RENEWABLE ENERGY LOGISTICS

RENEWABLE ENERGY LOGISTICS

BY

Alexander Boom

S4181832

University of Groningen

Faculty of Economics and Business

Pre-MSc Supply Chain Management

March 2020

J.a.boom.1@student.rug.nl Student number: S4181832 Supervisor: Jan Eise Fokkema

Noorderkerkstraat 2 9712 RB Groningen

ABSTRACT

The use of renewable energy is a sustainable solution to environmental pollution. Wind energy is the most widely used renewable energy. In this study, the goal is to explain how the size of a wind park and the size of hydrogen storage affect the percentage of the electricity demand that can be met for a self-sufficient city. This study shows that the size of a wind park does affect the percentage of electricity demand that can be met. The hydrogen storage only influences the percentage that meets the electricity demand, as soon as the wind park gets bigger. Since the wind park is generating more electricity, hydrogen storage is continuously replenished. Once the wind park does not produce energy, hydrogen storage can meet the electricity demand. This study shows the effect of a wind park and hydrogen storage. To determine if it is feasible, further research is required.

Key words:

Renewable energy Wind park

Hydrogen storage Self-sufficient city

CONTENT

4. FINDING AND DISCUSSION ... 16

4.1BASE CASE ... 16

4.2MAIN RESULTS ... 18

4.3SENSITIVITY ANALYSIS ... 22

5. CONCLUSION ... 23

1. INTRODUCTION

For the past decades, climate change and global warming are very often-discussed topics. The use of fossil energy as a primary energy source causes environmental pollution on a global scale (Ni et al., 2006). To minimise the problem of using fossil energy as a primary energy source, there is a need for sustainable developments. To create sustainable development, in general, there are three technological changes: energy savings, efficiency improvements in energy production and the replacements of fossil fuels by using renewable energy (Lund, 2007). So far, wind energy is the most widely used renewable energy, especially with the use of onshore installations. Due to the fact, there is a high wind resource availability and more technological experience with this kind of renewable energy these days. (Esteban et al., 2011). However, wind energy fluctuates and is, therefore, a variable source of energy (Burke & O'Malley, 2011). Wind turbines generate electricity that will be delivered directly to consumers. If there is more electricity generated than requested, the surplus needs to be stored. In order to make better use of the generated renewable energy, hydrogen storage can be a solution (Ni et al., 2006). Hydrogen is a clean energy carrier that can be produced from any primary energy source, by utilising an electrolyser electricity converts into hydrogen. The density of the hydrogen is increased using a compressor. This way, more hydrogen can be stored. If there is subsequently more demand for electricity than is generated, the hydrogen can be converted back into electricity with the aid of a fuel cell. This way, if there is enough hydrogen in storage, the demand can be met with hydrogen storage (Carton & Olabi, 2010).

This research studies the energy logistics of a self-sufficient city. A self-sufficient city is a city that is no longer dependent on the national or regional electricity grid (van den Dobbelsteen et al., 2014). It is interesting for cities to be self-sufficient because in this way the sustainability of the city is more measurable. To be complete independent and therefore self-sufficient, both the import and export of the electricity is linked to this specific city.

‘How does the size of a wind park and the size of hydrogen storage affect the percentage of electricity demand that can be met for a self-sufficient city?’

In this study, the goal is to explain what extent the size of a wind park and the size of hydrogen storage affect the percentage of the electricity demand that can be met for a self-sufficient city. What size must the hydrogen storage be to be able to precisely meet the electricity demand for a particular size of the wind park? This paper studies already existing literature to make and analyse relationships between different researches. This will result in new information which will be used to create a model.

2. LITERATURE REVIEW

In this section, the relevant literature and keywords are defined as support for answering the research question. Relationships between the work of different researches are showed and why they may or may not differ from this research. At last, this section contains a paragraph which explains why this research is relevant and different with respect to other similar researches.

Pao and Johnson (2009) stated that wind energy is a cost-effective and sustainable way to generate electricity, which results in that countries increasingly want to achieve to build more wind parks. Besides building more wind parks, multiple studies, such as Kusiak and Song (2010) and Marmidis et al. (2008), examines various ways to reduce costs with the use of wind energy by determining the ideal layout and location of a wind park. As soon as the wind park is not located at an ideal location and has an inadequate layout, this would lead to energy loss. Several types of research addressed the problem of power fluctuations of wind parks during unstable weather conditions. Burke and O'Malley (2011) and Sorensen et al. (2002) and Cutululis et al. (2007) studied various aspects of this subject. To ensure enough energy is generated, offshore wind parks can provide the solution. By building larger wind turbines, and having stronger wind coming in, which provides higher productivity, more energy is generated. Because of the continually increasing demand for energy and the scarcity of space for building onshore wind parks, offshore wind parks can provide as a solution as well (Leung & Yang, 2012). Leung and Yang (2012) do look at the offshore wind generation but do not look at the generation of electricity in combination with hydrogen storage.

A self-sufficient city is a city that is no longer dependent on the national or regional electricity grid. By constructing a wind park and connecting it directly to the city, the city will no longer need more energy than already comes from the wind park (van den Dobbelsteen et al., 2014). An example of self-sufficiency is that Yamagata et al. (2016) stated that batteries of electric vehicles are being used for the storage of electricity. One battery can supply a household enough electricity for two days. Another example is that Peterson (2016) declares that it is possible to supply a city or urban community with self-sufficient heat- and electricity. Besides, Nyholm et al. (2016) investigate the self-sufficiency of a city that is supplied with electricity generated by photovoltaic installations. However, Peterson and Nyholm et al. do not say anything about electricity generated from a wind park itself to supply a city. Also, Peterson is not mentioning anything about hydrogen storage, as a solution for the surplus of wind energy after supplying a self-sufficient city.

3. METHODOLOGY

In this first paragraph of this section the problem description can be found. Subsequently the conceptual model and an overview of the simulation study is shown. At last the experimental setup, which contains an explanation of the experiments, is given.

3.1 Problem description

This research will focus on how the size of a wind park and the hydrogen storage capacity affects the electricity demand that can be met for a self-sufficient city. The central problem in this research is the most favourable ratio between the size of hydrogen storage and the size of the wind park. Another problem that can arise in this research is that when the self-sufficient city needs large hydrogen storage, whether this can be achieved with current technology.

In this study, there are several parameters, which are already known in advance. To begin with, the amount of electricity produced, this parameter is related to the parameter of the size of the wind park. Because the size of a wind park influences the amount of electricity generated. Also, the size of hydrogen storage is a parameter, which is related to the beginning hydrogen inventory. It is necessary to provide a self-sufficient city with electricity immediately since no electricity is generated in the first hours. For this reason, a hydrogen conversion will have to take place immediately to supply the city with electricity and therefore to satisfy the demand. Therefore, the beginning hydrogen inventory is relevant. Changing the beginning hydrogen inventory can affect the outcome of the demand fulfilment because if the hydrogen storage is not initially filled and no electricity is generated the demand fulfilment decreases. In this research, the size of the wind park and the size of hydrogen storage are variable quantities, since this is necessary to determine how these aspects affect the percentage of electricity demand that can be met. The beginning hydrogen inventory is a fixed quantity because, in this study, it is equated with the size of the hydrogen storage.

variables are relevant in this research because the outcomes determine whether the electricity demand is satisfied.

3.2 Conceptual model

Conceptual modelling, as seen in figure 1, is the abstraction of a simulation model. In this chapter, the objectives, inputs, outputs, content, assumptions and the simplification of the conceptual model will be explained and finally presented schematically in a simulation model and a logic flow diagram. The simulation model is created to determine whether the size of a wind park and the size of hydrogen storage does or does not affect the percentage that can be met for a self-sufficient city.

Objectives

By the end of this research, the following objectives need to be achieved:

- How does the size of a wind park affect the percentage of electricity demand that can be met? - Hoe does the size of hydrogen storage affect the percentage of electricity demand that can be met?

- How does the size of a wind park and the size of hydrogen storage affect the percentage of electricity demand that can be met for a self-sufficient city?

Inputs (parameters)

The simulation model presents in figure 1 has several inputs and are, therefore, all parameters, because the quantities are already known in advance.

- The capacity of the wind park: What capacity should be assumed for the most favourable use of wind energy in relation to the size of hydrogen storage.

- Size of hydrogen storage: What capacity should be assumed for the most favourable use of hydrogen storage with regard to the produced electricity.

- Beginning hydrogen inventory: The beginning hydrogen inventory will begin as the maximum size of the hydrogen storage.

Outputs (variables)

With the given input, there are different outcomes through the process of the simulation model. The outputs are as follows:

- Hydrogen storage at the end of each period: The hydrogen storage at the end of the period is the amount of hydrogen left in storage after each hour. The hydrogen storage at the end of each period is determined by subtracting the beginning hydrogen inventory with hydrogen storage to demand and adding up the electricity to hydrogen storage (efficiency rates of 0.7 included).

- Electricity to hydrogen storage: If the demand is met and there is still electricity left, this will be converted into hydrogen and is going to storage. Based on the amount of electricity that is left, the efficiency of the electrolyser of 0,7 will determine how much electricity is absorbed in the hydrogen storage (Ananthachar & Duffy, 2005). The electricity to hydrogen storage is calculated by subtracting the electricity demand with the wind energy produced. If there is still electricity left this will be multiplied by the efficiency rate, which results in the amount of electricity going to hydrogen storage.

- Hydrogen storage to demand: As soon as the electricity demand is not met, hydrogen will be converted into electricity in order to meet the remaining demand. Based on the amount of electricity that is still required, the fuel cell with an efficiency of 0,7, will determine whether the demand can be met or not (Ananthachar & Duffy, 2005). Hydrogen storage to demand is calculated through the amount of electricity that is needed, multiplied by the efficiency rate.

Simulation model

Figure 1 shows the simulation model. The parameters are shown on the left side of the model. These parameters are the input of the simulation model. The variables are shown on the right, these are the outcomes of the model.

Figure 1: Overview of simulation study

Content

The scope and the level of detail are shown schematically in figure 2; a logic flow diagram.

Figure 2: logic flow diagram of the simulation model

The conceptual model is structured as follows. Based on figure 2, it can be concluded that the conceptual model is a one-hour repetitive process. The process in this research will take 8760 hours, which is exactly one year. This is because a certain amount of wind is generated in a given hour. Besides, the percentage of the demand that can be met is calculated of every hour in a year. This will now be explained step by step:

o No? Is there enough energy left in the hydrogen storage?

▪ No? The demand has not been met; nothing can be done about this. Go to the next hour.

▪ Yes? The fuel cell converts the hydrogen back into electricity and the electricity goes straight to the demand.

o Yes? The generated electricity goes straight to the demand.

- After the demand has been met, it is important to know whether there is any electricity left. This is important because the hydrogen storage, if necessary, can then be supplemented. Is there any electricity left?

o No? Go to next hour

o Yes? Is there enough space left in the hydrogen storage?

▪ Yes? The electrolyser converts the leftover electricity into hydrogen, which will be stored for the following hours.

▪ No? This means curtailment. Go to next hour - At last, is this the end of time horizon?

o No? Go to next hour.

o Yes? This is the end of time horizon; the process ends here.

Table 1: Different flows of the conceptual model

Once the size of the wind park and the size of the hydrogen storage have been determined, the above table can be filled in every hour, for the following 8760 hours. This way it is possible to look at the efficiency of the logic flow diagram seen in figure 2.

Simplifications of the model

The scope of this research is aims at the supply of electricity to one city, so other cities or not included in the model. Nevertheless, this research may apply to other cities, but they will not be mentioned in this research. Besides, this research does not include the curtailment of wind energy. If the electricity demand is met and the hydrogen storage is already filled, and there is still electricity left, this means there is curtailment. Although this could be an essential variable, curtailment is not included in this research.

Assumptions

The following assumptions were made:

- This research assumes that the capacity of the fuel cell and the capacity of the electrolyser are infinite (there is no capacity limit), so they will not be a bottleneck throughout the hydrogen conversion process.

- The conversion efficiency of both the electricity to hydrogen and the hydrogen to electricity is 0,7. The efficiency of an electrolyser is 0,68. Therefore this research chose to round this number to 0,7. The conversion loss of the fuel cell is 0,3 and therefore has an efficiency of 0,7 (Ananthachar & Duffy, 2005). These values will not be changed during this research.

- Kusiak and Song (2010) mentioned, the ideal location and layout of the wind parks are important because of the energy loss. This research assumes the wind park is ideal located with the ideal layout, to minimize the energy loss. So the potential energy loss from these aspects will not be included within this research.

3.3 Experimental setup

This section describes which data is used and where it comes from, and it afterwards it describes the base case experiment, the main experiment and the sensitivity analysis.

Wind energy data

The hourly wind generation data, used in this research, are made with specific wind speed data from the KNMI (Koninklijk Nederlands Meteorologisch Instituut, 2020). The KNMI is the weather forecast institute in The Netherlands, which has its headquarter in De Bilt. The KNMI collects all wind data that is generated across all stations in The Netherlands. The maximum of wind energy produced by a wind park size of 15 MWp is 100 MWh, the minimum is 0 MWh, and the mean is 28 MWh.

The electricity demand of households’ data

The data used in this research is gathered from Liander. Liander (2020) is a Dutch utility company which operates in the distribution of electricity in the Netherlands. The specific data, which is used in this research, is the electricity demand of a given group of households of every hour in one year. This demand fluctuates every hour. The total electricity demand contains 16.248 MWh over the whole year. The maximum electricity demand is 3,9 MWh, the minimum is 0,8 MWh, and the mean is 1,9 MWh. Furthermore, a simulation model was built to examine all the parameters and variables. Most of this data is gathered from literature and other researches.

To determine which values are used in this research, a realistic and efficient balance between the size of the wind park and the hydrogen storage capacity needed to be found. The values, which are shown in table 2, were chosen by continually filling in the simulation model. As soon as the most useful values came out, these values are determined and noted. Useful values in this research are the values which are in line with the efficient fulfilment of the electricity demand.

Table 2 shows that, for the main experiment, a total of 25 experiments are performed. Both the hydrogen storage (in MWh) and the wind park size (in MWp) have five different values. Based on the amount of electricity produced, in combination with these parameters, a matrix can be drawn up on the basis of these results.

Table 2: Experimental factors

The values used for the main experiment are based on the same method used for the base case. For example, with a wind park size of 5 MWp, the fulfilment is just over 20 percent, and with a wind park size of 25 MWp, the fulfilment is over 80 percent. Besides, the hydrogen storage capacity influences whether the above percentages rise or fall. As the hydrogen storage capacity increases, the percentage that has been met, increases as well. By continuously taking steps of 5 MWp, precise results emerge. Based on these values, the steps per parameter have been chosen. Therefore, the size of the wind park is a growing step of 5 (MWp), and the size of the hydrogen storage is a growing step of 200 (MWh).

4. FINDING AND DISCUSSION

In this chapter the results and the associated discussions will be presented. At first, the base case will be explained and discussed in section 1. At second, the main results will be shown and discussed in section 2. Afterwards, a sensitivity analysis is performed in section 3.

4.1 Base case

In this research, the base case experiment acts as the basis of the simulation model and the results. Table 3 describes all parameters and variables that are relevant to the base case experiment.

Parameters:

Wind park peak capacity 15 MWp

Capacity hydrogen storage 500 MWh

Beginning hydrogen storage 500 MWh

Conversion efficiencies 0,7

Variables:

Total electricity demand 16.248 MWh

Total electricity produced 16.987 MWh

Total electricity to demand 8255 MWh

Total electricity to hydrogen storage 8193 MWh

Total hydrogen storage to demand 6111 MWh

Percentage of demand fulfilment 77%

Table 3: Base case experiment

Demand fulfilment Hours

Winter 83% 8019 t/m 1416

Spring 77% 1417 t/m 3626

Summer 75% 3627 t/m 5834

Autumn 75% 5835 t/m 8018 Table 4: Demand fulfilment base case (per season)

Electricity produced Hours

Winter 25% 8019 t/m 1416

Spring 25% 1417 t/m 3626

Summer 20% 3627 t/m 5834

Autumn 30% 5835 t/m 8018 Table 5: Electricity produced base case

Storage to demand Hours

Winter 35% 8019 t/m 1416

Spring 23% 1417 t/m 3626

Summer 20% 3627 t/m 5834

Autumn 22% 5835 t/m 8018 Table 6: Storage to demand base case

Figure 3: Electricity produced (MWh) base case

As indicated in Table 3, the parameters of the base case do not meet the requirements of the city's self-sufficiency. Therefore, the parameters need different values. If the city wants to call itself self-sufficient, the demand fulfilment will have to be 100%. The next section will test multiple values of the parameters in order to achieve a demand fulfilment of 100% and thus complete self-sufficiency.

4.2 Main results

This section will show the main results of the research. All results are supported by tables, graphs or matrices.

Figures 4 and 5 show the extreme values of the size of the wind park and the hydrogen storage capacity. In figure 4, the x-axis shows the hydrogen storage capacity (in MWh) and the y-axis shows the percentage of demand fulfilled. If the wind park size is 5 MWp, it does only fulfil 20 till 23 percent of the electricity demand (with a build-up hydrogen storage from 100 to 900 MWh). Figure 4 clearly shows that hydrogen storage has little influence on the demand fulfilment if the wind park size is 5 MWp.

Figure 4: Wind park size 5 MWp

However, as seen in figure 5, once the wind park size (x-axis) goes up, the percentage of demand fulfilment goes up as well (y-axis). Therefore, the wind park size has much influence on the demand fulfilment, regardless of the size of the hydrogen storage (which in this case is 100 MWh).

Figure 5: Hydrogen storage size 100 MWh 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 05 10 15 20 25 % o f d em an d fu lf ill ed

Based on the research question, the matrix in table 7 is drawn up. The values of the hydrogen storage capacity (MWh) are shown on the vertical axis of the matrix. The horizontal axis shows the values of the size of the wind park (MWp). The values, shown in table 7, are the percentages of the fulfilled electricity demand. The heat map distinguishes the low percentages from the high percentages, where red is low and green is high.

Table 7: Overview of demand fulfilment

As seen in table 7, the total electricity demand of 16.248 MWh is fulfilled as soon as the hydrogen capacity is at least 700 MWh, and the size of the wind park is 25 MWp. However, if the size of the wind park is 5 MWp and the hydrogen storage capacity is 100 MWh, the fulfilment is only 20 percent, which is 3250 MWh.

Figure 6: Overview of demand fulfilment

Table 8 shows how the percentages of electricity demand fulfilment (as illustrated in table 7) are divided by the percentage of wind park supply and the percentage of hydrogen storage supply. It is interesting to see the dynamics how the share of fulfilled energy demand, through either direct energy supply of the wind park or the earlier accumulated energy stored in the hydrogen storage facility, changes with the wind park size and storage capacity. Hydrogen storage does have an impact, during specific period times in the year and at a particular wind park size. The impact of hydrogen storage mainly applies to the winter months. As table 5 shows that the hydrogen storage provides, relatively, the city with the most energy in winter. Table 8 shows, that especially when the wind park size is bigger, a big share of the fulfilled electricity demand is supplied through earlier accumulated energy in the hydrogen storage.

Based on the earlier accumulated energy in the hydrogen storage, table 9 is drawn up. Table 9 shows the average hydrogen storage utilization. It clearly shows that when the wind park size increases, the hydrogen storage is increasingly filled. Throughout the year, the hydrogen storage at a wind park size of 25 MWp and hydrogen storage of 900 MWh is, on average, 80% full. This high utilization is reflected in table 8 in the share of the hydrogen storage supply. For this reason, table 8 and 9 are a bit contradicted in relation to figure 6. This is because figure 6 shows that hydrogen storage does not have much influence on the demand fulfilment, while that is not always the case.

Table 9: Average hydrogen storage utilization

Discussion

4.3 Sensitivity analysis

A sensitivity analysis is performed with the parameter hydrogen storage at beginning of period. The size of the hydrogen storage and the size of the wind park are, in this analysis, the same values as in the base case. As seen in table 10, the values of the beginning hydrogen storage are set between 50 MWh and 500 MWh. Based on the ten experiments, table 8 shows that there is little difference between the lowest and highest value. With a difference of less than 2 percent of demand fulfilment, the parameter its sensitivity is very low and is therefore not remarkable. Therefore, it is more important that the hydrogen storage is filled at the beginning of the year, so that the demand can at least be met during the first hours. A sensitivity analysis is performed with more values of the hydrogen storage at beginning of period. However, none of the outcomes made the difference in the percentage of demand fulfilled.

Beginning hydrogen storage (MWh) Percentage of demand fulfilment (%) 50 75,6 100 75,6 150 75,8 200 76,2 250 76,4 300 76,0 350 76,6 400 76,9 450 76,9 500 77,3

5. CONCLUSION

This study aimed to answer the following question: ‘How does the size of a wind park and the

size of hydrogen storage affect the percentage of electricity demand that can be met for a self-sufficient city?’ This study shows that the size of a wind park enormously affects the percentage

of demand fulfilment. However, the size of hydrogen storage in itself does not affect the percentage that meets the electricity demand of a self-sufficient city to the same extent. Nevertheless, the combination of the size of the hydrogen storage and the size of the wind park does have a significant effect on this percentage. Once the wind park is getting bigger, the influence of the hydrogen stage on the demand fulfilment increases. Since the wind park is generating more wind energy, the hydrogen storage is continuously replenished. As soon as the wind park does not produce wind energy for a few periods, the hydrogen storage, when filled, can always meet the electricity demand if necessary. Hence, as long as the wind park is large enough to supplement the hydrogen storage continuously, both parameters affect the percentage of electricity demand that can be met for a self-sufficient city.

Another aspect, in addition to the demand fulfilment, is the parameter hydrogen storage at the beginning of each period. The sensitivity analysis shows that changing the value of this parameter has little to no influence on the demand fulfilment. Therefore, no further research is necessary for this parameter.

There are some limitations to this research. First of all, all data and findings are based on just one wind year, which affects the reliability and validity of the research. Secondly, this study did not include curtailment of wind energy, while curtailment is an essential aspect of this sustainable process. One way to reduce curtailment could be the supply of hydrogen to meet the heat demand of a city. This can be done by turning the hydrogen storage into a hybrid system that can both supply hydrogen and convert hydrogen into electricity. At last, this study did not look into the feasibility and affordability of the size of the wind park and the size of hydrogen storage. Based on these limitations, further research is recommended.

REFERENCES

A. van den Dobbelsteen, N. Tillie, S. Broersma, M. Fremouw (2014). ‘The Energy Master Plan:

Transition to self-sufficient city regions by means of an approach to local energy potentials’ 30th International Plea Conference 16-18 December 2014, CEPT University, Ahmedabad

Andrew Kusiak and Zhe Song (2010). ‘Design of wind farm layout for maximum wind energy

capture’ Renewable Energy 35 (2010) 685–694 Intelligent Systems Laboratory, Mechanical

and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242 – 1527, USA

Burke, D. J., & O'Malley, M. J. (2011). ‘Factors Influencing Wind Energy Curtailment.’ IEEE

Transactions on Sustainable Energy, 2 (2): 185-193 Dublin: Institute of Electrical and

Electronics Engineers.

Carton, J. G., & Olabi, A. G. (2010). ‘Wind/hydrogen hybrid systems: Opportunity for

Ireland's wind resource to provide consistent sustainable energy supply.’ Mechanical Engineering, Dublin City University, Dublin 9, Ireland.

Daniel Kroniger, Reinhard Madlener (2014). ‘Hydrogen storage for wind parks: A real

options evaluation for an optimal investment in more flexibility’ Applied Energy xxx (2014) xxx–xxx a Institute for Power Plant Technology, Steam and Gas Turbines (IKDG), Faculty of

Mechanical Engineering, RWTH Aachen University, Mathieustr. 9, 52074 Aachen, Germany Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University, Mathieustr. 10, 52074 Aachen, Germany

Dennis Y.C. Leung, Yuan Yang (2012). ‘Wind energy development and its environmental

impact: A review’ Renewable and Sustainable Energy Reviews 16 (2012) 1031–1039

Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China

Dolores Esteban, M., Javier Diez, J., López J., & Negro V. (2011).‘Why offshore wind energy?’

Renewable Energy 36 (2011) 444e450 Universidad Politécnica de Madrid, C/Profesor

Aranguren S/N, 28040 Madrid, Spain

Grigorios Marmidis, Stavros Lazarou, Eleftheria Pyrgioti (2008). ‘Optimal placement of wind

turbines in a wind park using Monte Carlo simulation’ Renewable Energy 33 (2008) 1455– 1460 High Voltage Laboratory, Department of Electrical and Computer Engineering,

University of Patras, 26500 Rio, Patras, Greece

Hendrik Lund (2007). 'Renewable energy strategies for sustainable development' Energy 32

(2007) 912–919 Department of Development and Planning, Aalborg University, Fibigerstraede

13, 9220 Aalborg, Denmark

Liander (2020) – Beschikbare data: https://www.liander.nl/partners/datadiensten/open-data/data

Lucy Y. Pao and Kathryn E. Johnson (2009). ‘A Tutorial on the Dynamics and Control of Wind

Turbines and Wind Farms’ American Control Conference, Hyatt Regency Riverfront, St. Louis, MO, USA

Ni, M., Leung, M. K., Sumathy, K., & Leung, D. Y. (2006). 'Potential of renewable hydrogen

production for energy supply in Hong Kong.' International Journal of Hydrogen Energy 31 (2006) 1401–1412. Pokfulam Road, Hong Kong SAR, PR China: Department of Mechanical

Engineering, The University of Hong Kong.

Nyholm, E., Goop, J., Odenberger, M. & Johnsson, F. (2016) ‘Solar photovoltaic-battery

systems in Swedish households - Self-consumption and self-sufficiency’ Applied Energy, 183: 148-159 Energiteknik, Chalmers Tekniska Högskola, SE - 412 96 Göteborg, Sweden

Petersen, J-P. (2016). ‘Energy concepts for self-supplying communities based on local and

renewable energy sources: A case study from northern Germany.’ Sustainable Cities and Society, 26, 1-8. Technical University of Denmark, ICIEE, Nils Koppels Allé, Building 402,

2800 Kgs. Lyngby, Denmark

Poul Sorensen, Anca D. Hansen, Pedro Andre, Carvalho Rosas (2002). ‘Wind models for

simulation of power fluctuations from wind farms’ Journal of Wind Engineering and Industrial Aerodynamics 90 (2002) 1381–1402 Wind Energy Department, Riso National

Laboratory, P.O. Box 49, DK-4000 Roskilde, Denmark. Department of Electrical Power Engineering, Technical University of Denmark, DK-2800 Lyngby, Denmark

Poul Sorensen, Nicolaos Antonio Cutululis, Antonio Vigueras-Rodriguez, Leo E. Jensen, Jesper Hjerrild, Martin Heyman Donovan, and Henrik Madsen (2007). ‘Power Fluctuations

From Large Wind Farms’ IEEE Transactions on Power Systems (Volume: 22, Issue: 3, Aug. 2007)

S.A. Sherif, F. Barbir , T.N. Veziroglu (2005). ‘Wind energy and the hydrogen economy -

review of the technology’ Solar Energy 78 (2005) 647–660 University of Florida, Department

of Mechanical and Aerospace Engineering, 232 MAE-B, P.O. Box 116300, Gainesville, FL 32611-6300, United States

Steward Robinson (2014). ‘Simulation – the Practice of Model Development and Use’ 2nd edition Professor of Management Science, Loughborough University

V. Ananthachar & J.J. Duffy (2005). ‘Efficiencies of hydrogen storage systems onboard fuel

cell vehicles.’ Solar Energy 78 (2005) 687–694 Energy Engineering Program, University of

Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, USA

Yoshiki Yamagata, Daisuke Murakami, Kazuhiro Minami, Nana Arizumi, Sho Kuroda, Tomoya Tanjo and Hiroshi Maruyama (2016). ‘Electricity Self-Sufficient Community

Clustering for Energy Resilience’ Energies 2016, 9, 543; doi:10.3390/en9070543