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Techno-economic optimization analysis of stand-alone renewable energy system for remote areas
Sustainable Energy Technologies and Assessments 38 (2020) 100673
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Techno-economic optimization analysis of stand-alone renewable energy system for remote areas
T
Fareeha Akrama, Furqan Asgharb, , Muhammad Asghar Majeedc, Waseem Amjadb, M. Owais Manzoorb, Anjum Munirb ⁎
a
Department of Electrical Engineering, University of Gujrat, Pakistan Department of Energy Systems Engineering, University of Agriculture, Faisalabad, Pakistan c School of Electrical Engineering, The University of Faisalabad, Faisalabad, Pakistan b
ARTICLE INFO
ABSTRACT
Keywords: Techno-economic optimization Hybrid renewable energy system (HRES) Demand-side management (DSM) Cost of energy (COE)
Hybrid renewable energy systems (HRES) are becoming popular as stand-alone power systems for providing electricity in remote areas due to the advancement in renewable energy technologies and subsequent rise in the prices of conventional fuels. A hybrid energy system, or hybrid power, usually consists of two or more renewable energy sources used together to provide increased system efficiency as well as greater balance in energy supply. Therefore, this paper aims at the design optimization of the hybrid renewable energy systems to meet the specific daily residential load profile for remote areas. The optimization problem regarding the design of a hybrid renewable energy system has been solved using Homer Pro Software depending on demand-side management during peak and off-peak hours. Four cases are used to evaluate the performance of the proposed design scheme. The simulation results have shown that the proposed design scheme is suitable for remote areas in comparison to the earlier proposed systems depicted in four cases. In addition, a properly planned hybrid system with demandside management will reduce the overall system cost and increase the system efficiency by reducing carbon emissions, balancing power system by managing overloading and reduction in load shedding as well as less complex design and easy implementation in remote areas.
Introduction To improve the human living standards, economic and industrial development has been associated with human’s ability to harness natural energy resources previously. With every passing year, the energy crisis is increasing in Pakistan, especially in rural and remote areas of Baluchistan, Khyber-Pakhtunkhwa, and Sindh, due to the longer distances from WAPDA (Water and Power Development Authority) grid stations. However, until recent times, the diesel generator was the only available energy resource in these areas even though it is uneconomical due to the fuel transportation to far distance areas with low-efficiency issues. Renewable energy resources especially wind and solar become the most reliable, profitable source of electricity for all stakeholders, as these resources are easily available in Baluchistan and Sindh coastal and offshore areas, and they are free of cost [1]. Moreover, these renewable energy technologies are a potential solution for current environmental problems. Furthermore, geographical, environmental and regional climate understanding is the most important factor to consider for HRES designing to maintain the power quality, reliability and energy demands
⁎
of that region. However, the high initial cost due to the intermittent nature of these resources and expensive machinery is the main reason behind the slow growth of these renewable energy systems [2,3]. Also, the high cost of the system’s design methodology is dependent on system capacity regarding peak load and resource availability. In severe energy crises, without any efficient load management system, load shedding may increase and eventually reduce the system overall efficiency. Previously, a lot of research work has been carried out to develop efficient techniques in which the objective function was the cost optimization and HRES efficiency, such as iterative technique [4–5], genetic algorithm [6], Hybrid Genetic Algorithm [7], GPSO [8], meta-PSO [9], mixed-integer Quadratic programming technique [10], graphical construction technique [11] and probabilistic approach [12]. However, these design techniques achieved optimum cost but the system efficiency decreases. The study [13] depicts the results of the techno-economic analysis of hybrid system comprising of Solar and wind energy for powering a specific remote mobile-based transceiver station (BTS) in Nigeria. Findings indicated that the PV array (10 kW)–DG (5.5 kW) – battery
Corresponding author. E-mail address: [email protected] (F. Asghar).
https://doi.org/10.1016/j.seta.2020.100673 Received 17 October 2018; Received in revised form 13 February 2020; Accepted 13 February 2020 2213-1388/ © 2020 Elsevier Ltd. All rights reserved.
Sustainable Energy Technologies and Assessments 38 (2020) 100673
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Fig. 1. Proposed Algorithm.
Fig.2. Monthly Average Solar Global Horizontal Irradiance data of Chaghi.
energy storage system (64 units Trojan L16P) is the most economically viable option with the total net present cost of $69,811 and per-unit cost of electricity of $0.409. The study proposed in [14] indicates that the analysis of PV/diesel/ battery hybrid renewable system configuration is found as optimum
architecture for both sensitivity cases of 1.1 and $1.3/l of diesel. The simulations concentrated on the net present costs, cost of energy and the renewable fraction of the given hybrid configurations for all the climatic zones. An optimal, reliable and cost-effective configuration of HRES has
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Fig. 3. Monthly Average Wind Speed Data of Chaghi.
with a vast land area of 347,190 km2 that contributes 43.6% of the total land area of Pakistan. According to the 1998 census, this province has a population of 20 million people. This province occupies vast barren, uneven and more than 200 rugged segments [15]. The energy department of Baluchistan states that this province has great potential to harness electricity from renewable resources especially from solar. According to the energy department, more than 95% of its area has an average solar irradiation of 5–7 kWh/m2 per day. This province has been blessed with an extensive potential of wind too. An average wind speed of 7–9 m/s blows in most of its districts like chaghi, Nokundi, and coastal area. Geothermal energy could be harnessed for HRES from Koh-e-sultan, which is in the Chaghi district [15].
Table 1 Daily Residential Load on Priority Lines. Appliances
HPLL KWh/day
LPLL KWh/day
Electric Iron Fan Personal Computer Energy Saver (CFL) (40 W) Energy Saver (CFL) (15 W) Refrigerator TV Washing Machine Motor Pump Total
0.5 1.05 – 1.6 – 2.4 – – 0.37 5.92
– – 0.4 – 0.3 – 0.75 0.35 – 1.8
Proposed algorithm
been provided in the proposed study. This research work presents a method to design a hybrid power system by considering an efficient load management technique. To check the credibility of proposed hybrid power system, a remote area of Pakistan (Baluchistan) was selected
Demand-side management (DSM) is common in smart grid networks due to its smart and swift capability of load shedding in the hours when demand surpasses the supply. Researchers are working to optimize and
Fig. 4. HPLL and LPLL in kWh/yr.
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Many strategies to overcome the load curtailment have been proposed till date [16–21]. The proposed algorithm combines DSM with a standalone hybrid power system design. It is assumed that a smart distribution board can perform automatic switching between load lines based on priority. Fig. 1. Shows the flowchart of the proposed algorithm. LPLL and HPLL are two priority load lines in the smart distribution board. The algorithm is designed in such a way that LPLL and HPLL remain activated in hours when power consumption is equal to power generation. When total power generation is less than the total power consumption of both lines but greater than the power required by HPLL, then it keeps feeding HPLL and disconnects the LPLL until the generation exceeds the consumption of both lines. Load shedding continues until the power generation is less than the power consumption in LPLL, which lead to disconnect all the connected lines. A load of 609 kWh on HPLL has been considered as system design load. The high priority load line includes the auxiliary appliances like electric iron, fan, energy saver, refrigerator and motor pump depending on the user requirement and usage on daily purposes. Low priority load lines include auxiliary appliances such as personal computers, low wattage energy saver, TV and washing machines. Monthly Average Solar Irradiance and Wind speed data of Chaghi can be seen in Figs. 2 and 3. Table 1 shows the distribution of load on different load lines (Fig. 4).
Table 2 Climate data of Chaghi (Source: NASA website) [21]. Month
Clearness index
Daily Radiation (kwh/m2 /day)
Wind speed m/s
January February March April May June July August September October November December Height (m)
0.630 0.632 0.590 0.609 0.609 0.615 0.606 0.614 0.645 0.651 0.637 0.601
3.830 4.610 5.230 6.250 6.760 7.000 6.790 6.470 6.010 5.040 4.040 3.420
5.320 5.940 5.900 5.940 6.660 6.960 5.840 5.450 5.970 6.490 5.210 5.300 50
Table 3 Average Energy demand for a Single Home (source: RET Screen Software). Appliances
Electric iron Fan Personal Computer Energy Saver (CFL) Energy Saver (CFL) Refrigerator TV Washing Machine Motor Pump Total
Load (W)
Quantity
Average Hourly usage/ day
Energy demand kWh/day
KWh/ month
kWh/yr.
Mathematical modelling of HRES components
1000 70 200
1 3 1
0.5 5 2
0.5 1.05 0.4
15 31.5 12
182.5 383.2 146
40
4
10
1.6
48
584
As shown in Fig. 1, the proposed hybrid renewable energy system has been modeled according to mathematical equations shown below.
15
2
10
0.3
9
109.5
PV system modelling
200 150 700
1 1 1
12 5 0.5
2.4 0.75 0.35
72 22.5 10.5
876 273.7 127.7
According to Markvard, the output power of the solar PV generator can be calculated from the following mathematical equations [22]:
370 2745
1 18
1 46
0.37 7.72
11.1 231.6
135.05 2817.8
PPV = N × Am ×
g
×G
(1)
t
where PPV is the output power of the PV generator, N is the total number of solar PV modules, Am is the area of a single solar PV module(m2 ), g is the efficiency of the PV generator andGt is the global irradiations incident on the tilted plane (W/m2 ) . Kolhe calculates PV generator efficiency according to the following equation [23]. g
=
pt r [1
(Tc
Tr )
t
Gt
t
NOCT 20 ( 800
pt r )
(2)
where g is the efficiency of PV generator, pt is the PowerPoint tracker efficiency, r is the reference efficiency of PV generator, Tc is the PV cell temperature, Tr is the reference temperature of the PV cell, t is the temperature coefficient of efficiency and NOCT is nominal operating cell temperature. Wind turbine modelling The estimated mathematical model of a wind turbine is shown in equation (3). Fig. 5. Schematic Diagram of HOMER for Diesel Generator.
PWT = save energy by applying the DSM scheme on domestic equipment with an aim to control the load when energy supply is less than energy demand for the residential load.
(
0 V Vin Vn Vin
Pm
)P
m
Vout < V < Vin Vin < V < Vn Vn < V < Vout
(3)
where V is the speed of the wind, Vin is the cut-in speed of the wind, Vn is the rated speed of the wind, Vout is cutout speed of wind and Pm is the
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The Levelized Cost of Electricity (LCOE) for grid extension is:
LCOEgrid = LRMC +
a1 L + a2 H E
(4)
The Levelized Cost of Electricity for HRES is:
LCOEHRES =
b1 H E
(5)
Where LRMC is the long-run marginal cost of electricity in $/kWh, a1is constant of the grid extension related to the distance in $, a2 is constant of the grid extension related to the number of homes in $, E is estimate electricity demand of household in kWh, L is the distance of grid extension in km, b1is the coefficient of the dispersed renewable energy source in $ and H is the number of homes in the remote area. The logical solution to equalize the distance between grid extension and hybrid renewable energy system is as follows:
dequalize = Fig. 6. Homer Schematic Diagram for HRES without DSM.
(b1
a1
LRMC × E ) H a1
(6)
Case study Chaghi has been selected as a case study for the installation of HRES in Baluchistan because the WAPDA power supply in these areas is not available and the national grid extension is not economically beneficial and is unfavorable because of the longer distance and rugged land. Fortunately, these areas are rich in renewable energy resources that can become an economical and reliable source of energy production. The climatic and energy requirement data is shown in Tables 2 and 3. The data is composed of NASA and RET Screen Software respectively [25]. In this case study, 105 residential houses with a maximum 66.78KW demand and 0.5 Load factors are considered that is almost 25% of the total load of Chaghi. The residential load is categorized into two sets of loads such as LPLL (washing machine, TV, etc.) and HPLL (lighting, Fan, etc.). The remotely controllable switch is attached to each load. HPLL is considered as the most sensitive and critical load that DSM tries to provide when residential power is inadequate [26–30]. Results and discussions Optimal system configurations
Fig. 7. Homer Schematic Diagram for HRES with DSM.
Diesel generator system The total designed electrical load is 809 kWh/d and only diesel generator is considered to fulfill this load demand as shown in Fig. 5.
rated power of the wind turbine. Wind turbine power (PWT ) is zero when the speed of the wind is less than cut in or greater than cut out speed. The power of the wind turbine is directly proportional to the speed of the wind in case the wind speed is less than the rated speed [24].
HRES without DSM As discussed in the previous section, the total designed electrical
Fig. 8. Optimization Results for Diesel Generator.
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Fig. 9. Electrical Production Summary for Diesel Generator.
Fig. 10. Optimization Results for HRES without DSM.
load is 809 kWh/d and multiple sources are used such as Photovoltaic cell, Wind turbine, battery, and diesel generator to fullfil this load demand. The schematic diagram can be seen in Fig. 6.
Fig. 8. The electrical production summary for diesel generator obtained from HOMER Pro software shows that the value of the unmet electric load for this system is 0% as shown below in Fig. 9.
HRES with DSM In this scenario, 609 kWh/d load of the HPLL has been considered from the total designed electrical load of 809 kWh/d. Hybrid system based on sources such as Photovoltaic cell, wind turbine, battery, and the diesel generator is being used as shown in Fig. 7. Optimum Results
Optimum solution for HRES without DSM The value of the cost of energy (COE) in this hybrid system is 0.0953 $/kWh which is acceptable in comparison with the diesel generator only system as shown below in Fig. 10. The Electrical production summary for hybrid energy system (HRES) without DSM obtained from HOMER Pro software shows that the value of the unmet electric load for this system is 2.41% as shown in Fig. 11.
Optimum solution for diesel generator The value of the cost of energy (COE) is 0.375 $/kWh as shown in
Optimum solution for HRES with DSM The value of the cost of energy (COE) in this hybrid system is
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Fig. 11. Electrical Production Summary for HRES without DSM.
Fig. 12. Optimization Results for HRES with DSM.
$0.0895/kWh, which is lower in comparison with HRES without demand-side management (DSM) and the diesel generator system as shown in Fig. 12. The Electrical production summary for HRES with DSM obtained from HOMER Pro software shows that the value of the unmet electric load for this system is 2.38% as can be seen in Fig. 13.
2. Grid extension is the economical option if the distance of the grid extension is lesser than the equalized distance. It is clear from the above formulae’s and analysis that HRES with DSM is most suitable in comparison to grid extension because Chaghi is 252 km away from the nearest grid (Nushki). The difference between the costs of grid extension and HRES is presented in Table 4.
Grid-Extension and proposed HRES comparison Technical and economical comparison can be carried out when the HRES and grid extension configurations are producing the same amount of electricity. Atd equalize , the total cost of HRES and grid extension is equal. Therefore, the following two consequences can be made:
Environmental aspects The carbon emissions in case of the diesel generators are proportional to the working time of diesel generators. A comparison of carbon emissions for three systems is shown in Table 5.
1. HRES is the most economical if the distance of the grid extension is larger than equalized distance.
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Fig. 13. Electrical Production Summary for HRES with DSM.
the system [27]. The design cost of the hybrid system includes the initial capital cost, replacement cost, operation & maintenance cost, fuel, salvage and interest for project lifetime [28–38]. In this research work, assumed lifetime is 25 years. The simulation result shows the comparison of four case studies: First system is Diesel generator, the second system considered in this study is HRES without DSM, the third System is HRES with DSM and the fourth one is grid extension. Table 6 shows the comparison of three systems which depicts HRES with DSM is the best optimal solution for the proposed hybrid system design (Table 7). It can be clearly observed from the comparative study that hybrid renewable energy system (HRES) with demand-side management (DSM) is the best suitable choice for the remote area of Baluchistan from technical and economical perspective as shown in Tables 5 and 6. This proposed technique would help in decreasing greenhouse gas (GHG) significantly, which in return reduces global warming and subsequent adverse environmental effects and human health degradation.
Table 4 Comparison between Grid extension and Proposed HRES. Grid Extension
Proposed HRES Cost Difference ($)
Capital cost ($/km)
2,016,000
O&M cost ($/km)
1,008,000
Total cost ($)
3,024,000
Capital cost ($) O&M cost ($) Total cost ($) 28,35,082
183,709 5,209 188,918
Table 5 Comparison of Different Emissions. Name
Carbon Dioxide Carbon Monoxide Unburned Hydrocarbons Nitrogen Oxides Sulfur Dioxide Particulate Matter
Emissions Diesel Power System
Hybrid System without DSM
Hybrid System with DSM
248,373 kg/year 1,566 kg/year 68.3 kg/year 1,471 kg/year 608 kg/year 9.49 kg/year
0 0 0 0 0 0
0 0 0 0 0 0
Conclusion Demand-side management control acting on the load profile for a stand-alone system is considered to minimize the unnecessary investment in the power plant. Diesel generator exhibits the highest generation cost of 0.375 $/kWh with a considerable source of carbon emissions. The comparison of the proposed demand-side management system with the conventional hybrid system concludes that the proposed system design reduces the initial capital cost of the system by 29%. The total unmet electrical load depicts a slight decrease from 2.41% to 2.38%. The cost of energy (COE) improved from 0.0953 $/kWh to 0.0895 $/kWh, with a 6.09% reduction. Based on this study, it is highly recommended to adopt proposed hybrid renewable energy management system (HRES) with the demand-side management (DSM) to fulfill the increased energy demand of Baluchistan due to the availability and accessibility of renewable resources in coastal and offshore
System analysis HOMER’s Pro software-based optimization and sensitivity analysis algorithms are used to calculate the power system configurations. Data provided by HOMER’s Pro serves energy balancing calculations for each power system configuration. Homer’s Pro sorting out has been completed by means of the net present cost to compare the design cost of
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Table 6 Comparison between Three Systems. Parameters
Energy-Economics Diesel Power System
Hybrid System without DSM
Hybrid System with DSM
System Architecture
Generator- 67 kW
Initial Capital Cost ($) Operating Cost ($) Total NPC ($) Total Production (kwh/yr) Unmet Elctircal Load (kwh/yr) COE ($)/kwh Diesel Consumed Renewable Fraction (%) Payback Period (Years)
33,500 108,272 1.43 M 296,835 0 0.375 94,885 0 –
PV- 190 kW Wind Turbine- 73 kW Batteries- 1591 kwh Bidirectional converter- 74.6 kW 258,714 7,451 355,037 399,052 7,122 (2.41%) 0.0953 0 100 6.8
PV- 139 kW Wind Turbine-57 kW Batteries-1139 kWh Bidirectional converter- 56.2 kW 183,709 5,209 251,055 312,978 5,293 (2.38%) 0.0895 0 100 6.4
Table 7 System constraints inputs. Parameters
Values
Maximum annual capacity shortage Minimum renewable fraction Operating Reserve As a Percentage of Load Load in the current time step Annual peak load As a Percentage of Renewable Output Solar power output Wind power output
3.00% 50.00%
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areas with low cost/kWh rather than integrating with the national grid or increasing the capacity of diesel power plants (high generation cost/ kWh). In future studies, the use of various types of power electronics circuitry with a better energy management system can manage the mismatch of supply and load, which will ultimately increase the system’s response time and overall efficiency. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.seta.2020.100673. References [1] Ashok S. Optimized model for community-based hybrid energy system. Renew Energy 2007;32(7):1155–64. [2] Kaabeche A, Belhamel M, Ibtiouen R. Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system. Energy 2011;36(2):1214–22. [3] Paudel, Subodh, et al. “Optimization of hybrid PV/wind power system for remote telecom station.”2011 International Conference on Power and Energy Systems. IEEE; 2011. [4] Yang HX, Lu L, Burnett J. Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong. Renew Energy 2003;28(11):1813–24. [5] Yang Hongxing, Lin Lu, Zhou Wei. A novel optimization sizing model for hybrid solar-wind power generation system. Sol Energy 2007;81(1):76–84. [6] Mohammad Sadigh Behzadi Mohsen Niasati “Comparative performance analysis of a hybrid PV/FC/battery stand-alone system using different power management strategies and sizing approaches Int J Hydrogen Energy 40.1 2015 538 548. [7] Atia, Raji, and Noboru Yamada. “Optimization of a PV-wind-diesel system using a hybrid genetic algorithm.” 2012 IEEE Electrical Power and Energy Conference. IEEE; 2012. [8] Nasser, Adil, Reji P. Optimal planning approach for a cost effective and reliable microgrid. 2016 International Conference on Cogeneration, Small Power Plants and District Energy (ICUE). IEEE; 2016.
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Techno-economic optimization analysis of stand-alone renewable energy system for remote areas
T
Fareeha Akrama, Furqan Asgharb, , Muhammad Asghar Majeedc, Waseem Amjadb, M. Owais Manzoorb, Anjum Munirb ⁎
a
Department of Electrical Engineering, University of Gujrat, Pakistan Department of Energy Systems Engineering, University of Agriculture, Faisalabad, Pakistan c School of Electrical Engineering, The University of Faisalabad, Faisalabad, Pakistan b
ARTICLE INFO
ABSTRACT
Keywords: Techno-economic optimization Hybrid renewable energy system (HRES) Demand-side management (DSM) Cost of energy (COE)
Hybrid renewable energy systems (HRES) are becoming popular as stand-alone power systems for providing electricity in remote areas due to the advancement in renewable energy technologies and subsequent rise in the prices of conventional fuels. A hybrid energy system, or hybrid power, usually consists of two or more renewable energy sources used together to provide increased system efficiency as well as greater balance in energy supply. Therefore, this paper aims at the design optimization of the hybrid renewable energy systems to meet the specific daily residential load profile for remote areas. The optimization problem regarding the design of a hybrid renewable energy system has been solved using Homer Pro Software depending on demand-side management during peak and off-peak hours. Four cases are used to evaluate the performance of the proposed design scheme. The simulation results have shown that the proposed design scheme is suitable for remote areas in comparison to the earlier proposed systems depicted in four cases. In addition, a properly planned hybrid system with demandside management will reduce the overall system cost and increase the system efficiency by reducing carbon emissions, balancing power system by managing overloading and reduction in load shedding as well as less complex design and easy implementation in remote areas.
Introduction To improve the human living standards, economic and industrial development has been associated with human’s ability to harness natural energy resources previously. With every passing year, the energy crisis is increasing in Pakistan, especially in rural and remote areas of Baluchistan, Khyber-Pakhtunkhwa, and Sindh, due to the longer distances from WAPDA (Water and Power Development Authority) grid stations. However, until recent times, the diesel generator was the only available energy resource in these areas even though it is uneconomical due to the fuel transportation to far distance areas with low-efficiency issues. Renewable energy resources especially wind and solar become the most reliable, profitable source of electricity for all stakeholders, as these resources are easily available in Baluchistan and Sindh coastal and offshore areas, and they are free of cost [1]. Moreover, these renewable energy technologies are a potential solution for current environmental problems. Furthermore, geographical, environmental and regional climate understanding is the most important factor to consider for HRES designing to maintain the power quality, reliability and energy demands
⁎
of that region. However, the high initial cost due to the intermittent nature of these resources and expensive machinery is the main reason behind the slow growth of these renewable energy systems [2,3]. Also, the high cost of the system’s design methodology is dependent on system capacity regarding peak load and resource availability. In severe energy crises, without any efficient load management system, load shedding may increase and eventually reduce the system overall efficiency. Previously, a lot of research work has been carried out to develop efficient techniques in which the objective function was the cost optimization and HRES efficiency, such as iterative technique [4–5], genetic algorithm [6], Hybrid Genetic Algorithm [7], GPSO [8], meta-PSO [9], mixed-integer Quadratic programming technique [10], graphical construction technique [11] and probabilistic approach [12]. However, these design techniques achieved optimum cost but the system efficiency decreases. The study [13] depicts the results of the techno-economic analysis of hybrid system comprising of Solar and wind energy for powering a specific remote mobile-based transceiver station (BTS) in Nigeria. Findings indicated that the PV array (10 kW)–DG (5.5 kW) – battery
Corresponding author. E-mail address: [email protected] (F. Asghar).
https://doi.org/10.1016/j.seta.2020.100673 Received 17 October 2018; Received in revised form 13 February 2020; Accepted 13 February 2020 2213-1388/ © 2020 Elsevier Ltd. All rights reserved.
Sustainable Energy Technologies and Assessments 38 (2020) 100673
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Fig. 1. Proposed Algorithm.
Fig.2. Monthly Average Solar Global Horizontal Irradiance data of Chaghi.
energy storage system (64 units Trojan L16P) is the most economically viable option with the total net present cost of $69,811 and per-unit cost of electricity of $0.409. The study proposed in [14] indicates that the analysis of PV/diesel/ battery hybrid renewable system configuration is found as optimum
architecture for both sensitivity cases of 1.1 and $1.3/l of diesel. The simulations concentrated on the net present costs, cost of energy and the renewable fraction of the given hybrid configurations for all the climatic zones. An optimal, reliable and cost-effective configuration of HRES has
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Fig. 3. Monthly Average Wind Speed Data of Chaghi.
with a vast land area of 347,190 km2 that contributes 43.6% of the total land area of Pakistan. According to the 1998 census, this province has a population of 20 million people. This province occupies vast barren, uneven and more than 200 rugged segments [15]. The energy department of Baluchistan states that this province has great potential to harness electricity from renewable resources especially from solar. According to the energy department, more than 95% of its area has an average solar irradiation of 5–7 kWh/m2 per day. This province has been blessed with an extensive potential of wind too. An average wind speed of 7–9 m/s blows in most of its districts like chaghi, Nokundi, and coastal area. Geothermal energy could be harnessed for HRES from Koh-e-sultan, which is in the Chaghi district [15].
Table 1 Daily Residential Load on Priority Lines. Appliances
HPLL KWh/day
LPLL KWh/day
Electric Iron Fan Personal Computer Energy Saver (CFL) (40 W) Energy Saver (CFL) (15 W) Refrigerator TV Washing Machine Motor Pump Total
0.5 1.05 – 1.6 – 2.4 – – 0.37 5.92
– – 0.4 – 0.3 – 0.75 0.35 – 1.8
Proposed algorithm
been provided in the proposed study. This research work presents a method to design a hybrid power system by considering an efficient load management technique. To check the credibility of proposed hybrid power system, a remote area of Pakistan (Baluchistan) was selected
Demand-side management (DSM) is common in smart grid networks due to its smart and swift capability of load shedding in the hours when demand surpasses the supply. Researchers are working to optimize and
Fig. 4. HPLL and LPLL in kWh/yr.
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Many strategies to overcome the load curtailment have been proposed till date [16–21]. The proposed algorithm combines DSM with a standalone hybrid power system design. It is assumed that a smart distribution board can perform automatic switching between load lines based on priority. Fig. 1. Shows the flowchart of the proposed algorithm. LPLL and HPLL are two priority load lines in the smart distribution board. The algorithm is designed in such a way that LPLL and HPLL remain activated in hours when power consumption is equal to power generation. When total power generation is less than the total power consumption of both lines but greater than the power required by HPLL, then it keeps feeding HPLL and disconnects the LPLL until the generation exceeds the consumption of both lines. Load shedding continues until the power generation is less than the power consumption in LPLL, which lead to disconnect all the connected lines. A load of 609 kWh on HPLL has been considered as system design load. The high priority load line includes the auxiliary appliances like electric iron, fan, energy saver, refrigerator and motor pump depending on the user requirement and usage on daily purposes. Low priority load lines include auxiliary appliances such as personal computers, low wattage energy saver, TV and washing machines. Monthly Average Solar Irradiance and Wind speed data of Chaghi can be seen in Figs. 2 and 3. Table 1 shows the distribution of load on different load lines (Fig. 4).
Table 2 Climate data of Chaghi (Source: NASA website) [21]. Month
Clearness index
Daily Radiation (kwh/m2 /day)
Wind speed m/s
January February March April May June July August September October November December Height (m)
0.630 0.632 0.590 0.609 0.609 0.615 0.606 0.614 0.645 0.651 0.637 0.601
3.830 4.610 5.230 6.250 6.760 7.000 6.790 6.470 6.010 5.040 4.040 3.420
5.320 5.940 5.900 5.940 6.660 6.960 5.840 5.450 5.970 6.490 5.210 5.300 50
Table 3 Average Energy demand for a Single Home (source: RET Screen Software). Appliances
Electric iron Fan Personal Computer Energy Saver (CFL) Energy Saver (CFL) Refrigerator TV Washing Machine Motor Pump Total
Load (W)
Quantity
Average Hourly usage/ day
Energy demand kWh/day
KWh/ month
kWh/yr.
Mathematical modelling of HRES components
1000 70 200
1 3 1
0.5 5 2
0.5 1.05 0.4
15 31.5 12
182.5 383.2 146
40
4
10
1.6
48
584
As shown in Fig. 1, the proposed hybrid renewable energy system has been modeled according to mathematical equations shown below.
15
2
10
0.3
9
109.5
PV system modelling
200 150 700
1 1 1
12 5 0.5
2.4 0.75 0.35
72 22.5 10.5
876 273.7 127.7
According to Markvard, the output power of the solar PV generator can be calculated from the following mathematical equations [22]:
370 2745
1 18
1 46
0.37 7.72
11.1 231.6
135.05 2817.8
PPV = N × Am ×
g
×G
(1)
t
where PPV is the output power of the PV generator, N is the total number of solar PV modules, Am is the area of a single solar PV module(m2 ), g is the efficiency of the PV generator andGt is the global irradiations incident on the tilted plane (W/m2 ) . Kolhe calculates PV generator efficiency according to the following equation [23]. g
=
pt r [1
(Tc
Tr )
t
Gt
t
NOCT 20 ( 800
pt r )
(2)
where g is the efficiency of PV generator, pt is the PowerPoint tracker efficiency, r is the reference efficiency of PV generator, Tc is the PV cell temperature, Tr is the reference temperature of the PV cell, t is the temperature coefficient of efficiency and NOCT is nominal operating cell temperature. Wind turbine modelling The estimated mathematical model of a wind turbine is shown in equation (3). Fig. 5. Schematic Diagram of HOMER for Diesel Generator.
PWT = save energy by applying the DSM scheme on domestic equipment with an aim to control the load when energy supply is less than energy demand for the residential load.
(
0 V Vin Vn Vin
Pm
)P
m
Vout < V < Vin Vin < V < Vn Vn < V < Vout
(3)
where V is the speed of the wind, Vin is the cut-in speed of the wind, Vn is the rated speed of the wind, Vout is cutout speed of wind and Pm is the
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The Levelized Cost of Electricity (LCOE) for grid extension is:
LCOEgrid = LRMC +
a1 L + a2 H E
(4)
The Levelized Cost of Electricity for HRES is:
LCOEHRES =
b1 H E
(5)
Where LRMC is the long-run marginal cost of electricity in $/kWh, a1is constant of the grid extension related to the distance in $, a2 is constant of the grid extension related to the number of homes in $, E is estimate electricity demand of household in kWh, L is the distance of grid extension in km, b1is the coefficient of the dispersed renewable energy source in $ and H is the number of homes in the remote area. The logical solution to equalize the distance between grid extension and hybrid renewable energy system is as follows:
dequalize = Fig. 6. Homer Schematic Diagram for HRES without DSM.
(b1
a1
LRMC × E ) H a1
(6)
Case study Chaghi has been selected as a case study for the installation of HRES in Baluchistan because the WAPDA power supply in these areas is not available and the national grid extension is not economically beneficial and is unfavorable because of the longer distance and rugged land. Fortunately, these areas are rich in renewable energy resources that can become an economical and reliable source of energy production. The climatic and energy requirement data is shown in Tables 2 and 3. The data is composed of NASA and RET Screen Software respectively [25]. In this case study, 105 residential houses with a maximum 66.78KW demand and 0.5 Load factors are considered that is almost 25% of the total load of Chaghi. The residential load is categorized into two sets of loads such as LPLL (washing machine, TV, etc.) and HPLL (lighting, Fan, etc.). The remotely controllable switch is attached to each load. HPLL is considered as the most sensitive and critical load that DSM tries to provide when residential power is inadequate [26–30]. Results and discussions Optimal system configurations
Fig. 7. Homer Schematic Diagram for HRES with DSM.
Diesel generator system The total designed electrical load is 809 kWh/d and only diesel generator is considered to fulfill this load demand as shown in Fig. 5.
rated power of the wind turbine. Wind turbine power (PWT ) is zero when the speed of the wind is less than cut in or greater than cut out speed. The power of the wind turbine is directly proportional to the speed of the wind in case the wind speed is less than the rated speed [24].
HRES without DSM As discussed in the previous section, the total designed electrical
Fig. 8. Optimization Results for Diesel Generator.
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Fig. 9. Electrical Production Summary for Diesel Generator.
Fig. 10. Optimization Results for HRES without DSM.
load is 809 kWh/d and multiple sources are used such as Photovoltaic cell, Wind turbine, battery, and diesel generator to fullfil this load demand. The schematic diagram can be seen in Fig. 6.
Fig. 8. The electrical production summary for diesel generator obtained from HOMER Pro software shows that the value of the unmet electric load for this system is 0% as shown below in Fig. 9.
HRES with DSM In this scenario, 609 kWh/d load of the HPLL has been considered from the total designed electrical load of 809 kWh/d. Hybrid system based on sources such as Photovoltaic cell, wind turbine, battery, and the diesel generator is being used as shown in Fig. 7. Optimum Results
Optimum solution for HRES without DSM The value of the cost of energy (COE) in this hybrid system is 0.0953 $/kWh which is acceptable in comparison with the diesel generator only system as shown below in Fig. 10. The Electrical production summary for hybrid energy system (HRES) without DSM obtained from HOMER Pro software shows that the value of the unmet electric load for this system is 2.41% as shown in Fig. 11.
Optimum solution for diesel generator The value of the cost of energy (COE) is 0.375 $/kWh as shown in
Optimum solution for HRES with DSM The value of the cost of energy (COE) in this hybrid system is
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Fig. 11. Electrical Production Summary for HRES without DSM.
Fig. 12. Optimization Results for HRES with DSM.
$0.0895/kWh, which is lower in comparison with HRES without demand-side management (DSM) and the diesel generator system as shown in Fig. 12. The Electrical production summary for HRES with DSM obtained from HOMER Pro software shows that the value of the unmet electric load for this system is 2.38% as can be seen in Fig. 13.
2. Grid extension is the economical option if the distance of the grid extension is lesser than the equalized distance. It is clear from the above formulae’s and analysis that HRES with DSM is most suitable in comparison to grid extension because Chaghi is 252 km away from the nearest grid (Nushki). The difference between the costs of grid extension and HRES is presented in Table 4.
Grid-Extension and proposed HRES comparison Technical and economical comparison can be carried out when the HRES and grid extension configurations are producing the same amount of electricity. Atd equalize , the total cost of HRES and grid extension is equal. Therefore, the following two consequences can be made:
Environmental aspects The carbon emissions in case of the diesel generators are proportional to the working time of diesel generators. A comparison of carbon emissions for three systems is shown in Table 5.
1. HRES is the most economical if the distance of the grid extension is larger than equalized distance.
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Fig. 13. Electrical Production Summary for HRES with DSM.
the system [27]. The design cost of the hybrid system includes the initial capital cost, replacement cost, operation & maintenance cost, fuel, salvage and interest for project lifetime [28–38]. In this research work, assumed lifetime is 25 years. The simulation result shows the comparison of four case studies: First system is Diesel generator, the second system considered in this study is HRES without DSM, the third System is HRES with DSM and the fourth one is grid extension. Table 6 shows the comparison of three systems which depicts HRES with DSM is the best optimal solution for the proposed hybrid system design (Table 7). It can be clearly observed from the comparative study that hybrid renewable energy system (HRES) with demand-side management (DSM) is the best suitable choice for the remote area of Baluchistan from technical and economical perspective as shown in Tables 5 and 6. This proposed technique would help in decreasing greenhouse gas (GHG) significantly, which in return reduces global warming and subsequent adverse environmental effects and human health degradation.
Table 4 Comparison between Grid extension and Proposed HRES. Grid Extension
Proposed HRES Cost Difference ($)
Capital cost ($/km)
2,016,000
O&M cost ($/km)
1,008,000
Total cost ($)
3,024,000
Capital cost ($) O&M cost ($) Total cost ($) 28,35,082
183,709 5,209 188,918
Table 5 Comparison of Different Emissions. Name
Carbon Dioxide Carbon Monoxide Unburned Hydrocarbons Nitrogen Oxides Sulfur Dioxide Particulate Matter
Emissions Diesel Power System
Hybrid System without DSM
Hybrid System with DSM
248,373 kg/year 1,566 kg/year 68.3 kg/year 1,471 kg/year 608 kg/year 9.49 kg/year
0 0 0 0 0 0
0 0 0 0 0 0
Conclusion Demand-side management control acting on the load profile for a stand-alone system is considered to minimize the unnecessary investment in the power plant. Diesel generator exhibits the highest generation cost of 0.375 $/kWh with a considerable source of carbon emissions. The comparison of the proposed demand-side management system with the conventional hybrid system concludes that the proposed system design reduces the initial capital cost of the system by 29%. The total unmet electrical load depicts a slight decrease from 2.41% to 2.38%. The cost of energy (COE) improved from 0.0953 $/kWh to 0.0895 $/kWh, with a 6.09% reduction. Based on this study, it is highly recommended to adopt proposed hybrid renewable energy management system (HRES) with the demand-side management (DSM) to fulfill the increased energy demand of Baluchistan due to the availability and accessibility of renewable resources in coastal and offshore
System analysis HOMER’s Pro software-based optimization and sensitivity analysis algorithms are used to calculate the power system configurations. Data provided by HOMER’s Pro serves energy balancing calculations for each power system configuration. Homer’s Pro sorting out has been completed by means of the net present cost to compare the design cost of
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Table 6 Comparison between Three Systems. Parameters
Energy-Economics Diesel Power System
Hybrid System without DSM
Hybrid System with DSM
System Architecture
Generator- 67 kW
Initial Capital Cost ($) Operating Cost ($) Total NPC ($) Total Production (kwh/yr) Unmet Elctircal Load (kwh/yr) COE ($)/kwh Diesel Consumed Renewable Fraction (%) Payback Period (Years)
33,500 108,272 1.43 M 296,835 0 0.375 94,885 0 –
PV- 190 kW Wind Turbine- 73 kW Batteries- 1591 kwh Bidirectional converter- 74.6 kW 258,714 7,451 355,037 399,052 7,122 (2.41%) 0.0953 0 100 6.8
PV- 139 kW Wind Turbine-57 kW Batteries-1139 kWh Bidirectional converter- 56.2 kW 183,709 5,209 251,055 312,978 5,293 (2.38%) 0.0895 0 100 6.4
Table 7 System constraints inputs. Parameters
Values
Maximum annual capacity shortage Minimum renewable fraction Operating Reserve As a Percentage of Load Load in the current time step Annual peak load As a Percentage of Renewable Output Solar power output Wind power output
3.00% 50.00%
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