Design of a Hybrid PV/Wind/Diesel Generator Energy System for 120 Residential Apartments in Gusau

Design of a Hybrid PV/Wind/Diesel Generator Energy System for 120 Residential Apartments in Gusau

Linus Idoko 1

1Ph D Student, Dept of EEE University of Strathclyde.

Glasgow, UK

Abstract – Gusau, a city in zamfara state, Nigeria is faced with the challenge of inadequate power supply as electricity supply from the national grid is unstable and inadequate, besides majority of those in the remote areas are isolated from the grid. The location has a potential for renewable resources, to overcome this challenge of inadequate power supply, a hybrid electric power system which employs the use of battery storage, solar and wind energy system with a diesel generator as reserve to provide adequate power to 120 residential apartments in a settlement isolated from the grid is being designed. The solar and wind energy resources in the location were assessed; the data from the two renewable energy resources available in the location was collected for the year 2015 from the Nigerian meteorological Agency, NIMET alongside the geographical coordinates and other useful parameters as input to the HOMER software simulator for analysis. The load profile of the location was carried out, the sizing of the energy mix from PV, wind, battery and generator was made using the software. The result of the simulation shows a yearly electricity production of 168,545KWh/yr, the yearly AC primary load is 67160KWh/yr. And the excess electricity is 88581Kwh/yr, this accounts for 52.6% of the total electricity generated hence, the hybrid energy system has the potential to provide the micro- grid with adequate power with high penetration of renewable energy.

Keywords HOMER; NIMET; Radiation; apartment; reserve.

1. INTRODUCTION

   The cost of energy and demand for electricity is both rising while the reserve for the sources is reduced e.g. fossil fuel globally is actually diminishing, and as such we are left with the idea that in years to come, the demand for electricity might exceed the supply of electricity from fossil fuel such that other sources of energy may be required, asides energy generation from fossil fuel gives rise to the emission of pollutants. As a result of the quest to cut down on pollution and prepare for the future energy demand requirement, the use of available renewable energy resources in every location for the provision of electricity is a necessity [1].

   To enable us to meet the electrical load demand using renewable energy sources, a hybrid energy system is required, this will help us to employ the use of wind and solar resources to provide power and in the process overcome the challenge which energy generation from a single renewable energy source experiences [2].

   Nigeria, popularly known as the giant of Africa, has a lot of mineral and renewable resources; daily average sunshine of

   Olimpo Anaya-Lara 2 2 Lecturer, Dept of EEE University of Strathclyde.

   Glasgow, UK

   6.25 hours is experienced in the country, the period of sunshine hours varies from 3.5 hours in locations in the coast of the country to 9 hours towards the north end. As a result of the long duration of sunshine hours, the value of its annual average daily radiation ranges from 7.0 KW/m2/day at the north end to 3.5KWh/m2/d at the coastal area with annual average daily radiation of 5.25KWh/m2/d and as such a high amount of energy can be generated from the sun. Consequently, the potential for the use of renewable resources to provide alternative power supply is high provided the maximum use of the available renewable resources is adopted [3].

   A. Description of research location and data

   The major source of electric power in developing countries, for example Nigeria, is usually the national grid in which most cases have limitations, as some areas cannot have access to the grid due to the geographical location of the place like many areas in Gusau in Nigeria situated at Longitude: 6.77 Degrees East and Latitude: 12.17 Degrees North. Hence an independent and reliable source of power is essential, where the majority depend on fuel-based generators for power supply which is usually associated with heavy cost of maintenance and environmental pollution [4].

   Gusau, the capital of Zamfara state was discovered to be one of the states in the country with a good potential for wind generated electricity and this is because the location experiences high temperature and long period of sunshine hours.

   The data for the average sunshine hours alongside the minimum and maximum temperature in Gusau for the year 2015 were obtained from the Nigerian Meteorological Agency, NIMET as shown in fig 1 and 2 respectively.

   Fig. 1: Average Monthly Sunshine hours for Gusau in 2015.

   Wind speed and solar radiation data for the research location is used to estimate the electricity potential at the location. The wind speed data were obtained from a research in the past [6]. The solar energy resources and clearness index for Gusau in the year 2015 is as shown in fig 4, and table 2 shows the clearness index, daily solar radiation and wind speed values for each of the different months.

   8 Global Horizontal Radiation

   Daily Radiation (kWh/m²/d)

   6

   1.0

   0.8

   Fig. 2: Minimum & maximum temperature for Gusau in 2015.

   From figure 1, it can be observed that the least of the sunshine hours are above 11 hours and the peak of the sunshine hours is slightly above 12.5 hours which implies there is an abundance of sunlight in the area. Conversely from figure 2, the peak of temperature in this location is around 38°C which puts the temperature of the location high. Other data obtained from NIMET include the monthly solar radiation for Gusau in the year 2015 as shown in table 1

   Table 1: Monthly solar radiation in Gusau in the year, 2015

   Month	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
   Monthly solar Radiation (Kwh/m2/day)	6.70	7.39	6.63	6.38	5.87	5.12	4.58	4.66	5.24	5.87	6.76	5.75

   Unlike in Europe, wind resources are low in several areas of Nigeria but the distribution is appreciable in some part of the country, including the location of study as shown in fig. 3.

   The areas in green in the figure are the areas in the country where there is a good potential for wind energy.

   4

   2

   0

   Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

   Daily Radiation Clearness Index

   Fig. 4: Solar energy resources at Gusau in 2015

   Month	Clearness Index	Daily Radiation (kWh/m2/d)	Wind Speed (m/s)
   January	0.780	6.700	7.500
   February	0.791	7.390	7.000
   March	0.655	6.630	7.000
   April	0.605	6.380	7.100
   May	0.556	5.870	7.200
   June	0.490	5.120	7.000
   July	0.438	4.580	5.900
   August	0.444	4.660	5.000
   September	0.513	5.240	4.000
   October	0.616	5.870	3.600
   November	0.775	6.760	5.000
   December	0.692	5.750	7.400
   Annual Average		5.94899	6.139

   Table 2: Solar and wind resources at Gusau in 2015

   0.6

   Clearness Index

   0.4

   0.2

   0.0

   Fig. 3: wind energy potential in Gusau, Zamfara state and other states in Nigeria [5]

   The month, which experiences the highest sunlight is February with a daily radiation of 7.390KWh/m2/d, on the other hand, July had a daily radiation of 4.580KWh/m2/d, which is the least, from the month of March to July, the daily radiation reduces progressively with the following differences: (0.25), (051), (0.75), and (0.54). During this period of reduction in daily radiation, energy from the wind energy system can be of tremendous support, besides during the nights, the energy stored in the battery alongside the wind energy can support the system.

   Conversely, the daily radiation increases progressively from the month of August to November with the following differences: (0.08), (0.58), (0.63), (0.89), after which it reduces in December by (1.01) followed by another increase in January and February by (0.95) and (0.69) respectively. A plot of the wind speed, solar radiation, maximum, and minimum temperatures in Gusau is as shown in fig 5.

   Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan

   Month vs. Wind speed, Solar radiation, max temp & min temp

   0 5 10 15 20 25 30 35 40 45

   wind speed (m/s) Solar radiation (KWh/m2/d Max temp (°C) Min temp(°C)

   Fig. 5: Month Vs solar radiation, max & min temperature

2. METHOD

   This project is specifically designed to provide electricity to people isolated from the grid and those who are connected to the grid but are dissatisfied with the use of electricity from the grid. A general description of this project is as shown in fig 6. The design requires energy majorly from renewable energy applications such as wind turbine and PV which requires battery storage, and a diesel generator as a reserve. The diesel generator is to serve in a situation where there is a failure of one of the sources or for the provision of reactive power to the micro-grid to help stabilize the voltage.

   The wind and solar energy resources in the location have the potential to provide adequate power supply to the community, but remains yet untapped and as a result the area is faced with inadequate power supply. Other reasons behind the challenge of inadequate supply in the area include:

   • The power supply is majorly from fossil fuel, which is adequate and capital intensive

   • Several people dont have electricity as they are being isolated from the grid

   • The efficiency of the Photovoltaic module reduces at the peak of the sun for those who can afford it.

   In most households in rural areas including Gusau, fuelwood appears to be the major source of energy for cooking, trees are being fell indiscriminately, since access to adequate

   electricity becomes more difficult, and people realized they can access fuelwood resources with ease, the use of fuelwood as an energy source became their choice, besides for lighting, kerosene appears to be in high demand [7].

   In a research carried out in the past [8] There has been no proof to confirm that those who live in remote locations make use of electricity supply from the grid.

   Solar and wind energy systems are still gaining awareness in the country as hydropower, energy from fossil fuel and fuelwood constitute the major source of energy consumed [9].

   In order to find a solution to this lingering electricity crisis, the use of island mode of hybrid energy system is a necessity. The main essence of employing renewable energy to provide electricity to the affected areas is due to the fact that the use of renewable energy resources for power generation provides security of supply, it reduces the amount of pollutants, there is a power quality enhancement and in the process create some form of employment to those in the area [10].

   The main contribution of this paper is to design a hybrid energy system to provide a stable and adequate power for a hundred and twenty residential buildings in the area with room for future expansion of the micro-grid.

   Fig. 6: Project description

   The method involves the general assessment of the location to identify the gravity of the power failure challenge in the area, the unavailability of the electricity grid, the extent of environmental pollution being experienced as a result of constant use of diesel generator as a source of power supply. The load requirement of the selected location was also designed; the data from the available renewable energy resources in the area, coupled with the geographical information of the study location were also gathered. HOMER software simulator was adopted for use to enable an efficient and reliable analysis of the energy requirement of the site in question and a systematic procedure for the implementation of the simulator software was used.

   The wind turbine and diesel generator are connected on the AC Busbar while the PV array, battery bank are all connected to the DC Busbar. With this arrangement in place energy storage can be achieved from any of the electrical sources with the help of the converter. The diesel generator is controlled with the help of the electrical switch as it serves as reserve and will be put to use in the event of total power failure. When both the Wind turbine and PV array is ON, the battery bank charges and stores energy for use when the load demand is more than supply.

   1. Load estimation of one hundred twenty residential apartments.

      The system load profile design for the location was carried out and represented as shown in fig 7 and table 3, its a design for 120 residential apartments and as such a diversity factor of 0.4 was used in the design to aid the accurate determination of the total demand.

      70W LED TV

      150W fridge

      32W fan (for airconditioning) 75W sound system

      4x15W exterior lighting

      4x15W CFL lamp for sitting room 15W CFL lamp for bedroom II 15W CFL lamp for bedroom I 15W CFL lamp for kitchen

      500

      450

      400

      350

      300

      250

      200

      150

      100

      Load [W]

      would have returned back home either from work, school etc. The load remains high from 6pm to 9pm and begins to reduce since most people are preparing to go to sleep. The procedure used to implement the design using a HOMER software simulator is as shown in fig 8.

      Start

      50

      0

      00h 01h 02h 03h 04h 05h 06h 07h 08h 09h 10h 11h 12h 13h 14h 15h 16h 17h 18h 19h 20h 21h 22h 23h

      Fig. 7: System load profile Table 3: System load requirement

      HOURLY LOAD [W]	00h	01h	02h	03h	04h	05h	06h	07h	08h	09h	10h	11h	12h	13h	14h	15h	16h	17h	18h	19h	20h	21h	22h	23h
      15W CFL lamp for kitchen																		15	15	15	15
      15W CFL lamp for bedroom I																				15	15	15	15
      15W CFL lamp for bedroom II							15													15	15		15
      4x15W CFL lamp for sitting room																			60	60	60	60	60
      4x15W exterior lighting	60	60	60	60	60	60	60												60	60	60	60	60	60
      75W sound system								75	75									75	75	75	75
      32W fan (for airconditioning)											96	96	96	96	96	96	96
      150W fridge	150			150			150			150			150			150			150			150
      70W LED TV																			70	70	70	70

      Energy [Wh] 60

      60

      60

      300

      780

      450

      672

      1200

      280

      0

      Identify the climatic region of the Research project

      Design & compute the load

      Identify & use

      the Longitude & Latitude of the location

      Obtain the Solar & wind resources of the location

      Obtain the size & cost of PV module, Wind turbine, diesel Generator, battery & Converter

      Use the values of the connected blocks as input to HOMER

      TOTAL [W] 210 60 60 210 60 60 225 75 75 150 96 96 246 96 96 246 96 75 430 310 310 370 150 60 3862 [Wh]

      Identify the best energy mix for the location

      TOTAL USERS [kW]

      25.2

      7.2

      7.2

      25.2

      7.2

      7.2

      27

      9

      9

      18

      11.52

      11.52

      29.52

      11.52

      11.52

      29.52

      11.52

      9

      51.6

      37.2

      37.2

      44.4

      18

      7.2

      Number of users 120

      Decide the Configuration & Size of the energy Mix

      TOTAL DEMAND [kW]

      10.08

      2.88

      2.88

      10.08

      2.88

      2.88

      10.8

      3.6

      3.6

      7.2

      4.608

      4.608

      11.81

      4.608

      4.608

      11.81

      4.608

      3.6

      20.64

      14.88

      14.88

      17.76

      7.2

      2.88

      Demand factor 0.4

      463.44 [kWh]

      1. [kWh]

         Evaluate Power output & its environmental impact

         End

         Table 4 represents a diversity factor table for residential apartment as specified by French standards NFC14-100 but this is used for a residential apartment that does not require electrical heating and this is applicable to the majority of the states in Nigeria but in a situation where electrical heat- storage is required for space heating, then irrespective of the number of residential apartment involved, a factor of 0.8 is used.

         Fig. 8: Systematic procedure

3. RESULTS

   The required inputs were fed into the HOMER software simulator using the systematic procedure in fig 8 to achieve the desired system configuration, several simulations were carried out to ensure a reliable and efficient system sizing and the results were obtained.

   Table 4: Diversity factor for residential apartments

   Apartment factor (KS)	Diversity
   2-4	1
   5-9	0.78
   10-14	0.63
   15-19	0.53
   20-24	0.49
   25-29	0.46
   30-34	0.44
   35-39	0.42
   40-49	0.41
   50 and above	0.40

   1. System load variations

   The load demand for this design varies from time to time during the day, from 11 pm in the night to 6 am in the morning as shown in table 3, the load demand is low, this is because most of the electrical devices are OFF and only the exterior lights and fridge are ON and also during this period the least load is observed when the fridge is not consuming energy, though there is a slight increase experienced by 6 pm. The load demand between the hours of 7am and 5pm is still low owing to less use of electrical devices, but the moment the time is 6pm, the load demand is observed to be at its peak (20.64KW) as most devices are in use since most individuals

   1. System configuration

      The system configuration comprises of a wind turbine, PV array, diesel generator, battery bank, the converter, the load requirements, DC and AC buses for connection, several values of the different energy sources and devices were used as input to ensure that the best energy mix for the project under consideration is obtained. The system configuration is as shown in fig 9.

      The simulated hybrid energy system is made of different sums of PV module of 250W up to 120KW but the range of PV sizes varies from 0KW 120KW, 50KW diesel generator, 50KW wind turbine and a set of converters which range from 0KW 80KW, the outcome of the simulation displays a number of the appropriate mix of the hybrid systems with the potential to cater for the required load demand. In this design, the peak load demand is 35KW and as such a diesel generator slightly higher than the peak load demand was selected to match the load demand and also provide an avenue for the storage of excess energy [11]

      The monthly average electricity production is as shown in fig 10

      Fig. 9: System configuration

   2. Simulation results

   30 Monthly Average Electric Production

   25

   Power (kW)

   20

   15

   10

   5

   0

   Jan FeFb ig. M1ar0: MApor nthMlyay aveJurnage JeullectrAiucg itySpeproduOccttionNov Dec

   PV

   Wind Generator 1

   Among the numerous simulations carried out, a section of the simulation results for sensitivity values of 5KWh/m2/d and 5m/s was considered as shown in table 5

   Table 5: Simulation result

   From fig. 10, it can be observed that the peak period of electricity production is January, with December, January and February as the periods of abundant renewable energy resources, the electrical output reduces progressively from the month of February to September, after which it begins to rise gradually. The electrical energy generated is also stored in the solar energy battery bank, which also gives back the energy to the system and as a result of the energy stored by the battery system; the AC production achieves a big boost to enable it serve the AC primary load as shown in fig. 11.

   40 AC Primary Load Served Monthly Averages

   Average Value (kW)

   30

   20

   10

   0

   Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann

   Month

   Fig. 11: AC primary load served monthly

   max

   daily high mean

   daily low min

   Several system architectures were considered from the simulation result to help ascertain the architecture that allows for high penetration of renewable energy with room for future expansion of the micro-grid, and on that basis, the architecture is selected as shown in table 6

   Table 6: System architecture

   60KW PV, 50KW Wind turbine, 50KW Diesel Gen, 480 Vision 6FM200D battery, 44KW Inverter & Rectifier
   Production KWh/yr % Consumption KWh/yr %
   PV array 90,638		54	AC Primary load	67160	100
   Wind turbine 77,907		46	Total	67160	100
   Generator 0		0
   Total 168545		100	Excess Electricity	88581	52.6
   			Unmet electrical load	0.00000226	0.0
   			Capacity shortage	0.00	0.0
   Quantity	Value
   Renewable fraction	1.00

   As shown in table 5 and 6, 100% energy generation is from renewable energy sources with the diesel generatr serving as reserve to serve the system in the event of system failure.

   The configuration of the battery bank is given in fig. 12, a total of 480 6FM200D batteries was implemented by the simulator software with 16 batteries in parallel in a string size of 30 for energy storage using a bus voltage of 360V. The minimum state of charge of the battery is slightly above 40%, the battery bank has a nominal capacity of 1,152 kW and autonomy of 90.2 hours, the battery bank gives out a total of 31,149 kWh/yr, with losses of about 7700KWh/yr and an expected life of 10years. The batteries monthly statistics show that in the month of January, February, March, April, May, June, July, November and December, the battery bank state of charge remains high and this is because there is enough energy produced by the mix that the battery does not need to be used up, but in the month of August, the state of charge of the battery drops and the least is obtained in September and that is the month when the least electricity production is obtained. In those months in which the energy generated is more than the load demand, the storage of the excess energy is done by the battery bank.

   Fig. 12: Battery bank state of charge

   Fig 10 shows the inclusion of a diesel generator in the energy mix, but with no output as show in table 6 and fig. 13

4. CONCLUSION

From the Homer Software simulator output, the total yearly electricity production is 168,545KWh/yr., the yearly AC primary load consumption is 67160 KWh/yr. And the excess electricity is 88581Kwh/yr. The excess electricity accounts for 52.6% of the total electricity generated. Conversely, from table 3, it can be seen that the peak demand is 20.64 KW, and the AC primary served as shown in fig. 11 shows that the AC primary load for each month was met.

Therefore, the hybrid energy system has the potential to provide the affected area with adequate power supply that is environmentally friendly (no emission of pollutants).

The next line of research in this work is the modelling of the hybrid PV/wind/diesel Gen energy system using MATLAB.

ACKNOWLEDGMENT

The author is grateful to the Petroleum technology development fund office, Nigeria for the financial assistance in carrying out this work, the author also acknowledges the

1.0

Average Value (kW)

0.8

0.6

0.4

0.2

0.0

Generator 1 Electrical Output Monthly Averages

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann

Month

Fig. 13: Generator electrical output.

max

daily high mean

daily low min

effort of NREL and HOMER Energy for giving me free access to HOMER software to enable me carry out the design.

REFERENCES

1. V. Baki, M. Pezo, . Stevanovi, M. ivkovi, and B. Grubor, Dynamical simulation of PV/Wind hybrid energy conversion system, Energy, vol. 45, no. 1, pp. 324328, 2012.

2. F. Jahanbani and G. H. Riahy, Optimum design of a hybrid

   The monthly electricity output generated by the wind turbine and the PV array is as shown in fig. 14 and 15 respectively.

   Renewable Energy System, Renew. Energy – Trends Appl., pp. 231250, 2008.

3. A. V. Anayochukwu, Simulation of Photovoltaic / Diesel

   50 Entegrity eW15 Power Output Monthly Averages

   Average Value (kW)

   40

   30

   20

   10

   0

   Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann

   Month

   Fig. 14: wind turbine power output

   50 PV Array Power Output Monthly Averages

   max

   daily high mean

   daily low min

   hybrid Power generation system with energy storage and supervisory control, vol. 3, no. 3, 2013.

4. A. V. Anayochukwu, Energy optimization map for off-grid health clinics in Nigeria, vol. 4, no. 1, 2014.

5. N. V. Emodi, Energy policies for sustainable development strategies. 2016.

6. O. O. Ajayi, R. O. Fagbenle, J. Katende, J. O. Okeniyi, and O.

   1. Omotosho, Wind energy potential for power generation of a local site in Gusau, Nigeria, Int. J. Energy a Clean Environ., vol. 11, no. 14, pp. 99116, 2010.

7. a. S. Sambo, Renewable energy for rural development: The Nigerian Perspective, ISESCO Sci. Technol. Vis., vol. 1, pp.

   Average Value (kW)

   40

   30

   20

   10

   0

   Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann

   Month

   Fig. 15: PV array power output.

   max

   daily high mean

   daily low min

   1222, 2005.

8. H. M. Muye, The international journal of science & technoledge: adoption of solar energy systems in remote and rural communities of Nigeria (a Review), vol. 4, no. 1, pp. 23 28, 2016.

9. D. Maijama, A. L. Maijama, and A. M. Umar, International Journal of sustainable energy and environmental research renewable sources of energy for economic development in Nigeria Keywords: Contribution / Originality, vol. 4, no. 2, pp. 4963, 2015.

10. S. Negi and L. Mathew, Hybrid Renewable Energy System: a Review, vol. 7, no. 5, pp. 535542, 2014.

11. S. M. Shaahid, Review of research on autonomous wind farms and solar parks and their feasibility for commercial loads in hot regions, Renew. Sustain. Energy Rev., vol. 15, no. 8, pp. 38773887, 2011.