How does the mix between solar and wind energy affect hydrogen storage capacity and the energy to load ratio?

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Research Paper – Renewable energy logistics

How does the mix between solar and wind energy affect hydrogen

storage capacity and the energy to load ratio?

Written by: Martijn van der Goot

S4183614

r.m.van.der.goot.2@student.rug.nl

22nd of June 2020

Supervisor: J. E. Fokkema University of Groningen Faculty of Economics and Business

Nettelbosje 2, P.O. Box 800 9700 AV Groningen

Declaration: I hereby certify that this is my own work and that the use of material from other

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ABSTRACT

This research paper aimed to contribute to developing local stand-alone grid for a group

households, by analysing how the mix between wind and solar energy affects the energy to load ratio (ELR) and the storage capacity. It was found that the highest ELR was managed between a 70 to 85 percent share of wind energy and a 30 to 15 percent share of solar energy. The effect of the wind and solar energy mix on the storage capacity was much dependent on the net production of the energy source within that year.

Keywords:

Renewable energy Hydrogen storage Stand-alone grid Wind Solar Mix

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One of today’s major global challenges is the transition from fossil fuels to renewable energy sources. One of the European Union’s key target for 2030, is a forty percent cut in greenhouse gas emissions opposed to 1990 levels (European Union, 2020). Two leading renewable energy sources (RES) to make this transition possible are wind and solar energy.

Energy from these sources is generated by capturing natural forces through wind turbines and solar panels. The amount of energy generated depends on the heaviness of these natural forces. The main problem using these renewable energy sources is that it varies greatly from season to season, day to day and hour to hour (A. Khosravi, 2018). This would not cause problems if the demand was well correlated with the availability, however this is mostly not the case. To overcome this problem, there is a need for storing renewable energy. Traditionally energy is stored in batteries, but these cannot store energy over longer periods. The introduction of hydrogen storage should help to overcome this problem. Hydrogen can easily be produced via electrolyses (power to gas) and converted back into electricity through fuel cells (gas to power) (Cilogullari, 2017). Hydrogen storage could be used to store energy in moments of overproduction and sent energy to demand in a moment of energy supply shortage. However, converting power to gas (P2G) and gas to power (G2P) does result in conversion losses.

Heide et al. (2011) mention that a 100% wind-only as well as a 100% solar-only scenario would require about twice as much storage. They also argue that wind and solar energy can cancel each other’s strong seasonal dependencies out and follow the weaker seasonal behaviour of the load. Therefore, it would be interesting to study what the effect of the mix between wind and solar energy production has on the hydrogen storage capacity. This raises the following research question: how does the mix between solar and wind energy affect hydrogen storage capacity and the energy to load ratio? The effect on the hydrogen storage capacity is measured in terms of what is the highest and lowest amount that is stored in a certain period. The energy to load ratio shows what percentage of the total energy production is sent directly to the load.

The purpose of this paper is to provide insights of the effects of the mix between wind and solar energy has on a stand-alone grid, which uses hydrogen storage. This study distinguishes itself from other research by combining multiple elements. This research is conducted through experimenting with different shares of wind and solar energy in a simulation model.

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2. LITERATURE REVIEW

Several researchers have studied the optimization of solar and wind energy to reduce the mismatch between energy production and demand (Heide, D., Greiner, M., Bremen, V., Hoffman, C., Speckmann, M., & Bofinger, 2010) often with only two components of renewable energy. This research aims to generate insights in how to utilize the energy production of wind and solar energy, in combination with hydrogen storage for a stand-alone grid for households. This research aims to fill the gap by using both wind and solar energy in combination with hydrogen storage. The focus lies in the adjustment of the share solar and wind energy.

There are several articles published which encourage balancing the mix of solar and wind energy. Heide et al. (2011) have conducted research somewhat similar to this research question. They found that balancing wind and solar energy will reduce the need of storage. Balancing the mix will reduce the amount of storage needed by a factor of two. Hybrid PV-wind energy generating systems can be characterized by higher availability in every month than any other systems utilizing solar energy and wind-power separately (Lakatos et al., 2011). So, the appropriate combination of solar and wind based generation can improve the efficiency and reliability of the system by resolving the problems caused by their variable nature (Kayal & Chanda, 2015; Yang & Burnett, 2002).

Heide et al. (2010) aimed at a seasonal optimal mix of solar and wind energy for a highly renewable Europe. The optimal mix to be found was 55% wind energy and 45% solar energy. When increasing the share of fossil fuels to generate energy, the share of wind energy will be become higher due to the wind characteristics in Europe. The insights of Heide et al. (2010) are generalized for Europe, however this could not be very accurate, as the weather characteristics will differ from northern to southern Europe. This research will specifically focus on the energy demand profile of households, which makes it applicable for communities which wishes to transition to a stand-alone grid with renewable energy sources.

In the literature there are a couple methods concerning sizing stand-alone systems. Many early on sizing methods have been based on the ‘worst month’ scenario, mainly useful for stand-alone PV-systems (Barra et al, 1984) and not a hybrid form. Another point of criticism is that the ‘worst month’ scenario does not offer an optimum (Celik, 2002) which is ought in this research. Protogeropoulos et al. (1997) present a method for sizing autonomous PV-wind hybrid systems. This is called the ‘yearly average monthly method’ in which the size of the PV panel and wind energy generator is derived from the yearly averaged monthly values of the component contributions. Similarly, the load is represented by the yearly mean monthly value. The system is sized at a point where the energy to load ratio (ELR), the ratio of the produced energy by renewable

components to energy demand, is equal to unity yearly.

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3. METHODOLOGY

3.1 Problem description

This research aims to understand how the mix of solar and wind energy will affect the moment of energy production and energy demand and also how this will affect storage capacity. The insights that result from this study could contribute to providing guidelines to size renewable energy sources for a group of households.

In the conceptual model (Figure 1) we consider a group of households which uses a hybrid model of energy supply through wind energy Pwt (MWh) and solar energy Ppv (MWh) in combination with a hydrogen storage tank S to fulfill energy demand D. The sum of both wind energy and solar energy will be the total energy production Pt (MWh). The control system determines whether total energy production is sufficient to fulfill energy demand or if it is needed to convert gas from the hydrogen tank into power in order to fulfill energy demand. The control system could also decide to store energy (power to gas) due to overproduction. The difference between the total energy production and the energy demand at any given time t will result in withdrawal w or deposit d of energy through the hydrogen storage tank (S). An important indicator for this study is how much energy is stored in the storage tank at any given time.

Figure 1 - Conceptual model

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3.2 Simulation model

The objective is to gather insight how the conceptual mix of wind and solar energy affect (1) the hydrogen storage capacity and (2) the energy to load ratio. Multiple years will be simulated to determine if there are forms of constants and possibly make uncertainty about renewable energy production less uncertain. In order to do this, it is measured how much of the total production is directly send to the load. The production of wind and solar is dependent on the weather, which is rather unpredictable. The input for the simulation is shown in table 1. In table 2 the component list of variables are described.

Table 1 – Component list Parameters

Component Detail Include/exclude Comment Wind energy

supply

Symbol Pw in kWh

Include Changes hourly

Photovoltaic power supply

Symbol Ppv in kWh

Include Changes hourly Total energy

production

Symbol Pt in kWh

Include Result of Pw + Ppv = Pt

Energy demand Symbol D in kWh

Include Changes hourly

Conversion losses Symbol E Include The conversion losses concerned with P2G and G2P. For both process the conversion loss is 0.7

Table 2 – Component list Variables

Component Detail Include/exclude Comment Energy to

Demand

Symbol m Include Energy which is produced and sent to demand in same hour

Energy to Storage Symbol d Include Energy which is overproduced and is sent to storage

Storage to Demand

Symbol w Include Energy which has to come from storage to fulfil demand

Stored energy in hydrogen storage

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Figure 2 - Logic flow diagram

3.2.1 Logic flow diagram

The simulation follows the structure of the logic flow diagram in figure 2. The diagram visualizes the decisions that are being made within the simulation model. The simulation starts with whether there is any energy produced by solar or wind energy. If energy is produced, the energy can be used to fulfill demand. However, when there is not sufficient energy produced to fulfill demand, the hydrogen storage has to convert gas to power to fulfill the remaining energy that is needed. This cycle will repeat itself until the end of the time horizon is reached. The simulation is rather simple because storage is unlimited and this makes situations of energy curtailment or loss of power supply unnecessary.

Table 1 - Flows of the simulation model

Flow in the model

Details Case 1: Overproduction Supply > Demand

Case 2: Shortage Supply < Demand

I Energy to Demand Yes Yes

II Energy to Storage Yes No

III Storage to Demand No Yes

IV Increase/Decrease of stored energy

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Table 2 shows the four flows that can occur in the simulation model. Flow I is energy which is produced which is sent to demand directly. Flow II represents the amount of energy which surpasses the needed energy to fulfill demand, which will be converted from power to gas to be able to be stored in the hydrogen storage tank. This also distinguishes case 1, which is the case of overproduction. Flow III occurs in the case when supply is less than demand. In that situation, the amount of energy which is deficit to fulfill demand has to retracted from storage. Flow IV is a result of over production or shortage. Evidently, when energy is sent to storage, this will increase the amount of stored energy and sending energy from storage to demand will result in the contrary. 3.2.2 Assumptions and simplifications

In this paper, certain assumptions and simplifications are made in order to make the research feasible in terms of time and not to add to many complexity. In order to make the share of wind and solar energy comparable the peak capacity of both energy sources used. Furthermore, the weather data of 2014 to 2016 is used as this was the most recent data of which both wind and solar numbers where available. In the simulation, no technical constraints were taken into account besides the conversion losses due to P2G and G2P.

3.3 Experimental setup

This research is interested in (1) how much energy is sent from production directly to demand and (2) how is the hydrogen storage capacity affected by the mix of wind and solar energy production. This will result in to two insights (1) How the mix of wind and solar affects the match between supply and demand and (2) to what extend could a hydrogen storage tank be (down)sized.

In order to analyse the effect of the conceptual mix of solar and energy on the ELR (Energy To Load Ratio) and how storage could be minimized, the share of wind and solar energy is adjusted. Table 3 shows the set of experiments that are conducted in this research paper.

Table 2 - Experiments

Set of experiments Number of experiments Peak capacity wind park (MWh) Peak capacity solar park (MWh) Amount of households (demand) Storage capacity mWh Period

Base case 3 20 20 10.000 Unlimited

2014-2016 Wind/solar ratio and

Energy to load ratio

60 0 to 40 MWh (in steps of 2) / 0 to 100 percent (in steps of 5 percent) 40 to 0 MWh (in steps of 2) / 100 to 0 percent (in steps of 5 percent) 10.000 Unlimited 2014-2016

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Base case

In the base case, the share of wind and solar energy is divided equally. The base case is interesting, because in this case the solar and wind energy have the same peak capacity, so it becomes clear what their net share to the total production is. The performance indicators in the base case are the same as in the second and third set of experiments. These are the ELR and width of the storage capacity.

Energy to load ratio (ELR)

The second set of experiments will be conducted to analyse the effect of the wind/solar ratio on the ELR. The share of wind energy will be incrementally increased from zero percent up to hundred percent in steps of five percent. Simultaneously to the increase of wind energy, the amount of solar energy will be lowered from hundred to zero percent. The energy to load ratio is the percentage of direct energy from production to demand (not from storage to demand).

Experiment – Width of the hydrogen storage capacity.

In this experiment the wind/solar ratio will be adjusted in the same manner as the previous experiment. In this experiment the effect on the hydrogen storage will be analysed by the following indicators:

Minimum load: this is the point where storage has decreased the most compared to the base load. Maximum load: this is the point where storage capacity has reached the highest level compared to the base load.

Width: this is difference between the minimal and maximum load. When the difference is lower, this will mean the supply and demand is more stable. This is the most important performance indicator

End of year load: This is amount of energy what is left in the hydrogen storage tank at the end of the year.

3.3.1 Data input Energy demand

To generate input for the local electricity demand of households, data from Liander is used. Liander provides a typical energy demand profile of the year 2020, which is the most representative dataset available. The input for the simulation is 10.000 households because the demand has to be significantly sized to have any room to adjust with wind and solar energy supply.

To calculate the energy demand of 10.000 households, we have extrapolated the 10.000 times an average of 2.800 kWh energy consumption per household. This is in total 28.000 MWh energy demand in a full year. Because 2016 is a leap year, this has one day extra energy demand.

Solar park

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Wind energy production

The data used for wind speeds was retrieved from KNMI. Average wind speeds of each hour between 2014 and 2016 is translated to power output. The power output of a wind turbine at various wind speeds is conventionally described by the power curve. The power curve has three key points on the velocity scale:

• Cut-in wind speed: the minimum wind speed at which the machine will deliver useful power.

• Rated wind speed – the wind speed at which rated power is obtained (rated power is generally the maximum power output of the electrical generator).

• Cut-out wind speed – maximum wind speed at which the turbine is allowed to deliver power (usually limited by engineering loads and safety constraints).

Table four shows the used cut-in, rated and cut-out wind speed. Figure 2 shows the power curve of the wind turbine used. The hourly data is divided by 1.5, because the peak capacity of one wind turbine is 1.5 MWh and thus it is made equal to 1 MWh peak capacity of solar panels. The peak capacity for the wind turbine has impact on the finite results, as the size of wind turbines is important for the translation of wind speeds to energy production.

Table 4 – Key points power curve

Windspeed (m/s) Energy production

Cut-in 3 0.1 MWh

Rated 12 1.5 MWh

Cut-out 20 -

Figure 2 – Power curve wind turbine

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4. RESULTS

In this chapter, the findings will be structured accordingly. Firstly, the base case will be discussed. In the base case experiment, we will discuss the different performance criteria related to answering the research question. In the second section, wind and solar parameters have been adjusted to simulate how this adjustment affects the ELR. In the third section, the effect of the wind and solar parameters are adjusted to see the effect on the hydrogen storage capacity. Lastly, there will be a discussion about how the results of this study compares to other literature.

4.1 Base case

In the base case, it is determined that the energy supply is equally divided. The peak capacity of solar and wind is 20MWh each. This also translates to a 0.5/0.5 ratio. The performance indicators for this paper are the ELR and hydrogen storage capacity. For this experiment, three consecutive years were modelled (2014-2016). Firstly, the energy production of each respective energy source is shown in table 5. The percentages shown in the table represent the relative contribution of the energy source compared to the total energy production.

The first distinction we can make of the table is that wind energy is more variable in supply than solar energy. Solar energy has relatively stable energy production and produces more energy overall.

Table 5 – Energy production by source

Year Wind (MWh) % W Solar (MWh) % S Total energy (MWh)

2014 15.020 43% 19.772 57% 34.792

2015 21.814 53% 19.701 47% 41.515

2016 15.861 44% 20.325 56% 36.186 Total 52.695 59.797 112.493

4.1.1 Energy to load ratio (ELR)

The ELR will show what percentage of the total energy production is directly sent to demand. This ratio has to be looked into from two viewpoints. The first viewpoint is the percentage compared to the total energy. The second viewpoint is compared to what percentage the energy to demand is compared to the demand. This is shown respectively in table 6 and 7.

Tabel 6 – ELR based on total production

Year Energy to demand ELR Energy to Storage % Total energy (MWh) 2014 14.989 43% 19.803 57% 34.792 2015 15.898 38% 25.617 62% 41.515 2016 14.177 39% 22.009 61% 36.186

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Table 7 – Storage to demand

Year Energy to demand % of

D Storage to demand* % Demand (MWh) 2014 14.989 54% 13.012 46% 28.001 2015 15.898 57% 12.103 43% 28.001 2016 14.177 50% 13.919 50% 28.096

Total 45.064 54% 39.034 46%

*this is after conversion loss

In table 7 is shown that between 54 – 60 percent of demand could be directly met in the same hour as production. On the contrary, about 43 – 50 percent of energy has to come from storage.

4.1.2 Storage capacity

The second part of the research question includes the effect on the hydrogen storage capacity. Figure 3 visualizes the lapse in the load of the hydrogen storage. The red line shows how much energy is stored in the hydrogen storage tank. The base load of the storage was set on 10.000 MWh in the experiment, so the storage never had a shortage. The dotted line represents the linear trend. Table 4 shows what hour belongs to which year. The simulation is chronologically and starts and 01:00 on the 1st of January. When looked at the figure more closely, it shows that in summer periods the storage ‘charges up’ the load. Whereas in winter period of 2014 and 2016, the storage load gets tanked. In 2015 (hour 8761 to 17.520) the storage capacity remains relatively stable at the last months of the year.

Figure 3 – Hydrogen storage load

Table 8 shows the results on the storage capacity. The column ending load shows how much energy was left in storage at the end of the specific year. The delta shows the difference of the ending load opposed to the starting load of the year. Years 2014 and 2016 show a substantive decrease, whereas 2015 manages to have a net increase.

80000 85000 90000 95000 100000 105000 1 753 ₁₅₀₅ ₂₂₅₇ ₃₀₀₉ ₃₇₆₁ ₄₅₁₃ ₅₂₆₅ ₆₀₁₇ ₆₇₆₉ ₇₅₂₁ ₈₂₇₃ ₉₀₂₅ ₉₇₇₇ 10529 11281 12033 12785 13537 14289 15041 15793 16545 17297 18049 18801 19553 20305 21057 21809 22561 23313 24065 24817 25569 MWh Hour

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Table 8 - Results storage capacity Year Begin (hour) End (hour) Ending load (MWh) Delta (MWh) 2014 1 8.760 5.277 -4.723 2015 8.761 17.520 5.919 642 2016 17.521 26.304 1.440 -4.479 Main insights

• While in 2015, the renewable energy sources produced 8.403 MWh more energy than in 2014, only 800 MWh more energy was sent directly to demand.

• The ELR shows a divergent line opposed to the total energy production. When the total energy production increases, the ELR decreases.

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4.2 Wind / solar mix and ELR

In the second set of experiments, the mix between wind and solar energy production is adjusted. In this experiment, there is a peak capacity of 40 MWh to be distributed between wind and solar energy. The starting point is zero wind energy and 40MWh solar energy. With each experiment the wind energy peak capacity is increased with 2 MWh and the solar peak capacity decreased with the same amount. This step of 2MWh represents five percent as 2 MWh divided by 40 MWh is five percent. In summary, each step accounts for a five percent shift in the wind / solar ratio mix. Figure 4 shows the ELR ratio.

The horizontal axis of figure 4 shows the percentage of wind energy increasing from 0 to 100 percent with steps of 5 percent. On the contrary with every step solar energy decrease mix is decreasing. Figure 4 shows that the ELR ratio increases together with the share of wind energy up until 70 percent for 2015 and 85 percent for years 2014 and 2016

Table 9 - ELR

Table 9 shows the exact number of the highest ELR ratio. A full table of all different wind/ solar mixes can be seen in Appendix A. The green cells indicate the turning points, which are also the highest ELR ratios. The delta between the ELR of 2014 and 2016 is a rather large 0.0515, while the total production of both years is rather similar amounts. This difference in ELR indicates that not only the total production is influencing the ELR.

The scenarios which the energy share is hundred percent wind or solar, it can be seen that a hundred percent wind share has a higher ELR of 0.109 (2015) up to 0.2177 (2014) compared to a hundred percent solar share scenario.

Main insights

• The ELR decreases when the total production increases, This is partly explained by the fact that the energy to demand is divided by a larger number.

• Because the dominant energy source differs from year to year, the highest ELR is somewhere between the mix of .70/.30 (wind/solar) and .85/.15 (wind/solar).

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4.3 Wind / solar mix and hydrogen storage capacity

In this section, the effect of the mix between the share of wind and solar energy on the storage capacity is discussed. In this analysis, the main performance indicator is the Width. The width means the difference between the maximum and minimum in a certain period. Conceptually, at the point where the width is the lowest, will also mean that necessary storage is the lowest. For this experiment, the storage capacity was set on unlimited and the base load was sufficiently sized so there were no constraints.

Figure 5 - Width

In figure 5 the development of the width can be seen at different mixes of wind and solar. The horizontal axis shows the percentage of wind from zero to hundred percent in steps of five percent. At each increase of five percent in the share of wind energy, the share of solar energy decreases with five percent. In appendix B the detailed numbers are shown at each percentage. The period of 2015 is the most representable, as the other years have insufficient energy production to regenerate the storage, therefore the width increases very steeply as can be seen in the figure. The turning point in 2015 which indicates the lowest width is 70% wind and 30% solar energy. At this point the width between the minimum and maximum amount is 3.226 MWh.

Main findings

• The effect of the wind/solar ratio is dependent on the net energy produced per percentage. For example, in 2015 the amount of energy produced by wind per percentage is higher than solar. In that scenario, the width indicated in figure 5 advocates for more dominant share of wind energy. Whereas in 2014 and 2016 this is the contrary.

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4.4 Discussion

The goal of this research was to answer: how does the mix between solar and wind energy affect hydrogen storage capacity and the energy to load ratio? Based on the results discussed in this chapter, it is seen that a hybrid use of wind and solar energy production positively influences the hydrogen storage capacity and ELR as opposed to a solar or wind only scenario. In the base case, where the share of wind energy and solar energy was equal, it was visualized that the summer period functions as a ‘charge up’ for the load of the hydrogen storage, while in the winter period the hydrogen storage load will decrease. The base case also showed that when the total energy production is higher, the energy to load becomes relatively lower. This is partly explained by the fact that the energy to demand is divided by a larger number.

Greiner et al. (2011) argued in their paper that .55 wind and .45 solar energy would be optimal in terms of minimizing energy storage for European countries. The findings in this paper showed that ‘optimal mix’ in terms of minimizing storage the mix between solar and wind energy varies between .45 wind and .55 solar energy (2016) to .70 wind and .30 solar energy (2015). Greiner et al. (2012) also argued that when a grid becomes more self-sufficient that the share of wind energy will become more important. This is inline with the results of this study as the simulation of 2015, which was the only year that ended with more stored energy than it started with, showed that the mix of .70 wind and .30 solar energy reduced the need for storage capacity the most.

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5. CONCLUSION

Based on our findings, it is shown that the mix of wind and solar energy influences the hydrogen storage capacity and the energy to load ratio. Solar energy is a stable source of energy, as it is more certain in which season or time of day energy is generated. Wind energy is more unpredictable, but energy production is more evenly distributed throughout the year and within the day. In this study, the mix between wind and solar energy was most optimal, in terms of ELR and minimizing storage capacity, between .70/.30 and .85/.15 wind/solar.

Implications for practice

This study was based on a demand of 10.000 households. It is thought that for communities which are transitioning to a grid which energy sources are renewable, this research provides interesting insights. These insights can be used for sizing a hybrid wind/solar park.

Implications for theory

As seen in the base, the summer period is used a ‘charge up’ for the storage, due to a large share of solar energy. While in the winter period the storage load drains very quickly. Given this analysis, this could raise the question for decision-makers if they rather have a more reliable energy supply of solar panels, but at the same more necessity for high storage capacity. Or rather have an unpredictable wind energy supply, but more evenly distributed so the needed storage capacity could be lower.

Furthermore, the results imply that the energy to demand ratio is optimizable to a certain point for a self-sufficient grid for a group of households. Primarily, since to the fact that on average wind and solar energy peaks at daytime, while demand peaks in the evening. This implicates that there be looked for ways to shift the demand peak more towards daytime or search for other renewable energy sources which can be utilized in evening hours.

Critical reflection

Suggestions for further research are related to the limitations of this study. Leeuwarden was taken as location as input for weather data. Future research should focus on the impact of location on the mix between wind and solar energy. The same research could also be conducted with different sizes of wind turbine peak capacity.

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REFERENCES

A. Khosravi, R. K. (2018). Energy, exergy and economic analysis of a hybrid renewable energy. Energy, 1087 - 1102.

Barra L, C. S. (1984). An analytical method to determine the optimal size of a PV plant. Solar Energy (57), 509-514.

Celik, A.N. (2003). Energy Conversion and Management (44), 195101968.

Cilogullari, M. E. (2017). Investigation of hydrogen production performance of a photovoltaic and thermal system. International Journal Hydrogen Energy, 2547-2552.

European Union. (2020, 6 21). 2030 climate & energy framework. Retrieved from ec.europa.eu: https://ec.europa.eu/clima/policies/strategies/2030_en

Graabak, I., Korpas, M., Belsnes, M., & 14th International Conference on the European Energy Market, EEM 2017 14 2017 06 06 - 2017 06 09. (2017). Balancing needs and measures in the future west central european power system with large shares of wind and solar resources. International Conference on the European Energy Market, Eem, (2017 07 14). doi:10.1109/EEM.2017.7981934

Heide, D., Greiner, M., Bremen, v., & Hoffman, C. (2011). Reduced storage and balancing needs in a fully renewable European power. Renewable Energy 36, 2515-2523.

Heide, D., Greiner, M., Bremen, v., Hoffman, C., Speckmann, M., & Bofinger, S. (2010). Seasonal optimal mix of wind and solar power in a future,. Renewable Energy 35, 2483-2489. Kayal, P., & Chanda, C. (2015). Optimal mix of solar and wind distributed generations

considering performance improvement of electrical distribution network. Renewable Energy, 173-186.

Lakatos, L., Hevessy, G., & Kovács, J. (2011). Advantages and Disadvantages of Solar Energy and Wind-Power Utilization. World Futures 67:6, 395-408.

Protogeropoulos C, B. B. (1997). Sizing and techno-economical optimization for hybrid solar PVwind power systems with battery storage. Int J Energy Res (21), 1-15.

Yang, H., Lu, L., & Burnett, J. (2003). Weather data and probability analysis of hybrid. Renewable Energy 28, 1813-1824.

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APPENDICES

Appendix A – ELR

Green indicates the turning point

Wind Solar 2014 2015 2016 0% 100% 0,2475 0,2481 0,2333 5% 95% 0,2725 0,2793 0,2578 10% 90% 0,2966 0,3059 0,2801 15% 85% 0,3186 0,3262 0,2995 20% 80% 0,3391 0,3414 0,3165 25% 75% 0,3577 0,3533 0,3316 30% 70% 0,3744 0,3623 0,3455 35% 65% 0,3899 0,3694 0,3585 40% 60% 0,4044 0,3751 0,3704 45% 55% 0,4180 0,3795 0,3814 50% 50% 0,4308 0,3829 0,3918 55% 45% 0,4428 0,3854 0,4015 60% 40% 0,4541 0,3871 0,4107 65% 35% 0,4646 0,3880 0,4194 70% 30% 0,4742 0,3881 0,4273 75% 25% 0,4824 0,3871 0,4337 80% 20% 0,4888 0,3850 0,4384 85% 15% 0,4922 0,3811 0,4407 90% 10% 0,4911 0,3746 0,4392 95% 5% 0,4819 0,3637 0,4311 100% 0% 0,4652 0,3490 0,4161

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Appendix B – Storage capacity

Wind/solar ratio Period 2014

Wind Solar Min Max Width EoY

0% 100% -5184,98 1584,28 6769,26 -5184,98 5% 95% -4890,34 1496,84 6387,18 -4890,34 10% 90% -4642,22 1388,76 6030,98 -4642,22 15% 85% -4464,61 1252,83 5717,43 -4464,61 20% 80% -4347,73 1081,77 5429,5 -4347,73 25% 75% -4293,41 869,916 5163,32 -4293,41 30% 70% -4303,29 616,793 4920,09 -4303,29 35% 65% -4356,84 385,637 4742,48 -4356,84 40% 60% -4445,82 162,878 4608,7 -4445,82 45% 55% -4614,84 2,21556 4617,05 -4570,84 50% 50% -4875,62 13,3183 4888,94 -4722,78 55% 45% -5162,85 62,7103 5225,56 -4903,93 60% 40% -5474,87 132,376 5607,24 -5113,29 65% 35% -5809,07 200,821 6009,89 -5346,13 70% 30% -6174,25 268,218 6442,47 -5611,17 75% 25% -6589,55 334,936 6924,48 -5913,99 80% 20% -7073,42 505,506 7578,93 -6265,58 85% 15% -7624,56 707,523 8332,08 -6689,23 90% 10% -8276,16 904,799 9180,96 -7216,35 95% 5% -9105,4 1096,93 10202,3 -7923,06 100% 0% -9999,79 1281,72 11281,5 -8695,89

Wind/solar ratio Period 2015

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100% 0% -3310,69 2144 5454,68 1630,36 Wind/solar ratio Period 2016

How does the mix between solar and wind energy affect hydrogen storage capacity and the energy to load ratio?

How does the mix between solar and wind energy affect hydrogen

storage capacity and the energy to load ratio?

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