE SS SS SW SS SW SW SW SW SW
0.39 0.40 0.41 0.42 0.43 0.45 0.45 0.45 0.45 0.46 0.46 0.46 0.47 0.47 0.48 0.49 0.49 0.50 0.50 0.51 0.51 0.51 0.51 0.51 0.52 0.53 0.54 0.55 0.55 0.55 0.56 0.57 0.58 0.58 0.59 0.61 0.65 0.67 0.68 0.69
60.92 61.69 63.40 65.26 66.19 69.13 69.44 69.60 70.22 70.53 70.84 71.30 73.16 73.47 73.94 75.18 75.80 77.66 77.97 78.43 78.59 79.05 79.21 79.36 79.98 81.53 83.55 84.48 85.10 85.41 87.27 88.35 89.44 89.75 91.76 94.24 100.91 103.85 105.25 106.33
651,364 660,209 678,939 697,700 709,239 739,743 742,881 744,880 751,504 846,003 758,255 763,742 783,222 785,726 789,780 804,935 865,771 831,354 832,253 839,733 870,270 755,019 848,787 810,419 847,979 913,600 894,637 840,111 909,158 981,260 932,645 956,322 904,256 958,655 942,807 1,001,368 1,078,265 1,110,556 853,813 1,135,489
100,961,420 102,332,395 105,235,545 108,143,500 109,932,045 114,660,165 115,146,555 115,456,400 116,483,120 131,130,465 117,529,525 118,380,010 121,399,410 121,787,530 122,415,900 124,764,925 134,194,505 128,859,870 128,999,215 130,158,615 134,891,850 117,027,945 131,561,985 125,614,945 131,436,745 141,608,000 138,668,735 130,217,205 140,919,490 152,095,300 144,559,975 148,229,910 140,159,680 148,591,525 146,135,085 155,212,040 167,131,075 172,136,180 132,341,015 176,000,795
233
Fig. 8 presents the mapping of the forty sites based on the same Sun-power mono crystalline solar array rated at 15 MW for each site. The solar PV power mapping is for embedded generation in Nigeria. The purpose of employing a 15 MW PV array across the sites is for uniformity of assessment and to have a basis for comparison between viable and inviable sites. The excess in energy generated annually from a design set up to cater for the energy needs of rural communities are utilized in the form of distributed generation. The feed-in tariff (2012e2017) is reflected in the Multi Year Tariff Order (MYTO) of the Federal Government of Nigeria [13] which provides the specific price set in the government's regulatory document stipulating the actual payment per MW payable to an investor for the generation and feed-in of PV electricity per MW. The validity of the document elapses in May, 2017, and afterward the prices will be reviewed for another five years. Therefore, the number of years used for the economic study was 10 years, since it will be a little difficult to preempt the exact feed-in tariff over a 25year economic life cycle. Therefore government and willing investors seeking to meet the energy needs of rural communities may take into consideration the value in profits that could also be derived via such endeavour as presented in Table 5. This value in profit can be referred to as the levelised value of energy (LVOE). It is given as negative levelised cost of energy (LCOE). The LVOE is obtained from LCOE when substantial revenue above other cost is obtained. This substantial revenue is reflected in the value of annualized cost when profit is made. The sites with values in the excellent performance category for PV power embedded generation ranged between $ 0.07/kWh and $ 0.17/kWh by LVOE (i.e. profit per kWh). Those in good performance range between $ 0.02/kWh and $ 0.05/kWh by LVOE. The fair performance ranged between $ 0.007/kWh and $ 0.017/kWh by LVOE, while those sites that are infeasible are those that do not yield any form of profit via PV power embedded generation. From Fig. 8 and Table 4, the northern regions are observed as being the most effective for PV power, with the north east (NE) and north-west (NW) having the edge, while the north central (NC) follows closely behind. Select sites in southern Nigeria produce good return on investment, such as those in the south east (SE) with 2 of its 3 sites in the good category and one producing a fair return on investment i.e. only a little profit margin per kWh of generation. The south-south (SS) and the south west (SW) are the poorest regions in Nigeria for PV power generation. Their techno-economic analysis proved to be infeasible for any profit via embedded generation for most of their stations. The SS region though, is the worst, as it has 5 of its 7 sites in this category while the SW has 6 of its 10 available sites in the category. 5. Conclusion The use of solar energy in RE technologies can be employed for electrical purposes as it will alter and improve the life of rural dwellers by helping to realize the education and technology targets of the millennium development goals (MDGs). This is because, beforehand, due to the intense poverty of rural areas in subSaharan Africa, the provision of energy to these locations were regarded as mere social activities as the rural dwellers did not have the economic wherewithal to sustain such infrastructure. Consequently, both government and private corporations exercised a lack of eagerness in providing energy to the rural poor. However, with the declining prices of renewable energy technologies [28e32] as well as the provision for embedded generation by government [13,14] it has become essential that RE technologies be employed for electrical purposes as it will provide incentive for private
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Table 5 Photovoltaic Classification for Nigeria for embedded generation (based on a PV array rating of 15 MW). Geopolitical zone
State
LCOE ($/kWh)
PV array production (MWh/yr)
Profit ($/yr)
Total NPC ($)
Remark
North-West
Sokoto Yelwa Gusau Kaduna Katsina Zaria Kano Maiduguri Bauchi Potiskum Nguru Yola Ibi Jos Ilorin Bida Abuja Lokoja Makurdi Minna Iseyin Ikeja Lagos Ijebu-ode Abeokuta Oshogbo Ondo Akure Shaki Ibadan Onitsha Enugu Owerri Calabar Port-Harcourt Ikom Ogoja Warri Uyo Benin City
0.078 0.091 0.084 0.069 0.171 0.086 0.109 0.123 0.089 0.122 0.134 0.114 0.077 0.041 0.079 0.068 0.049 0.07 0.05 0.097 0.007 0.143 0.003 0.115 0.149 0.068 0.021 0.017 0.043 0.028 0.028 0.026 0.009 0.026 0.038 0.017 0.081 0.023 0.024 0.008
33,953.30 25,780.00 25,344.90 24,244.50 33,953.30 25,419.00 27,026.50 29,639.00 26,775.00 29,570.70 30,739.30 28,225.80 25,897.80 23,570.20 26,070.70 25,297.00 20,084.20 25,402.40 24,075.70 27,350.90 20,939.70 15,665.20 20,435.40 16,436.20 15,505.30 17,914.80 21,599.60 19,843.30 22,753.50 21,970.10 21,961.00 21,841.00 20,981.80 19,475 18,996.50 21,359.30 17,473.90 19,581.90 19,549.00 20,952.80
1,737,650 2,064,987 1,857,079 1,468,796 4,551,722 1,915,160 2,579,459 3,076,927 2,028,408 3,045,125 3,465,908 2,870,181 1,710,444 828,865 1,754,555 1,468,210 1,009,796 1,519,831 1,031,865 2,254,757 129,048 1,985,957 54,115 1,674,165 2,019,681 1,077,504 404,558 297,674 872,107 548,653 548,561 499,860 166,505 447,017 637,777 320,028 1,252,765 397,899 414,187 147,750
20,705,284 8,726,117 7,828,542 6,202,823 20,705,284 8,147,658 10,910,516 13,023,272 8,620,154 12,913,231 14,701,202 12,099,003 7,217,161 3,522,634 7,469,036 6,245,072 4,298,451 6,483,655 4,356,211 9,518,773 557,413 8,445,949 229,066 7,119,349 8,612,531 4,569,929 1,706,355 1,274,391 3,719,671 2,359,933 2,353,585 2,121,434 686,342 1,902,406 2,712,817 1,328,640 5,321,449 1,690,608 1,773,985 623,373
Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Good Excellent Excellent Good Excellent Good Excellent Fair Infeasible Infeasible Infeasible Infeasible Infeasible Good Infeasible Good Good Good Good Fair Infeasible Infeasible Fair Infeasible Infeasible Infeasible Fair
North-East
North-Central
South-West
South-East
South-South
enterprises to make profit as well as alter and improve the life of rural dwellers. Hence the private sector can propel development in the renewable energy sector by establishing PV power plants in excess of 100 MW in a number of the studied locations as proven by the results on a 10-year project life as shown in Table 4. These plants could help improve on the present problem of inadequate supply of energy to propel the nations' economy as well as serve as a viable business opportunity for willing investors. References [1] Obeng G, Evers H. Impacts of public solar PV electrification on rural microenterprises: the case of Ghana. Energy Sustain Dev 2010;14:223e31. [2] Sharma D. Transforming rural lives through decentralized green power. Futures 2007;39(5):583e96. [3] Kuldeep O. Need of independent rural power producers in Indiadan overview. Clean Technol Environ Policy 2009;12(5):495e501. [4] Kaundinya D, Balachandra P, Ravindranath N. Grid-connected versus standalone energy systems for decentralized power e a review of literature. Renew Sustain Energy Rev 2009;13(8):2041e50. [5] Ghobadian B, Najafi GH, Rahimi H. Future of renewable energies in Iran. Renew Sustain Energy Rev 2009;13:689e95. [6] Ghorashi A, Rahimi A. Renewable and non-renewable energy status in Iran: art of know-how and technology-gaps. Renew Sustain Energy Rev 2011;15: 729e36. [7] Moehlecke A, Zanesco I. Pilot plant to develop cost effective photovoltaic modules. In: Proceedings of the 22nd European Photovoltaic Solar Energy Conference and Exhibition; 2007. Milan, Italy, http://www.pucrs.br/cbsolar/ pdf/22europeu_pp.pdf. [8] Bloomberg, new energy finance estimates global module production capacity in 2012 to be 50% in excess of demand. Week Rev April 16e23.2012;6(131).
[9] USITC. Crystalline silicon photovoltaic cells and modules from China. 2011. p. 1e13. Publication 4295. [10] SEIA. U.S. Solar market insight report, Q4 2011 and 2011 year-in-review full report. March 2012. p. 10e7. [11] David A. U.S. solar photovoltaic (PV) cell and module trade overview, vol. 1. U.S. International Trade Commission, Executive Briefings on Trade; June 2011. Available online, http://www.usitc.gov/publications/332/executive_briefings/ Solar_Trade_EBOT_Commission_Review_Final2.pdf [accessed 07.02.14]. [12] Ohijeagbon OD, Ajayi OO. Potential and economic viability of standalone hybrid systems for a rural community of Sokoto, North-west Nigeria. Front Energy 2014;8(2):145e59. [13] Nigerian Electricity Regulatory Commission. Multi-year tariff order for the determination of the cost of electricity generation for the period 1st June 2012 to 31st May 2017. 2012. p. 1e37. [14] Overview of the NERC regulations on embedded generation and independent electricity distribution networks. 2012. Available online, http://www. businessdayonline.com/NG/index.php/law/legal-culture/38733-overview-ofthe-nerc-regulations-on-embedded-generation-a-independent-electricitydistribution-networks [accessed 02.07.13]. [15] Akinboro FG, Adejumobi LA, Makinde V. Solar energy installation in Nigeria: observations, prospect, problems and solution. Transnatl J Sci Technol May Ed 2012;2(4):73e84. [16] Anayochukwu AV, Nnene EA. Simulation and optimization of hybrid diesel power generation system for GSM base station site in Nigeria. Electron J Energy Environ 2013;1(1):37e56. [17] Akpama EJ, Okoro OI, Chikuni E. In: Designing a photovoltaic sustained sector: a review of current practice, domestic use of energy conference; 2011. p. 155e60. [18] Habib SL, Idris NA, Ladan MJ, Mohammad AG. Unlocking Nigeria's solar PV and CSP potentials for sustainable electricity development. Int J Sci Eng Res 2012;3(5). ISSN: 2229-5518. [19] Oodo OS, Liu Yanli, Zhou Wei, Sun Hui. Impact of PV generation for small autonomous electricity generation in Nigeria. Transnatl J Sci Technol Ed 2012;2(7):81e90.
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[27] Ajayi OO, Ohijeagbon OD, Nwadialo CE, Olasope O. New model to estimate daily global solar radiation over Nigeria. Sustain Energy Technol Assess 2014;5:28e36. [28] Branker K, Pathak M, Pearce JA. Review of solar photovoltaic levelised cost of electricity. Renew Sustain Energy Rev 2011;15:4470e82. [29] Lorenz P, Pinner D, Seitz T. The economics of solar power. The McKinsey Quarterly; 2008. p. 67e79. Issue 4. [30] Summary for policy makers: renewable power generation costs. International Renewable Energy Agency (IRENA); 2012. p. 1e11. [31] Mustafa SG. Economic analysis of large-scale wind energy conversion systems in Central Anatolian Turkey. In: Eguchi Kei, editor. Clean energy systems and experiences. InTech; 2010, ISBN 978-953-307-147-3. Available online, http:// www.intechopen.com/books/clean-energy-systems-and-experiences/ economicanalysis-of-large-scale-wind-energy-conversion-systems-incentral-anatolian-turkey [accessed 03.08.13]. [32] Xie T, Pejnovic N, Lees F, Ewing E. Wind energy: a thorough examination of economic viability. Energy and energy policy. University of Chicago; 2008. Available online, http://www. humanities.uchicago.edu/orgs/…/Energy/BPEnergy-Wind-Energy.doc [accessed 03.08.13].
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Solar regime and LVOE of PV embedded generation systems in Nigeria O.D. Ohijeagbon a, Oluseyi O. Ajayi b, * a b
Mechanical Engineering Department, University of Lagos, Akoka, Lagos, Nigeria Mechanical Engineering Department, Covenant University, P.M.B. 1023, Ota, Nigeria
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 February 2014 Accepted 5 January 2015 Available online
The study assessed the potential and economic viability of solar PV standalone systems for embedded generation, taking into account its benefits to small off-grid rural communities in forty meteorological sites in Nigeria. A specific electric load profile was developed to suite the communities consisting 200 homes, a school and community health centre. Data (daily mean relative humidity, maximum and minimum temperatures, and daily global solar radiation for 24 years spanning 1987-2010) obtained from the Nigeria Meteorological Agency were used. It focused on the assessment of design that will optimally meet daily load demand for the rural communities with an LOLP of 0.01. The HOMER® software optimizing tool was engaged for the feasibility study and design. A 15 MW PV distributed generation system was utilized to economically compare the different sites in terms of life cycle cost as well as levelised cost of producing energy. A profit for potential investors in the range of $ 0.01/kWh to $ 0.17/kWh was discovered for 29 of the 40 available meteorological sites, while the remaining sites were not profitable with the present tariff regime in Nigeria. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Photovoltaic power Distributed generation Cost per kWh Clean energy Nigeria
1. Introduction Photovoltaic (PV) technology has immense capacity of meeting the energy needs of rural and remote communities in developing countries [1e4]. The high capital cost of this technology though, means that innovative approaches to its execution are required with new models so as to encourage more pervasive exploitation of the technology [5]. The amount of solar energy said to reach the earth's surface annually is about 10,000 times more than the annual global energy demand. This exceeds all the earth's available energy reserves. It has thus been stated that only 0.01% of the solar energy reaching the earth is required to meet the world's energy needs [6]. However, energy produced using PV systems is presently more expensive than the price of grid electricity in many countries due to high production costs. Nevertheless, between 1976 and 2008 the capital cost of PV modules/watt has shrank from more than $58 per watt to less than $4 [6]. This is in addition to being environmentally safe with no harmful gas or noise emissions, as solar energy is known to be a clean and non-
* Corresponding author. Tel.: þ234 8036208899. E-mail address: [email protected] (O.O. Ajayi). http://dx.doi.org/10.1016/j.renene.2015.01.014 0960-1481/© 2015 Elsevier Ltd. All rights reserved.
depleting resource of energy. Also because of its advantage of a lack of moving parts, it requires very little maintenance. The momentous growth of this technology has however been principally credited to new manufacturing plants established to produce low cost PV cells [7e11]. One of the world's largest PV markets is Germany with the driving force being the favourable feed-in tariff law on solar electricity. These regulations have enabled consumers to supply additional green (PV) electrical energy to the grid network at prices above that of network electricity [6]. In Nigeria, a similar regulation exists through embedded generation [12], which can be described as a form of generation where excess renewable energy generated by a consumer above the 1 MW mark may be sold to a nearby distribution network at prices that are higher than grid electricity. This is presented in the multi-year tariff order for 2012e2017 [13]. Therefore PV technology can supply a significant amount of electricity, thus making significant contributions to improving the nations' energy deficit [14]. This study therefore made effort at presenting options that could optimize the use of PV technology in isolated remote communities with minimum environmental degradation. It also made a case for Nigeria by exposing the solar profile/regime across the different states and geopolitical zones of the country. One major attraction to investor is the ability to have first glance opportunity to know and ascertain the potential and likely cost-benefit of a project.
O.D. Ohijeagbon, O.O. Ajayi / Renewable Energy 78 (2015) 226e235
2. Review of solar energy studies and opportunities for its application in Nigeria Akinboro et al. [15] stated that less than 40% of Nigeria is connected to the national electric grid and less than 60% of the energy demand of this 40% is generated and distributed. Therefore the study sought to proffer the use of solar system in Nigeria as an alternative energy source. It highlighted the problems that have been encountered so far. The paper also presented a review of the technical information on the solar energy standalone and hybrid installations, with more attention being allotted to domestic and industrial installations. Anayochukwu and Nnene [16] investigated a photovoltaic (PV)diesel hybrid power generation system suited to a Global System for Mobile communication (GSM) base station site located in Abuja (FCT), Nigeria. The Hybrid Optimization Model for Electrical Renewables (HOMER) software was engaged for the design of the proposed power system. The daily load was 318 kWh/day with annual average solar radiation being 5.45 kWh m2 d1. The study estimated the savings related with the use of a PV-diesel hybrid power system as against a standalone diesel powered system. From the simulation results, the proposed system has about 72%e87% savings in Total Net Present Cost (NPC) and amount of CO2 emission when compared with the diesel only system. Akpama et al. [17] reviewed the current investments and projects in relation to photovoltaic technology in Nigeria and proposed a photovoltaic incentive program that would enhance performance. They recommended a governmenteconsumer partnership for the installation of PV power systems to remote areas. Habib et al. [18] researched the potential for electrical energy generation through the use of either solar Photovoltaic or Concentrating Solar Power (CSP). Solar radiation potentials were stated to range between 3.5 and 7.0 kWh/m2-day, while the projected total potential for solar PV with 1% area for twenty select states was estimated at 1189321.65 MWh. The value range of direct normal irradiance (DNI) in northern Nigeria was said to meet the minimum DNI threshold required for economically feasible CSP project. The total potential for CSP within the fourteen frontline northern states was projected at 427,829 MW, with an electricity potential of 26,841 TWh/yr. Oodo et al. [19] studied the possibility of developing a component PV system to be integrated into the Nigerian rural micro distribution grid. This hybrid diesel-PV Autonomous generation system is said to be a practical way of enhancing rural electrification for use in remote areas far removed from the national grid. Melodi et al. [20] studied the solar energy potential in Akure, South-West Nigeria as compared with grid electricity. The work, cost-comparatively, investigated both sources of energy with a view to determining the timing of its evenness with grid supply and factors which may force earlier attainment. The study discovered that solar PV-grid parity is achievable only on the long term for the test case region. The work further then proposes an action plan for the attainment of this goal. Abdulkarim [21] studied the global and regional availability of solar energy and its feasibility for power generation. The technologies researched include concentration, solarethermal and solareelectric systems. The costs of solar energy systems were investigated and solar technologies were contrasted economically with conventional technologies of power generation. The terrestrial radiation on Nigeria's land area was said to be 2.079 1015 kWh/ year, while the average annual consumption of all forms of energy in Nigeria said to be 2.4026 1011 kWh. Based on the previous studies of PV systems in Nigeria, most papers were focused on investigations into the feasibility of generating energy via PV systems. Not much has been done so far on optimization for rural communities that specifically determine
227
the technical and econometric results as well as provision for distributed generation. This is part of the focus of this study. Another focus of the study was to investigate the solar regime over the different geopolitical zone and states of Nigeria. This is in a bid to characterize the PV potential of the different sites/states across the nation. The study takes advantage of the geopolitical differentiates that spans the land mass and topography of the nation. 3. Research methodology The location parameters of the selected sites are as shown in Table 1. Cumulative solar panels of 15 MW were utilized for all locations. 3.1. Data collection The twenty-four years (1987e2010) daily global solar radiations that were employed for this study were sourced from the Nigeria Meteorological agency (NIMET), Oshodi, Lagos, Nigeria. 3.2. Load calculation The load profiles of rural communities have been reported to be an average of 1 kWh/day per home [22,23]. Conversely, for the purpose of this study, the assumed energy demand requirement of the rural communities were analyzed to be 1.4 kWh/day, based on the individual power rating of the appliances utilized in each home. Table 1 Location parameter of the studied sites. Geopolitical zone
State
Latitude ( N)
Longitude ( E)
North-West
Sokoto Yelwa Gusau Kaduna Katsina Zaria Kano Maiduguri Bauchi Potiskum Nguru Yola Ibi Jos Ilorin Bida Abuja Lokoja Makurdi Minna Iseyin Ikeja Lagos Ijebu-ode Abeokuta Oshogbo Ondo Akure Shaki Ibadan Onitsha Enugu Owerri Calabar Port-Harcourt Ikom Ogoja Warri Uyo Benin City
13.0833 10.8331 12.1667 10.5167 12.9833 11.0667 12.0031 11.8333 10.5000 11.7333 12.8750 9.2300 8.1850 9.9167 8.5000 9.0833 9.0667 7.8167 7.7333 9.6167 7.9667 6.5833 6.4500 6.8208 7.1500 7.7667 7.1667 7.2500
5.2500 4.7387 6.6667 7.4333 7.6000 7.7000 8.5288 13.1500 10.0000 11.1500 10.4550 12.4600 9.7450 8.9000 4.5500 6.0167 7.4833 6.7500 8.5333 6.5500 3.6000 3.3333 3.3833 3.9208 3.3500 4.5667 5.0833 5.1950
7.3907 6.1667 6.4500 5.4833 4.9500 4.7833 6.0833 6.6667 5.5167 5.0500 6.3176
3.8923 6.7833 7.5000 7.0333 8.3250 7.000 8.6167 8.800 5.7500 7.9333 5.6145
North-East
North-Central
South-West
South-East
South-South
228
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Fig. 1. Daily load profile used as input in HOMER® for the design of hybrid energy systems in rural areas of Nigeria.
The primary peak load consumption per home was calculated as 46 kW. Fig. 1 presents the 24 h hourly load profile for the communities. The mode of analysis for this load profile is presented in Tables 2 and 3. The HOMER® software receives an hourly load profile and a monthly average solar radiation data for use in simulating the optimized technical and economic result. Hence, the 24 years average monthly solar radiation data were utilized in the analysis. 3.3. Modelling the photovoltaic (PV) project The study takes up two different forms: 1. Community focused, such that the optimal techno-economic results that will supply electricity at 0.01 LOLP to each community made up of 200 homes is discovered. 2. Distributed generation focused, such that 15 MW PV arrays are designed for each site so as to analyze their respective return on investment. This analysis also covers the same rural communities of 200 homes to be supplied electricity for free.
3.3.1. Description of the solar radiation algorithm The solar radiation data utilized for about 50% of the sites were derived from the model developed by Ref. [27] due to insufficient
(up-to-date) and unreliable data for some of the sites. Worthy of note is the fact that, while some of the sites have complete datasets covering the entire period of 1987e2010, others have data up to few years below 2010. Based on this, a robust model [27], developed from complete and reliable ground based datasets covering several sites across the nation was employed for the sites without up-to-data and reliable datasets. This was done in order to have uniformity of assessment period. To this end, the model [27] utilized the instantaneous value of temperature and other related meteorological variables which represents the precise environmental situation for each particular site to develop the instantaneous value of solar radiation for each precise latitudinal position. The data period covered the same 24 years of this study and obtained from NIMET. It must however be noted that this model is more effective than satellite-based solar irradiation databases. This is because satellite models give average values over a range of longitudes and latitudes. Hence for a number of yearly results they generate exactly same values for a number of sites within such widespread regions. 3.3.2. PV array model HOMER® uses the equation below to calculate the output of the PV array:
Fig. 2. 24-year monthly average solar radiation (kWh/m2-day) for sites in North-West Nigeria.
O.D. Ohijeagbon, O.O. Ajayi / Renewable Energy 78 (2015) 226e235
where: Cann,tot ¼ total annualized cost, i, the annual real interest rate (the discount rate), Rproj the project lifetime, and CRF() is the capital recovery factor, given by the equation:
Table 2 General wattage chart for some household appliances [24,25]. Power rating
Household appliance
24 W 55e90 W 150e340 W 60 W 18 W
4200 ceiling fan (low speed) 1900 CRT television Desktop Computer and 1700 CRT monitor 60-Watt light bulb (incandescent) CFL light bulb (60-Watt equivalent)
PPV ¼ YPV fPV
GT GT; STC
!
1 þ aP Tc Tc;STC
(1)
CRFði; NÞ ¼
ið1 þ iÞN
(3)
ð1 þ iÞN 1
where, i is the annual real interest rate and N is the number of years (25 years). The annualized capital cost of each component is calculated using the following equation:
Cacap ¼ Ccap $CRF i; Rproj
where: YPV ¼ rated capacity of the PV array, meaning its power under standard test conditions (kW) fPV ¼ PV derating factor (%) Gt ¼ solar radiation incident on the PV array in the current time step (kW/m2) GT; STC ¼ incident radiation at standard test conditions (1 kW/ m2 ) aP ¼ temperature coefficient of power (%/ C) Tc ¼ PV cell temperature in the current time step ( C) Tc, STC ¼ PV cell temperature under standard test conditions (25 C)
(4)
where: Ccap ¼ initial capital cost of the component The annualized replacement cost of a system component is the annualized value of all the replacement costs occurring throughout the lifetime of the project, minus the salvage value at the end of the project lifetime. It must be noted that the annualized replacement cost can be negative because it includes the annualized salvage value. The following equation is used in calculating each component's annualized replacement cost:
Carep ¼ Crep $frep $ SFF i; Rcomp S$ SFF i; Rcomp
3.4. Cost benefit analysis Economics is vital in the selection of energy resources, as renewable and non-renewable energy sources usually have very diverse cost characteristics. Renewable sources tend towards having high initial capital costs and low operating costs, while conventional non-renewable sources usually tend to have low capital and high operating costs. The life-cycle cost (or NPC) analysis comprises the costs of initial construction, component replacements, maintenance, fuel, cost of buying power from the grid, and miscellaneous costs such as fines ensuing from pollutant emissions. Revenues take into account income from selling power to the grid, plus any salvage value that occurs at the end of the project lifetime. As for the NPC estimation, costs are seen as positive and revenues are negative. A negative NPC value typifies a net present value (NPV). For each component, the capital, replacement, maintenance, and fuel costs, along with the salvage value and other costs or revenues, make up the component's annualized cost. The annualized costs are therefore totalled for each component, together with any miscellaneous costs, so as to find the total annualized cost of the system. The total net present cost is:
CNPC ¼
229
Cann;tot CRF i; Rproj
(2)
(5)
frep, is a factor arising due to the fact that the component lifetime can be different from the project lifetime, as given by:
frep ¼
CRF i; Rproj CRF i; Rrep ; 0;
Rrep > 0 Rrep ¼ 0
(6)
Rrep, the replacement cost duration, is given by:
Rrep ¼ Rcomp $INT
Rproj Rcomp
(7)
where, INT ( ) is the integer function, returning the integer portion of a real value. The integer function does not round up but only returns the integer portion of the number. The salvage value of each component at the end of the project lifetime is assumed proportional to its remaining life. Therefore the salvage value S is given by:
Rrem S ¼ Crep $ Rcomp
(8)
where Rrem, the remaining life of the component at the end of the project lifetime, and it is given by:
Table 3 Electricity consumption analysis for a rural community of 200 homes [26]. Description
AC/DC
Intermittent resourceeload correlation
Base case load/home (watt)
No. of appliance per home (watt)
Hours of use per day (hr/day)
TV Bulb Fan Water Pump Radio Clinic equipment School electronics Electricity e daily e AC (KWh)
AC AC AC AC DC AC AC
Negative Negative Zero Positive Zero Positive Positive
90 18 24 Community based 6 Community based Community based
1 6 6 7 3 8 Community based 3 1 5 Community based 5 Community based 5 Electricity e daily e AC (KWh)
Days of use per week
Base case load for community (watt)
7 7 7 3 7 5 5
18,000 21,600 14,400 20,000 1200 2000 2400 357.256571
230
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Rrem ¼ Rcomp Rproj Rrep
(9)
The levelised cost of energy (LCOE) is therefore:
LCOE ¼ Crep ¼ replacement cost of the component. SFF ( ) ¼ sinking fund factor Rcomp ¼ lifetime of the component The sinking fund factor is a ratio used to calculate the future value of a series of equal annual cash flows, (the future value being the equivalent value at some designated future date of a sequence of cash flows, taking into account the time value of money). The equation for the sinking fund factor is:
SFFði; NÞ ¼
i
(10)
ð1 þ iÞN 1
The total O&M cost is the sum of: the system fixed O&M cost, the penalty for capacity shortage and penalty for emissions (if any). The following equation is used in calculating the total annual O&M cost:
Com;total ¼ Com;fixed þ Ccs þ Cemissions
(11)
where: Com,fixed ¼ system fixed O&M cost ($/yr) Ccs ¼ the penalty for capacity shortage ($/yr) Cemissions ¼ the penalty for emissions ($/yr) The following equation is used to calculate the penalty for capacity shortage:
Ccs ¼ ccs $Ecs
Cann;tot Eprim þ Edef þ Egrid;sales
(14)
where, Cann,tot is the total annualized cost, Eprim and Edef are the total amounts of primary and deferrable load, respectively, that the system serves per year, and Egrid,sales is the amount of energy sold to the grid per year. It must be noted that when revenues from the project far supersedes other incurred costs, i.e. Com,total (the annual operating cost of the project), and the summation of (Cacap,total and Carep,total). It will result in a negative total annualized cost and by extension, a negative LCOE which could be termed levelised value of energy (LVOE); which reveals the profitability of the project from an investors' stance. Hence,
ðLCOEÞ ¼
Cann;tot ¼ LVOE Eprim þ Edef þ Egrid;sales
3.5. Specifications of solar panel used in this study The solar cell specification was that of a 1 kW SUNPOWER mono-crystalline silicon cell [26]. It has a collector area of 5.1 m2 and an efficiency of 19.6%. It also has a nominal operating cell temperature of 45 C and temperature coefficient of 0.40%/ C. Miscellaneous losses were taken as 10%. Therefore as required to meet the load demand for each site, more solar cells are added such that the size of the solar array is increased above 1 kW with other parameters remaining constant.
(12) 4. Results and discussion
where: 4.1. Solar profile/regime ccs ¼ capacity shortage penalty ($/kWh) Ecs ¼ total capacity shortage (kWh/yr) Therefore, the total annualized cost is:
Cann;tot ¼ Cacap;total þ Carep;total þ Com;total þ Rann;proj where, Rann,proj ¼ annual project revenue ($/yr)
(13)
Based on the data from NIMET and the solar radiation model discussed in Section 3.3.1, the solar regime across the country is presented per geopolitical zones in Figs. 3e8. The figures present the average monthly solar radiation profiles covering the period between 1987 and 2010. Fig. 2 shows that the 24 years monthly average solar radiation across the studied North-Western region's sites ranged between 4.0
Fig. 3. 24-year monthly average radiation (kWh/m2-day) for sites in North-East Nigeria.
O.D. Ohijeagbon, O.O. Ajayi / Renewable Energy 78 (2015) 226e235
Fig. 4. 24-year monthly average radiation (kWh/m2-day) for sites in North-Central Nigeria.
Fig. 5. 24-year monthly average solar radiation (kWh/m2-day) for sites in South-West Nigeria.
Fig. 6. 24-year monthly average radiation (kWh/m2-day) for sites in South-East Nigeria.
231
232
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Fig. 7. 24-year yearly average radiation (kWh/m2-day) for sites in South-South Nigeria.
(kWh/m2/d) in August for Gusau and 6.7 (kWh/m2/d) in April for Yelwa. Fig. 2 also shows that the period between July and August experiences the least solar radiation across the sites/states. Kano and Katsina appear to be the sites/states with the better solar profiles and Gusau and Yelwa with the worst. Fig. 3 shows that the 24 years monthly average solar radiation profiles across the North-East sites ranged between 3.6 (kWh/m2/ d) in September for Bauchi and 6.7 (kWh/m2/d) in April for Nguru. Fig. 3 also shows that the period between June and September experiences the least solar radiation across the sites/states. Maiduguri, Nguru and Potiskum appear to be the sites/states with the better solar profiles and Ibi and Bauchi with the worst.
Fig. 4 shows that the 24 years monthly average solar radiation for sites in North-Central Nigeria ranged between 3.2 (kWh/m2/d) in August for Abuja and 5.8 (kWh/m2/d) in March for Minna. Fig. 4 also shows that the period between June and September experiences the least solar radiation across the sites/states. Minna, Ilorin, Lokoja and Bida appear to be the sites/states with the better solar profiles and Jos with the worst in terms of 24-year monthly average. Fig. 5 shows that the 24 years monthly average solar radiation ranged between 2.3 (kWh/m2/d) in august for Abeokuta and 5.8 (kWh/m2/d) in March for Ondo. Fig. 5 also shows that the period between June and September experiences the least solar radiation
Fig. 8. Solar PV power mapping for embedded generation in Nigeria.
O.D. Ohijeagbon, O.O. Ajayi / Renewable Energy 78 (2015) 226e235
across the sites/states. Ibadan, Ondo, Iseyin and Shaki appear to be the sites/states with the better solar profiles and Ikeja and Abeokuta with the worst. On the other hand, Fig. 6 demonstrates that the 24 years monthly average solar radiation over the South-Eastern sites ranged between 2.9 (kWh/m2/d) in July for Owerri and 5.4 (kWh/m2/d) in December for Enugu. The figure further shows that the period between July and August experiences the least solar radiation across the sites/states. Enugu and Onitsha appear to be the sites/states with the better solar profiles and Owerri with the worst. Fig. 7 shows that the 24 years monthly average solar radiation ranged between 3.0 (kWh/m2/d) in August for Ogoja and 5.5 (kWh/ m2/d) in March for Benin. Fig. 7 also shows that the period between May and September experiences the least solar radiation across the sites/states. Benin and Ikom appear to be the sites/states with the better solar profiles and Ogoja and Port-Harcourt with the worst. 4.2. Solar photovoltaic mapping and classification for Nigeria Table 4 reveals a techno-economic ranking based on LCOE for all forty sites in Nigeria. It reveals in descending order the most viable site in terms of providing electricity to the rural poor without considering any commercial incentive to the project proponent.
Table 4 Community focused techno-economic optimization for supplying electricity at 0.01 LOLP to each rural community made up of 200 homes. S/n
Site
Region
PV LCOE ($/kWh)
PV LCOE (NGN/kWh)
PV total NPC ($)
PV total NPC (NGN)
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
Nguru Kano Katsina Maiduguri Potiskum Yola Ibadan Zaria Yelwa Minna Sokoto Kaduna Ibi Gusau Bauchi Lokoja Ilorin Onitsha Enugu Shaki Benin Bida Abuja Makurdi Jos Calabar Akure Ikom Ondo Ogoja Owerri P/H Uyo Iseyin Warri Oshogbo Abeokuta Ikeja Lagos Ijebu-Ode
NE NW NW NE NE NE SW NW NW NC NW NW NE NW NE NC NC SE SE SW SS NC NC NC NC SS SW SS SW SS SE SS SS SW SS SW SW SW SW SW
0.39 0.40 0.41 0.42 0.43 0.45 0.45 0.45 0.45 0.46 0.46 0.46 0.47 0.47 0.48 0.49 0.49 0.50 0.50 0.51 0.51 0.51 0.51 0.51 0.52 0.53 0.54 0.55 0.55 0.55 0.56 0.57 0.58 0.58 0.59 0.61 0.65 0.67 0.68 0.69
60.92 61.69 63.40 65.26 66.19 69.13 69.44 69.60 70.22 70.53 70.84 71.30 73.16 73.47 73.94 75.18 75.80 77.66 77.97 78.43 78.59 79.05 79.21 79.36 79.98 81.53 83.55 84.48 85.10 85.41 87.27 88.35 89.44 89.75 91.76 94.24 100.91 103.85 105.25 106.33
651,364 660,209 678,939 697,700 709,239 739,743 742,881 744,880 751,504 846,003 758,255 763,742 783,222 785,726 789,780 804,935 865,771 831,354 832,253 839,733 870,270 755,019 848,787 810,419 847,979 913,600 894,637 840,111 909,158 981,260 932,645 956,322 904,256 958,655 942,807 1,001,368 1,078,265 1,110,556 853,813 1,135,489
100,961,420 102,332,395 105,235,545 108,143,500 109,932,045 114,660,165 115,146,555 115,456,400 116,483,120 131,130,465 117,529,525 118,380,010 121,399,410 121,787,530 122,415,900 124,764,925 134,194,505 128,859,870 128,999,215 130,158,615 134,891,850 117,027,945 131,561,985 125,614,945 131,436,745 141,608,000 138,668,735 130,217,205 140,919,490 152,095,300 144,559,975 148,229,910 140,159,680 148,591,525 146,135,085 155,212,040 167,131,075 172,136,180 132,341,015 176,000,795
233
Fig. 8 presents the mapping of the forty sites based on the same Sun-power mono crystalline solar array rated at 15 MW for each site. The solar PV power mapping is for embedded generation in Nigeria. The purpose of employing a 15 MW PV array across the sites is for uniformity of assessment and to have a basis for comparison between viable and inviable sites. The excess in energy generated annually from a design set up to cater for the energy needs of rural communities are utilized in the form of distributed generation. The feed-in tariff (2012e2017) is reflected in the Multi Year Tariff Order (MYTO) of the Federal Government of Nigeria [13] which provides the specific price set in the government's regulatory document stipulating the actual payment per MW payable to an investor for the generation and feed-in of PV electricity per MW. The validity of the document elapses in May, 2017, and afterward the prices will be reviewed for another five years. Therefore, the number of years used for the economic study was 10 years, since it will be a little difficult to preempt the exact feed-in tariff over a 25year economic life cycle. Therefore government and willing investors seeking to meet the energy needs of rural communities may take into consideration the value in profits that could also be derived via such endeavour as presented in Table 5. This value in profit can be referred to as the levelised value of energy (LVOE). It is given as negative levelised cost of energy (LCOE). The LVOE is obtained from LCOE when substantial revenue above other cost is obtained. This substantial revenue is reflected in the value of annualized cost when profit is made. The sites with values in the excellent performance category for PV power embedded generation ranged between $ 0.07/kWh and $ 0.17/kWh by LVOE (i.e. profit per kWh). Those in good performance range between $ 0.02/kWh and $ 0.05/kWh by LVOE. The fair performance ranged between $ 0.007/kWh and $ 0.017/kWh by LVOE, while those sites that are infeasible are those that do not yield any form of profit via PV power embedded generation. From Fig. 8 and Table 4, the northern regions are observed as being the most effective for PV power, with the north east (NE) and north-west (NW) having the edge, while the north central (NC) follows closely behind. Select sites in southern Nigeria produce good return on investment, such as those in the south east (SE) with 2 of its 3 sites in the good category and one producing a fair return on investment i.e. only a little profit margin per kWh of generation. The south-south (SS) and the south west (SW) are the poorest regions in Nigeria for PV power generation. Their techno-economic analysis proved to be infeasible for any profit via embedded generation for most of their stations. The SS region though, is the worst, as it has 5 of its 7 sites in this category while the SW has 6 of its 10 available sites in the category. 5. Conclusion The use of solar energy in RE technologies can be employed for electrical purposes as it will alter and improve the life of rural dwellers by helping to realize the education and technology targets of the millennium development goals (MDGs). This is because, beforehand, due to the intense poverty of rural areas in subSaharan Africa, the provision of energy to these locations were regarded as mere social activities as the rural dwellers did not have the economic wherewithal to sustain such infrastructure. Consequently, both government and private corporations exercised a lack of eagerness in providing energy to the rural poor. However, with the declining prices of renewable energy technologies [28e32] as well as the provision for embedded generation by government [13,14] it has become essential that RE technologies be employed for electrical purposes as it will provide incentive for private
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Table 5 Photovoltaic Classification for Nigeria for embedded generation (based on a PV array rating of 15 MW). Geopolitical zone
State
LCOE ($/kWh)
PV array production (MWh/yr)
Profit ($/yr)
Total NPC ($)
Remark
North-West
Sokoto Yelwa Gusau Kaduna Katsina Zaria Kano Maiduguri Bauchi Potiskum Nguru Yola Ibi Jos Ilorin Bida Abuja Lokoja Makurdi Minna Iseyin Ikeja Lagos Ijebu-ode Abeokuta Oshogbo Ondo Akure Shaki Ibadan Onitsha Enugu Owerri Calabar Port-Harcourt Ikom Ogoja Warri Uyo Benin City
0.078 0.091 0.084 0.069 0.171 0.086 0.109 0.123 0.089 0.122 0.134 0.114 0.077 0.041 0.079 0.068 0.049 0.07 0.05 0.097 0.007 0.143 0.003 0.115 0.149 0.068 0.021 0.017 0.043 0.028 0.028 0.026 0.009 0.026 0.038 0.017 0.081 0.023 0.024 0.008
33,953.30 25,780.00 25,344.90 24,244.50 33,953.30 25,419.00 27,026.50 29,639.00 26,775.00 29,570.70 30,739.30 28,225.80 25,897.80 23,570.20 26,070.70 25,297.00 20,084.20 25,402.40 24,075.70 27,350.90 20,939.70 15,665.20 20,435.40 16,436.20 15,505.30 17,914.80 21,599.60 19,843.30 22,753.50 21,970.10 21,961.00 21,841.00 20,981.80 19,475 18,996.50 21,359.30 17,473.90 19,581.90 19,549.00 20,952.80
1,737,650 2,064,987 1,857,079 1,468,796 4,551,722 1,915,160 2,579,459 3,076,927 2,028,408 3,045,125 3,465,908 2,870,181 1,710,444 828,865 1,754,555 1,468,210 1,009,796 1,519,831 1,031,865 2,254,757 129,048 1,985,957 54,115 1,674,165 2,019,681 1,077,504 404,558 297,674 872,107 548,653 548,561 499,860 166,505 447,017 637,777 320,028 1,252,765 397,899 414,187 147,750
20,705,284 8,726,117 7,828,542 6,202,823 20,705,284 8,147,658 10,910,516 13,023,272 8,620,154 12,913,231 14,701,202 12,099,003 7,217,161 3,522,634 7,469,036 6,245,072 4,298,451 6,483,655 4,356,211 9,518,773 557,413 8,445,949 229,066 7,119,349 8,612,531 4,569,929 1,706,355 1,274,391 3,719,671 2,359,933 2,353,585 2,121,434 686,342 1,902,406 2,712,817 1,328,640 5,321,449 1,690,608 1,773,985 623,373
Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent Good Excellent Excellent Good Excellent Good Excellent Fair Infeasible Infeasible Infeasible Infeasible Infeasible Good Infeasible Good Good Good Good Fair Infeasible Infeasible Fair Infeasible Infeasible Infeasible Fair
North-East
North-Central
South-West
South-East
South-South
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