Glensaugh Farm monthly load demand data in kWh [14].
Open access peer-reviewed article
This Article is part of the special issue HYBRID RENEWABLE-HYDROGEN GREEN ENERGY SYSTEMS led by Prof. Dallia Ali and Ms. Ayyate Atteya
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Article Type: Research Paper
Date of acceptance: May 2023
Date of publication: June 2023
DoI: 10.5772/geet.16
copyright: ©2023 The Author(s), Licensee IntechOpen, License: CC BY 4.0
Table of contents
Renewable-hydrogen (H2) is a key component in Scotland’s decarbonisation plans and its implementation in farming communities can support achieving net-zero goals. HydroGlen, a demonstrative renewable-powered farming community at Glensaugh, is used as a case-study to investigate the potential of renewable-hydrogen in enabling Scotland farms’ energy transition.
For our case-study farm, two renewable-hydrogen configurations (Solar-H2 and Wind-H2) were proposed, sized, and assessed to identify their capability in supplying most of the farm’s residential and commercial demands by clean renewable-energy as well as the transport demands by green hydrogen stored during renewables’ surplus. The effectiveness of the proposed configurations was then assessed against that of the Solar-Wind-H2 configuration proposed by RINA (RINA 2021).
The study started by assessing the currently installed renewables-system in meeting the farm’s demands and results showed that the system can only meet 11% of farm’s commercial and residential demands and none of the transport fuel demands. To allow meeting more residential and commercial demands as well as transport demands, a hybrid Solar-H2 system was proposed with an additional photovoltaic (PV) capacity that was sized to feed a higher percentage of the demands with renewable power and a hydrogen energy-storage system to store the surplus in PV production in the form of green H2 to be used in feeding the transport fuel demands. Components of the proposed green-H2 energy-storage system (electrolyser and storage-tank) were accordingly sized. The effectiveness of the proposed hybrid PV-H2 configuration was then assessed, and results showed it was capable of supplying 35% of the residential and commercial demands from solar energy and 100% of the transport demands by green H2. This generous amount of green H2 resulted from the plenty PV daytime surplus given that most of the residential demand is not during sun availability hours.
A hybrid Wind-H2 configuration was then proposed, sized and assessed. Results showed that this configuration was capable of supplying most of the residential and commercial demands from wind energy as the wind-generation profile closely matched these demands, and around 44% of transportation fuel demand by green H2.
The levelized cost of energy (LCOE) was then estimated for each of the proposed hybrid configurations showing that the LCOE for the hybrid PV-H2, 0.3 £/kWh, is more cost competitive than that of the Wind-H2 of 0.4 £/kWh; thus, the hybrid PV-H2 system was recommended for the farm.
Finally, a Simulink model was developed to simulate and assess the operation of the proposed PV-H2 system given that this has not been considered in RINA study.
solar energy systems
wind energy systems
hydrogen energy storage systems
modelling
sizing
LCOE
Author information
The implementation of clean energy production from renewables is a key component towards achieving the net-zero target. However, given the intermittent nature of renewable energy systems (RES), energy storage is critical to mitigate this intermittency problem and realise the full potential of renewable energy. Energy storage devices can be classified according to a range of characteristics, including their storage capacity and duration, life expectancy, size, cost and safety, and environmental effect, including their recyclability [1]. There are numerous storage options; these include flow batteries which store energy directly in the electrolyte but are still in their infancy in terms of deployment, sodium-sulfur batteries which have a higher energy density than Li-ion batteries but have an inconvenient hot liquid metal electrolyte partially reducing the battery performance [2], supercapacitors which cannot provide electricity for an extended period of time, and compressed air and flywheels energy storage installations which are restricted by location requirements [3].
Hydrogen energy storage (HES) systems are distinguished from other types of renewable energy storage systems by their adaptability and capacity to deliver multiple services [4]. This quality is essential for grid operators to maintain system dependability and the integration of RES into the electricity, heating, and transportation infrastructures [4, 5]. Energy can be stored at large-scale using HES systems, ranging from 1 GWh to 1 TWh, whereas batteries generally range from 10 kWh to 10 MWh [5]. Fuel cell electric vehicles have additional hydrogen utilisation potential of interest [6]. The degree to which HES systems may enter energy storage markets will rely on a number of variables, including non-technological hurdles such as regulatory, safety, and economic concerns [7]. H2 energy-storage is an emerging key enabler in Scotland’s decarbonisation plans, and there is a need to demonstrate how its implementation can contribute to meeting the net-zero greenhouse gas (GHG) emission goals. Sizing the capacity of the HES system components needed with renewables and their economic viability represent key research components in assessing the potential of HES in enabling the clean energy transition. To this extent, many researchers have carried out valuable insights into sizing and assessing the feasibility of HES technologies for stand-alone and grid-connected hybrid renewable energy systems. Castaneda
This paper investigates the potential of hybrid renewable-hydrogen energy storage systems in enabling the energy transition of Scotland’s farms using Glensaugh farm as a case study for the investigation. At Glensaugh farm, just outside Fettercairn in Aberdeenshire, the HydroGlen Project aims turning the farm into a net-zero carbon emissions farm through feeding the farm’s (electricity, heating, and transport) energy needs from a combination of renewable energy sources and on-site hydrogen production, compression, and storage [14]. Currently, Glensaugh farm has a renewable energy mix composed of a 50-kW wind turbine and 50 kW solar PV and is looking into installing more renewable capacity to meet all their residential, commercial, and transportation demands [14]. The farm’s energy demands data and RINA feasibility report [14] have been provided by the James Hutton Institute (JHI).
This study started by evaluating the farm currently installed renewable system in meeting the farm’s residential, commercial and transport demands. Based on this evaluation, two hybrid renewable-H2 systems were proposed. Option (1), a Hybrid Solar-H2 system that utilizes the existing PV together with an extra grid-connected PV capacity that was sized to allow meeting more residential and commercial demands and a H2 generator that was sized to allow storing the solar surplus in the form of green H2 to be used for meeting the transport demands. The effectiveness of this proposed and sized hybrid PV-H2 system was then assessed. For option (2), a hybrid Wind-H2 system is proposed, sized and assessed. The proposed hybrid Wind-H2 system utilizes the currently installed wind turbine as it was found capable of meeting most of the residential and commercial demands, together with a H2 generator that was sized to allow storing the wind surplus in the form of green H2 to be used for meeting the transport demands. The green H2 produced in each of the proposed hybrid renewable-H2 systems is utilized as fuel for meeting the farm transport demands rather than converting the H2 back to electrical energy for ‘on-grid’ consumption because H2 fuel is more competitively priced if it is marketed as a high-value gas [15]. The LCOE for each of the proposed hybrid configurations was estimated and they were compared to select the more cost-competitive option. Based on this comparison, a Simulink model was developed for the selected hybrid PV-H2 system to assess in more detail its operation and potential.
Table 1 shows the data provided for the farm demands [14], where the transport demand is comprised of 12 vehicles accounting for 5110 kWh with no variation throughout the year. The currently installed renewable capacity comprises a mixture of a 50-kW solar PV and 50-kW wind turbine [14]. The on-site 50 kW wind turbine is not included in the calculations as it feeds directly into the national grid, not the farm demands. The RINA report proposes a new, 800 kW turbine to be installed for use in combination with the solar PV.
Month | Residential monthly demand (kWh) | Commercial monthly demand (kWh) | Transport monthly demand (kWh) |
---|---|---|---|
January | 6413 | 37,397 | 5110 |
February | 6413 | 29,560 | 5110 |
March | 6413 | 31,565 | 5110 |
April | 5318 | 17,012 | 5110 |
May | 4628 | 18,856 | 5110 |
June | 3968 | 16,887 | 5110 |
July | 3947 | 14,586 | 5110 |
August | 4427 | 14,857 | 5110 |
September | 5207 | 14,117 | 5110 |
October | 6528 | 25,020 | 5110 |
November | 6618 | 38,493 | 5110 |
December | 7308 | 38,792 | 5110 |
The energy output of the currently installed 50-kW PV system was calculated using the PVsyst software and results are shown in Table 2. The PVsyst includes extensive meteorological and PV system components databases that allows the user to select the PV system components (module and inverter) [16]. Based on the information provided by the farm owner, the PV and inverter modules were set in PVsyst to JC250M-24/Bb-v and SolarLake 15000-TL respectively, the tilt was set to 20°, and the azimuth, which is the direction the PV is facing, was set at 50°. From the PVsyst simulation results shown in Table 2, it was found that the annual energy output of the currently installed 50-kW solar system can only meet 11% of the residential and commercial total demand (40,316 kWh/364,330 kWh).
Month | Monthly residential and commercial total demand (kWh) | PVsyst simulation output monthly production from currently installed PV (kWh) |
---|---|---|
Jan | 43,810 | 1,034 |
Feb | 35,973 | 1,791 |
Mar | 37,978 | 3,609 |
Apr | 22,330 | 4,868 |
May | 23,484 | 6,266 |
Jun | 20,855 | 5,884 |
Jul | 18,533 | 5,271 |
Aug | 19,284 | 4,984 |
Sep | 19,324 | 3,300 |
Oct | 31,548 | 2,207 |
Nov | 45,111 | 1,102 |
Dec | 46,100 | 661 |
Total annual | 364,330 | 40,316 |
Given that the currently installed PV capacity is insufficient to meet the farm’s energy demands alone without adding an 800 kW wind turbine as proposed by RINA’s study [14], we here propose an alternative solution of extra PV capacity that allows meeting the farm’s demands by 100% solar PV. The capacity of the proposed PV system was accordingly sized, and its potential was assessed. A hydrogen energy storage system was also proposed and sized to store the excess in the PV production in the form of green hydrogen fuel to be used in clean fuelling of the farm’s 12 vehicles. Figure 1 shows the proposed PV-H2 Energy System.
The size of the solar PV array that would be required for a grid-connected PV-H2 system is calculated using Equation (1) [11]
From the results of the 12-months calculated PV system sizes shown in Table 3, the required size of the new PV capacity is found to be 598 kW. Thus, in addition to the existing 50 kW solar capacity, an extra PV capacity of 548 kW is suggested to be installed in order to fully meet all energy requirements of the farm and community.
Month | Monthly load demand (kWh) | Average monthly peak sun-hours (PSH) | Temperature loss | Inverter efficiency | Derate factor | Monthly PV array size (kW) |
---|---|---|---|---|---|---|
January | 43,810 | 48.1 | 0.88 | 0.96 | 0.774 | 1392 |
February | 35,973 | 90.0 | 0.88 | 0.96 | 0.774 | 612 |
March | 37,978 | 111.7 | 0.88 | 0.96 | 0.774 | 520 |
April | 22,330 | 174.9 | 0.88 | 0.96 | 0.774 | 195 |
May | 23,484 | 231.0 | 0.88 | 0.96 | 0.774 | 155 |
June | 20,855 | 186.2 | 0.88 | 0.96 | 0.774 | 171 |
July | 18,533 | 180.8 | 0.88 | 0.96 | 0.774 | 157 |
August | 19,284 | 154.8 | 0.88 | 0.96 | 0.774 | 190 |
September | 19,324 | 140.1 | 0.88 | 0.96 | 0.774 | 211 |
October | 31,548 | 88.6 | 0.88 | 0.96 | 0.774 | 544 |
November | 45,111 | 54.3 | 0.88 | 0.96 | 0.774 | 1270 |
December | 46,100 | 40.0 | 0.88 | 0.96 | 0.774 | 1764 |
Given the high cost of electrolysers, it is desirable to maximise their utilisation [15]. Based on industrial standards, the electrolyser size is usually chosen to be between 20% and 40% of the PV capacity to increase the electrolyser’s utilisation level. The possible downside is that there will be moments when total renewable generation surpasses the total electricity that the combined load and storage can absorb [15]. Selecting a 40% of the proposed 598 kW PV capacity suggests an electrolyser size of 239.2 kW. To allow better utilization, three units of 80 kW HySTAT 15–10 electrolysers from Hydrogenic manufacturer (highlighted in Figure 2) are accordingly selected.
To identify the storage tank size, it is first necessary to identify the amount of H2 produced by the electrolyser based on the surplus in the solar output from the proposed PV system. Using Pvsyst, the surplus in solar production was estimated by comparing the monthly energy output of the newly sized PV capacity to the farm’s total residential and commercial load demand. The monthly energy output of the PV system was estimated using the PVsyst software after setting the following inputs: the location was set to a Latitude of 57.20° N and a longitude of −2.20° E. PV Tilt angle was set as 37° after examining different tilt values to find the optimal, additionally Fordham [20] proved that the optimal tilt angle of a PV is equal to the site’s latitude minus 20° thus in Scotland it is (57° − 20° = 37°). The azimuth was set at 0° as Scotland is in the northern hemisphere facing south. The PV module and inverter types were selected the same as the ones already installed on the farm, Figure 3 shows the system details. An optimized selection of inverter size is done by the PVsyst.
Table 4 demonstrates the PVsyst simulation results showing the proposed PV system monthly DC energy output and the monthly AC solar energy excess which takes into account the inverter and wiring losses.
Month | Monthly DC production from the proposed 598 kW PV system (kWh) | Monthly AC residential and commercial demand (kWh) | Monthly AC solar energy supplying the demand during the daytime (kWh) | Monthly AC solar energy excess (kWh) |
---|---|---|---|---|
January | 19,740 | 43,810 | 7,850 | 11,450 |
February | 29,900 | 35,970 | 9,630 | 19,660 |
March | 54,040 | 37,980 | 14,220 | 38,790 |
April | 65,220 | 22,330 | 10,960 | 53,020 |
May | 82,320 | 23,480 | 13,240 | 67,540 |
June | 74,280 | 20,860 | 12,600 | 60,250 |
July | 73,070 | 18,530 | 11,090 | 60,560 |
August | 63,230 | 19,280 | 10,070 | 51,950 |
September | 54,020 | 19,320 | 8,680 | 44,300 |
October | 32,920 | 31,580 | 10,030 | 22,200 |
November | 19,340 | 45,110 | 9,010 | 9,880 |
December | 13,740 | 46,100 | 6,610 | 6,770 |
Annual | | | | |
Based on the PV’s monthly energy surplus, the amount of hydrogen produced monthly by the previously selected and sized electrolyser was calculated by dividing the PV energy excess by the electrolyzer energy consumption of 5.4 kWh/N m3. The monthly hydrogen required for fuelling each vehicle in the farm was also calculated by using the 12 vehicles given total monthly consumption (5110 kWh) and the onboard H2 fuel cell and sub-systems Round-trip efficiency (RTE) of 30% [21]. Given the RTE, the energy demand of the 12 vehicles becomes 17,033 kWh (5110/0.3), this equates to a monthly demand of 1419.4 kWh for each vehicle [14]. This amount is then converted from 1419.4 kWh to normal cubic meters (N m3 of hydrogen) by multiplying it by the conversation factor 0.333 giving 473 N m3 of H2 required per month for each vehicle. This conversion factor was calculated based on the fact that at low heat value (LHV), 11.1 N m3 of hydrogen is equivalent to 33.3 kWh [19]. Using the calculated monthly H2 produced by electrolyser and the H2 required by each vehicle, the number of vehicles that can be fed by clean green H2 fuel every month was then calculated. The monthly accumulation of H2 excess was then calculated and used in fuelling more vehicles. Finally, the residual monthly accumulated H2 after feeding the farm 12 vehicles was calculated to be sold as a commodity or used in generating clean electricity. Results of all these calculations are shown in Table 5.
Month | Monthly excess in PV energy (kWh) | Electrolyser energy consumption (kWh/N m3) | Monthly H2 Production (N m3) | One vehicle monthly H2 consumption needs (N m3) | No of vehicles that can be supplied each month | Monthly H2 excess after feeding the possible number of vehicles (N m3) | No of extra vehicles that can be supplied from the H2 monthly accumulation | Monthly accumulated H2 after feeding the farm’s 12 vehicles (N m3) |
---|---|---|---|---|---|---|---|---|
Jan | 11,450 | 5.4 | 2120.4 | 473 | 4 | 228.4 | 0 | 228.4 |
Feb | 19,660 | 5.4 | 3640.7 | 473 | 7 | 329.7 | 1 | 85.1 |
Mar | 38,790 | 5.4 | 7183.3 | 473 | 12 | 1507.3 | 0 | 1592.4 |
Apr | 53,020 | 5.4 | 9818.5 | 473 | 12 | 4142.5 | 0 | 5735.0 |
May | 67,540 | 5.4 | 12,507.4 | 473 | 12 | 6831.4 | 0 | 12,566.4 |
Jun | 60,250 | 5.4 | 11,157.4 | 473 | 12 | 5481.4 | 0 | 18,047.8 |
Jul | 60,560 | 5.4 | 11,214.8 | 473 | 12 | 5538.8 | 0 | 23,586.6 |
Aug | 51,950 | 5.4 | 9620.4 | 473 | 12 | 3944.4 | 0 | 27,531.0 |
Sep | 44,300 | 5.4 | 8203.7 | 473 | 12 | 2527.7 | 0 | |
Oct | 22,200 | 5.4 | 4111.1 | 473 | 8 | 327.1 | 4 | 28,493.8 |
Nov | 9,880 | 5.4 | 1829.6 | 473 | 3 | 410.6 | 9 | 24,647.4 |
Dec | 6,770 | 5.4 | 1253.7 | 473 | 2 | 307.7 | 10 | 20,225.1 |
The volume of the H2 storage tank was then determined based on the maximum amount of accumulated hydrogen, which is 30,058.7 N m3. The volume of hydrogen can be lowered by employing a compressor. Using Boyles’ law (Equation (2)), the new volume of hydrogen following compression with the temperature remaining constant is calculated.
As specified in the HySTAT 15-10 electrolyser specifications, the hydrogen is supplied at a pressure
A Wind-H2 system, as illustrated in Figure 5, is proposed, sized and assessed as option (2). The RINA feasibility study (RINA 2021) identified that
Based on the assessment of several potential development areas (PDA) against several criteria (like terrain, wind speed, noise risk, etc.) as seen in Figure 8, the RINA feasibility study concluded that PDA 3 is the most suitable location for installing the new wind turbine capacity [14]. By matching the HydroGlen feasibility study location picture with Google maps, as illustrated in Figures 6 and 7, PDA 3 was found to be at a latitude of 56.913 and a longitude of −2.551.
Using Global wind Atlas, which is a free, web-based application, the wind speed was found by drawing a 3 km by 3 km rectangular on the PDA 3 location using the webpage map [24]. However, the wind speed was normalized as the result display the wind speed index as shown in Figure 8.
The denormalized wind speed was then calculated by multiplying the monthly wind speed index by the location wind speed which was found by the Global wind Atlas to be 9.81 m/s. Equation (3) [25] was then used to transform the obtained monthly wind speed at reference height of 100 m to the equivalent speed at the new wind turbine target height of 50 m as shown in Table 6.
Month | Monthly wind speed index at height 100 m (normalized wind speed) | Location wind speed (m/s) | Monthly denormalized wind speed at height 100 m ( | Monthly wind speed at height 50 m ( |
---|---|---|---|---|
January | 1.21 | 9.81 | 11.8701 | 10.86 |
February | 1.23 | 9.81 | 12.0663 | 11.04 |
March | 1.09 | 9.81 | 10.6929 | 9.78 |
April | 0.87 | 9.81 | 8.5347 | 7.81 |
May | 0.85 | 9.81 | 8.3385 | 7.63 |
June | 0.75 | 9.81 | 7.3575 | 6.73 |
July | 0.78 | 9.81 | 7.6518 | 7.00 |
August | 0.84 | 9.81 | 8.2404 | 7.54 |
September | 0.91 | 9.81 | 8.9271 | 8.16 |
October | 1.03 | 9.81 | 10.1043 | 9.24 |
November | 1.15 | 9.81 | 11.2815 | 10.32 |
December | 1.32 | 9.81 | 12.9492 | 11.84 |
The wind speed time series (from Table 6) was then merged with the wind turbine power curve (yellow curve) shown in Figure 9 to find the available/theoretical wind power at each speed (
Month | Monthly wind speed at 50 m height (m/s) | Power coefficient | Wind power | Capacity factor | Wind production using Equation (5) (kWh) | Monthly residential and commercial demand (kWh) | Monthly wind energy excess after supplying residential and commercial total demand (kWh) | |
---|---|---|---|---|---|---|---|---|
Jan | 10.86 | 0.41 | 722 | 0.35 | 744 | 77,083.60 | 43,810 | 33,273.60 |
Feb | 11.04 | 0.42 | 744 | 0.35 | 672 | 73,495.30 | 35,973 | 37,522.29 |
Mar | 9.78 | 0.47 | 600 | 0.35 | 744 | 73,432.80 | 37,978 | 35,454.80 |
Apr | 7.81 | 0.49 | 300 | 0.35 | 720 | 37,044 | 22,330 | 14,714 |
May | 7.63 | 0.49 | 290 | 0.35 | 744 | 37,002.80 | 23,484 | 13,518.84 |
Jun | 6.73 | 0.49 | 200 | 0.35 | 720 | 24,494.40 | 20,855 | 3,639.40 |
Jul | 7.00 | 0.49 | 228 | 0.35 | 744 | 29,091.90 | 18,533 | 10,558.88 |
Aug | 7.54 | 0.49 | 290 | 0.35 | 744 | 37,002.80 | 19,284 | 17,718.84 |
Sep | 8.16 | 0.49 | 366 | 0.35 | 720 | 45,193.70 | 19,324 | 25,869.68 |
Oct | 9.24 | 0.49 | 500 | 0.35 | 744 | 63,798 | 31,548 | 32,250 |
Nov | 10.32 | 0.46 | 672 | 0.35 | 720 | 77,898.20 | 45,111 | 32,787.24 |
Dec | 11.84 | 0.36 | 790 | 0.35 | 744 | 74,057.80 | 46,100 | 27,957.76 |
Annual | | 364,330 | |
The wind output energy was then calculated using Equation (5) [26]:
Based on industrial standards, the electrolyser size is often selected to be around (1/3) of the wind capacity [15]. This suggests that for the 800-kW wind capacity a 266.6 kW electrolyser is recommended. For better utilization, three units of 80 kW Hydrogenic HySTAT 15-10 electrolysers were therefore chosen.
To size the storage tank, the amount of hydrogen produced by the electrolyser was first calculated based on the surplus wind energy given in Table 7. The previously calculated amount of hydrogen needed for fuelling the farm vehicles was then deducted from the hydrogen produced to find the monthly excess of H2. Finally, the amount of accumulated hydrogen after supplying the vehicles was calculated to size the storage tank accordingly. Table 8 shows the results for those calculations.
Month | Monthly wind energy excess after supplying the residential and the commercial demand (kWh) | Energy consumption of the selected Alkaline electrolyser (kWh/N m3) | Monthly hydrogen production by electrolyser (N m3) | Monthly H2 consumption for one vehicle (N m3) | No of vehicles that can be supplied by the electrolyser each month | Monthly H2 excess after supplying the vehicles (N m3) | No of extra vehicles that can be supplied from the accumulated H2 excess | Monthly accumulated H2 after feeding the 12 vehicles (N m3) |
---|---|---|---|---|---|---|---|---|
Jan | 33,273.608 | 5.4 | 6161.78 | 473 | 12 | 485.78 | 0 | 485.78 |
Feb | 37,522.296 | 5.4 | 6948.57 | 473 | 12 | 1272.57 | 0 | 1758.35 |
Mar | 35,454.8 | 5.4 | 6565.70 | 473 | 12 | 889.70 | 0 | |
Apr | 14,714 | 5.4 | 2724.81 | 473 | 5 | 359.81 | 6 | 169.87 |
May | 13,518.84 | 5.4 | 2503.49 | 473 | 5 | 138.49 | 0 | 308.36 |
Jun | 3,639.4 | 5.4 | 673.96 | 473 | 1 | 200.96 | 1 | 36.32 |
Jul | 10,558.888 | 5.4 | 1955.35 | 473 | 4 | 63.35 | 0 | 99.67 |
Aug | 17,718.84 | 5.4 | 3281.27 | 473 | 6 | 443.27 | 1 | 69.94 |
Sep | 25,869.68 | 5.4 | 4790.68 | 473 | 10 | 60.68 | 0 | 130.62 |
Oct | 32,250 | 5.4 | 5972.22 | 473 | 12 | 296.22 | 0 | 426.84 |
Nov | 32,787.24 | 5.4 | 6071.71 | 473 | 12 | 395.71 | 0 | 822.55 |
Dec | 27,957.76 | 5.4 | 5177.36 | 473 | 10 | 447.36 | 2 | 323.92 |
The size of the storage tank is selected based on the maximum amount of accumulated hydrogen which is 2648.06 N m3. Using Boyle’s law, the volume of the needed tank after compression to 200 bars is:
The levelized cost of energy (LCOE) was calculated for each of the proposed systems. The LCOE for a renewable source with hydrogen storage is given by Equation (6) [15]:
CAPEXRES&H 2, | Capital cost of renewable energy and hydrogen system in year ( |
OPEXRES&H 2, | Operating and maintenance (O&M) cost of renewable system and hydrogen generator in year ( |
Discount rate 3% [30] | |
Project lifetime (typically 20 years) | |
ERes, | Renewable energy output utilised in meeting demands in year ( |
EH 2, | Energy produced in form of H2 in year ( |
O2, | Oxygen produced in year ( |
The CAPEX and OPEX of the PV-H2 system, calculated by adding the costs of all units in the system as shown in Table 10, was found to be £1,332,328 and £5279.59 respectively.
Component | Manufacturer | Source | Number of units | Cost per unit | Total cost |
---|---|---|---|---|---|
CAPEX | |||||
Renesola virtus II 250 W solar PV panels | Renesola | Go GreenMan Solar | 2392 | £94.74 [31] | £226,618 |
PV installation | 2392 | £53 [32] | £126,776 | ||
SolarLake 15000TL-PM 15 kW inverter | SamilPower | Renugen | 31 | £2195.07 [33] | £68,047.17 |
HyStat-15 electrolyser | Hydrogenics | — | 3 | £66,964.76 [34] | £200,894.28 |
Compressor system | Pure Energy Centre | Pure Energy Centre | 1 | £100,000 [14, 35] | £100,000 |
Hydrogen storage tank | BOC | BOC | 12 | £4999.37 [23] | £59,992.42 |
Hydrogen vehicle refuelling station | — | IRENA | 1 | £550,000 [14] | £550,000 |
OPEX | |||||
O&M of PV panels | 1% of PV panel cost [36] | £2266.18 | |||
O&M of electrolyser | 1.5% of electrolyser capital cost [37] | £3013.41 |
The CAPEX and OPEX of the Wind-H2 system, calculated by adding the costs of all units in the system as shown in Table 11, was found to be £2,055,894 and £43,141 respectively.
Component | Manufacturer | Source | Number of units | Cost per unit | Total cost |
---|---|---|---|---|---|
CAPEX | |||||
Enercon E53/800 Wind Turbine | Enercon | Go GreenMan Solar | 1 | £1,003,200 | £1,003,200 |
Wind turbine installation | £196,800 [14] | £196,800 | |||
HyStat-15 electrolyser | Hydrogenics | 3 | £66,964.76 [34] | £200,894.28 | |
Compressor system | Pure Energy Centre | Pure Energy Centre | 1 | £100,000 [14, 38] | £100,000 |
Hydrogen storage tank | BOC | BOC | 1 | £4999.37 [23] | £4999.37 |
Hydrogen vehicle refuelling station | IRENA | 1 | £550,000 [14] | £550,000 | |
OPEX | |||||
O&M of wind turbine | 4% of wind turbine cost | £40,128 | |||
O&M of electrolyser | 1.5% of electrolyser cost [37] | £3013.41 |
To calculate the LOCE of PV-H2 and wind H2 system, the ERes,
The above values were then substituted in Equation (4) along with the CAPEX and OPEX to find the LCOE for each of the proposed systems which were found to be 0.3 £/kWh for the PV-H2 system and 0.4 £/kWh for the Wind-H2 system.
From Table 4 results, it can be found that the proposed PV system can meet just 35% of the load demand (123,990 kWh/364,350 kWh) because most of the demand is not during the sun availability. Only 21% (123,990 kWh/581,820 kWh) of the total solar energy is being utilized, resulting into around 77% (446,370 kWh/581,820 kWh) of excess in solar energy production (see Table 12). This solar energy excess is stored in the form of green H2 to be used as clean fuel for the farm’s vehicles. On analyzing Table 5 results, it can be found that the proposed HES system was able to meet all the 12 vehicles’ transportation demand from March until September. From September to December, all the transportation demand was met after using the stored accumulation of hydrogen from previous months. Although not all the 12 vehicles were supplied in January and February, the accumulated hydrogen after one year will be sufficient to cover these months in the following year. Therefore, it can be concluded that PV-H2 system is almost capable of meeting 100% of the transport fuel needs by green H2. Furthermore, after supplying all vehicles, there are still extra H2 that can be either sold as a commodity or can be converted back to electricity using a fuel cell to feed more residential and commercial demands during the lack of solar energy thus minimising grid imports.
PV-H2 system | Wind-H2 system | |
---|---|---|
ERes (kWh) | 123,990 | 364,330 |
EH 2 (kWh) | 133,911 | 85,580 |
(solar energy excess ∗ H2 RTE) | (wind energy excess ∗ H2 RTE) |
In comparison, it can be concluded from Tables 7 that the proposed Wind-H2 system can meet almost 100% of the farm’s residential and commercial demands by wind energy, and 44% (285,265.35 kWh/649,595.35 kWh) of the wind production is converted to H2 for vehicles fuelling.
Figure 10 was then constructed using columns 3 and 4 from Tables 4 and column 7 from Table 7 that represent the monthly solar production, the monthly residential and commercial demands, and the monthly wind production respectively to investigate the ability of the PV-H2 and the Wind-H2 in meeting residential and commercial demands while producing green H2 fuel from the excess to powering the transport demands. It can be seen that the wind generation profile closely matches the monthly residential and commercial demand, given the higher demand in the winter and lower demand in the summer. On the other hand, the Solar profile can be seen out of synchronism with the residential and commercial demand but allows plenty of green H2 production to be used as fuel for meeting the transport needs as well as being sold as commodity.
From Table 10, it was found that LCOE for the PV-H2 system is 0.3 £/kWh lower than that of the Wind-H2 system of 0.4 £/kWh. This implies that the PV-H2 system is more cost competitive than the wind-H2 system at current prices, which might be attributed to the wind turbine’s high capacity and O&M costs. Reducing the CAPEX and/or increasing the round-trip efficiency of the hydrogen system in the future will allow reducing the LCOE of renewable-H2 systems to become more financially competitive with other technologies such as natural gas, which is 213 US/MWh [39], this equates to 0.16 £/kWh.
In order to assess in more detail, the potential of the PV-H2 system (given that a PV-H2 system was not considered by RINA), a Simulink model is developed in this section to simulate the generation of the proposed PV system and its overall utilization in the electrolyser to produce green H2. An electrochemical model has been used to model the electrolyser’s green hydrogen production. The developed electrochemical model gives more accurate results as it considers the hydrogen production as function of the current output from the proposed PV system modelled using MATLAB/Simulink, as seen in Figure 11.
The amount of hydrogen produced by the electrolyser is also calculated using the electrolyser’s energy consumption calculation method in order to compare the result to the Simulink model results.
The proposed 598-kW photovoltaic capacity was modelled using the MATLAB/Simulink PV Array block. The PV Array block is a five-parameter model that employs a light-generated current source (IL), a diode, series resistance (Rs), and shunt resistance (Rsh) to simulate the modules’ irradiance and temperature-dependent
Diode current (A) | |
Thermal voltage (V) | |
Diode voltage (V) | |
Diode saturation current (A) | |
Diode ideality factor, a number close to 1.0 | |
Boltzmann constant = 1.3806 × 10−23 J⋅K−1 | |
Electron charge = 1.6022 × 10−19 C | |
Cell temperature (K) | |
Number of cells connected in series in a module |
The PV array block was then modified to model the proposed 598 kW solar capacity. The module was set as RenSola America J250M to be similar to the farm currently installed PV. The PV array block’s module parameters were then set to correspond to the PV module parameters listed in the PV datasheet [41]. The parallel strings and series modules were set to 104 and 23, respectively, as determined from the PVsyst simulation results shown in Figure 3. Based on this, the following values resulted for the output voltage and current:
Figure 14 shows the output
To ensure the PV array provides maximum power at all times, an MPPT (Maximum power point tracking) controller with incremental conductance technique block was integrated with the PV array to account for variables such as fluctuating irradiance (sunlight) and temperature [42]. The file exchange of the MPPT block can be found here [43].
An Alkaline electrolyzer electrochemical model, as shown in Figure 15, is developed in this section.
The production rate of hydrogen in an alkaline electrolyser is related to the input current as given by Equation (12) [44]:
The flow rate obtained from Equation (12) is then converted from moles to N m3/h to facilitate comparison in the results section. The H2 production in N m3/h (
A MATLAB/Simulink Hydrogen Production block was then developed as seen in Figure 16 using Equations (12) to (14).
However, the H2 production rate obtained by using Equation (12) is independent of the input voltage which is incorrect since water can only split into hydrogen and oxygen when the cell voltage is sufficient to activate this [45]. Thus, the cell voltage is expressed as shown in Equation (15) [44].
Symbol | Description | Unit | Value |
---|---|---|---|
Area of electrode | m2 | 0.25 | |
Overvoltage parameter of electrode | V | 0.185 | |
Empirical overvoltage parameter of electrode | A−1 m2 | 1.002 | |
Empirical overvoltage parameter of electrode | A−1 m2 °C | 8.424 | |
Empirical overvoltage parameter of electrode | A−1 m2 °C2 | 247.3 | |
Electrolyte ohmic resistive parameter | 𝛺m2 | 8.05 × 10−5 | |
Electrolyte ohmic resistive parameter | 𝛺m2 °C−1 | −2.5 × 10−7 |
Equation (15) was then used to develop the Electrolyser V-I block components, as shown in Figure 17.
Finally, the boundary condition was added to the developed Hydrogen Production modelling block to allow hydrogen production only when cell voltage is greater than
To simulate the electrolyser’s hourly H2 output, the hourly solar irradiance and hourly ambient temperature data are required as input to the developed PV array block. To obtain this data, the PVsyst software was used to generate this hourly data for the 598 kW PV system. PVsyst generates various types of irradiance data, including global irradiation in the horizontal plane (GlobHor), global irradiation in the collector plane (GlobInc), and “Effective” global irradiation on collectors. GlobInc was the one utilised as input for the Simulink PV array block because refers to the total irradiance received (“viewed”) by the tilted plane [16].
Table 15 demonstrates some of the findings of the estimated H2 output derived based on the electrolyser’s energy consumption (5.4 kWh/N m3), versus the estimated H2 output from the developed Simulink model which is based on the electrolyser electrochemical model.
Month/day/hour | Hourly solar irradiance (W/m2) (GlolbInc using PVsyst) | Hourly T Amb (°C) (using PVsyst) | Hourly DC power from PV (kW) (using PVsyst) | Hourly DC PV power (kW) (using Simulink) | Estimated H2 output based on the electrolyser’s energy consumption and the PVsyst hourly excess power (N m3 /h) | Estimated H2 output from the developed Simulink model (N m3 /h) |
---|---|---|---|---|---|---|
Jan/31/13:00 | 74.38 | 10.5 | 41.07 | 44.75 | 7.60 | 8.05 |
Feb/28/13:00 | 564.91 | 12.86 | 311.54 | 358 | 57.69 | 60 |
Mar/15/13:00 | 522.50 | 8.54 | 296.02 | 337 | 54.81 | 56.17 |
Apr/15/13:00 | 298.69 | 8.47 | 171.25 | 186.32 | 31.71 | 34.25 |
May/15/13:00 | 719.95 | 14.01 | 389.71 | 435 | 72.17 | 79 |
Jun/15/13:00 | 683.62 | 17.35 | 366.75 | 421 | 67.91 | 75 |
Jul/15/13:00 | 384.51 | 24.90 | 215.43 | 227 | 39.89 | 42 |
Aug/15/13:00 | 790.33 | 18.79 | 414.15 | 417 | 76.69 | 87 |
Sep/15/13:00 | 326.98 | 15.7 | 182.96 | 201.75 | 33.88 | 35.85 |
Oct/31/13:00 | 159.85 | 8.25 | 91.13 | 97.5 | 16.87 | 17.66 |
Nov/30/13:00 | 303.96 | 7.28 | 171.72 | 191 | 31.8 | 34.05 |
Dec/15/13:00 | 42.76 | 7.5 | 21.81 | 22 | 4.04 | 4.5 |
Dec/31/13:00 | 192.39 | 9.1 | 107.701 | 119.6 | 19.94 | 21.9 |
The effect of the irradiance and temperature variations on the solar power generation and H2 production were investigated over different hours of the years. Figure 18 shows the solar power produced from the proposed PV system when using PVsyst simulation versus the Solar power production on using the Simulink model; it can be observed that solar production in summer days is higher than that of winter days as irradiance decreases. It can also be observed that the estimated power generation on using Simulink is higher than that using PVsyst, and this is due to the fact that Simulink model does not account for losses such as soiling loss and wiring loss.
To minimize the number of parameters involved in the electrolyser simulation analysis, a simpler Faraday efficiency equation with non-temperature-dependent coefficients (Equation (13)) was utilized. A deeper examination of the hydrogen generation results on accounting for the varying electrical current (the orange bar in Figure 19) reveals that the previously predicted H2 based on based on the electrolyser’s energy consumption (5.4 kWh/N m3) correspond well with the modelled one and thus validate the calculations undertaken in the design process. However, the H2 generated by the electrolyser electrochemical model was found slightly more than that produced based on the electrolyser’s energy consumption (calculation method), this is because the Simulink model does not account for losses such as soiling loss and wiring loss.
Two combinations of Renewable-H2 energy systems were proposed, sized and assessed in this paper to identify the scenario that meets most of Glensaugh farm residential and commercial demands with green energy as well as providing green H2 fuel for the farm transport demand. It was found that the proposed grid-connected PV-H2 system is capable of feeding almost 100% of Glensaugh transportation fuel requirements with green hydrogen and 35% of Glensaugh residential and commercial demands with clean solar energy, with the gird meeting the remaining demands. The proposed wind-H2 system was found capable of meeting most of the residential and commercial demands by clean wind energy in addition to around 44% of the transport demand by green H2.
The results obtained for the PV-H2 system is due to the fact that most of the residential demands are during evenings resulting into a lot of solar daytime energy converted to green H2 for powering the vehicles. The H2 accumulated after feeding all vehicles could be either sold as a commodity or converted back to electricity by using a fuel cell to feed the residential and commercial demands during the shortage of solar energy, thus improving the overall system efficiency. The results obtained for the wind-H2 system, on the other hand, is because the wind energy profile is very closely matched with the residential and commercial demand, and thus most wind energy is consumed by this demand, leaving only a small amount of wind energy excess to meet the green H2 fuel transport demands.
It was also found that the levelized cost of energy of the proposed PV-H2 system is 0.3 £/kWh, more cost competitive than that of the wind-H2 of 0.4 £/kWh. On the other hand, the reduction in carbon footprint achieved on using Wind-H2 system was found higher that of PV-H2 system. Given that this paper is focusing on assessing the PV-H2 system, a Simulink model was developed for the PV-H2 system, and it utilized an electrolyser electrochemical model to model the system green hydrogen production.
Implementing the proposed Renewable-H2 systems in Scottish farms will provide an excellent opportunity in maximizing the implementation of green energy and green H2 to meet the current Scottish Government’s goal of reaching at least 5 GW of renewable and hydrogen generation by 2030 and at least 25 GW of hydrogen production by 2045 [38]. Developing and promoting such ecologically sustainable green concepts will be a crucial step in transforming Scottish Farms into an energy-efficient and environmentally sustainable communities.
For future work, it is recommended to investigate a renewable energy mix scenario, with wind and solar employed together to reduce the grid import and meet the transport fuel demand by green H2. Wind energy may be sized to meet the commercial and residential demand since its generation profile closely matches the monthly consumption. At the same time, a solar-hydrogen system could be employed to meet the transportation demand fuel needs and the excess in green H2 to be sold as a commodity. Furthermore, the system can be investigated with a grid connection import/export capacity to facilitate additional revenue through grid export.
It is also recommended to investigate the sale of the O2 produced by the electrolyser as a commodity to increase the system economic efficiency (overall competitive value) of the system.
To allow investigating a large number of data such as examining the variation of irradiance every hour of the year, it is suggested to use a strain generator rather than a constant block to allow input a series of irradiance data. This could be more efficient. Further research into the impact of thermal transients in electrolysers could also be investigated.
The authors declare no conflict of interest.
The data and feasibility reports used for this case study have been provided by the James Hutton Institute (JHI).
Written by
Article Type: Research Paper
Date of acceptance: May 2023
Date of publication: June 2023
DOI: 10.5772/geet.16
Copyright: The Author(s), Licensee IntechOpen, License: CC BY 4.0
© The Author(s) 2023. Licensee IntechOpen. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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