1. Introduction
In addressing net-zero targets, it is recognized that green electricity alone is very unlikely to meet the requirements of the complete energy system, for example in areas such as heavy transport [
1]. Therefore, alternative energy sources will be required if ambitious decarbonization targets are to be achieved. Green hydrogen has been identified as a low carbon solution that can be used to meet these requirements and a number of countries have already set low carbon hydrogen production targets for 2030 [
2,
3].
One approach to producing low carbon hydrogen is through the electrolysis of water using renewable electricity generation (for example wind or solar). However, the cost of electrolyser technology is currently high, and they are not typically designed for intermittent operation [
4]. A novel alternative which is currently being explored to address these challenges is a battolyser. The battolyser acts like a battery until it is fully charged and then produces hydrogen gas. A single device which can produce both electricity and hydrogen has the potential to be more economically viable. There are a range of options for running a hybrid wind/battery/electrolyser/battolyser solution as shown in
Figure 1.
Current figures suggest that the levelised cost of electricity (LCOE) is between
$58 to
$76/MWh for wind energy [
5]. There exists some recent literature looking at hybrid systems including wind and battery technology [
6,
7] and wind and hydrogen technology [
8,
9]. Reference 6 uses a LCOE for a wind farm of
$60/MWh and then calculates separately the cost of storage (battery and compressed air). They size the system to provide ancillary support services such as frequency regulation or load shifting. This results in a high calculated LCOE for the storage system of up to
$435/MWh. Reference [
7] also looks at adding battery storage to wind farms. Their numbers predict that by 2050, the cost of storage will have come down sufficiently such that the LCOE of the storage is in the range €68–€83/MWh depending on replacement costs and of comparable value to the LCOE of wind.
Reference [
8] looked at using 4% waste energy from wind farms to produce hydrogen. They showed that even if the cost of electricity from wind farms were free, the levelised cost of energy through hydrogen from fuel cells was in the order of £130/MWh. This is still very high and also suffers from the issue of fluctuating supply. Reference [
9] assumed that the full potential for wind farms off the coast of China were utilised and that all excess energy is turned to hydrogen. They show that the wind and hydrogen LCOE combination is estimated to be
$249–
$301/MWh. These values are significantly higher than that of a wind farm on its own because of the high cost of the electrolysers.
The published research mostly focuses on conventional revenue streams for example ancillary services as a source of revenue for batteries. This means the sizing of the systems are large and the capital costs of the equipment to provide large scale services combined with the high capital cost means the LCOE is also high and the combinations of wind with these other types of services are considered not viable.
The aim of this research is to validate, through economic and energy modelling, an offshore wind generator and battolyser hybrid system as a viable economical technological solution that could be used to decarbonise the electricity and transport networks.
This work makes several novel contributions in this area. Firstly, it includes the use of a battolyser which is a new technology and for which there are few published data. The paper pulls together cost data for a battolyser based on published data for batteries and electrolysers using an acid chemistry. Secondly, it is more common to look at electricity revenue based on either wholesale market values or the frequency response markets. This paper considers revenue from wind curtailment as a defining revenue factor using a systems approach to the LCOE calculation (combining wind with the storage technology). With the integration of new technology into the market there is a risk. This paper looks at that de-risking the technology by comparing the NPV and LCOE to a wind farm and a wind farm with more conventional technology; a battery and an electrolyser to provide comparable data.
Description of Battolyser Technology
Integrating batteries and electrolysers into single units will help to reduce the burden on grid infrastructure and will allow for more flexible power management at a singular site [
10]; furthermore, it allows for a single unit to be in quasi-constant operation compared with separate units where both are used with less regularity [
10]. However, battolysers are in their infancy and there has been limited research into them since their conception in 2017 [
10,
11].
It is important that battolysers have a flowing electrolyte as bubbles in the electrolyte increase electrical losses by reducing the conductivity of the electrolyte and therefore these bubbles need to be moved on.
Figure 2 shows a high-level concept diagram of a battolyser. It contains an electrochemical cell made up of electrodes and a separator. Depending on the cell chemistry, the electrolyte could be common to both electrodes or different. The electrolyte is pumped round the cell, to help with battery action and to help remove the products of electrolysis. The separator stops the plates from touching but also prevents cross contamination of hydrogen and oxygen.
The inaugural battolyser used iron–nickel battery chemistry [
10]; this research has been replicated [
11] and an additional chemistry based on lead acid battery chemistry has been demonstrated [
12].
This paper is different from previous work because it does not focus on the technical or chemical aspects of the battolyser, but instead considers the business case behind using this technology. This research looks at including a battolyser with a wind farm. The paper includes a description of the modelling along with results.
2. Methodology
To determine the economic feasibility of the battolyser system in combination with an offshore wind farm a stochastic time-series model was created. This model uses wind speed data and wind turbine power curves to estimate the electrical power produced from an offshore wind farm, along with historical curtailment data to estimate the amount of curtailed energy that is available to the battolyser system. The model then calculates the amount of energy stored and released and the hydrogen that is produced by the battolyser system. The revenue generated from the electricity and hydrogen output from the combined system are calculated. Wind farm and battolyser capital and operational costs are then used to calculate the levelised cost of energy and the net present value of the system.
This paper investigates whether a battolyser is likely to be profitable over its lifetime. The paper defines profitability as a positive NPV as shown in Equation (1) [
13,
14]. which represents a positive income over its lifetime.
where:
: number of operational years;
: index for counting years;
: net cashflow during the jth year;
: discount rate.
A case is an economically feasible case if , and economically infeasible otherwise.
The net cash flow,
is calculated as
where:
is the net revenue from the wholesale electricity market in the jth year;
is the net revenue from hydrogen sales in the jth year;
is the operational and maintenance cost of the battolyser and wind farm in the jth year;
is the capital cost of the battolyser (
) and wind farm (
).
The registered site capacity (export limits) will remain the same and therefore no extra connection costs will be required and there will be no changes to capacity market revenue.
An alternative metric to NPV is the LCOE—levelised cost of energy [
15,
16], typically written as sum of the costs over the lifetime divided by the sum of the energy produced over the lifetime.
Levelized cost of electricity metrics can have some limitations. In particular, time dependency effects of matching generation to load. For example, the generator may have a dispatch or ramp up time associated with coming online or the generation availability does not match market profile.
An alternative analysis around energy storage has been developed to take into account the impact of the battery; LCOS—levelised cost of storage [
17]. LCOS is defined as the discounted cost per unit of discharged electrical energy. This calculation is complicated when the battery can operate as both a generator and a load. The LCOS calculation is comparable in form to the LCOE as a generator where charging cost replaces fuel cost. It is dependent on storage type and application and quantifies the present discounted value of the storage [
18].
An equivalent formula relating to electrolysers has also been developed. The levelised cost of hydrogen (LCOH) is calculated by dividing the lifetime cost by the lifetime thermal energy generation [
19,
20]. In this instance
includes the cost of the electrolyser.
A LCOWB (levelised cost of wind/battolyser) is a modification to the LCOE, LCOS and LCOH to reflect that the battolyser includes both storage and hydrogen. However, the charging cost are not required, as this is “free” energy from the wind farm. In this instance
is the cost of the battolyser and wind farm.
At this time, the end-of-life costs have been set to zero. This is because a lead acid battolyser chemistry is assumed and this can directly feed into the lead acid battery recycling chain. This paper uses a case study approach using real data related to existing windfarms. To narrow down the scope, this paper looks a large wind farm scenario (1 GW), and considers comparisons between the battolyser and an electrolyser and battery solution.
2.1. Techno-Economic Model
A high-level overview of the model is shown in
Figure 3. This consists of three sub modules (wind-farm revenue, curtailed-wind revenue and the business case) which are described in more detail throughout this section.
2.2. Wind Farm Revenue Model
The aim of the wind farm revenue model is to calculate the amount of energy produced by a case-study offshore wind farm, how much of this may be curtailed and how much revenue is generated from energy exported to the grid. The inputs and outputs for this model along with the key steps are summarised in
Figure 4. The model randomly selects a year from a range of historical offshore wind speed data at 10 min intervals. This is used with a chosen wind turbine power curve to generate a year-long time series worth of turbine and wind farm power production. It is assumed that there are no wake losses between turbines and that the same wind speed was seen at each turbine, hence each turbine produced identical power.
Similarly, historical wholesale market electricity prices were gathered, and a random year chosen amongst the data collected. This was scaled by a factor to represent the shift in energy prices from a historical average to a future predicted average. The energy produced from the windfarm over each 10 min interval is multiplied by the scaled price for that 10 min interval period to generate the revenue for that period. This was then summed over the period of a year. This process was repeated over the user-defined operational lifetime.
The wind farm generates revenue through exporting electricity at market price. However, the wind farm may need to be curtailed occasionally and the payment for this is market dependent. It is assumed that the windfarm continues to be paid at the wholesale market value even though its export is curtailed. If the wind farm continues to generate power but not export it—this then becomes “free” electricity that can be used for other purposes and provide an additional source of income through this stored energy.
Wind farm curtailment is estimated using two years of historical curtailment data for a UK offshore wind farm. As there were very few data available, two different methods were used to estimate wind farm curtailment in the model as explained in
Table 1.
Note—the majority of curtailments start when it is windy and at night when generation is not required.
Table 2 was produced from the curtailment data set and represents the probability of curtailment happening over a half hour period in a year (e.g., 344 events out of 17,520) based on a combination of the wind farm output and the time of the day. The reason there are some zero numbers is that at this time of day no recorded curtailment data were present in the original data set.
Table 3 shows that the wind farm is never fully curtailed but only partially curtailed.
There are instances where a wind farm maybe completely curtailed, (e.g., maintenance on a Network or under dynamic generation); however, these data were not available. The result of this is that curtailment is potentially under-estimated, and the business case would be better if these numbers could be established. In addition, there are onshore wind projects that exist (e.g., in the Orkneys) where automatic network management is used and curtailment may be imposed as part of this. However, data from this were not available.
At the time of writing this paper, the future of curtailment is not clear. Re-enforcement of the network could reduce curtailment while more active network management schemes would increase curtailment. From discussions with network and system operators, it is most probable that curtailment is more likely to increase with increases in renewable generation leading to a better business case. The results of this study are therefore on the conservative side. Sensitivity analysis in this area will form future work.
Table 4 shows the majority of the curtailment periods are between 1 h and 7 h long; however, the majority of this is at less than 17.5% curtailment depth.
2.3. Battolyser Revenue Model
The aim of the battolyser revenue model is to calculate the electricity revenue generated when the battolyser is run as a battery exporting power and the volume of hydrogen and associated revenue produced by the battolyser system using curtailed energy output from the wind farm revenue model. In order to calculate the revenue from the battolyser it is first necessary to develop a control system around the operational state of the battolyser. The battolyser can operate under three different regimes: (1) charging as a battery, (2) discharging as a battery or (3) producing hydrogen as an electrolyser. The control system implemented in the model is based on the flow diagram in
Figure 5. Any curtailed energy is first used to charge the “battery” part of the battolyser. When the battery is full, any additional curtailed energy is used to produce hydrogen. When the curtailment period is over, the battery is discharged until it is either empty or another curtailment period is reached. The model considers the efficiency of the battery and discharges less energy than is used in charging. The power the battery discharges plus the un-curtailed power being exported by the wind farm cannot exceed the site export limit (assumed to be the rated power of the wind farm). All charge/discharge is limited to the power limit of the battolyser. The energy the battery discharges over each ½ period is used to calculate the additional wholesale market electricity revenue. The overall methodology of the revenue model is summarised in
Figure 6. At this time there is no attempt to optimise when this stored electrical energy could be exported. However, designing a control system to optimise this will improve the business case. Any hydrogen that is produced is turned into revenue through a £/MWeH
2 lookup table which also takes into account conversion efficiency. This is based on a single figure at today’s prices. It should be possible to generate a future average hydrogen cost and use time series market figures similar to the wholesale market costs. However, this would have introduced more complexity and a complex optimisation problem around storage sizing. This is considered too complex for the research at this stage which is looking at early feasibility studies when figures for battolyser costs are themselves not exactly known, but could be considered as an area of investigation in the future.
The model also includes the ability to include a battery or electrolyser as additional separate items. In this case the battery is charged/discharged before the battolyser and the electrolyser produces hydrogen before the battolyser.
2.4. Business Case Model
The aim of the business case model is to calculate the LCOE and NPV of the case study scenario, as outlined in
Section 2. This is the most straightforward aspect of the modelling and is summarised in
Figure 7. The wind farm and battolyser (or battery/electrolyser) size is used to calculate the capex and yearly opex costs of the system. These are then used with the revenue values calculated in the wind farm revenue and battolyser revenue model in conjunction with Equation (1) to calculate the NPV over the number of operating years and Equation (6) for the LCOE. For the purposes of this paper, this value is set to 15 years.
It is difficult to validate a model of this nature because there are no existing data on which to validate the output results based on operational parameters. This work therefore uses conceptual validation (i.e., by determining whether the theory, calculations, input data and assumptions underlying the model are justifiable). Independent checking of each stage of the model was undertaken by someone independent to the person who coded the model to check the outputs at each stage were as expected. Once this had been full checked over all different scenario types, the model was assumed to be validated.
2.5. Scenario Studies
The model was run for a number of different scenarios as shown in
Table 5 each over 100 times to understand the variability due to the stochastic nature of the model. All the models assumed a 9.5 MW turbine and the time series wholesale market values. The other options (using the strike price and other turbine types were assumed to have negligible impact). A retrofit wind farm (one where the capex for the wind farm has already been accounted for) has not been included but would improve the economic case significantly. As the depth of curtailment from
Table 3 is lower than 15% the size of the different energy storage options is investigated up to 150 MW/150 MWh. Additional studies were undertaken over and above what is presented here. As the average depth of curtailment is low and the duration of curtailment is mostly less than 8 h, one hours’ worth of energy storage was adequate to deal with the majority of curtailment events. Energy storage sizing over 1 h therefore had a negative impact on the business case and the results below are limited to 1 h storage.
5. Discussion
Figure 9,
Figure 10 and
Figure 11 show the NPV and LCOE for different sizes of hybrid units along with a 1 GW new windfarm. The results from the modelling studies show the following main points:
It makes no financial sense to include an electrolyser on its own with a wind farm as the capex and opex costs for an electrolyser results in a higher LCOE than just the wind farm on its own. As the size of the electrolyser increases, the financial case reduces further. At the upper end of the electrolyser capex limits—even a small electrolyser is unprofitable over a 15-year period.
Adding a small battery or battolyser at the lower end of the cost range could prove to be more profitable than just having a wind farm on its own. The LCOE figure remains stable over a range of battery and battolyser sizes and is of comparable value to that of the wind farm on its own.
A battery at the upper end of its cost range may not be profitable and the LCOE increases in line with this. As the battery was sized based on MW rather than MWh, there is little difference in the cost between the different MWh figures. This is because the cost does not increase between these scenarios and the increase in revenue is not significant after the first 50 MWh because this is the most significant part of the storage. As the MWh increases beyond that which is produced in curtailment, the storage is then underutilised.
A battolyser at the upper end of its cost range looks to be more profitable than a battery of similar size. This is because the upper cost of a battolyser is lower than that of an expensive battery. In addition, the LCOE is not significantly higher than that of a wind farm on its own. Increasing the size of the hydrogen beyond 50 MW acts to decrease the profitability as there is cost dependency on the hydrogen storage size.
The results are significant because they show that adding a small battolyser or battery of around 5–10% of the size of the windfarm with 1 h of storage capability to a new wind farm can help with profitability. It can be more profitable to have a windfarm/battery or windfarm/battolyser hybrid than just a wind farm when the wind farm is subject to curtailment.
Where the windfarm already exists, the economic case is better still as some/all of the wind farm costs have already been covered.
The costs of the batteries and battolysers have minimum and maximum values based on the published literature. Where the minimum cost for both are used, there is little difference financially between adding a battery or a battolyser. However, an expensive battery is less profitable than an expensive battolyser. There is no techno-economic case for adding an electrolyser to a new wind farm as results indicate that in all scenario’s the profitability of a wind farm with an electrolyser is less than the wind farm on its own.
6. Conclusions
The aim of this research was to validate, through economic and energy modelling, an offshore wind generator and battolyser hybrid system as a viable economical technological solution that could be used to decarbonise the electricity and transport networks. A techno-economic model was developed that looked at wind farm revenue, battolyser revenue and combined them to evaluate the business case. The results indicated that the main aim of the paper to confirm the hybrid system as a viable economic technological solution was achievable under certain circumstances, such as a battolyser set to 5–10% of the size of a new wind farm.
To conclude, there is less risk to the business case of adding a battolyser to a wind farm to help with curtailed wind than adding an electrolyser. The size of the energy storage needs to be carefully sized to avoid under-utilised assets. This work looked at curtailed power that has been brought about through Network constraints and is competed and paid for in the open market. It is likely that curtailment brought about through active network management (e.g., in the Orkneys) will have a different business case that may be made more helpful with the addition of a battolyser. It is also likely that curtailment will increase in volume as more wind is added to the system as additional constraints around inertial response may need to be included. It should be noted that adding additional energy storage could reduce the market value of the curtailed wind in the future.