Fuel Cell Electric Vehicle as a Power Plant: Techno-Economic Scenario Analysis of a Renewable Integrated Transportation and Energy System for Smart Cities in Two Climates

Featured Application

Fuel cell electric vehicles (FCEVs) balancing electricity demand and supply through vehicle-to-grid (V2G) have the potential to become the world’s biggest virtual power plants. Especially in regions with large seasonal effects in electricity generation and demand, FCEV2G could replace large-scale fast-reacting back-up power plants facing low capacity factors.

Abstract

Renewable, reliable, and affordable future power, heat, and transportation systems require efficient and versatile energy storage and distribution systems. If solar and wind electricity are the only renewable energy sources, what role can hydrogen and fuel cell electric vehicles (FCEVs) have in providing year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban areas in European climates? The designed system for smart urban areas uses hydrogen production and FCEVs through vehicle-to-grid (FCEV2G) for balancing electricity demand and supply. A techno-economic analysis was done for two technology development scenarios and two different European climates. Electricity and hydrogen supply is fully renewable and guaranteed at all times. Combining the output of thousands of grid-connected FCEVs results in large overcapacities being able to balance large deficits. Self-driving, connecting, and free-floating car-sharing fleets could facilitate vehicle scheduling. Extreme peaks in balancing never exceed more than 50% of the available FCEV2G capacity. A simple comparison shows that the cost of energy for an average household in the Mid Century scenario is affordable: 520–770 €/year (without taxes and levies), which is 65% less compared to the present fossil situation. The system levelized costs in the Mid Century scenario are 71–104 €/MWh for electricity and 2.6–3.0 €/kg for hydrogen—and we expect that further cost reductions are possible.

1. Introduction

The Paris Agreement, which pledges to keep global warming well below 2 degrees Celsius above pre-industrial levels and to limit the increase to 1.5 degrees Celsius, needs a boost [1]. The highest emitting 100 cities, or so-called urban areas, account for 18% of the global carbon footprint [2,3]. Therefore, cities are increasingly focusing on and shaping the trajectory and impacts of climate change and air quality [4,5,6,7,8,9]. The C40 Cities Climate Leadership Group connects more than 90 of the world’s largest cities, representing over 650 million people and one-quarter of the global economy [10]. C40 is focused on tackling climate change and driving urban action that reduces greenhouse gas emissions and climate risks.

More than 54% of the world’s population lives in urban areas (cities, towns, or suburbs) [11]; in Europe, this is almost 75% [12]. Energy consumption is growing rapidly in urban areas [7]. A smart, integrated, and combined centralized and decentralized approach is essential for creating sustainable urban energy systems [12,13,14,15,16]. By coupling energy sectors through electrification and hydrogen [17,18,19,20], major problems related to the intermittent nature of many renewables, such as wind and solar, can be solved, and synergies benefiting all sectors can be created [21,22,23,24,25,26]. Both the Hydrogen Council and the World Energy Council support and leverage the enabling role of hydrogen and fuel cell solutions around the world [27,28].

Inspired by the concept of a “Hydrogen Economy” [29,30,31,32,33,34,35], the authors designed a 100% renewable, reliable, and cost-effective energy system for power, heat, and transportation for smart urban areas in Europe [36]. The system covers the annual energy consumption of the main energy functions in urban areas, namely road transportation and, in residential and services buildings, space heating and cooling, hot water, lighting, and electrical appliances. The heating and transportation system is all-electric in its final energy use. Heating is by means of electric powered heat pumps and transportation by hydrogen fuel cell-powered electric vehicles; no other technologies are used for these applications. Local solar and large-scale wind electricity provide all renewable energy, together with hydrogen and electricity, as intermediate energy carriers. Fuel cell electric vehicles (FCEVs) provide transportation and energy distribution and balance the intermittent solar and wind electricity production by converting renewable hydrogen into electricity. This concept of grid-connected FCEVs providing grid services when parked—also known as vehicle-to-grid (V2G)—has already been demonstrated on a small scale with one V2G-ready commercial Hyundai ix35 FCEV and an all-electric house [37,38]. FCEVs providing power to electric appliances (also referred to as vehicle-to-load, V2L), small grids, or homes (vehicle-to-home, V2H) [39] are being developed by several FCEV manufacturers [40,41,42,43], although none of them have reported connecting an FCEV to a low-voltage national AC grid.

European regions have different climatic conditions [44] (including supplement of [44]), which have an impact on the energy consumption of buildings [45,46,47], especially for space heating and cooling [48,49,50,51,52]. In addition, the different average building and household types, sizes, and compositions in European countries also impact the energy consumption in buildings [53,54,55]. Vehicle ownership and the average number of kilometers driven per year determine the final road transportation energy consumption, which varies among European countries [56,57]. The regional availability and magnitude of solar and wind energy differ significantly across Europe [58,59,60,61,62]. Wind and solar power generation across European regions exhibits hourly, diurnal, and strong seasonal behavior [63,64], as well as intra-annual [65,66,67] or decadal/multi-decadal variability [68,69,70,71,72].

Average European statistics, average hourly energy consumption, and production profiles for an average day during an average year were used to calculate system component sizes, including safety margins [36]. Rough estimations, such as several days without sun or wind power, were used to define the required back-up and balancing power and energy storage sizes [36]. Hourly modeling will capture the biggest variations for larger systems and is, therefore, more adequate to dimension flexibility requirements [73]. Modeling on an hourly basis and tailoring to geographical energy demand and climatic conditions will give a better insight into hourly, diurnal, and seasonal energy production and consumption mismatch, in other words, the energy storage requirements, and the system design and its related cost.

The question is: can solar and wind electricity, together with fuel cell electric vehicles and hydrogen as an energy carrier, provide year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban/city areas in two different European climates?

To address this question, this study performed a techno-economic scenario analysis and design for a 100% renewable, reliable, and cost-effective energy system. The energy systems provide year-round power, heat, and transportation for smart urban areas. The total system cost and energy performance are compared for two different technology development scenarios and two European climate zones for five years (2012–2016). Analyzing the system over five years will give insight into the inter-annual variability of the cost and energy performance. To our knowledge, no such comprehensive study has been performed up to now. Many studies and pilot projects investigate stand-alone and national grid-connected renewable energy systems using hydrogen as energy storage and stationary fuel cells for the reconversion of the stored hydrogen [74,75,76,77,78]. Some studies use the produced hydrogen for transportation [79,80,81,82,83,84] or solely use the fuel cell in the vehicle as an electric generator [85,86,87,88,89] without considering hydrogen production. Integration of FCEVs through V2G into a local electricity network for operating in island mode, emergency power, or balancing local renewables has been done mostly on a smaller or a very large scale [90,91,92,93,94]. Some studies include a cost analysis [95,96,97], do not compare with a future scenario with improved cost and efficiency (scenario and trend analysis) [98], are dependent on the grid electricity, do not compare different climate zones nor include inter-annual variability [99], or do not include seasonal hydrogen storage [98]. The authors of [100] focus on a small-scale system in a specific region without considering hydrogen transportation, although includes a future cost scenario. The authors of [101] look into urban areas and road transportation in different regions in different Japanese climate zones, but the described system is not 100% renewable and does not include economics or consider V2G electricity services with FCEVs. A study [102] performs a future techno-economic 100% renewable energy analysis, including multi-annual variability for multiple large national and trans-national regions. Various energy sectors are coupled, where hydrogen is used as energy storage and road transportation fuel along with several other energy carriers and storage techniques. However, here too, but also here V2G electricity services with FCEVs are not considered.

2. Materials and Methods

2.1. Approach

The techno-economic scenario analysis of a fully autonomous renewable and reliable integrated transportation and energy system for a smart city area is performed in four steps:

• Location selection, system design and dimensioning, technological and economic characterization for the system components in two technology development scenarios (Section 2.2).
• Developing a calculation model for hourly simulation of all energy flows for multiple years and sizing of system components, for two different European climates zones in two technology development scenarios (Section 2.3).
• Calculating the cost of energy for the two technology development scenarios in two climate zones based on the sizing and economic characterization of the system components (Section 2.4).
• Inter-annual variability analysis of wind and solar energy production on the cost of energy (Section 2.5).

2.2. Location Selection, System Design and Dimensioning, System Components, and Scenarios

2.2.1. Location Selection

The following criteria apply to the selection of two locations in different European climate zones. They are listed in order of significance (Figure 1):

• Close to a large European functional urban area [12] or city with at least 50,000 inhabitants, preferably in one of Europe’s five most populous countries [103].
• Located in different European climate zones, as defined by the Köppen–Geiger climate classification [44] and supplement of [44].
• Located in a region with underground salt formations suitable for underground gas storage [104].
• One location should have a relatively high, and one location should have a relatively low solar global irradiation compared to European measurements [59,60,62].
• One location should have a relatively low annual precipitation compared to European measurements [105].
• All required statistical and hourly modeling data should be available for the selected locations (wind velocity, solar irradiation, precipitation, building energy consumption, etc.).

The urban area of Hamburg in Germany and Murcia in Spain were selected, see Figure 1. Hamburg is the cooler, windier, and rainier area; Murcia is the warmer, sunnier, and dryer area. In Appendix A.1,

Table A1. Key figures, characterizing the climate of the two locations, Hamburg and Murcia.

Key Figures	Locations
	Hamburg, Germany	Murcia, Spain
No. of inhabitants of urban area (# x 1,000,000) [231,232,233]	1.8	1.5
Climate zone (Köppen–Geiger) (-) [44,234,235,236,237]	temperate oceanic (Cfb)	hot semi-arid (BSh)
Weather station data
Weather station height above sea-level (m) 1 [238,239]	11	61
Weather station location 1 [238,239]	53°38′ N, 9°59′ E	38°0′ N, 1°10′ W
Weather data 2012–2016 means and standard deviations	µ (CV)	µ (CV)
Wind speed at 10 m above ground (m/s) 1 [238,242]	4.1 (4.3%)	3.9 (4.3%)
Solar global horizontal irradiation (kWh/m2/year) [238,240]	1020 (4%)	1855 (1.8%)
Precipitation (l/m2/year) [238,240]	735 (4.9%)	255 (24%)
Air temperature (°C) [238,240]	9.9 (5.9%)	19.1 (2.8%)
Daily maximum air temperature (°C) [238,240]	13.4 (5.1%)	25.5 (2.2%)
Daily minimum air temperature (°C) [238,240]	6.3 (8.7%)	13.7 (4.4%)
Heating Degree Days (°C·day/year) 2 [238,240]	3066 (6.5%)	854 (16%)
Cooling Degree Days (°C·day/year) 2 [238,240]	101 (24%)	1245 (6.9%)

¹ Wind speeds measured at the nearby Almeria Airport weather station are used [242,243] because, at the Murcia weather station [239,240], wind speeds are economically less favorable for wind turbines. The non-wind weather data of the Murcia weather station is more complete than that of the Almeria Airport weather station. The Almeria Airport weather station is 21 m above sea-level and has the following coordinates: 36°50′ N, 2°21′ W. The Murcia weather station five-year average (2012–2016) and coefficient of variation of the average annual wind speed at 10 m above ground are 2.4 m/s and 2.8%, respectively. ² Calculated with a base temperature of 18 °C as in [50,51,127].

shows key figures characterizing Hamburg in Germany and Murcia in Spain and their climates.

2.2.2. System Design and Dimensioning

The smart city area energy and transportation system is designed in such a way that it fulfills the following design requirements:

• uses only electricity and hydrogen as energy carriers and is all-electric in end-use
• uses only hydrogen as seasonal energy storage and fuel to power all road vehicles
• can be applied to an average European city area and is a scalable design
• can be operated in a network of multiple smart city areas and renewable hydrogen and electric energy hubs or centers [32,106,107,108,109,110]
• can be integrated into existing infrastructure and buildings
• is not dependent on an in-urban area underground hydrogen pipeline transportation network
• uses abundant renewable energy sources in Europe: local solar and large-scale wind only
• is independent of high and medium voltage electricity grids, natural gas, and district heating grids or the expansion of these.

By applying the design requirements, the integrated system design of the smart city area has the following seven major elements and functional energy performance and conversion steps (Figure 2 and

Table 1. Components, energy, and water flow in the smart city area (Figure 2).

Label	Components	Label	Components
S	Local solar electricity and hydrogen production	TT	Hydrogen tube trailer transportation
S1	Solar electricity system	TT1	Tube trailers
S2	Water purification (reverse osmosis)	TT2	Trailer tractors
S3	Pure-water tank	FCEV	Fuel cell electric vehicle-to-grid (V2G)
S4	Electrolyzer	FCEV1	Fuel cell in fuel cell electric vehicle (FCEV) for V2G use
S5	Hydrogen purifier	FCEV2	V2G infrastructure
S6	Low-pressure compressor		Energy and water flows
W	Large-scale and shared wind hydrogen production	E	Electricity
W1	Shared wind turbine park	EW	Electricity from wind
W2	Water purification (reverse osmosis)	ES	Electricity from solar
W3	Pure water tank	EDC	Direct consumption solar electricity
W4	Electrolyzer	Esurp	Surplus solar electricity
W5	Hydrogen purifier	EB	Electricity consumption in buildings
W6	Low-pressure compressor to SHS	EV2G	Electricity from hydrogen via V2G
HFS	Hydrogen fueling station (HFS)	EHFS	Electricity consumption HFS
HFS1	High-pressure compressor	ESHS	Electricity consumption SHS
HFS2	High-pressure stationary storage	H	Hydrogen
HFS3	Chillers	HW	Hydrogen from wind electricity
HFS4	Dispensers	HS	Hydrogen from surplus solar electricity
SHS	Large-scale and shared seasonal hydrogen storage (SHS)	HHFS	Dispensed hydrogen at HFS
SHS1	Shared seasonal hydrogen storage (SHS)	HRoad	Hydrogen consumed by road vehicles
SHS2	Low-pressure compressor	HV2G	Hydrogen consumed for V2G electricity
		H2O	Water
		H2OW	Water for hydrogen production via wind
		H2OS	Water for hydrogen production via solar

):

• Local solar electricity and hydrogen production (orange): Local rooftop solar electricity and rainwater collection, purification, and storage systems (S1–S3) produce solar electricity (E_S) and pure water (H₂O_S). A part of the solar electricity is directly consumed (E_DC) in buildings and other sub-systems. The remaining surplus solar electricity (E_S) is used with purified water (H₂O_S) in the hydrogen production, purification, and compression system (S4–S6) for filling tube trailers (TT1) with hydrogen (H_S).
• Fuel cell electric vehicle-to-grid, building electricity consumption, and smart grid control (yellow): The smart electric grid is managed by a controller, which connects all buildings, grid-connected FCEVs (FCEV1and2), the hydrogen fueling station (HFS1-HFS4), solar electricity and hydrogen production (S1–S6), and the tube trailer filling station (SHS2) at the seasonal hydrogen storage (SHS1). The directly consumed solar electricity (E_DC) is divided amongst the all-electric residential and services sector buildings (E_B), HFS (E_HFS), and SHS (E_SHS) electricity consumption. Any shortage of electricity is met by the electricity produced from hydrogen (E_V2G) through parked (at home or in public or commercial spaces) and V2G connected FCEVs (FCEV1and2).
• Hydrogen tube trailer transportation (grey): Tube trailers (TT1) towed by tube trailer tractors (TT2) transport hydrogen from either the local solar hydrogen production or the SHS to the HFS, or from the local solar hydrogen production to the SHS.
• Hydrogen fueling station (blue): Hydrogen from tube trailers is further compressed (HFS1) and stored at high pressure (HFS2). A chiller (HFS3) cools the dispensed hydrogen (H_HFS), including sufficient dispensers (HFS4) to provide hydrogen for both road transportation (H_road) and V2G (H_V2G) use.
• Road transportation (purple): A fleet of road transportation FCEVs, namely passenger cars, vans, buses, trucks, and tractor-trailers.
• Large-scale and shared wind hydrogen production (green): A large-scale wind turbine park (W1) that is not located near or in smart city areas is shared with other smart city areas and renewable hydrogen hubs and consumers. All wind electricity (E_W) is used with purified water (H₂O_W) from local surface water or seawater in hydrogen production (W4), purification (W5), and compression system (W6), which includes a water collection and purification system (W2 and W3). The hydrogen produced (H_W) is stored in a large-scale underground seasonal hydrogen storage (SHS1).
• Large-scale and shared seasonal hydrogen storage (red): Large-scale underground seasonal hydrogen storage (SHS1), including a tube trailer filling and emptying station (SHS2).

The system design configuration is sufficiently flexible to allow other renewable energy sources, if present, to be used (e.g., offshore wind, biomass, or hydropower). However, this was not analyzed in this study. The smart urban area operates in a network of multiple smart urban areas, hydrogen fueling stations, other renewable hydrogen and electric energy hubs, and other hydrogen and electricity consumers (not part of this study). Hydrogen is produced within the smart urban areas from local surplus solar electricity and at large-scale wind parks. These large-scale wind parks, as well as the large-scale seasonal underground hydrogen storage, are jointly owned by the smart urban areas and other hydrogen consumers. Hydrogen is transported via tube trailers from the smart urban areas to hydrogen fueling stations, or the large-scale and shared underground seasonal hydrogen storage [104,111].

The size of a Hamburg- or Murcia-based illustrative smart city area for this study was determined using the dispersion of supermarkets and gas stations in Europe, Germany, and Spain. In the EU 28 countries, for every 2000 households, there is one medium-sized supermarket and one gas station [55,112,113,114]. In Germany and Spain, there is one gas station per 2600 and 1700 households, respectively [55,113,114]. Thus, 2000 households are a good indicator for dimensioning the smart integrated city area; see

Table 2. Characteristics of the modeled smart city areas.

Characteristics	Quantity
	Hamburg, Germany	Murcia, Spain
Common parameters (based on European statistics)
Gas stations (#) [114]	1	1
Retail food shops (#) [112]	1	1
Households and dwellings 1 in smart integrated city (#) [54]	2000	2000
Local parameters (based on national statistics)
People (#) [118]	4310	5083
Passenger cars (#) [57,119,120]	2364	1846
Vans (#) 2 [57,119,120]	115	356
Trucks (#) [57,119,120]	27 3	31 4
Tractor-trailers [57,119,120]	10	12 4
Buses (#) [57,119,120]	4.1	4.5
Floor area of residential buildings (m2) 5,6 [54]	183,200	183,550
Floor area of services buildings (m2) 6 [55]	92,940	38,330
Roof area available for solar electric modules (m2) [125,126]	56,000	56,000 7

¹ Assuming that only one household lives in a dwelling. ² German data [119,120] defines a van as a vehicle with a weight of less than 3.5 tons; the Odyssee database [57] used for Spain defines a van as a vehicle with a weight of less than 3 tons. ³ Including commercial vehicles of 3.5–6.0 tons. ⁴ No distinction is made between trucks and tractor-trailers in [57]; therefore, the same relation between the number of trucks and tractor-trailers as in Germany is used. ⁵ Based on the surface area of permanently occupied dwellings [54]. ⁶ The floor area represents the floor space that needs to be heated, cooled, or illuminated [127]. ⁷ For ease of comparison, the value is kept equal to the Hamburg case.

(common parameters). This hydrogen fueling station will serve a similar vehicle population as current gasoline stations [115,116]. Total capital cost per capacity for large HFS (≥1500 kg/day) is lower than for smaller HFS [117], thus also defining the minimum size of this scalable and illustrative smart city area.

On average, 2000 households in Germany and Spain correspond to, respectively, 4310 and 5083 people, with 2364 and 1846 passenger cars and 156 and 410 other vehicles, according to German and Spanish national statistical data [55,57,113,118,119,120]. See Table 2 (local parameters).

The floor area of residential and services buildings is derived from national statistical data and scaled to 2000 households: German and Spanish average household floor area S_-hh is, respectively, 91.60 and 91.78 m² [54,55]. Residential and service sector roofs will be used for solar electricity systems and rainwater collection [121,122,123,124]. Solar electricity systems are installed on all technically suitable roof areas: 9 m² per person on residential buildings and 4 m² per person on service sector buildings area [125,126]. Facçades are not considered.

For ease of comparison between Hamburg and Murcia, the roof area available for solar electric modules and rainwater collection in Murcia is based on the Hamburg parameters.

2.2.3. Technological and Economic Characterization of System Components in Two Scenarios

The technological and economic characteristics of the selected components will be listed according to the latest available figures in two technology development scenarios. The two scenarios, in different time frames, can be characterized as follows:

• The Near Future scenario uses current state-of-the-art renewable and hydrogen technology and current energy demand for buildings and transportation. It is an all-electric energy system, which means space heating is done using heat pumps, meeting the present heat demand for houses and buildings. Only commercially available hydrogen technologies are used. For all systems, including hydrogen technologies, current technology characteristics and cost figures are used. The Near Future scenario presents a system that could be implemented in 2020–2025.
• In the Mid Century scenario, a significant reduction in end-use energy consumption is assumed. Hydrogen and fuel cell technologies have become mature with mass production and performing on the cost and efficiency targets projected for 2050. Also, for all the other technologies, such as solar, wind, and electrolyzers, the learning curves are taken into account.

The detailed technical and cost-related parameters of the system components are presented in Appendix A.2

Table A2. Specific energy consumption SEC or production SEP (kWh_e/kg H2) of the energy conversion processes in the smart city area for both scenarios and locations.

Label (See Figure 2 and Table 2)	Energy Conversion Processes	Specific Energy Consumption/Production (SEC/SEP)
		Near Future [kWhe/kg H2]	Mid Century [kWhe/kg H2]
W4 and S4	Alkaline water electrolysis [246,255,256,257,258,259]	44.4–50.0 1,2	42.6–47.7 1,2
S5 and W5	Hydrogen purification [260,261]	1.3	1.1
S6	Compressor at local solar (500 bar) [115,263,264,265,266]	3.0 3	1.8 3
W6	Compressor at wind turbine park to SHS (180 bar) [115,263,264,265,266]	1.9 3	1.0 3
HFS1	Compressor at HFS ([30–500]–875 bar) [115,263,264,265,266]	0.5–3.1 1	0.4–2.5 1
SHS2	Compressor at SHS (180–500 bar) [115,263,264,265,266]	0.8	0.6
HFS3	Chiller [277,278]	0.20	0.15
S2 and W2	Reverse Osmosis—rainwater/surface water [272,273,274]	0.006	0.006
FCEV1	FCEV hydrogen to electricity [275,276,279]	20.3	23.6

¹ Direct current electrical consumption [259] at 15–100% load in the Near Future scenario and 10–100% load in the Mid Century scenario. ² 15 and 30 bar hydrogen outlet pressure in, respectively, the Near Future and the Mid Century scenario. ³ 15 and 30 bar hydrogen inlet pressure in, respectively, the Near Future and the Mid Century scenario.

and

Table A3. Economic parameters of the smart city area components for the Near Future and the Mid Century scenario. IC_i = installed capital cost; OM_i = annual operational and maintenance cost expressed as an annual percentage of the installed investment cost; LT = lifetime.

Label	Subsystems and Components	Near Future			Mid Century
		ICi	OMi [%/year]	LTi [years]	ICi	OMi [%/year]	LTi [years]
	Solar and wind electricity production
S1	Solar electricity system [134,280,281]	725 €/kWp	2.8%	25	440 €/kWp	2.3%	30
W1	Wind turbine park [132,133,280,281,282,283,284]	975 €/kW	2%	25	800 €/kW	1%	25
	Hydrogen production and compression
S4 and S5	Alkaline electrolyzer, including H2 purification at solar system [136,285,286,287,288]	575 1 €/kW	2.5% 2	203	200 €/kW	2.5% 2	30 3
W4 and W5	Large-scale alkaline electrolyzer, including H2 purification at wind turbines [136,285,286,287,288]	480 1 €/kW	4.2% 2	203	200 €/kW	4.4% 2	30 3
S6	Compressor at solar system [267,289]	10,030/9630 €/kg H2/h 4	4%	15	3445/3325 €/kg H2/h 4	2%	15
W6	Compressor at wind turbine park to SHS [267,289]	8250/8915 €/kg H2/h 4	4%	15	3515/6260 €/kg H2/h 4	2%	15
	Hydrogen transport
TT1	Tube trailer storage [136,190,267]	830 €/ kg H2	2%	30	510 €/ kg H2	2%	30
TT2	Trailer tractors [136,190,267]	160,000 €/ tractor	61/65% 5	8 6	160,000 €/tractor	63/62% 5	8 6
	Hydrogen fueling station (HFS)
HFS1	Compressor at HFS [267,289]	8375/8820 €/kg H2/h 4	4%	10	3630/3670 €/kg H2/h 4	2%	10
HFS2	Storage HFS 875 bar [263,290,291]	920 €/kg H2	1%	30	575 €/ kg H2	1%	30
HFS3	Chiller units [263,289]	143,875 €/kg H2/min	2%	15	118,520 €/kg H2/min	2%	15
HFS4	Dispensers units [260,261,263,289]	91,810 €/unit	1.1%	10	72,890 €/unit	0.9%	10
	Fuel cell electric vehicle to grid (FCEV2G)
FCEV1	Replacement of fuel cell system in FCEV for V2G use [36,38,275,276,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307]	3970 €/100 kW	5%	4100 h 7	2650 €/100 kW	5%	8000 h 7
FCEV2	Smart grid, control, and V2G infrastructure [134]	6400 €/4-point dischargers	5%	15	3200 €/4-point dischargers	5%	15
	Seasonal hydrogen storage (SHS)
SHS1	SHS plant (3733 ton H2 cavern) [104]	107,000,000 €/plant	0.5%	30	107,000,000 €/plant	0.5%	40
SHS2	Compressor at SHS [267,289]	3470/3825 €/kg H2/h 4	4%	15	1560/1665 €/kg H2/h 4	2%	15
	Water purification and storage
S2 and W2	Water purification [272]	1,200 €/m3/day	4.8%	25	1,200 €/m3/day	4.8%	25
S3 and W3	Pure-water tank [121,122,123,124]	120 €/m3	0.33%	50	120 €/m3	0.33%	50

¹ The size of the electrolyzer at the solar system is smaller than 10 MW and, therefore, still has a higher IC in the Near Future scenario than the wind connected electrolyzer. ² The OM includes stack replacement [36], which is calculated based on an average of 13 and 24 operating hours per day for, respectively, solar and wind-powered electrolyzers. ³ System LT is 20 and 30 years, and stack economic operating lifetime is 80,000 and 90,000 h in, respectively, the Near Future and the Mid Century scenario. ⁴ Figures for, respectively, Hamburg/Murcia. The compressor IC costs are dependent on, for example, mass flow capacity, discharge pressure, and pressure ratio [36]. ⁵ Figures for, respectively, Hamburg/Murcia. The OM is defined by the amount of hydrogen transported [36] and differs per location and scenario. ⁶ Lifetime of 8 years based on approximately 75,000 km/year; for the cost calculation [36], only the required driven kilometers are included, i.e., there are no “idling” costs. ⁷ Lifetime = economic lifetime in driving operating hours. The economic lifetime calculation when using the FCEV for both driving and V2G is explained in [36].

. The technology selection for the system components and sizing methods is based on the component description in [36].

2.3. Calculation Model and Hourly Simulation

Figure 3 shows the simplified simulation scheme of the calculation model, consisting of five major steps that are executed hourly for an entire year. A detailed description and input data are described in Appendix B,

Table A4. Specific annual energy consumption SEC_B (kWh/m²/year) per energy consumption category and total annual energy consumption E_B (MWh/year) in buildings for the residential and the services sector for the Hamburg- and Murcia-based smart city areas at Present and the Near Future and Mid Century scenarios.

Energy Consumption Category	Specific Energy Consumption Buildings SECB [kWh/m2/Year]
	Hamburg, Germany			Murcia, Spain
	Present Situation	Near Future	Mid Century	Present Situation	Near Future	Mid Century
Residential sector
Space heating [54]	131.1 1	29.2	6.6	13.8 1	2.7	0.7
Space cooling [51]	0.9 b	0.9	0.3	30.2 2	30.2	9.1
Water heating [54]	32.3 1	24.5	16.2	16.4 1	13.7	8.2
Cooking [54]	7.8 1	7.4	7.4	7.7 1	6.7	6.7
Lighting [54]	2.9 2	2.3	0.6	4.9 2	3.9	1.0
Electrical appliances [54]	18.9 2	18.9	18.9	25.7 2	25.7	25.7
Total	194.0	83.2	49.8	98.6	82.9	51.3
Services sector
Space heating [55]	80.3 1	18.3	12.1	48.3 1	11.4	7.2
Space cooling [50]	3.4 2	3.4	1.0	43.0 2	43.0	12.9
Water heating [55]	8.3 1	7.3	4.1	7.7 1	6.4	3.8
Cooking [55]	13.1 1	11.5	11.5	4.1 1	3.5	3.5
Lighting 3 [55]	28.8 2	23.0	5.8	71.5 2	57.2	14.3
Electrical appliances [55]	39.7 2	39.7	39.7	49.0 2	49.0	49.0
Total	173.6	103.3	74.2	223.5	170.5	90.8
	Total annual Energy consumption buildings EB [MWh/year]
	Hamburg			Murcia
	Present Situation	Near Future	Mid Century	Present Situation	Near Future	Mid Century
Residential	35,541	15,241	9127	18,105	15,225	9422
Services	16,130	9597	6893	8567	6535	3479
Total	51,671	24,838	16,020	26,672	21,760	12,901

¹ Fuel mix [54,55] and useful thermal energy fractions, electricity: 0.97, heat: 0.95, gas: 0.80, oil: 0.72, coal: 0.65, wood 0.55 [127]. ² electrical energy. ³ Including the electricity used for public lighting [320].

,

Table A5. Specific energy consumption savings for the Near Future and Mid Century scenarios compared to the Present Situation.

Energy Consumption Category	Specific Energy Consumption Savings Compared to Present Situation
Residential Sector	Near Future	Mid Century
Space heating [309,310,311,312,313]	71% 1	95%
Space cooling [51,309,310,313]	0%	70%
Water heating [54,55,127,311,313]	24/16% 2	50%/50% 3
Cooking [54,55,127]	5/13% 2	5/13% 2
Lighting [314]	20%	80%
Electrical appliances [317,318,319]	0% 4	0% 4
Services sector
Space heating [309,310,311,312,313]	71% 1	85%
Space cooling [50,309,310,313]	0%	70%
Water heating [54,55,127,313]	12/17% 2	50%/50% 3
Cooking [54,55,127]	12/15% 2	12/15% 2
Lighting [314,315,316]	20%	80%
Electrical appliances [317,318,319]	0% 4	0% 4

¹ Savings due to heat pump usage, conversion of the Present Situation fuel mix [54,55] with the useful thermal energy fractions (electricity: 0.97, heat: 0.95, gas: 0.80, oil: 0.72, coal: 0.65, wood 0.55) [127] and a heat pump COP of 3.5 [309,310,311,312] into the electrical equivalent energy. ² Hamburg/Murcia savings due to the electrification of existing primary fossil energy demand for thermal purposes. Conversion of the Present Situation fuel mix [54,55] with the useful thermal energy fractions (electricity: 0.97, heat: 0.95, gas: 0.80, oil: 0.72, coal: 0.65, wood 0.55) [127] into the electrical equivalent energy for the thermal demand [127]. ³ Hamburg/Murcia combined savings due to the application of electrification, heat pump usage, solar thermal heating, and other heat recuperation techniques. ⁴ Although energy savings will be significant, the net savings will be zero due to an increased number and use of electrical appliances, home automation, and IT services [317,318,319].

and

Table A6. The average annual distance driven d and Near Future and Mid Century scenario-specific energy consumption of transport (SEC_T) for van, truck, tractor-trailer, and bus type FCEVs.

	Specific Energy Consumption Transportation SECT [kg H2/100 km]		Average Annual Distance Driven d [km/year/vehicle]
Vehicle Type	Near Future [36]	Mid Century [36]	Hamburg, Germany [119,120]	Murcia, Spain [57]
Passenger car	1.0	0.6	13,728	12,535
Van	1.3	0.9	19,388	17,704 a
Truck	4.6	3.7	31,870 b	37,077
Tractor-trailer	6.9	5.5	96,211	151,513
Bus	8.6	6.9	55,883	147,398
	Hamburg, Germany		Murcia, Spain
Annual hydrogen consumption Hroad	Near Future	Mid Century	Near Future	Mid Century
Hydrogen [kg H2 /year]	479,909	316,129	545,192	381,732
Hydrogen Energy c [MWhHHV/year]	18,913	12,459	21,486	15,044

^a No data is present for vans in [57]; therefore, the same relation between the average annual distance driven of cars and vans as in Germany is used. ^b Including commercial vehicles 3.5–6.0 tons. ^c Based on a higher heating value (HHV) of 39.41 kWh/kg H₂.

.

• Electricity consumption and production (yellow; see description in Appendix B.1)
• Road transport hydrogen demand (blue; see description in Appendix B.2)
• Electricity and hydrogen hourly balance (red; see description in Appendix B.3)
• Hydrogen tube trailer and tractor fleet (grey; see description in Appendix B.4)
• Wind hydrogen production and seasonal storage balance (green; see description in Appendix B.5)

Two sets of energy balances are calculated, on both an hourly and an annual basis (Figure 3 in red and green) for both hydrogen and electricity energy carriers. Energy consumption takes place in buildings and for mobility. Energy production is by roof-top solar and wind turbines and covers all energy consumption needs, taking into account all efficiencies of the different energy conversion and storage processes.

The amount of rooftop area available for solar electricity systems is fixed in both scenarios and locations for ease of comparison of the system performance between the two climates. The amount of installed wind capacity is the degree of freedom in the calculation model and completes the annual energy balance.

The system is simulated for five years using weather data from 2012 to 2016, which results in varying hourly electricity production consumption profiles, as well as electricity production per installed capacity. For ease of comparison between the years, the annual building electricity demand is kept constant.

2.4. Calculating the Cost of Energy

Three components of the cost of energy (CoE) will be calculated for each location in both scenarios.

• Smart city area total system cost of energy (TSCoE_SCA) in euros per year (Appendix C.1).
• System levelized cost of energy for electricity (SLCoE_e) in euros per kWh and for hydrogen (SLCoE_H) in euros per kg of hydrogen (Appendix C.2).
• Cost of energy for households (CoE_hh) in euros per household per year (Appendix C.3).

2.4.1. Smart City Area Total System Cost of Energy

The TSCoE_SCA in euros per year is the sum of the total annual capital and operation and maintenance costs TC_i (€/year) of the total number of components (n) in the smart city area. The TCi of an individual component is calculated using the annual capital cost CCi (€/year) and operation and maintenance cost OMCi (€/year); cost formulas used are listed in Appendix C.1.

The cost analyses are in constant 2015 euros. An exchange rate of 0.88 USD to 1 EUR is used as in [36]. The website [128] is used to convert all USD values to USD₂₀₁₅ values. A weighted average cost of capital WACC of 3% is used from Appendix A of [102].

2.4.2. System Levelized Cost of Energy

The system levelized cost of energy, for either electricity SLCoE_e (€/kWh) or hydrogen SLCoE_H (€/kg H₂), is calculated by allocating a share of the TSCoE_SCA (€/year) related to either electricity TSCoE_SCA,e (€/year) or hydrogen consumption TSCoE_SCA,H (€/year). These shares are then divided by either the annual electricity consumption EC_e (kWh/year) or the annual hydrogen consumption EC_H (kg H₂/year), resulting in, respectively, the SLCoE_e (€/kWh) or the SLCoE_H (€/kg H₂). The cost formulas used are listed in Appendix C.2.

2.4.3. Cost of Energy for Households (Without Taxes and Levies)

Cost of Energy for a single household CoE_hh (€/hh/year), here calculated without taxes and levies, consists of the cost of energy for the building energy CoE_hh,B (€/hh/year) and the transportation energy CoE_hh,T (€/hh/year). The cost formulas used are listed in Appendix C.3.

2.5. Inter-Annual Variability Analysis

Multiple years of hourly solar global irradiation data and hourly average wind speed data recorded at both locations will be used to analyze the inter-annual variability and its impact on the smart city area total system cost of energy (TSCoE_SCA).

3. Energy Balance Results and Discussion

3.1. Annual Energy Balance Results

Key energy balance parameters for FCEV2G, solar electrolyzer, and SHS usage for Hamburg and Murcia in the Near Future and Mid Century scenarios are summarized in

Table 3. Key energy balance parameters for FCEVs through vehicle-to-grid (FCEV2G), solar electrolyzer, and SHS usage for Hamburg and Murcia in the Near Future and Mid Century scenarios.

Location	Hamburg		Murcia
Scenario	Near Future	Mid Century	Near Future	Mid Century
FCEV2G
Fleet average FCEV2G hours at 10 kW (hours/year)	880	440	670	330
Annual electricity production (MWh)	20,794	10,388	12,247	6112
Max. power (MW)	7.60	3.91	7.72	2.75
Date max. power (dd-mm)	3 January	4 January	12 June	3 September
Max. FCEV2Gs (#) / Max fleet percentage (%)	760/32.1	391/16.5	772/41.8	275/14.9
FCEV Driving
Average driving time passenger car (hours/year)	310	310	280	280
Solar electrolyzer
Capacity factor (%)	4.1	8.6	7.8	15.5
Annual electricity consumption (MWh)	2680	12,428	5658	7648
Max. absorbed power (MW)	7.43	16.47	8.26	19.05
Date max. power (dd-mm)	27 July	27 July	23 February	23 February
SHS
Max. H2 storage (×1000 kg H2)	191	163	122	92
Max. H2 storage relative to typical SHS 3733 ton H2 (%)	5.1	4.4	3.2	2.5
No. similar smart city areas served by one typical SHS (#)	20	23	30	40
Date max. storage (dd-mm)	4 September	29 September	29 May	6 October
Date min. storage (dd-mm)	24 January	15 March	3 February	17 February
Annual hydrogen production (×1000 kg H2)	1504	753	1149	640
Max. H2 storage relative to annual hydrogen production (%)	13	22	11	14
Max. H2 storage relative to annual electricity production (%)	8.9	15	6.7	9.3

. Detailed background figures that serve as input to Table 3 can be found in Appendix D (Figure A1, load duration curves, Figure A2, hourly electricity balance for an entire year, Figure A3, SHS storage level, and monthly hydrogen flows).

The annual energy balances of Hamburg and Murcia in the Near Future and Mid Century scenarios are shown in Figure 4 and Figure 5.

The key energy balance parameters and annual energy balances of the years 2012–2015 show similar outcomes. Several major trends can be seen when looking at the FCEV2G, wind and solar electricity production, direct consumption of solar electricity, and seasonal hydrogen storage.

• Reliable electricity supply can be realized at all times, as extreme FCEV2G peaks never exceed 50% of the car fleet. Maximums of 760 and 772 cars, 32% and 42% of the car fleet in Hamburg and Murcia in the Near Future scenario, are reduced to 391 and 275 cars, 17% and 15% of the car fleet in the Mid Century scenario. The above maximums are extreme outliers, and values close to these occur for only a few hours per year (Figure A1).
• In the Mid Century scenario, FCEV2G usage is comparable to driving. In the Near Future scenario, the fleet average FCEV2G hours are 880 h/year compared to 440 h in Mid Century scenario at 10 kW/car output for Hamburg. For Murcia, this is 670 h and 330 h. The Mid Century scenarios’ FCEV2G hours are similar to the average driving hours for passenger cars: 310 and 280 h/year for, respectively, Hamburg and Murcia.
• The 87% higher solar electricity output in the Mid Century scenario in both locations results in less required external wind-to-hydrogen production to close the energy balance. This, together with more than a 30% reduction in building and road transportation energy consumption, and improvements in energy conversion processes, results in reductions of 70% and 90% of wind electricity production for, respectively, Hamburg and Murcia.
• The 490% higher solar hydrogen production in the Mid Century scenario in both locations compared to the Near Future scenario. Due to lower building electricity consumption and higher solar electricity production, there is more solar surplus electricity for hydrogen production. In Hamburg, solar electrolyzer power consumption always peaks in the summer’s time, whereas, in Murcia, solar electrolyzer power consumption peaks in winter (Figure A2).
• The 40% and 56% higher coverage of electricity consumption with direct solar electricity production in the Mid Century scenario in, respectively, Hamburg and Murcia compared to the Near Future scenario. Due to higher solar radiation and lower building and system electricity consumption, a higher percentage can be met directly with solar electricity. Nighttime electricity consumption has to be met with FCEV2G electricity production.
• The 15%–25% lower seasonal hydrogen storage requirements in the Mid Century scenario due to a better match of higher solar electricity production and lower building electricity demand compared to the Near Future scenario. For Hamburg, the maximum storage content of hydrogen occurs in the fall for both scenarios, whereas, in Murcia, this period shifts from spring to fall. The minimum storage content occurs in winter for both locations and scenarios. In the Mid Century scenario, a typical salt cavern [104] (Table A3) could serve approximately 23 similarly operating smart city areas in Hamburg and 40 Murcia smart city areas.
• The 40% lower seasonal hydrogen storage and FCEV2G requirements in Murcia compared to Hamburg, in all scenarios. In the Mid Century scenario, solar electricity alone is almost able to supply all of Murcia’s energy needs for buildings and road transportation (despite its 21% higher consumption of road transportation hydrogen; Appendix B.2). If approximately 15% more solar panels were to be installed, either on facades, in public spaces, or nearby solar fields, the entire energy demand could be met with solar energy. The reason for the lower SHS and FCEV2G requirements in Murcia compared to Hamburg is the better match in time (daily and seasonal) between solar electricity production and building electricity consumption. In addition, Murcia also has a relatively higher solar electricity output and lower building demand compared to Hamburg. In the Mid Century scenario in Murcia, the same solar system produces 73% more electricity than in Hamburg.
• Relatively, 70% and 30% more seasonal hydrogen storage is needed in the Mid Century scenario for, respectively, Hamburg and Murcia. Even though absolute hydrogen and electricity production, energy consumption, and seasonal hydrogen storage decrease in the Mid Century scenario, the higher dependency on solar electricity production increases the seasonal effect. Hence, there is an increase in relative seasonal hydrogen storage compared to the annual hydrogen and electricity production in the Mid Century scenario.

3.2. FCEV2G Usage and Electricity Balance Discussion and Results

Figure 6 provides further insight into seasonal and hourly FCEV2G usage. The FCEVs needed for producing V2G electricity (# cars left y-axis, % of car fleet right y-axis) are shown by means of boxplots for every hour of the day. For both locations and scenarios, usage is shown separately for both the colder winter period (in blue, left, 1 October–31 March) and the warmer summer period (in orange, right, 1 April–30 September).

• Reliable electricity supply can be realized at all hours of the day, as extreme FCEV2G peaks never exceed 50% of the total car fleet. The number of cars needed to balance the system peaks in the morning (06:00–09:00) and the late afternoon/early evening (16:00–20:00) and correspond to driving rush hours. These peaks are extreme outliers, and values close to these occur for only a small number of hours per year (Figure A1).
• In Murcia, virtually no cars are required during daylight hours. This is valid in all scenarios and seasons, except for some moments. In Hamburg, this is only the case in the summer period, for both scenarios.
• Hamburg faces a greater seasonal, and Murcia a greater day-night storage challenge, particularly in the Mid Century scenario. In Hamburg, peak FCEV2G electricity production occurs in the winter period, whereas, in Murcia, the production is highest in both the summer and the winter period (see also Figure A2).
• On average, less than 22% and 13% of all cars are required during peak hours (17:00–19:00), in, respectively, the Near Future and the Mid Century scenario (black crosses).
• In Murcia, the mean FCEV2G usage is highest in summer. In Hamburg, the mean FCEV2G usage is highest in winter. Electricity demand in Murcia is dominated by space cooling, whereas, in Hamburg, it is dominated by space heating. In the Mid Century scenario, the mean daily FCEV2G usage in the winter period in Hamburg is 7.3% of all cars, whereas, in Murcia, the figure is 4.6%. In summer, this is 3% of all cars in Murcia and 2.7% of all cars in Hamburg.
• Relatively more FCEV2G electricity is produced outside regular driving hours (20:00–06:00) [129] than during regular driving hours (06:00–20:00). In the Mid Century scenario, up to 60% of all FCEV2G electricity production in Murcia takes place during the 10 night hours (20:00–06:00); the remaining 40% FCEV2G electricity is produced during the 14 regular driving hours (06:00–20:00). In Hamburg, in the Mid Century scenario, the figures are 50% during the 10 regular driving hours and 50% during the 14 regular driving hours.

4. Cost of Energy Results and Discussion

4.1. Total System Cost of Energy

The total system cost of energy per year TSCoE (k€/year) in the Near Future and Mid Century scenarios for Hamburg and Murcia is shown in Figure 7. The subsystems are grouped into hydrogen and electricity. The average component installed capacities and their total annual costs (TC_i) are listed in Appendix E

Table A7. Installed component capacities (Q) and component total annual costs (TC_i) in the smart city area Hamburg and Murcia in the Near Future and Mid Century scenario, 2012–2016 average (μ) and coefficient of variation (CV).

		Hamburg								Murcia
		Near Future				Mid Century				Near Future				Mid Century
Label	Subsystems and Components	Qi		TCi		Qi		TCi		Qi		TCi		Qi		TCi
		µ	CV [%]	µ [k€/yr]	CV [%]	µ	CV [%]	µ [k€/yr]	CV [%]	µ	CV [%]	µ [k€/yr]	CV [%]	µ	CV [%]	µ [k€/yr]	CV [%]
	Electricity production
S1	Solar electricity system [MWp]	11.20	-	690		18.67	-	600	-	11.20	-	690	-	18.67	-	600	-
W1	Wind turbines park share [MW]	23.36	8.3	1760	8.3	7.26	10.1	390	10.1	18.60	6.5	1400	6.5	1.43	16.4	80	16.4
	Hydrogen production and compression
S4 and S5	Alkaline electrolyzer—solar [MW]	6.20	5.7	330	4.5	14.63	4	220	3.7	6.97	4.8	360	3.8	16.23	5.5	240	4.9
W4 and W5	Alkaline electrolyzer share—wind [MW]	21.95	8.3	1150	8.3	6.95	10.1	130	10.1	17.48	6.5	920	6.5	1.36	16.4	30	16.4
S6	Compressor—solar [kg H2/h]	125	5.7	150	3.7	308	4	110	2.6	140	4.8	170	3.1	342	5.5	120	3.6
W6	Compressor share—wind [kg H2/h]	441	8.3	450	5.5	146	10.1	50	6.7	351	6.5	390	4.3	29	16.4	20	10.5
	Hydrogen transport
TT1	Tube trailer storage [kg H2]	4620	9.5	270	9.5	4400	0	160	0	4400	-	260	0	4400	-	160	-
TT2	Tractor-trailers [#]	1.9	6.6	200	1.7	1.1	9.5	120	2.3	1.4	8.7	160	1.9	1.3	-	140	0.6
	Hydrogen fueling station (HFS)
HFS1	Compressor [kg H2/h]	489	4	640	4.6	240	3.4	120	2.4	343	9.6	480	8.3	172	5.1	90	8.4
HFS2	Stationary storage 875 bar [kg H2]	5705	4.6	320	4.6	2715	3.2	100	3.2	4051	10.7	230	10.7	1954	5.5	70	5.5
HFS3	Chiller capacity [kg H2/min]	9.5	4.6	140	4.6	4.5	3.2	60	3.2	6.7	10.7	100	10.7	3.3	5.5	40	5.5
HFS4	Dispensers units [#]	29.2	4.6	340	4.6	4.5	3.2	40	3.2	20.7	10.7	240	10.7	3.3	5.5	30	5.5
	FCEV2G
FCEV1	Replacement of FC system in FCEV due to V2G use only [#100 kW systems]	755	7.5	1190	1.2	389	3.3	230	0.8	774	4.3	750	1.7	265	2.2	140	1.2
FCEV2	Smart grid, Control, and V2G infrastructure [# 4-point dischargers]	189	7.5	160	7.5	97	3.2	40	3.2	193	4.3	170	4.3	66	2.2	30	2.2
	Seasonal hydrogen storage (SHS)
SHS1	Share of SHS plant (3733 ton H2 cavern) [%]	4.1	14.7	250	14.7	3.8	7.9	200	7.9	3.9	12.9	230	12.9	2.1	12.2	110	12.2
SHS2	Tube trailer filling (compressor) at SHS [kg H2/h]	488	4.2	210	8.5	239	3.4	40	12.5	341	9.7	160	6.3	171	5	30	3.2
	Water purification and storage
S2	Reverse osmosis—solar [m3/day]	95	8.3	12	8.3	31	10.1	4	10.1	75	6.4	10	6.4	6.2	16.4	1	16.4
W2	Reverse osmosis—wind [m3/day]	6.7	3.8	0.8	3.8	20	4.5	2.6	4.5	7.6	5.7	1	5.7	21	2.1	2.6	2.1
S3	Pure-water tank—solar [m3]	13	3.8	0.1	3.8	41	4.5	0.2	4.5	15	5.7	0.1	5.7	42	2.1	0.2	2.1
W3	Pure-water tank—wind [m3]	189	8.3	1	8.3	63	10.1	0.3	10.1	150	6.4	0.8	6.4	12	16.4	0.1	16.4
	Total			8290	4			2620	2.2			6710	3.7			1920	2.7

and serve as input for Figure 7. The following major trends can be observed when comparing both locations and scenarios.

• The 70% reduction in TSCoE in the Mid Century compared to the Near Future scenario for both locations. Higher efficiencies, lower final energy consumption, and increased favorable match between solar electricity production and final energy consumption significantly reduce installed capacities, thus costs. Economies of scale also reduce both installed capital and operation and maintenance costs.
• The 20–30% lower TSCoE for Murcia compared to Hamburg for both scenarios. For Murcia, the TSCoE is 1.9 million euros/year in the Mid Century scenario, whereas, for Hamburg, it is 2.6 million euros/year. The reason for this is the lower final transportation and building electricity demand and lower storage and reconversion requirements.
• Variations in TSCoE from year to year are very small, 2.2–4.0% (coefficient of variation CV in Table A7 in Appendix E). This can be explained by the variations in daily and annual wind and solar electricity production, as well as the varying mismatch between solar electricity production and consumption. Seasonal hydrogen storage has relatively higher cost variations (8–12%) in comparison to other components, as the SHS is responsible for coping with all the above-mentioned variations.
• The cost of hydrogen components in the Mid Century scenario drops up to 75%. For both locations, in the Near Future scenario, the hydrogen components represent about 70% of the TSCoE; this reduces to 63% on average. As hydrogen technology is relatively new, economies of scale have a bigger impact on future cost reductions than on solar and wind electricity technology. In addition, the increase in solar output reduces storage requirements.
• Hydrogen transportation, seasonal hydrogen storage, and the solar system are the only components that share in the total costs’ relative increase compared to all other components. This is because the cost reductions for these components are relatively lower compared to the other components. The relatively higher use of seasonal hydrogen storage in the Mid Century scenario compared to the annual hydrogen production (see Section 3.1) is another contributing factor.

4.2. System Levelized Cost of Energy

The levelized and system levelized cost of electricity and hydrogen for Hamburg and Murcia in the Near Future and the Mid Century scenario are listed in

Table 4. Levelized (LCoE) and system levelized cost of energy (SLCoE) parameters for Hamburg and Murcia in the Near Future and Mid Century scenarios.

		Hamburg		Murcia
Levelized Cost Parameter	Involved Cost (TCi) of Components (i) (Table A7 Appendix E)	Near Future	Mid Century	Near Future	Mid Century
LCoEe,S [€/MWh]	S1	68	31.7	37.6	17.5
LCoEe,W [€/MWh]	W1	23.5	16	26.5	18.2
LCoEH,S [€/kg H2]	S1–6	13.7	2.9	6.5	1.5
LCoEH,W [€/kg H2]	W1–6	2.3	1.2	2.7	1.4
LCoEH,W&S [€/kg H2]	W1–6 and S1–6	2.7	1.7	3	1.5
System levelized cost parameter
SLCoEH [€/kg H2]	W1–6, S1–6 (surplus), TT1and2, SHS1and2, HFS1–4,	4.9	3	5.2	2.6
SLCoEe,V2G [€/MWh]	W1–6, S1–6 (surplus), TT1and2, SHS1and2, HFS1–4, FCEV1and2	307	154	332	139
SLCoEe [€/MWh]	W1–6, S1–6, TT1and2, SHS1and2, HFS1–4, FCEV1and2	239	104	179	71.2

. The values represent the average of the five simulated years. The levelized cost of energy (LCoE) and SLCoE parameters are calculated using the total costs (TC_i, Appendix E) of the various components and the corresponding energy flows (Figure 4 and Figure 5). Detailed calculation methods can be found in Appendix C and [36].

• The system levelized cost of energy of electricity (SLCoE_e) is 239 and 176 €/MWh in the Near Future scenario for, respectively, Hamburg and Murcia, and 104 and 71 €/MWh in the Mid Century scenario. The SLCoE_e is calculated by summing the costs of solar and FCEV2G electricity for buildings and dividing it by the total building electricity consumption. The total costs of solar electricity for buildings are calculated by multiplying the solar electricity consumption of buildings (Figure 4 and Figure 5) by the levelized cost of energy of solar electricity (LCoE_e,S). The total FCEV2G electricity costs are calculated by multiplying the FCEV2G electricity for buildings by the system levelized cost of energy of FCEV2G electricity (SLCoE_e,V2G).
• All SLCoE_e reduce by approximately 60% in the Mid Century scenario compared to the Near Future scenario. Also, in Murcia, the SLCoE_e is about 30% lower compared to Hamburg. In Murcia, a larger part of the building load can be directly covered by cheap and abundant solar electricity (even for hydrogen production) in both scenarios. As a result, less hydrogen production, storage, dispensing, and FCEV2G electricity are required.
• The levelized cost of energy of hydrogen from surplus solar electricity (LCoE_H,S in €/kg H₂) in this system is always higher than the levelized cost of energy of hydrogen from wind electricity (LCoE_H,W in €/kg H₂). The levelized cost of energy of hydrogen (LCoE_H,W&S) before transportation and storage is based on hydrogen from both wind and solar. Even in Murcia, in the Mid Century scenario, the cost of solar electricity (LCoE_e,S) is lower than the cost of wind electricity LCoE_e,W. The reason for this is that a significantly higher capacity factor is achieved when the electrolyzer is connected to the wind turbine than to the solar electricity system, which only uses surplus solar electricity peaks.
• The system levelized cost of energy of hydrogen (SLCoE_H) is 70–80% higher than the combined levelized cost of energy of hydrogen from solar and wind (LCoE_H,W&S). The SLCoE_H includes the costs of hydrogen transportation by tube trailers, seasonal and fueling station storage, and dispensing on top of the solar and wind electricity costs, and the electrolyzers and low-pressure compressors, which is only the case for the LCoE_H,W&S.

4.3. LCoE and SLCoE Comparison with Other Studies

Studying “100% renewable energy systems” is relatively new [130], and no integrated transportation and energy systems are the same. Comparing the SLCOE_e with other 100% renewable energy systems should be taken as a general indication since there are many differences; for example, differences in geographical locations, renewable energy sources, energy carriers, storage technologies, and simulation criteria, such as energy self-sufficiency ratios or cost input parameters. Despite such differences, we can, to a certain extent, compare some subsystem costs, onshore wind and solar electricity, stored and dispensed hydrogen, and all-time available system electricity costs, including daily and seasonal storage.

• Onshore wind electricity costs (LCoE_e,W) are relatively low in comparison with other studies. Near Future scenario 24–27 €/MWh compared to 30–50 €/MWh for 2025 [131], and Mid Century scenario 16–18 €/MWh with 20–35 €/MWh for 2050 [131]. There are three reasons for this. First, the exclusion of grid connection costs of 11.5% [132,133] in this study, because of the direct coupling between the wind turbine and the electrolyzer. Second, the use of a lower WACC (3%) compared to other studies (3.5–10%) [131]. Third, the placement of wind turbines on sites with good wind conditions, resulting in good onshore wind capacity factors (33–38%).
• Rooftop solar electricity costs (LCoE_e,S) are comparable to the average small rooftop and utility-scale solar electricity costs, also known as community-scale or large rooftop. Near Future scenario costs of 38–68 €/MWh are similar to 20–90 €/MWh [134,135] in 2025, and Mid Century scenario costs of 18–32 €/MWh to 15–44 €/MWh [134] in 2050. The aforementioned values from the literature have similar global horizontal irradiation, although higher WACC (4–5%) [134,135].
• Stored and dispensed hydrogen costs (SLCoE_H) are similar or lower compared to other studies. Near Future scenario costs of 4.9–5.2 €/kg H₂ are similar to the 4–7 €/kg H₂ according to studies by the Fuel Cell Hydrogen Joint Undertaking (FCH JU) and United States Department of Energy (US DoE) [136,137,138,139]. The SLCoE_H in the Mid Century scenario of 2.6–3.0 €/kg H₂ is slightly lower than the US DoE targets of dispensed hydrogen (3.3–3.9 €/kg H₂) [140]. The major reasons for this are the higher electricity and expensive electrolyzer costs assumed by the US DoE.
• System electricity costs (SCLoE_e) are similar to or lower than those in other studies on 100% renewable energy systems, including energy and transportation. The Near Future scenario SCLoE_e of 179–239 €/MWh is lower compared to the transportation and energy system of the United States National Renewable Energy Laboratory (NREL) [3]. The difference can be explained by the system’s smaller scale, higher, and older component cost figures, and the use of stationary fuel cells instead of FCEV2G technology. The Mid Century scenario SLCoE-_e of 71–104 €/MWh is close to the SLCoE_e of 88 €/MWh for an average European smart city area, excluding seasonal hydrogen storage [36]. Several hydrogen electricity reconversion pathways in the north of Germany have been designed and evaluated for the year 2050, including underground seasonal hydrogen storage [141]. The study reports higher values of 176–247 €/MWh, although it confirms that the costs are dominated in all pathways by the costs of purchasing electricity [141]. The authors of [102] and [142] report similar values of 75–85 €/MWh and 100 €/MWh for 100% renewable and self-sufficient energy systems in 2050. Although they have similar system electricity costs, there are several differences: [102] and [142] use different storage technologies simultaneously, include more sectors (industry, agriculture, fishing, and forestry) and renewable energy sources, and either simulate for entire countries (Germany and Spain) [102] or cities in a different continent (North America) [142].

4.4. Cost of Energy for Households (Without Taxes and Levies)

Total system costs or system levelized energy costs do not represent the combined effect of energy-saving measures, higher efficiencies, and decreased costs. Therefore, the cost of energy for an average household CoE_hh (€/hh/year) is introduced as an example. To put the designed system into perspective, a comparison with today’s household energy costs would be interesting to make. This, however, is not as straightforward as it seems.

The developed system and the technologies used are very different from today’s fossil-based energy and transportation system. Cities today are not self-sufficient: They import energy from both the national and the international power and fuel network. These national and international electricity and fuel supply chain networks also come at a cost. This, however, falls outside the scope of this study.

The analyzed size of this system is very small; one could compare it to a neighborhood within these big urban areas or a very small village. In addition, only the building and the road transportation sector are analyzed and integrated here. Increasing the system size and combining several different sectors would create more integration opportunities and reduce costs. For example, the equipment could be shared to avoid underutilization.

Environmental and health savings and welfare creation (e.g., jobs) [143] compared to the present fossil system are difficult to express in costs for this specific and small-scale system. In the present situation, taxes and levies on energy can represent a great part of the energy costs for household consumers, but future estimates of taxes and levies are not within the scope of this study.

Summarizing, it is very difficult to make a fair cost comparison. Nevertheless, a very simple energy cost comparison for an average household is shown below, without any taxes or levies. The present fossil situation is compared with the designed 100% renewable system in the Near Future and the Mid Century scenarios. Additional background data for the present situation can be found in Appendix F.

The cost of energy for a single household CoE_hh (€/hh/year) consists of the cost of energy for the building energy CoE_hh,B (€/hh/year) and the transportation energy CoE_hh,T (€/hh/year); see

Table 5. The annual cost of energy for households (CoE_hh) without taxes and levies for the Present, Near Future, and Mid Century scenarios in Hamburg and Murcia.

	Hamburg			Murcia
Annual Cost of Energy for Households (Without Taxes and Levies)	Present	Near Future	Mid Century	Present	Near Future	Mid Century
Building CoEhh,B [€/hh/year]	1050	1820	480	1120	1360	340
Transportation CoEhh,T [€/hh/year]	460	790	290	350	570	180
Total CoEhh [€/hh/year]	1510	2610	770	1470	1930	520

. The Near Future scenario CoE_hh shows an increase compared to the present situation, although not by several magnitudes. For Murcia, the increase is only 30% in the Near Future scenario. This shows that even though new hydrogen technologies are used, Near Future scenario costs can come close to the present situation costs and thus give reason to explore further. We should bear in mind that the Near Future scenario only changes technologies (e.g., electric water heating and heat pumps for heating) and has no significant energy savings as in the Mid Century scenario. However, in reality, the installation of a heat pump often goes hand in hand with energy-saving measures like insulation. What’s more, any further integration with other sectors and increasing the system size could also further reduce costs.

The cost of energy for households (without taxes and levies) in the Mid Century scenario is significantly lower (up to 65%) compared to the present situation—namely 770 and 520 €/year per household for Hamburg and Murcia, respectively.

Therefore, the designed system is not only renewable and reliable but also affordable.

5. Discussion

The designed and analyzed integrated transportation and energy system is an extreme hypothetical scenario, because:

• The city area is not connected to any national electricity or natural gas grid or a transportation fuel network. It is self-sufficient and stand-alone.
• Only the residential, services, and road transportation sectors have been taken into account as energy consumers (e.g., not industry, agriculture, rail, or air transportation sectors).
• Space heating and hot water production are all-electric.
• It uses a single set of technologies for road transportation, transportation fuel, energy storage, and balancing, namely hydrogen, hydrogen production, and fuel cells (FCEVs), (no batteries or Battery Electric Vehicles, BEVs).
• The city area is relatively small, based on approximately 5000 people.

In the future, a mix of multiple energy carriers, storage methods, and energy technologies could all work together. Cities in Europe already have connections to national electricity and sometimes natural gas grids. In addition, all sectors should be considered, not only the residential, services, and road transportation sectors. Increasing the system size and combining several different sectors would create more integration opportunities and could reduce costs.

However, the calculated energy costs of the designed system are affordable and in line with other studies. This gives reason to explore whether variations in system designs and balancing methods can reduce total system costs even further. The system designs and balancing methods discussed below are a non-exhaustive selection of possible options.

5.1. Other System Designs

• A national electricity grid connection would make it possible to import electricity or export peaks of solar electricity to other cities or electricity consumers in different sectors, such as industry, for example, by importing lower-cost onshore or offshore wind electricity during periods of insufficient solar electricity production (e.g., at night). This would reduce the need for hydrogen storage and FCEV2G electricity. High solar output at midday in the Mid Century scenario results in high surplus peaks to be absorbed by the solar electrolyzer. Exporting these high peaks of solar electricity to, for example, industrial cooling warehouses would reduce solar electrolyzer installed capacity and costs. Using only one electrolyzer connected to the national grid and placed next to the hydrogen station could reduce hydrogen transportation. Smart placement of electrolyzers in the electricity grid could obviate electricity grid congestion and reduce or avoid the need for expensive capacity expansion [144].
• A hydrogen pipeline network [32,145,146,147,148,149] could reduce hydrogen transportation via tube trailers and fueling station capacity. Multiple electrolyzers and hydrogen fueling stations could be interconnected via a pipeline network [150]. In this way, tube trailer hydrogen transportation could be replaced, and hydrogen transportation costs reduced. Furthermore, the partial re-compression of hydrogen when emptying a tube trailer could also be reduced or avoided altogether. The compressor could even be omitted, provided the electrolyzer hydrogen output pressure is higher than the pipeline pressure. In the case of parked FCEVs delivering V2G electricity, the fuel cell could be connected directly to the hydrogen distribution pipeline network, instead of using hydrogen from the on-board hydrogen tank [151]. Not using hydrogen from the 700 bar tank eliminates the need for refueling for V2G purposes, which in turn reduces the required capacity of hydrogen fueling stations.
• Import of low-cost renewable hydrogen could partially replace, possibly costlier, local hydrogen production and seasonal hydrogen storage, and thus total system costs. Locally and at certain times of the year, there could be insufficient solar and onshore wind sources available to produce hydrogen. Regions with abundant and low-cost hydro, solar, or wind power [152,153,154,155,156,157,158] could produce low-cost hydrogen for export. This hydrogen could be imported at demand centers instead of being produced and stored on-site. Several ideas already exist, for example, producing hydrogen (far) offshore [159] from fixed or floating wind [32,160,161,162] and solar structures [163,164], or wave energy [165] and bringing the hydrogen onshore via existing natural gas or newly built pipelines [32] or ships [166,167]. The onshore pipeline network would then distribute the hydrogen to the consumers.
• Using a lower-cost mix of renewable energy sources. In this study, the rooftop solar surface area was kept equal in both locations, even though solar electricity is more expensive in Hamburg than in Murcia. Therefore, using the lowest cost renewable energy source locally available could reduce total system costs even further. For example, hydropower, offshore wind, biomass, concentrated solar power, by-product hydrogen, or tidal or wave energy could result in lower-cost electricity than onshore wind or solar Photovoltaic (PV).
• Tailor electricity mix and its supply pattern to local demand. In Murcia, solar electricity production has a better time match with electricity consumption on both a daily and a seasonal basis. During the day, solar electricity production in summer aligns well with electricity demand in buildings for space cooling. Therefore, a lower total system cost can be achieved by tailoring the renewable energy mix to allow for a better match between the production pattern and the demand pattern [61,63,65,102,142,168,169,170,171,172,173,174,175,176,177]. This would result in lower hydrogen production, storage, transportation, fueling, and FCEV electricity production costs.

5.2. Other Balancing Methods

• Using a mix of FCEVs, BEVs, or fuel cell plug-in hybrid electric vehicle (FCPHEV) and stationary batteries [84,87,178,179,180]. Instead of only using hydrogen and FCEV2G for both daily and seasonal energy balancing, other technologies could be used in parallel. For example, batteries in BEVs or FCPHEV, as well as stationary batteries, could be used for storing or releasing peak surplus or shortage of electricity [181] for day-to-day storage. Especially in Murcia, this could result in lower total system costs, as the day-to-day storage is more prevalent in Murcia [182]. Capacity factors of electrolyzers could be improved, and so decrease costs. FCEVs and hydrogen production and storage could subsequently be used for energy balancing for longer periods, up to entire seasons [182].
• Using other CO₂-free hydrogen carriers for energy transportation, short and long-term energy storage. There are several other proven and available carriers today, such as liquefied hydrogen [183,184,185,186,187], ammonia (NH₃) [188], or liquid organic hydrogen carriers (LOHC) [189,190]. Transporting liquid hydrogen can be less costly compared to compressed hydrogen when volumes and distances are larger. Ammonia storage and LOHC storage are becoming commercial applications at scale, and both represent reasonable alternatives in the absence of salt caverns.
• Increase passenger car FCEV2G power output, use other FCEVs and stationary fuel cells for combined heat and power. At the moment, only passenger cars with an output of 10 kW/car while having a 100 kW fuel cell system on-board are used for FCEV2G electricity. This limitation is mainly because of potential insufficient cooling radiator capacity when parked and providing FCEV2G electricity [38]. If V2G output could be increased by enhancing cooling capacity, then proportionally fewer passenger cars would be needed. Cooling capacity could be enhanced by installing, for example, a bigger radiator and cooling fans, or by using two-phase cooling fluids with a higher cooling capacity [191]. Commercial vehicles (vans, trucks, buses) are more widely used than passenger cars, although often not during the night. By also using commercial for V2G purposes [192], the number of passenger cars would be reduced. In the case of an underground hydrogen pipeline network, stationary fuel cells [193,194,195,196,197,198] could provide heat and power to buildings, and when necessary, FCEV2Gs could provide peak power.
• Internet Technology (IT) usage for demand response forecasting, scheduling, virtual power plants, and autonomous driving. Weather and electricity demand forecasting [199,200,201,202,203,204,205,206,207,208] in combination with demand response [21,26,209,210,211] could potentially avert peaks in temporal surplus or shortage of electricity. This would reduce installed capacity cost. Combining the output of thousands of grid-connected FCEVs would create so-called virtual power plants [212,213] with potentially large capacities. Similar to mobility as a service (MaaS) [214,215,216,217,218], power or electricity as a service (PaaS or EaaS) could be introduced. To create these markets, additional pricing structures, contract types, and aggregators scheduling and operating the cars will be required [219,220,221,222]. Upcoming technologies could facilitate the scheduling of cars, for example, self-driving, free-floating, cloud-connected car-sharing fleets [223,224,225], together with inductive (wireless) self-connecting V2G infrastructure [226,227,228,229,230]. As mentioned earlier, most FCEV2G electricity is required at night, whereas most people travel and work during the day. So, even if car-sharing spreads widely and the total number of cars decreases, at night, car-sharing fleets will be used less and, therefore, will be available to provide power.

6. Conclusions

The designed and modeled system for smart urban areas is based on wind, solar, and hydrogen, where fuel cell electric vehicles provide year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation in two different European climates.

The two locations in different climate zones—namely Murcia in Spain and Hamburg in Germany—were selected based on several criteria. Both are close to or in a large European urban area in one of the five most populated countries. Located in a different climate zone according to the Köppen–Geiger classification, Hamburg has a temperate oceanic climate (Cfb), and Murcia a hot semi-arid climate (BSh). Both locations have salt formations suitable for underground hydrogen gas storage. One location has a high level of solar radiation (Murcia), while the other has a low level (Hamburg).

The two designed smart city areas have the climate characteristics of Hamburg and Murcia; the dimensions are based on, respectively, German and Spanish statistical data. The smart city areas consist of 185,000 m² floor area of residential sector buildings, and for Hamburg and Murcia, respectively, 93,000 m² and 38,000 m² floor area services sector buildings. Hamburg and Murcia have, respectively, a total of 2500 and 2250 fuel cell electric vehicles (FCEVs), of which 2360 and 1850 are passenger cars that can be used for producing electricity via vehicle-to-grid (V2G), so-called fuel cell electric vehicle to grid (FCEV2G). Two thousand households with a total of approximately 4300–5000 inhabitants are the minimum viable economic size for dimensioning the smart city area, as statistically, there is one gas station and one retail food shop per 2000 households. Smaller capacity fueling stations are relatively costlier.

The designed smart city area system is 100% renewable. All electricity and hydrogen can be supplied by solar and wind to fulfill the energy demand for power, heat, and transportation. The transportation and energy sectors are fully integrated, and their final energy use is all-electric. Electricity is generated by solar modules on all roofs. Surplus solar electricity is converted via water-electrolysis with rainwater into pure hydrogen. The hydrogen is compressed and transported by tube trailer modules to the nearby hydrogen fueling station (HFS) or underground seasonal hydrogen storage (SHS). At the HFS, the hydrogen is further compressed to fuel all types of FCEVs, from passenger cars, vans, buses to trucks, and tractor-trailers. In the case of a temporary shortage of solar electricity, the fuel cells in the parked and grid-connected passenger cars provide the necessary electricity (FCEV2G) by converting hydrogen from the on-board hydrogen storage tanks. At parking places at home, the office, or the local shopping area, vehicle-to-grid points connect the cars to the smart city electrical grid. The SHS is filled with hydrogen from surplus solar electricity and via a very short pipeline with hydrogen produced from wind electricity from an onshore wind turbine park. The stored hydrogen in the SHS is transported via tube trailers to the hydrogen fueling station.

The designed smart city area system is reliable at all times and independent of other energy systems and grid connections. The energy balance is simulated on an hourly basis for an entire year for a Near Future and a Mid Century scenario. Five years are simulated using climate data input from the years 2012–2016, although no significant differences in the energy balance are observed.

Reliable electricity supply can be realized at all times, as extreme peaks in the FCEV2G electricity supply never exceed 50% of the car fleet. Maximums of 32% and 42% of all cars (760 and 772 cars) in Hamburg and Murcia in the Near Future scenario drop to 17% and 15% of all cars (391 and 275 cars) in the Mid Century scenario. These maximums are extreme outliers, and values close to these only occur for a few hours per year. On average, less than 13% of all cars are required during peak hours (17:00–19:00) in the Mid Century scenario. FCEV2G usage is comparable to driving in the Mid Century scenario. There is an average of 440 FCEV2G hours per year per car compared to 310 driving hours per year for Hamburg. For Murcia, there are about 330 FCEV2G hours per year and 280 driving hours. The average number of FCEV2G hours could be reduced significantly by increasing the output per car or using other vehicles, such as buses, trucks, or vans. The passenger cars are limited to 10% (10 kW) of their maximum output (100 kW).

The underground seasonal hydrogen storage (SHS) guarantees year-round storage of hydrogen for driving and electricity production. A typical size SHS can serve around 20 smart city energy and transportation systems based on Hamburg in both scenarios, the equivalent of 86,000 people and 50,000 vehicles (passenger cars, vans, trucks, buses, and tractor-trailers). In the case of Murcia, this is about 30 smart city systems in the Near Future and 40 in the Mid Century scenarios. For the Near Future and Mid Century scenarios in Murcia, this is, respectively, the equivalent of 153,000 and 203,000 people with 68,000 and 90,000 vehicles (passenger cars, vans, trucks, buses, and tractor-trailers).

The designed smart city area system is affordable, and further cost reductions are possible. It is very difficult to make a fair cost comparison between today’s energy system and the one proposed in this study in the Mid Century scenario. Nevertheless, a very simple energy cost comparison for an average household shows that the cost of energy for households (without taxes and levies) in the Mid Century scenario can be 65% lower compared to the present situation—namely 770 and 520 €/year per household for, respectively, Hamburg and Murcia.

The developed system and the technologies used are very different from today’s fossil-based energy and transportation system. The designed and analyzed integrated transportation and energy system is an extreme hypothetical scenario because:

• The city area is not connected to any national grid; it is self-sufficient and stand-alone.
• Only the residential, services, and road transportation sectors have been taken into account as energy consumers.
• Space heating and hot water production are all-electric.
• It uses a single set of technologies for road transportation, transportation fuel, energy storage, and balancing; hydrogen, hydrogen production, and fuel cells (FCEVs and no batteries or BEVs).
• The city area is relatively small, based on approximately 5000 persons.

Increasing the system size and combining several different sectors would create more integration opportunities and could reduce costs. Environmental and health savings and welfare creation (e.g., jobs) compared to the present fossil system are difficult to express in costs for this specific and small-scale system. In the future, multiple energy carriers, storage methods, and energy technologies could work in parallel. The system levelized costs in the Mid Century scenario are 71–104 €/MWh for electricity and 2.6–3.0 €/kg for hydrogen. These results compare favorably with other studies describing fully renewable power, heat, and transportation systems. This gives reason to explore whether variations in system designs and balancing methods and technologies can further reduce total system costs.