Research Paper: Supply Chain Management Pre-MSc
University of Groningen, Faculty of Economics and Business
Biomass as a variation management strategy in a
wind-hydrogen storage system
Author:
Remco Timmermans
Student number:
S4034627
Date:
22 June 2020
Lecturer:
JE Fokkema
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Abstract
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Content
Abstract ... 3 1. Introduction ... 5 2. Theoretical background ... 6 3. Methodology ... 8 3.1 Problem Description ... 8 3.2 Conceptual model ... 9 3.2.1 Objectives ... 9 3.2.2 Context ... 93.2.1 Parameters and Variables ... 10
3.2.2 Scope and limitations ... 10
3.2.3 Assumptions and simplifications ... 10
3.3 Experimental setup ... 11
3.3.1 Data input ... 11
3.3.2 Experimental setup ... 13
4. Results and Discussion ... 14
4.1 Base case experiment ... 14
4.2 Storage Capacity ... 15
4.3 Size wind park ... 16
4.4 Sensitivity analysis: storage capacity & size wind park ... 17
4.4.1 Demand satisfaction ... 17
4.4.2 Conversion losses ... 18
4.5 Extra analysis: Seasonal deployment of bioenergy ... 19
5. Conclusion ... 22
References ... 24
Appendix ... 26
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1. Introduction
In 2015, many countries in the world signed the Paris Agreement. This agreement states that these countries are committing to reduce greenhouse gas emissions to not exceed the 1.5 °C temperature above pre-industrial levels (IPPC, 2018). To succeed in this goal, many counties need to part from fossil fuels to reduce the expelling of carbon dioxide. To do that, countries are investing in renewable energy technologies to provide green electricity and are setting goals for the upcoming decades to be fully dependent on RE.
The characteristics of power generation by RE are more complex and different than fossil fuels. Fossil fuel has the benefit that the electricity production is demand-driven and production levels can be regulated more easily than most RE. Which means it is easier to meet the required demand of customers. There are three major RE sources, these are biogas, wind, and solar energy. Wind and solar power plants are characterized by high fluctuation. These power plants are dependent on wind speeds and the number of sun hours which leads to a variable power generation (Katrin Schaber, et al., 2012). This fluctuating production makes it difficult to balance mismatching energy supply and demand. The key is to be flexible in energy systems (Huber et al., 2014). Energy storage, for example, is a tool to increase flexibility. The options for energy storage are, for example, by batteries or hydrogen. But it has been acknowledged that hydrogen storage is more feasible economically and in terms of infrastructure on a large scale (Faaij & Blanco, 2018). Statistics from CBS (2019) show that the largest contribution to The Netherlands will come from wind energy. And planned projects in the future, as project NortH2 (Mulder, 2020), are indicating that the production of green hydrogen from wind energy will be the most important renewable source for the Dutch government to reach their climate goals in 2050. The downside of hydrogen storage, however, is the conversion losses that occur. The more energy is stored for balancing energy supply and demand, the more conversion losses there are. The electricity production by biomass, more precisely biogas, is a RE that is not dependent on weather conditions and the production is very predictable (Holm-Nielsen, et al., 2009). Therefore, biogas plants could play a beneficial role in the integration of weather-dependent RE (wind/solar energy) in future energy systems. It can also help, next to balancing mismatching energy supply and demand, reducing the conversion losses that go with stored (hydrogen) energy (Hahn, et al., 2013). Thus combined heat and power (CHP) plants, which produce electricity from biogas, can be used in combination with weather dependent RE. However, this has not yet been done in the existing literature.
This paper is, therefore, going to investigate how a wind-hydrogen storage system in combination with bioenergy affects conversion losses and the alignment of matching demand with supply. This is going to be modeled in different wind years. The following research question is determined: How does
electricity from biomass, combined with a wind-hydrogen storage system, affect demand satisfaction and energy conversion losses?
6 described in the results and discussion section. Finally, the overall conclusion will be provided next to suggestions for possible future research.
2. Theoretical background
The energy system of the future will have to integrate very fluctuating VRE’s caused by weather-dependent sources such as wind and solar PV. Electricity produced from biomass is a dominant renewable power source that can produce demand-driven and is dispatchable on a large scale. The current literature shows that biomass is widely determined as an essential RE that can be used as a strategic complement to VRE’s by supplying electricity during hours of high peaks in electricity demand (Johansson, et al., 2018) (Bijlsma, 2010) (Katrin Schaber, et al., 2012) (Laleman, et al., 2012) (Steinke, et al., 2012). However, we see that the calculations and estimations in these researches are not made specific in a practical context but held in a more general context. Besides, the actual hour-to-hour decision making process in a weather-dependent storage system in combination with biomass, and the impact of this biomass in these energy systems is currently missing in the literature on this topic. Therefore, this research is needed to fill in this gap.
Balancing fluctuating VRE’s with biomass generation
In the current electricity system, most electricity is generated with highly controllable assets that have guaranteed availability and predictable production. The production of electricity generally responds to changes in the electricity price. The expectation is that the share of wind and solar generation is increasing in future energy systems. As a consequence, assets with limited availability and limited predictability need to be integrated. Therefore, biomass offers the significant advantage of high availability as well as high predictability. On the other hand, biomass could overcome also a resource scarcity just like fossil fuels (Lalemana, et al., 2012).
In the study of Laleman, et al. (2012)the potential of biomass electricity as a backup for VRE in the electricity mix was studied. Their findings showed that in scenario's were VRE's are dominant, biomass capacity can significantly lower the total back-up needs.
In (Bijlsma, 2010), Tenergy services published a report in cooperation with 2-G Energietechnik about the possible role CHP’s could play in delivering electricity in combination with wind energy on peak moments. They concluded that steerable CHP’s can balance the required (German) demands unless there is enough biomass available and wind energy will increase in the future. This is not a scientific paper but research done by two companies. Therefore, the outcomes could be featured more positively. Critically, they analyzed demand and production data from random days in a year and based their calculations on that. They did not look at the hour to hour demand quantities but total demands for a certain period.
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Biomass in combination with storage of VRE’s
Several studies have indicated that a 100% renewable energy system (RES) on a large scale is impossible without variation management strategies such as storage (hydrogen), demand-side management (DSM), supply-side management (SSM). The fluctuation of VRE's would be too much to fulfill 100% demand. The right mix of VRE's and variation management strategies is depending on the number of resources that are available in certain areas to meet the required demand. The research of Steinke et al. (2012) examined, partly, the amount of back-up generation is needed for a 100% RES in Europe. The result was that the combination of storage and biomass (dominant dispatchable power source) is vital for a 100% RES for Europe. These outcomes are confirmed in other papers as well (Huber, et al., 2014) (Weitemeyer, et al., 2015).
Thus, a combination of enough back-up generation (storage) and biomass production is needed for a 100% RES. But the electricity mix is also dependent on beneficial weather and wind climate. Johansson, et al. (2018) added different climatic variables to their model. Outcomes showed that regions with, on average, a stable wind/sun climate rely more on biomass when there are no other variation management strategies such as storage (hydrogen).
The literature is showing that a 100% RES on a large scale is possible with a combination of multiple VRE’s, hydrogen storage capabilities, and baseload technologies (mainly biomass). Where hydrogen storage is a very important complement. The energy storage capabilities of hydrogen are causing also disadvantages. Energy losses occur when (wind) energy is converted by an electrolyzer into hydrogen. And more losses occur when the hydrogen is converted back by a fuel cell into electricity (Lamy, 2016). This disadvantage implies that storing all of the energy that is generated by VRE’s is not efficient. The earlier described studies, that have hydrogen storage as an important variable in their models, did not look critical at conversion losses. Besides, the discussed studies were mainly based on general estimations and calculations.
In conclusion, we see in the current literature papers that investigated biomass as a potential grid stabilizer. Further on we see that it is combined with VRE's, variation management strategies, and climatic variables. As an addition to this, we are going to simulate a weather-dependent (wind) hydrogen storage system in combination with firm bioenergy production in different (wind) years. This will be simulated in an hour-to-hour decision making process to investigate the mechanisms behind the impact on conversion losses and demand satisfaction. And to research what the impact of bioenergy is as a grid stabilizer and balancing function between demand and supply. This has never been done in the existing literature and would be a relevant addition to this topic. Mainly the hour-to-hour process simulation of a wind-hydrogen system in combination with biomass is something that is missing in the current literature.
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3. Methodology
3.1 Problem Description
As discussed in the introduction and theoretical section, it is difficult to integrate high fluctuating RES, like wind, into the energy system. Implications arise in terms of balancing demand and supply. Biomass in combination with a hydrogen storage tank could be an important RES to balance the demand and supply. So, the main problem addressed in this paper is concerned with how a group of households can be realistically provided with electricity from wind- and bioenergy, in a way that the demand is satisfied and the conversion losses are limited.
To investigate this, a theoretical model was developed (see figure 1). Here, a wind park (on land) and a CHP in The Netherlands is going to supply a small group of Dutch households. The wind park is connected with a hydrogen storage tank that can store the maximum amount of hydrogen measured in megawatt-hour (MWh).
The wind park is consisting of wind turbines that produce and supply an amount of MWh per year that goes directly to the electricity grid. The energy surplus of the wind turbines is converted into hydrogen and stored in a storage tank. This tank has a limited storage capacity. The CHP is producing an amount of MWh per year and supplies it straight to the electricity grid. The process of converting wind energy into hydrogen (P2G) and hydrogen into electricity (G2P) causes conversion losses because of the amount of electricity that is needed in the conversion process. However, it is vital to investigate how the number of wind turbines, bioenergy production, and maximum storage capacity affects demand satisfaction and conversion losses in an hour-to-hour simulation. Decision-makers can use this knowledge to make future choices on this topic.
The input data of the model is the hourly wind production, electricity demand, and biogas production. More details on the input data will be given in paragraph 3.3.1.
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3.2 Conceptual model
3.2.1 Objectives
The main objective of this research is to answer the research question. Therefore, additional objectives can be determined. These objectives will be explained in the same sequence in the result section.
(1) Determine the relationship of the fluctuating wind speeds on the hydrogen storage inventory development
(2) Determine the impact of bioenergy on the wind-hydrogen storage system to satisfy demand (3) Determine the impact of bioenergy on the wind-hydrogen storage system and how this affects
conversion losses
(4) Determine what is needed to fully supply the group of households within different wind years
3.2.2 Context
In the extend of paragraph 3.1, a more detailed explanation is given on the decisions that are made in the simulation model. In figure 2, a logic flow diagram of the model is shown with a time frame of one hour.
10 The simulation starts with the generation of bioenergy and wind energy. A fixed amount of bioenergy is produced by a CHP. This generated electricity is going straight to the electricity grid to fulfill the demand. When wind energy is generated there arises three possibilities. Firstly, if the amount of MWh (wind energy) is lower than the demand minus the bioenergy, then there would not be enough electricity to fulfill the demand. Then the remaining demand will be delivered by the storage tank if there is enough available. Not enough storage available leads to demand that could not be satisfied and results in a power outage. Secondly, if the generated wind energy minus bioenergy is higher than the actual demand, the amount that is needed for the demand will be delivered and the surplus will be stored in the storage tank. If the maximum capacity of the storage tank is reached, the energy surplus will then unfortunately be curtailed. Thirdly, if the generated amount of wind energy minus bioenergy is equal to demand then the electricity will go straight to the electricity grid to fulfill the demand. After the described processes and decisions, the simulation ends and will start again with the next hour. This will be repeated every hour for one year in total. This results in an 8760 simulation run.
3.2.1 Parameters and Variables
The parameters that can be managed and that are influencing the model are the wind park capacity, the maximum hydrogen storage capacity (MWh), the start hydrogen inventory (MWh), and the amount
of bioenergy produced per hour (MWh). The wind park capacity is simply the number of wind turbines
used. The maximum hydrogen storage capacity is the maximum amount of MWh that a storage tank can store. The start inventory of a hydrogen tank is the amount of MWh storage at the beginning of the simulation. The fixed amount of bioenergy is the number of MWh that is produced every hour. These parameters are indirectly influencing the variables. These variables are demand satisfaction and
conversion losses. The demand satisfaction is noted by a percentage and measured by dividing ‘the
hours in which demand could not be supplied’ by ‘the total of hours in simulation run’. The calculation of the conversion losses is more complicated. Every hour in which a G2P and P2G process were occurring, the conversion losses were noted in MWh and summed up. The total conversion losses were then divided by the total of wind energy that was produced in that year. This gives a good view of the amount of wind energy that is lost due to the conversion process.
3.2.2 Scope and limitations
The scope of the research is limited to the supply of electricity and not the supply of other energy sources such as gas. Other sources require different technical aspects that are not relevant to the aim of this research. Besides, factors such as costs and infrastructural design are excluded from the research because it is not relevant for the aim of this research as well.
3.2.3 Assumptions and simplifications
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Another assumption concerns the capacity and conversion losses of the electrolyzer and fuel cell. It is assumed that there is no limit on the amount of MW that the electrolyzer and fuel cell can convert per hour to ensure that the model is kept simple. The conversion rate of the P2G process is set on 70% and the G2P process is set on 75% (Lamy, 2016). Further conversion losses that occur by transport and storing hydrogen are not included in the model because the losses are very minimal (Yang & Aydin, 2001) and would have therefore not a significant impact on the outcomes.
Further assumptions are made in the calculation of the amount of biomass that is available in The Netherlands, and that is intended for the electricity consumption of households. More details are given in 3.31.
3.3 Experimental setup
This section will describe the specific data input, base case experiment, and other experiments.
3.3.1 Data input
As mentioned before, there are three sorts of hourly data input in the model that will be discussed in this section. First, the production of wind energy, secondly the electricity demand of households and thirdly the bioenergy that is produced.
Wind energy data
The hourly generation data wind is created by using wind speed data from the Royal Netherlands Meteorological Institute (KNMI, n.d.). This institute collects hourly wind speed data (m/s) on various stations in The Netherlands. For this research, the data of average hourly wind speeds in Almere (Dutch city in the province Flevoland) were used. The reason is that it comes close to reality because it is in line with the choice to use wind turbines of a wind park in Flevoland as mentioned in paragraph 3.2.3. The wind speed data were now the input for the following formula: Power output = 1/2 * density
of wind * blade length^2 * 𝜋 * efficiency. (Lydia, et al., 2014). The cut-in speed and cut-out speed was
set on 3 m/s and 21 m/s (Lydia, et al., 2014). The maximum capacity of the wind turbine is 1.65 MW, an efficiency rate of 40%, and the blade length is 66 meters (Nuon, 2002). The power curve of one wind turbine is shown in figure 3, with on the y-axis the power output in MWh and on the x-axis the wind speed in m/s. These calculations resulted in hourly generated power (MWh) for the year 2017, 2018, 2019.
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The electricity demand of households
The hourly electricity demand data were retrieved from a Dutch network operator that delivers energy to households (LIander, 2009). The raw data represents the hourly expected electricity consumption for 10.000 Dutch households during a full year. It is based on the average consumption profiles of the last 20 years. This gives a realistic representation of the electricity demand per hour of households. However, it is chosen to use the profiles of 5000 households (small village) in the theoretical model because it is not realistic to provide a large number of households with electricity from biomass (CE Delft, 2020). The data of 10.000 households is divided by 2 to scale it to 5000 households. See figure 4 for an example of the electricity demand (MWh) profile of three random days. The curves represent the electricity demand per hour for 5000 households. With on the y-axis the MWh.
Bioenergy produced
The use of realistic data of bioenergy production is concerned with the amount of biomass that is available in The Netherlands. The consultancy group CE Delft, did independent research, commissioned by the Dutch government, to the applications and availability of sustainable biomass (CE Delft, 2020). The perspectives of biomass availability for electricity consumption towards 2050 is moderate (CE Delft, 2020). To have a realistic calculation of biomass in the model, the following calculation is made. The share of electricity consumed by households of the total electricity consumption is calculated at 18%. So the estimate of biomass available for electricity consumption for households was assumed to be 18% of the total (biomass electricity consumption) as well, which is approx. 830.000 MWh. Therefore, it has been chosen to equally divide that number by the 5000 households (assumed that there are approx. 7.000.000 Dutch households). This results in that max. 7% of the electricity demand (of 5000 households) is satisfied by electricity from biomass. See table 1 for the calculations (blue rows are numbers form sources).
Estimation of biomass availability for 5000 households
Total electricity consumption 2017 (CBS, 2017) 120000000 MWh Household electricity consumption 2017 (CBS, 2017) 21111111 MWh Percentage households in total consumption 2017 18% Available electricity from biomass (CE Delft, 2020) 4722222 MWh Available electricity from biomass to households 830761 MWh Available electricity from biomass for 5000 households 593,4 MWh Percentage of total electricity consumption of 5000 households 7%
Table 1: estimation of biomass availability
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3.3.2 Experimental setup
In this paragraph, the settings of the base case experiment and other experiments will be described. See table 2 for the parameter settings of the base case experiment and other experiments.
The base case experiment stands for the ‘basic’ or ‘normal’ system. It should imply a realistic functioning system. The size of the wind park and the storage capacity is highly related to each other. To find a certain realistic and efficient balance to supply 5000 households, there was experimented with the number of wind turbines and storage capacity to bridge the months when wind speeds are low. A realistic balance was found at a storage capacity of 500 MWh and a wind park capacity of 10 turbines. Analysis of the generated wind energy showed that the highest peaks, per day in the wintertime, were approx. around 250 MW generation. It is vital to capture those high peaks to supply demand when wind speeds are much lower in the summer. Therefore is decided to double the 250 MW as maximum storage capacity to ensure that high peaks can be captured sufficiently. Besides, the bioenergy production is set on 7% of the total demand, approx. 0,067 MWh per hour. This is the maximum generation of electricity by biomass available (for 5000 households) in The Netherlands, as explained in paragraph 3.3.1. The ‘beginning hydrogen inventory’ is set on 300 MWh for all the experiments. This is to ensure that the model can capture high peaks right away at the beginning of the year. The other fixed parameters are explained in paragraph 3.2.3.
To determine how the ‘mix’ of a wind-hydrogen storage system and bioenergy affects demand satisfaction and conversion losses, it is needed to first understand how the wind-hydrogen storage system alone affects these variables. This is answered in the first two experiments by changing the storage capacity (4.2) and wind park size (4.3). This follows into a sensitivity analysis (4.4) on how both parameters in combination affect the variables. The results of the sensitivity analysis are then tested in a scenario with 0% and 7% bioenergy production which gives insight on the impact of the firm bioenergy supply. In addition, there is an extra analysis (4.5) done on how the available 7% bioenergy can have a bigger impact on the variables by deploying the CHP only during a certain period. This experiment is conducted with the base case parameter settings and executed ones. The bioenergy production is deployed in June, July, and August because research showed that in those months the hydrogen storage inventory was low caused by prolonged low wind speeds.
Parameters Number of experiments Storage capacity (MWh) Size Wind Park (MWp) Bioenergy produced (% of total demand) Conversion rate P2G Conversion rate G2P Beginning hydrogen inventory (MWh) Base case 1 500 10 7% 0,7 0,75 300 Storage capacity (MWh) 15 300 to 1700 10 7% 0,7 0,75 300
Size wind park 10 500 5 to 15 7% 0,7 0,75 300
Bioenergy produced (MWh) 2 500 10 0% and 7% 0,7 0,75 300
Fixed Changeable
Extra analysis:
Seasonal deployment of CHP plant with base case parameter settings
All the experiments are modeled for 3 wind years
(2017, 2018, 2019)
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4. Results and Discussion
In this chapter, the findings of the experiments will be described with graphs and tables. The mechanisms behind the results will be explained and discussed. The results are structured in the order of the experiments as shown in table 2. This means that the base case experiment is discussed first, followed by the storage capacity and wind park capacity (sensitivity) experiments and lastly the experiments on the impact of bioenergy. The focus of the results section is on the relationship between all the experiments because that is what will answer the research question.
4.1 Base case experiment
The base case is one experiment that stands for the ‘basic’ system as explained in paragraph 3.3.2. Table 3 shows the results of the experiment on the investigated variables per wind year and table 4 shows the outcomes of additional numbers.
The notable result, concerning demand satisfaction, is that the percentage of 2017 is 5 percent higher than 2018 despite the total wind energy production (table 4) of 2017 is even 3% lower than 2018. This can be explained by the variance and standard deviation of wind energy production per day (table 5).
For example, the variability is significantly higher in 2018 relative to 2017. This means that the wind speed differences in 2018 were more extreme which led to 15% more curtailment of wind energy (table 4). The curtailment occurred because it could not be stored due to the storage capacity maximum. Therefore, the demand could not always be supplied during periods of low wind speeds. Besides, the demand satisfaction in 2019 is, as well as in 2017, 92% despite even a higher variance. This is can be rebutted by the fact that wind energy production in 2019 was 12% higher than in 2017. These outcomes are not surprising as in other studies have indicated that high fluctuations occur and develop differently each year (Böttcher, et al., 2017).
The same reasoning applies to the conversion losses. The more variance in wind speeds and curtailment of wind energy, the less efficient the storage system works and the fewer conversion losses occur. In 2019, relative to 2017, the variability and wind energy curtailment (table 4) were high but the conversion losses low.
Base case experiment 2017 2018 2019
Demand satisfaction p/h: 92,1% 86,6% 92,1% Conversion Losses: 32,8% 26,6% 28,0%
Wind years
Wind energy production 2017 2018 2019
Variance 1334,3 1716,8 2218,7 Standard deviation 36,5 41,4 47,1
Wind years
Base case (additional outcomes) 2017 2018 2019
Wind energy produced (MWh) 10284 10641 11480
Bioenergy produced (MWh) 587 587 587
Hydrogen end inventory (MWh) 472 80 303 Hydrogen storage capacity average p/h 37,8% 37,6% 44,1% Local electricity demand (MWh) 8124 8124 8124
Wind to demand 3754 3799 3869
Wind to hydrogen storage 4503 3618 4225
Wind to curtailment 97 1674 1576
Bioenergy to demand 587 587 587
Storage to demand** 4326 3838 4222
Bioenergy produced / total demand 7% 7% 7%
Table 3: Base case variable outcomes
Table 4: Base case outcomes of additional numbers
15 For decision-makers, it is therefore important to investigate the variance of wind speeds and determine a storage capacity that can store enough hydrogen to bridge periods with prolonged low wind speeds.
4.2 Storage Capacity
In this paragraph, the experiment with the ascending storage capacity is being discussed. See table 6 for the outcomes.
In this experiment, the storage capacity was expanded with 100 MWh per simulation to investigate the impact on demand satisfaction and conversion losses. Remarkable is that the demand satisfaction in 2017 is reaching his maximum at 600 MWh which is 93% in total. The curtailment of wind energy is then 0 MWh which implies that demand satisfaction can never reach 100%, with base case settings, due to the conversion losses of the supply from P2G and G2P.
Another example of this phenomenon is shown in the graph of figure 5 The blue line represents the conversion losses in 2019 in percentage and the yellow line the curtailment of wind in 2019 in MWh. With on the x-axis the steps of 100 MWh storage capacity and the left y-axis the conversion losses percentage and on the right y-axis the MWh curtailment. The graph illustrates that the increasing storage capacity leads to more conversion losses because more electricity can be stored and
therefore the curtailment of wind energy decreases. So you could say that conversion losses are indicating how efficient the storage system is working.
Storage capacity (MWh) 2017 2018 2019 2017 2018 2019 300 89% 84% 88% 30% 25% 25% 400 91% 85% 91% 32% 26% 27% 500 92% 87% 92% 33% 27% 28% 600 93% 88% 93% 33% 27% 29% 700 93% 89% 95% 33% 28% 29% 800 93% 91% 96% 33% 29% 30% 900 93% 92% 97% 33% 29% 31% 1000 93% 93% 98% 33% 30% 31% 1100 93% 95% 99% 33% 31% 32% 1200 93% 96% 100% 33% 32% 33% 1300 93% 97% 100% 33% 32% 33% 1400 93% 98% 100% 33% 33% 33% 1500 93% 99% 100% 33% 34% 34% 1600 93% 100% 100% 33% 34% 34% 1700 93% 100% 100% 33% 35% 34%
Demand satisfaction Conversion losses
Table 6: Storage capacity experiment outcomes
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4.3 Size wind park
In this paragraph, the experiment of the expanding wind park capacity is going to be discussed. The graph in figure 6 shows the relationship
between the demand satisfaction % and the number of wind turbines used. The remarkable thing that the graph illustrates, is that the lines first rise quickly and then deflects. The deflection visualizes the fact that the closer it comes to a 100% demand satisfaction, the harder it is to supply these remaining demand. The increase of wind turbines enlarges the peaks in wind generation while the storage capacity stays
on 500 MWh. Therefore, more wind energy is curtailed and causes that the percentage increase in demand satisfaction is going slower in the last 10-15%.
The graph in figure 7 shows the relationship between the conversion losses % and the number of wind turbines used. The line of 2017 is an example of how the conversion losses are developing while there is (almost) enough storage capacity available, at all times, to capture all the high peaks of wind generation (as discussed in 4.1, curtailment almost 0 MWh in 2017). The line goes first gradually to the top and then in smaller steps downwards. The
reason is that from 10 wind turbines on, the generated wind can more often be supplied straight to the grid so less demand has to be supplied from storage which leads to fewer conversion losses. The lines of 2018 and 2019 go different because more generated electricity has to be curtailed caused by the higher variance in wind speeds (paragraph 4.1) and the fixed storage capacity of 500 MWh. For decision-makers, it is important to understand, based on the results above, that installing more wind turbines ensures that the surplus in wind generation increases, and more energy has to be curtailed. Therefore, only installing more wind turbines to satisfy 100% of the demand is not efficient. Also expanding the storage capacity should then be taken into account. The trade-off between more wind turbines and curtailment versus fewer wind turbines with a higher storage capacity therefore more conversion losses should be further investigated.
Figure 6: Graph of demand satisfaction% per wind turbine
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4.4 Sensitivity analysis: storage capacity & size wind park
In this paragraph, the sensitivity analysis will be discussed based on the experiments in paragraphs 4.2 and 4.3. With this experiment, the impact of a wind-hydrogen storage system (wind park size and storage capacity) in combination with two bioenergy production scenarios, on the two variables is determined. The results of this analysis are structured in two sub-sections of the two variables.
4.4.1 Demand satisfaction
The graphs in figure 8 are illustrating the outcomes of the experiment on the demand satisfaction variable. The different lines are representing the capacity storage from 300 MWh till 1700 MWh in steps of 200. On the x-axis is the amount of wind turbines and on the y-axis the percentage scale. The graph is divided, for each wind year, into a 7% bioenergy scenario and a 0% bioenergy scenario. The interesting phenomenon is that the deflection of the lines, in the 7% bioenergy scenario, is starting at a higher demand satisfaction percentage than in the 0% bioenergy scenario. This deflection towards 100% is also discussed in paragraph 4.3, which is dependent on the variability of the wind speeds and total wind energy generation during a year. But the use of bioenergy does not accelerate that deflection towards 100%, because the impact to fill the so-called ‘valleys’ is minimal.
18 This statement is related to the conclusions of the research of Johansson, et al (2018). More availability of biomass is needed to make a real impact on peak shaving when the deployment is equally spread over the year.
4.4.2 Conversion losses
The graphs in figure 9 are illustrating the outcomes of the experiment on the conversion loss variable. The different lines are representing the capacity storage (MWh) from 300 MWh till 1700 MWh in steps of 200. On the x-axis is the amount of wind turbines and on the y-axis the percentage scale. The graph is divided, for each wind year, into a 7% bioenergy scenario and a 0% bioenergy scenario.
The phenomena discussed in paragraph 4.3 is similar to the findings here. Namely, more conversion losses occur when the storage capacity is large, and the more rounded the line is in the graph. Besides, the impact of bioenergy on this phenomenon is visible. The line in the bioenergy scenario goes up steeper and faster than in the other scenario. This proves that bioenergy is giving the system a certain
19 boost in capturing wind generation surplus and supplying wind energy from storage with fewer wind turbines.
4.5 Extra analysis: Seasonal deployment of bioenergy
In this paragraph, the extra analysis of seasonal bioenergy deployment is described. This experiment is conducted to try a more efficient deployment of bioenergy production to increase the impact on the demand satisfaction variable. This is within the assumption that the total biomass availability per year for The Netherlands could be available in only 3 months, and as well that the CHP's can produce this amount. This assumption is made because it is not sure whether this is possible. The experiment is executed with the base case parameter settings of 2018.
See the graph in figure 10. The graph is illustrating the development of demand satisfaction per day during 2018 of the base case experiment. The yellow curve on top represents the demand from households per day. Three flows of electricity supply are ‘trying to close’ the space toward this yellow curve in order to satisfy demand. The green bars (1) are representing the bioenergy production, the blue bars (2) the wind generation that went straight to the grid (wind to demand), and the orange bars (3) are the supply from the hydrogen inventory (storage to demand). In this way, demand satisfaction is visualized. The grey circles in the figure are indicating certain ‘holes’ which are periods in where demand could not be satisfied. The red curve is showing the average hydrogen storage development per day (relative to the right y-axis). The inventory drops in June and stays low till September/October whereby the demand cannot be satisfied. Prolonged low wind speeds are causing these periods of low inventory and missed demand.
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The green block in the graph (figure 11) represents the seasonal production of bioenergy. Compared to figure 10, there is an improvement in demand satisfaction during the three summer months, two
big gaps have shrunk. There is still one big gap visible (grey circle) which is an improvement, compared to figure 10. Overall the improvement on the demand satisfaction is a 3,6% increase (table 7). However, the results of this experiment show that seasonal bioenergy production and deployment has a bigger impact on satisfying demand compared to the same amount of bioenergy divided over the full year (paragraph 4.4).
Besides, the new results are added to the graphs (figure 12) of the experiment of paragraph 4.4 to see the development of the demand satisfaction in relation to the storage capacity and amount of wind turbines.
Base case experiment 2018 2018
Demand satisfaction p/h: 86,6% 89,9%
Figure 11: Visualization of demand satisfaction and storage development (2018)
Table 7: impact of SD on demand satisfaction (2018)
21 The seasonal deployment of bioenergy causes an even faster rise of the lines towards the 90% and then deflects. The ‘deflection’ phenomenon that is discussed in 4.4.1 is comparable but, as the graph shows, the acceleration is going faster towards the 100% satisfaction. This improvement is proving that a seasonal deployment is causing a significant impact on peak shaving. The ‘faster rise and deflection’ towards the 100% is caused by the fact that the seasonal deployment of bioenergy creates extra wind energy surplus that can be stored instead of satisfying the demand. In this way, the hydrogen storage inventory is increasing again (figure 10) and can satisfy more demand later when there is a shortage of wind energy.
The ability to spread the peaks is a result of more efficient use of the hydrogen storage system despite the same amount of total energy produced. The numbers in table 8 prove this statement. In the seasonal deployment (SD 7%) scenario, the ‘wind to hydrogen’ and ‘storage to demand’ increases while ‘wind curtailment’ decreases relative to the stable amount of bioenergy scenario (7%).
22
5. Conclusion
In this research, we simulated the supply of electricity for a small group of Dutch households by a wind-hydrogen storage system in combination with a limited share of biomass for three (wind) years. The output of the simulation was used to determine how it affected the demand satisfaction and conversion losses. Therefore, we first needed to understand how the wind-hydrogen storage system affected the variables alone. Secondly, the impact of the wind-hydrogen storage system in combination with the limited share of biomass on the variables was determined. Thirdly, the impact of a seasonal deployment of biomass on the variables was investigated.
The results showed that a wind-hydrogen storage system is mostly affected by how strong the wind speeds fluctuate, which is measured by the variance. The variance of wind speeds and total generation differs every year but have consequences for the storage capacity needs. It ensures the need for bigger storage capacity to continue meeting the demand. The variance of wind speeds is indirectly affecting the conversion losses. Conversion losses are increasing as when storage capacity is increasing. So to meet the demand, large hydrogen storage capacity is needed in years with high wind fluctuations. Moreover, this research showed that an increase in wind turbines is giving the demand satisfaction a boost to the 85-90% of the demand. The satisfaction growth deflects in the last 10-15%. This phenomenon is caused by the fact that extra wind turbines are enlarging the peaks in wind generation whereby curtailment of wind energy is increasing. Decision-makers should understand that there is a certain trade-off to make between the number of extra wind turbines that are needed to satisfy the last percentages of demand, with the consequence of more curtailment. Or expanding extra storage capacity to capture all the peaks in wind energy with more conversion losses as a result. Cost is here the main factor that should be investigated in future research.
The impact of deploying bioenergy production (7% of total demand) is mild. The amount of bioenergy is spread equally among every hour in the year, thereby is the peak shaving impact moderate. However, the impact of seasonal deployment (also 7% of total demand), divided over only June July and August, is much greater. Satisfying the last 10-15% of the demand accelerates in the seasonal deployment scenario. This acceleration is caused by the fact that the seasonal deployment of bioenergy ensures extra wind energy surplus that can be stored, instead of satisfying the demand. In this way, the hydrogen storage inventory is increasing again and can satisfy more demand later when there is a shortage of wind energy. So you could say that the seasonal deployment of bioenergy production is ‘shaving the peaks’ more efficiently than when the bioenergy is spread over the full year. Decision-makers should know that a greater impact with bioenergy is feasible when it is seasonally deployed. Especially when the predictions of the ‘moderate’ availability of biomass in The Netherlands (CE Delft, 2020) will become true in the future.
23 year as well. Besides, the research gives also relevant generalizable insight on optimizing the impact of limited biomass on demand satisfaction with seasonal deployment as mentioned earlier.
However, The weakness of this research is the assumed availability of biomass, the feasibility of a seasonal deployment of bioenergy, and not taking the cost perspective into account. It is hard to predict the future biomass availability and it differs per region (CE Delft, 2020). Future research should investigate these implications further.
24
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26
Appendix
Table 9: Example of simulation
Table 10: Dashboard of the simulation
Wind Park Size 2017 2018 2019 2017 2018 2019 5 49% 48% 53% 24% 26% 27% 6 59% 59% 63% 27% 28% 27% 7 68% 68% 71% 29% 29% 28% 8 77% 75% 80% 31% 28% 29% 9 85% 82% 87% 32% 27% 28% 10 92% 87% 92% 33% 27% 28% 11 96% 91% 97% 31% 26% 28% 12 98% 94% 100% 29% 25% 26% 13 99% 97% 100% 27% 24% 24% 14 99% 100% 100% 25% 24% 21% 15 99% 100% 100% 23% 22% 20% Conversion losses % Demand Satisfaction %
27
7% bioenergy 7% bioenergy 7% bioenergy
Demand satisfaction 5 7 9 10 11 13 15 Demand satisfaction 5 7 9 10 11 13 15 Demand satisfaction 5 7 9 10 11 13 15 300 48,7% 67,7% 83,1% 88,6% 92,3% 96,1% 96,9% 300 47,3% 65,6% 78,8% 83,7% 88,1% 94,6% 99,1% 300 50,6% 68,6% 82,7% 87,9% 92,4% 96,3% 99,5% 500 48,7% 68,0% 85,5% 92,1% 96,2% 98,8% 99,3% 500 48,5% 68,3% 81,5% 86,6% 90,9% 97,2% 100,0% 500 53,5% 71,5% 86,6% 92,1% 97,5% 100,0% 100,0% 700 48,7% 68,0% 85,5% 93,1% 97,9% 98,8% 99,7% 700 48,5% 70,7% 84,3% 89,3% 93,6% 99,3% 100,0% 700 53,9% 74,7% 89,4% 94,8% 99,7% 100,0% 100,0% 900 48,7% 68,0% 85,5% 93,1% 97,9% 98,8% 99,7% 900 48,5% 70,7% 87,0% 92,1% 96,1% 100,0% 100,0% 900 53,9% 77,0% 92,2% 97,3% 100,0% 100,0% 100,0% 1100 48,7% 68,0% 85,5% 93,1% 97,9% 98,8% 99,7% 1100 48,5% 70,7% 89,8% 94,5% 98,2% 100,0% 100,0% 1100 53,9% 77,0% 94,7% 99,4% 100,0% 100,0% 100,0% 1300 48,7% 68,0% 85,5% 93,1% 97,9% 98,8% 99,7% 1300 48,5% 70,7% 92,2% 96,7% 100,0% 100,0% 100,0% 1300 53,9% 77,0% 96,6% 100,0% 100,0% 100,0% 100,0% 1500 48,7% 68,0% 85,5% 93,1% 97,9% 98,8% 99,7% 1500 48,5% 70,7% 92,7% 98,7% 100,0% 100,0% 100,0% 1500 53,9% 77,0% 96,6% 100,0% 100,0% 100,0% 100,0% 1700 48,7% 68,0% 85,5% 93,1% 97,9% 98,8% 99,7% 1700 48,5% 70,7% 92,7% 100,0% 100,0% 100,0% 100,0% 1700 53,9% 77,0% 96,6% 100,0% 100,0% 100,0% 100,0%
Conversion losses 5 7 9 10 11 13 15 Conversion losses 5 7 9 10 11 13 15 Conversion losses 5 7 9 10 11 13 15
300 24,0% 28,4% 30,4% 29,9% 28,5% 24,8% 21,1% 300 24,1% 26,7% 25,5% 25,1% 24,4% 22,7% 20,8% 300 23,9% 26,2% 25,7% 25,2% 24,3% 21,3% 19,0% 500 24,1% 28,7% 32,1% 32,8% 31,4% 27,0% 22,9% 500 25,8% 28,7% 27,1% 26,6% 26,1% 24,4% 21,8% 500 26,6% 28,3% 28,5% 28,0% 27,5% 23,5% 19,7% 700 24,1% 28,7% 32,1% 33,3% 33,1% 27,6% 23,7% 700 25,8% 30,5% 28,7% 28,0% 27,4% 25,8% 22,3% 700 27,0% 30,2% 30,0% 29,4% 29,0% 24,1% 20,2% 900 24,1% 28,7% 32,1% 33,3% 33,7% 28,3% 24,3% 900 25,8% 30,5% 30,3% 29,5% 28,7% 26,6% 22,8% 900 27,0% 31,6% 31,4% 30,7% 29,8% 24,7% 20,7% 1100 24,1% 28,7% 32,1% 33,3% 33,7% 28,9% 24,8% 1100 25,8% 30,5% 31,9% 30,9% 30,1% 27,2% 23,4% 1100 27,0% 31,6% 32,9% 32,0% 30,5% 25,3% 21,2% 1300 24,1% 28,7% 32,1% 33,3% 33,7% 29,6% 25,4% 1300 25,8% 30,5% 33,5% 32,3% 31,3% 27,8% 23,9% 1300 27,0% 31,6% 34,2% 33,0% 31,1% 25,8% 21,7% 1500 24,1% 28,7% 32,1% 33,3% 33,7% 30,2% 25,9% 1500 25,8% 30,5% 33,8% 33,8% 32,1% 28,5% 24,4% 1500 27,0% 31,6% 34,2% 33,7% 31,8% 26,4% 22,2% 1700 24,1% 28,7% 32,1% 33,3% 33,7% 30,8% 26,5% 1700 25,8% 30,5% 33,8% 34,9% 32,8% 29,1% 25,0% 1700 27,0% 31,6% 34,2% 34,1% 32,5% 27,0% 22,7% 2019
Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh)
2017 2018
Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh)
Table 12: outcomes of sensitivity analysis (7% bioenergy)
0% bioenergy 0% bioenergy 0% bioenergy
Demand satisfaction 5 7 9 10 11 13 15 Demand satisfaction 5 7 9 10 11 13 15 Demand satisfaction 5 7 9 10 11 13 15 300 44,3% 62,6% 77,9% 84,0% 88,3% 94,3% 95,7% 300 43,2% 61,3% 74,3% 80,0% 83,9% 90,8% 96,8% 300 46,0% 63,6% 78,0% 83,6% 87,8% 93,5% 96,7% 500 44,3% 62,7% 79,8% 87,2% 92,1% 98,4% 98,9% 500 44,2% 64,0% 76,8% 82,5% 86,7% 93,5% 99,1% 500 48,8% 66,2% 81,9% 87,3% 92,3% 99,6% 100,0% 700 44,3% 62,7% 79,8% 87,6% 93,9% 98,4% 99,2% 700 44,2% 64,5% 79,4% 85,1% 89,1% 95,9% 100,0% 700 48,8% 69,0% 84,5% 89,9% 94,8% 100,0% 100,0% 900 44,3% 62,7% 79,8% 87,6% 93,9% 98,4% 99,2% 900 44,2% 64,5% 82,1% 87,5% 91,7% 97,9% 100,0% 900 48,8% 70,9% 87,1% 92,4% 97,1% 100,0% 100,0% 1100 44,3% 62,7% 79,8% 87,6% 93,9% 98,4% 99,2% 1100 44,2% 64,5% 84,5% 90,0% 93,9% 99,8% 100,0% 1100 48,8% 70,9% 89,6% 94,7% 99,2% 100,0% 100,0% 1300 44,3% 62,7% 79,8% 87,6% 93,9% 98,4% 99,2% 1300 44,2% 64,5% 85,9% 92,3% 96,1% 100,0% 100,0% 1300 48,8% 70,9% 90,4% 97,0% 100,0% 100,0% 100,0% 1500 44,3% 62,7% 79,8% 87,6% 93,9% 98,4% 99,2% 1500 44,2% 64,5% 85,9% 94,4% 98,0% 100,0% 100,0% 1500 48,8% 70,9% 90,4% 98,5% 100,0% 100,0% 100,0% 1700 44,3% 62,7% 79,8% 87,6% 93,9% 98,4% 99,2% 1700 44,2% 64,5% 85,9% 94,9% 99,7% 100,0% 100,0% 1700 48,8% 70,9% 90,4% 98,5% 100,0% 100,0% 100,0%
Conversion losses 5 7 9 10 11 13 15 Conversion losses 5 7 9 10 11 13 15 Conversion losses 5 7 9 10 11 13 15 300 22,8% 27,4% 29,5% 29,8% 28,8% 26,3% 22,6% 300 22,9% 26,4% 25,4% 25,1% 24,5% 23,2% 21,8% 300 23,0% 25,3% 25,4% 25,1% 24,4% 22,1% 19,7% 500 22,8% 27,5% 31,0% 32,2% 31,9% 29,2% 24,8% 500 24,6% 28,9% 27,0% 26,6% 26,1% 24,9% 23,3% 500 25,7% 27,3% 28,4% 27,9% 27,4% 25,5% 21,6% 700 22,8% 27,5% 31,0% 32,4% 33,2% 29,8% 25,5% 700 24,6% 29,3% 28,6% 28,0% 27,4% 26,1% 24,3% 700 25,8% 29,2% 29,9% 29,3% 28,9% 26,3% 22,1% 900 22,8% 27,5% 31,0% 32,4% 33,2% 30,4% 26,1% 900 24,6% 29,3% 30,2% 29,4% 28,7% 27,2% 24,9% 900 25,8% 30,5% 31,4% 30,6% 30,1% 26,9% 22,6% 1100 22,8% 27,5% 31,0% 32,4% 33,2% 31,1% 26,6% 1100 24,6% 29,3% 31,8% 30,9% 30,0% 28,3% 25,4% 1100 25,8% 30,5% 32,8% 31,9% 31,3% 27,5% 23,1% 1300 22,8% 27,5% 31,0% 32,4% 33,2% 31,7% 27,2% 1300 24,6% 29,3% 32,7% 32,3% 31,3% 29,0% 26,0% 1300 25,8% 30,5% 33,2% 33,3% 32,2% 28,0% 23,6% 1500 22,8% 27,5% 31,0% 32,4% 33,2% 32,4% 27,7% 1500 24,6% 29,3% 32,7% 33,7% 32,6% 29,6% 26,5% 1500 25,8% 30,5% 33,2% 34,4% 32,9% 28,6% 24,1% 1700 22,8% 27,5% 31,0% 32,4% 33,2% 33,0% 28,3% 1700 24,6% 29,3% 32,7% 34,1% 33,9% 30,2% 27,0% 1700 25,8% 30,5% 33,2% 34,4% 33,6% 29,2% 24,6% 2019
Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh) 2017
Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh)
2018 Wind park size
Storage capacity (MWh)
Wind park size
Storage capacity (MWh)
