Capturing flexibility in an inflexible market: energy
system flexibility from a service perspective
MSc thesis Supply Chain Management
Faculty of Economics and Business, University of Groningen
Supervisors: prof. dr. ir. J.C. Wortmann & ing. R.J.H. van der Burg, MSc
Acknowledgements
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Abstract
The energy transition confronts the traditional energy system with a number of challenges. Supply of energy from intermittent resources and demand for electricity increases, resulting in serious balancing challenges for stakeholders in the energy system. This development requires a lot of our electricity infrastructure but also provides business opportunities: for example, parties may offer services such as energy storage, flexibility in production processes or demand side management. These kinds of flexibility options can be deployed as a service to support the stability and reliability of energy systems that rely on renewable energy sources. Moreover, such flexibility services can reduce the investments in the power grid. However, up to now flexibility gained hardly attention from a service perspective. By collecting data from various stakeholders in the energy system this study provides more clarity regarding energy system flexibility and flexibility services. Findings show that the definition of energy system flexibility consists of a technical and commercial dimension. In order to develop, offer and manage flexibility services operational and financial insights play an essential role for flexibility service providers. Furthermore, the constraints that set boundaries to the available flexibility of energy storage and demand side management practices are identified. Findings show that the available flexibility is bounded by a number of operational constraints, but even more constraining are the current market structure and laws and regulations. For flexibility services to flourish it is essential that legislation changes such that it is financial viable for stakeholders to get involved with flexibility services. Finally, this paper proposes additions and improvement to an operationalization of energy system flexibility. This operationalization enables to provide insights in the available flexibility of individual storage devices in a real-time planning perspective and supports the operations management of flexibility services.
Key words: Flexibility, Flexibility services, Operationalization, Renewable energy systems, Energy
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Table of content
1. Introduction ... 6
2. Theoretical background ... 8
2.1. Stakeholders in the energy system ... 8
2.1.1. The transitioning energy system ... 8
2.2. Flexibility services ... 9
2.2. Defining flexibility in energy systems ... 10
2.3. Measuring flexibility in energy systems ... 11
2.4. Sources of flexibility and constraints on their available flexibility ... 11
2.5. Conclusion ... 12
3. Methodology ... 13
3.1. Research context and stakeholders ... 13
3.2. Data collection ... 14
3.3. Data analysis ... 15
4. Findings ... 16
4.1. Definition of energy system flexibility... 16
4.2. Insights flexibility providers for developing, offering and managing flexibility services ... 17
4.3. Constraints that set boundaries to the availability of flexibility ... 17
4.3.1. Energy storage ... 18
4.3.2. Demand side management ... 18
4.4. Additional findings regarding factors that limit ESF ... 19
4.5. Measuring ESF ... 19
4.6. Summary ... 20
5. Discussion ... 21
5.1. General flexibility options & dimensions of flexibility ... 21
5.2. Relation flexibility options and sources of flexibility ... 22
5.3. Constraints that set boundaries on available flexibility ... 23
5.3.1. Energy storage ... 23
5.3.2. Demand side management ... 24
5.4. Expressing available flexibility ... 25
5.5. Evaluation of the operationalization of energy system flexibility ... 27
6. Conclusion ... 28
6.1. Managerial implications ... 29
6.2. Limitations and directions for future research ... 29
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Appendices ... 33
Appendix A: interview protocol ... 33
Appendix B: case descriptions ... 36
Appendix C: safeguard measures to ensure reliability and validity ... 37
Appendix D: coding tree ... 38
Appendix E: required parameters, variables and constant in the case of energy storage ... 44
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1. Introduction
The recent developments in the energy system, including the increasing integration of renewable energy, ensure a transition which has an effect on all levels and actors within the energy system. A large part of the rising share of renewable energy production comes from intermittent sources which enlarge the variation in the resulting demand. To guarantee the reliability, security, and effectivity of the energy system it is essential to match supply and demand at any moment in time. However, guaranteeing this match is a serious challenge for all stakeholders within the renewable energy system. In order to meet this challenge, it is essential for the energy system to be flexible, where we define flexibility of an energy system as a power adjustment sustained for a given duration in order to balance supply and demand at a given moment in time (Eid, Codani, Perez, Reneses, & Hakvoort, 2016).
An energy system should be able to balance supply and demand of energy always and everywhere, also in the future when supply and demand are fluctuating even stronger (Donker, Huygen, Westerga, Weterings, & Bracht, 2015). Also, an energy systems should be flexible enough to cope with uncertainty and variability in production and consumption to maintain a reliable energy system at reasonable additional costs (Ma, Silva, Belhomme, Kirschen, & Ochoa, 2013). Fortunately, there are several ways for energy systems to cope with the inherent variability of wind and solar power (Kondziella & Bruckner, 2016). Integration of variable renewable energy sources can be supported by different sources of flexibility, like demand side management, supply side management, energy storage, and energy conversion (Lund, Lindgren, Mikkola, & Salpakari, 2015). The integration of these sources of flexibility requires investments of stakeholders in the existing energy market. By offering flexibility as a service, the sources of flexibility could be deployed and integrated in an economic viable way (Heussen, Bondy, Hu, Gehrke, & Hansen, 2013; Zhang et al., 2013).
Flexibility is often addressed in scientific literature, but it is not systematically elaborated in such a way that it allows to develop, offer and study flexibility services from a business point of view. In order to deploy sources of flexibility and to offer and manage flexibility services it is essential to know which insights are needed regarding energy system flexibility for potential flexibility service providers. However, the current literature does not provide clear knowledge about which insights in energy system flexibility are needed for developing flexibility services and mainly describes the sources of flexibility from a technical point of view (Ulbig & Andersson, 2015). For deploying sources of flexibility and developing and managing flexibility services it is essential to operationalize flexibility such that it is applicable to the different sources of flexibility and provides insights in the available amount of flexibility. This makes it possible to relate and compare the different sources of flexibility and their underlying techniques. However, the current literature falls short in describing energy system flexibility in such a way that it is clear how flexibility can be operationalized from a service perspective. Furthermore, only few papers describe the constraints that set boundaries on the availability of flexibility while these constraints can have a significant impact on the available flexibility of a flexibility source (Lannoye, Flynn, & O’Malley, 2012b).
- 7 - the availability of flexibility, and which insights in flexibility are needed for potential flexibility service providers. In order to achieve these objectives, the following research questions are developed:
How can energy system flexibility be operationalized from a service perspective and what insights in energy system flexibility are needed for flexibility service management?
- How can flexibility in energy systems be defined and quantified?
- What are the constraints that set boundaries on the available flexibility that sources of flexibility can provide?
- Which insights do flexibility service providers require about the available flexibility for developing and managing flexibility services?
To start, a literature review about energy system flexibility will be conducted. Then, an empirical study of multiple cases will be used to study how the concept of flexibility services in energy systems is used in practice and what insights in energy system flexibility are needed by flexibility service providers. By answering the above-mentioned research questions, this scientific knowledge is expected to contribute to practice by supporting the implementation of flexibility services.
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2. Theoretical background
The following section provides the theoretical background of this study. To start with, a short overview of the current energy system, which is undergoing a transition, is provided including a description of the important stakeholders and their relations. Then, flexibility services in current energy systems are described, showing the need for more knowledge about these services and why insights about how these service can be developed are essential. Thereafter, various definitions and measures of flexibility are discussed. Finally, the different sources of flexibility and their constraints are discussed and a conclusion is provided.
2.1. Stakeholders in the energy system
An energy system can be defined as a collection of energy devices (producing and consuming energy), energy networks (linking production and consumption), multiple independent stakeholders (producers, system operators, traders, regulators, and consumers), markets, and regulations that all together enable energy production, transport, trade, and consumption (Alanne & Saari, 2006; Verzijlbergh, De Vries, Dijkema, & Herder, 2014). Figure 1 displays the physical energy flow in the current energy system, with all actors involved.
An important note is that the energy producer or retailer always retains title of the electricity throughout the whole system, until it is consumed. The transmission network operator (TSO) and the distribution network operator (DSO) are only responsible for the transmissions and distribution grid. Respectively, the DSOs have to guarantee that consumers always have access to enough electricity capacity (Harbo & Biegel, 2013). However, the past few years a new group of consumers has emerged, the energy prosumers. Energy prosumers are those consumers, e.g. households and industries, which not only consume energy, but also produce energy. For instance, households that have solar panels can produce more energy than they need at a certain moment in time and deliver energy to the power grid instead of consuming it. Each stakeholder in the energy system can benefit from having insights in flexibility. It can help TSOs to balance supply with demand, prosumers to optimize their energy production and consumption, DSOs to reduce investment costs and energy producers to optimize their production. However, current scientific literature falls short in describing which specific insights in flexibility each one of these stakeholders requires.
2.1.1. The transitioning energy system
The traditional fossil based energy system is transitioning towards a system with an increasing share of energy from renewable sources. This requires rethinking and a new design of the energy system as well on the production as the consumption side (Mathiesen et al., 2015). Two important changes are key drivers for the current challenges that the energy system faces. First, the traditional energy system is hierarchical and based on centralized production and decentralized consumption with energy flowing in one direction. However, we are moving towards a system with decentralized production
- 9 - and consumption with energy flowing from and to both sides. This increases the load in the decentralized parts of the grid, resulting in managerial and operational problems in relation to energy transfer and distribution of renewable energy sources across the grid (Eid, Codani, Chen, Perez, & Hakvoort, 2015). Second, the old energy system is based on gas and coal-fired power plants which are capable of adjusting supply to the fluctuating demand, thereby maintaining a balance in the energy system (Biegel, Hansen, Stoustrup, Andersen, & Harbo, 2014). However, now we strive to replace these flexible coal and gas power plants with intermittent renewable resources. The increasing penetration of decentralized and intermittent renewable energy production capacity leads to increasing variability and uncertainty, causing an imbalance between demand and supply (Verzijlbergh et al., 2014). Flexibility services can provide solutions to these problems by enabling supply to match demand at all times, improving investment opportunities, and lowering emissions (Cochran et al., 2014; van Gerwen & de Heer, 2015).
2.2. Flexibility services
Flexibility services are needed to implement sources of flexibility within the energy system. In this context, flexibility services are those services that deploy sources of flexibility in such a way that it fulfils a specific need of a stakeholder in the energy system (van der Burg & Wortmann, 2017). According to literature, these flexibility services should be managed by aggregators in order to uncap the flexibility potential (Papaefthymiou & Dragoon, 2016). The aggregator is a new commercial entity, which brings demand and supply of several parties together and therewith trades on different short-term markets, or offers direct flexibility services (Donker et al., 2015). Figure 2 displays the introduction of the aggregator within the energy system, e.g. the flexibility value chain, adopted from van der Burg & Wortmann (2017). The aggregator can bundle decentralized flexibility from flexibility providers, e.g. energy prosumers, enter the market with that flexibility, and provide flexibility services to energy producers/retailers, the TSO, and DSOs (Sijm et al., 2015).
For instance, a flexibility service an aggregator could offer to a DSO is grid capacity management, which optimizes operational performance and asset use by reducing grid losses. Grid capacity management helps the DSO to enhance the management of their distribution grid by managing demand and supply, and storing energy. It is important that the aggregator is reliable since certain flexibility services require the guaranteed availability of flexibility (van Gerwen & de Heer, 2015). However, supply of renewable energy is hard to predict and fluctuates over time, which makes it also hard to predict when there is a need for flexibility services. Hence, for flexibility service providers it is important to have insights in the amounts of available flexibility (Lannoye, Flynn, & O’Malley, 2012; Oree & Sayed Hassen, 2016). This requires a sound and generalized operationalization of energy system flexibility which can provide aggregators with the required insights into real-time operations and long-term planning of flexibility.
- 10 - The past few years there have been quite some developments in managing renewable resources via aggregators or flexibility services. Van Gerwen & de Heer (2015) defined some flexibility services in their paper about the flexibility value chain, showing that potential new players in the energy system can capture economic value by integrating, selling, and trading flexibility. However, offering such flexibility services at distribution level has been underdeveloped since there is no clarity regarding flexibility service definitions and the potential market-based coordination of flexibility services (Heussen et al., 2013). In order to capture this flexibility for distribution grids, flexibility services need to be defined which can be integrated with distribution grid operations and are also able to provide a profit which can be compared to other investments (Heussen et al., 2013).
2.2. Defining flexibility in energy systems
Thus, flexibility is an essential aspect of the (future) renewable energy system. The term flexibility is widely used in the context of energy systems, although at times without a proper definition (Ulbig & Andersson, 2012). There are several interpretations available concerning the definitions of flexibility and it is of importance for analysis to establish clearly the type of flexibility that is addressed (González et al., 2015). Table 1 displays some definitions of flexibility that have been developed over the past years in the energy system literature.
(Ulbig & Andersson, 2015, p. 156) “the technical ability of a power system unit to modulate electrical power feed-in to the grid and/or power out-feed from the grid over time” (Huber, Dimkova, & Hamacher, 2014, p. 236) “the ability of a power system to respond to
changes in power demand and generation” (Lannoye, Flynn, & O’Malley, 2012, p. 922) “the ability of a system to deploy its resources to
respond to changes in net load, where net load is defined as the remaining system load not served by variable generation”
(Ma, Silva, Belhomme, Kirschen, & Ochoa, 2013, p. 1)
“the ability of a power system to cope with variability and uncertainty in both generation and demand, while maintaining a satisfactory level of reliability at a reasonable cost, over different time horizons”
(Denholm & Hand, 2011, p. 1819) “the general characteristic of the ability of the aggregated set of generators to respond to the variation and uncertainty in net load”
(Bertsch, Growitsch, Lorenczik, & Nagl, 2012, p.1)
``the capability to balance rapid changes in renewable generation and forecast errors within a power system``
Table 1. Definitions of flexibility in energy systems
- 11 - Most of the definitions in table 1 focus on flexibility at a system level. However, this study focusses on flexibility at an operational level. The definition of Ulbig & Andersson (2015) does take on an operational perspective and seems to be quite well applicable for the purpose of this study. So, for the aim of this study we apply the operational definition of Ulbig & Andersson (2015) of flexibility. However, seen the wide variety of definitions it still is interesting to discuss the definition of energy system flexibility with flexibility service providers in the energy system context.
2.3. Measuring flexibility in energy systems
Metrics or analytical frameworks which measure flexibility can support the operations management of flexibility services. For instance, a metric can show if an energy system has enough flexibility to match production and consumption at all times, including scenarios of projected renewable energy generation growth and changes to demand profiles (Cochran et al., 2014). Also, a flexibility metric can recognize the time period over which an energy systems is most likely to have a shortage of flexible resources, and can measure the influence of different operational policies and the inclusion of flexible resources (Lannoye et al., 2012). Hereby, it can provide insights to stakeholders about how to modify their system in order for them to improve flexibility and how to manage their flexibility services. Furthermore, an agreed-upon analytical framework can help inform policy, raise stakeholder acceptance of incorporating renewable energy sources, and raise investor confidence that the energy system can integrate renewable energy sources without the system being subject to shortcomings (Cochran et al., 2014).
A literature review showed that there are multiple methods employed to assess and depict flexibility of energy systems, each with its own focus, scope, complexity, and purpose. However, most of these metrics measure flexibility from a rather technological or engineering point of view (Capasso et al., 2014; Cochran et al., 2014; IEA, 2014; Lannoye et al., 2012a; Yasuda et al., 2013). Ulbig & Andersson (2015) propose a metric for assessing the required operational flexibility of energy systems, based on the method of Makarov et al. (2009). Alongside three dimensions, ramp-rate
up/down (MW/min), in/outfeed (MW), and volume/time (MWh) the flexibility need for accommodating high shares of renewable energy sources can be measured. Figure 3 represents this metric, also called the ‘flexibility envelope’. This is an accepted and established metric in the energy system flexibility context, but still rather technical. It could be questioned whether this metric also allows and helps businesses to gain insights in energy system flexibility. A proper metric which allows to measure flexibility and is applicable to all the different sources of flexibility is missing. Such a model would allow for offering generic flexibility services which are not based on and limited by a specific source of flexibility but can be created by combining various sorts of techniques.
2.4. Sources of flexibility and constraints on their available flexibility
The prospect for managing large energy systems with high shares of renewable energy in terms of options looks promising. Different approaches, technologies, and strategies exist to manage renewable energy systems (Donker et al., 2015; Lund et al., 2015; Ulbig & Andersson, 2015). All of these methods can be divided into the four main sources of flexibility, being energy storage, demand side management, supply side management, and energy conversion. And these operational flexibility options can be divided into flexibility options that increase or decrease infeed- and outfeed of the grid.
- 12 - All these sources of flexibility can provide flexibility to balance electricity production and consumption. However, for deploying these sources of flexibility it is important to consider the constraints, i.e. minimum/maximum ramping, power and energy constraints, of the individual flexibility sources when assessing their available flexibility (Ulbig & Andersson, 2015) When an analytical framework for measuring flexibility is used without consideration of the operational constraints it is not able to provide accurate results (Poncelet, Delarue, Six, & William, 2014). Also, a model that does not include technical constraints can give a false impression of the capability of a system to integrate renewables (González et al., 2015). Therefore, insights in the operational constraints are important for the management of flexibility services.
Although several metrics for assessing the availability of flexibility exist, only few include the operational constraints. The available flexibility of the different sources of flexibility is bounded by various constraints. For example, energy can only be stored in a storage vessel up to its maximum storage capacity (MWh) and energy can only be supplied at its maximum supply rate (MW). Next to technical constraints there are also other constraints, like time. For example, wind energy can only be produced and consumed at certain times. Also, someone who charges his or her electrical vehicle during the night requires the battery to be full at a certain time when he or she leaves the next morning. A few technical constraints like ramping rate restrictions, minimum up and down times, operating reserves, maintenance and resource availability have been addressed in literature (Bruce, Gibbins, Harrison, & Chalmers, 2015; González et al., 2015; Keane & Pearce, 2011; Palmintier, 2014). However, a clear overview of all the operational constraints that are of importance for the service management of flexibility services is missing and the current literature falls short in describing the operational constraints that set boundaries to the availability of flexibility sources. Therefore, this study aims to contribute in generating scientific knowledge about these operational constraints.
2.5. Conclusion
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3. Methodology
In order to answer the underlying research questions and for the operationalization of energy system flexibility, we opt for an empirical study by applying a multiple case study methodology. By studying multiple cases within the energy systems context it allows to generate meaningful, relevant theory from understanding gained through observing actual practice (Benbasat, Goldstein, & Mead, 1987; Meredith, 1998). By collecting data among the different cases the specific and complex phenomenon of energy system flexibility can be explored in-depth in a real-life context (Yin, 2009). Seen the explorative nature of this research and the difficulty of reconciling the many interpretations and applications of energy system flexibility, it asks for a flexible process and rich data collection (Voss, Tsikriktsis, & Frohlich, 2002). The unit of analysis for this study is flexibility services providers, i.e. aggregators, and the organizations which are involved with these aggregators.
This study aims to define the measurement of a phenomenon, energy system flexibility, which is not directly measurable, although its existence is indicated by other phenomena. This will not only be done by developing explanations, but also by building theory about the phenomenon of interest and thereby improving practice (Simon, 2002). In order to develop a comprehensive model that satisfies the objective it is essential to also gain insights from practice next to the gathered knowledge from scientific literature. By comparing findings obtained from literature and case study research with a model of energy system flexibility which already has been develop by van der Burg & Wortmann (2017) as part of the ‘FlexiForFuture1’ it allows for providing possible refinements to that model. Finally, by using information from the knowledge base a descriptive approach will be applied to build a convincing argument for the utility of the flexibility model (Hevner et al., 2004).
Summarized, the following three steps will be taken to conduct this research:
1. Identifying the needs of providers of flexibility services, i.e. aggregators. Clearly describing which insights into flexibility are needed by aggregators to deploy sources of flexibility and developing, offering and managing flexibility services. Data regarding these subjects will be collected by interviewing different cases in the energy context.
2. Identifying all factors that set boundaries to the available flexibility for the four sources of flexibility. Data regarding these factors will also be collected by interviews with domain experts in the field together with factors addressed in available scientific literature.
3. Reflecting the results of data collection against the model for energy system flexibility which has been developed as part of the FlexiForFuture project, depicted in the working paper of van der Burg & Wortmann (2017). Hereby testing if the model and included formulas are valid or need some alterations or additions. Finally, checking if the developed and validated model offers valuable insights for flexibility service providers.
3.1. Research context and stakeholders
In order to study the phenomenon of energy system flexibility, multiple organizations in the flexibility value chain (figure 2) were chosen to cover the overall research context. These organizations have different roles concerning flexibility. A description of the various cases can be found in appendix B. Aggregators 1, 2, and 3 are providers of flexibility services. They unlock flexibility from customers of energy producers and use that flexibility as input for delivering services to other stakeholders in the energy system, like a TSO or DSO. So, the TSO or DSO is the consumers of flexibility services of aggregators in this context.
1
- 14 - All of these organizations have an interest in obtaining more knowledge regarding energy system flexibility and applying the operationalization of energy system flexibility in practice. The most important group of stakeholders regarding this study are those organizations that are involved in offering flexibility services, or are willing to do so. Furthermore, the model of flexibility is not only developed for managers and businesses, but also for managerial scientists in the energy system context.
3.2. Data collection
The overall data collection mainly incorporates qualitative methods. Data is obtained via different sources in order to develop a more comprehensive perspective on the unit of analysis. Table 2 represents an overview of the data collection. The collected data serves for the data analysis in order to answer the research questions.
Table 2. Interview details
Interviews were selected as the main source of information in order to be able to collect the rich data needed for the explorative research. In total 6 individual interviews were conducted, with an average length of 40 minutes. Based on their knowledge regarding relevant topics concerning this study, different employees were selected from organization that offer or consume flexibility services in the energy system. The main semi-structured interviews questions were formulated based on topics that current literature lacked in describing up till now and serve the following purposes:
- Discussing the definition of energy system flexibility;
- Identifying the insights that flexibility providers require for developing, offering and managing flexibility services;
- Identifying the constraints that set boundaries to the available sources of flexibility;
- Developing a metric/model of energy system flexibility for managers, businesses, and managerial scientists that comprises all the different sources of flexibility;
The interview protocol is located in appendix A. The interviews were set-up such that they allowed for new questions and discussions. The interview questions were formulated such that it was able to compare answers afterwards. By recording and transcribing every semi-structured interview and presenting them to the interviewees again for verification the reliability of the interview process was ensured (Yin, 2009). Next to the semi-structured interviews, unstructured meetings and interactions, and personal observations also allowed for collection of explorative and unanticipated data to gain understanding of beliefs, activities, ideas and meanings from the inside (Eisenhardt, 1989). Also, additional background information was gained by collecting additional data, reports and other archival information referenced by interviewees (Voss et al., 2002).
No. Role in the flexibility value chain
Position of the interviewee
Length of the interview (minutes)
1. Aggregator Project Manager 45
2. DSO Manager Business
Analysis
50
3. Aggregator Business Analyst 35
4. Aggregator Project Team Member 45
5. DSO Program Manager 35
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3.3. Data analysis
The two main sources of data are the interviews with the various organizations and the working paper (van der Burg & Wortmann, 2017) developed as part of the FlexiForFuture project. The interviews were recorded and transcribed verbatim and thereafter reduced into categories by coding for the aim of organization and interpretation (Miles, Huberman, & Saldana, 2014). Figure 4 shows the underlying coding tree of this analysis based on Miles et al. (2014).
Figure 4. Coding process
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4. Findings
This section will first discuss the definition of flexibility in the context of energy systems and flexibility services. Thereafter, an overview of the required insights of flexibility service providers for developing, offering and managing flexibility services is provided. Subsequently, a profound analysis of all the constraints that set boundaries to the availability of flexibility is provided. Finally, the various sorts of data that are needed for measuring energy system flexibility will be illustrated. Together will these findings be the input for validating the model of energy system flexibility developed by van der Burg & Wortmann (2017), depicted in chapter 5.
4.1. Definition of energy system flexibility
The term flexibility is often used in the context of energy systems, although sometimes without a proper definition. There are different interpretations available on the definition of flexibility, and it is for this research of importance to clearly establish what type of flexibility is meant.
According to TSO (F. ‘t Hoen, personal communication, December 19th, 2016), who defines flexibility as ‘the ability to change from one electricity supply state to another2’, flexibility has two dimensions. First a technical dimension which is concerned with:
Balancing: ‘the capability to adapt power generation or consumption (MW) at a certain
moment, over a specific period of time (h), at a certain speed (MW per second), at a certain location or within a certain area, and thus to inject or withdraw energy (MWh) in or from the power system.’
Transmission and distribution: ‘the capability to use assets to control network flows within
secure limits.’
Second, a commercial dimension which is about ‘the ability to conduct commercial transactions
between market participants, TSOs and DSOs that utilize the technical capabilities’. These can be
used for portfolio management of energy schedules (i.e. energy balancing by BRPs), system balancing (i.e. power balance by TSO), redispatch measures and voltage control (TSOs and DSOs). The transactions can be facilitated by explicit products that specify required capabilities, e.g. lead time to response, location, power profiles.
Results show that other stakeholders in the energy system also agree with the technical dimension of flexibility: “having a system that is able to deal with the intermittent character of energy production
from renewable sources”[DSO].This technical dimension also has an important balancing aspect: “being able to ramp up or down if the power grid reaches a certain load”[Aggregator 1]. Eventually,
supply and demand need to be in balance at any moment: “…to ultimately keep the grid
stable…”[Aggregator 3].When these technical capabilities are utilized to develop services the
commercial dimension arises since: “You are looking for parties or resources that can offer the
flexibility you need all together”[DSO]. This commercial dimension is of high importance for
aggregators: “we respond to moments of high or low energy prices in order to create
business”[Aggregator 1]. Eventually, it is about developing services: ”…and monetizing those services.”[Aggregator 3].
Next to the technical and commercial dimension, results show that the definition also is concerned with a behavioral dimension. Eventually you need consumers of your services: “It is offering,
enabling, and also making it attractive to adapt the behavior of energy consumption…”[Aggregator 3]. And these consumers are in the end the ones who determine how much flexibility can be unlocked
2
- 17 - in the grid: “… it always depends on what the users of the power grid are doing, they are the ones who
have to be smart”[DSO].
4.2. Insights flexibility providers for developing, offering and managing flexibility
services
Results show that, from the perspective of an aggregator, primarily operational and financial insights are essential to determine if potential customers can deliver the required flexibility. Before engaging in a service cooperation an aggregator needs to know how much flexibility they can unlock at a certain customer: “How much flexibility can we collect from a certain customer?”[Aggregator 1]. It is very important for aggregators to have insights in the exact amount of flexibility so they know which sorts of services they can offer. For instance, the entry standard to be allowed to be active on the primary reserve market of TSO is 1MW. So an aggregator needs to be absolutely sure they can guarantee that amount at any moment in time. In order for an aggregator to make a judgement on the available flexibility of a certain customer they for instance ask for historical readings, which allows them to conduct a data analysis. However, this is very dependent on the sort of equipment that provides the flexibility: “For a pumped hydro storage facility you need to set other parameters compared to a
deep-freezing warehouse.”[Aggregator 1].
In the case there is enough available flexibility a financial consideration follows: “What needs to be
done and how much does it cost to make equipment steerable?”[Aggregator 1]. After an aggregator
has unlocked flexibility at customers and wants to offer that flexibility to a DSO for instance they need to know how much they are going to charge for that flexibility. However, it is not that easy to determine the price: “The hard thing is, what you pay for energy at a certain moment?”[Aggregator
3]. Payment plays an important role for these new sorts of services and yet there has not been
developed a standard pricing structure for flexibility services. One of the reasons is the fact that aggregators offer all kinds of services and have various sorts of customers: “It really depends on the
business model. For customers where we only capture flexibility, we apply a profit share. We make profit on their flexibility and we agree in advance on a percentage of the earnings they receive.”[Aggregator 1]. Some aggregators also take on the role of energy supplier, which allows
them to use flexibility and offer an energy price which is below the market average or APX price: “If
we succeed in managing the flexibility efficient it is very useful for us and the customer can enter a cheap contract”[Aggregator 1]. And of course is that price also decisive for a DSO: “For a DSO the price should be attractive enough compared to investing. It is a trade-off: am I going to expand my power grid or do I buy flexibility from aggregators?”[DSO]. Aggregators stated that for doing
business with DSO’s they usually make a customized deal for the service the DSO would like to receive. Furthermore, it is for consumers of flexibility services of importance that the aggregator who offers the service is reliable: “An aggregator should demonstrate that they are able to deliver their
service and needs to be able to guarantee us the amount they offer”[DSO].
4.3. Constraints that set boundaries to the availability of flexibility
- 18 - in the future energy system with an increasing share of renewable energy sources. And conversion of energy can be applied in times of electricity surplus but is subject to conversion losses and cannot provide flexibility on a large scale as DSM practices or energy storage do. Nevertheless, storage and DSM are widely applied in practice and will be extensively discussed in the following two sections.
4.3.1. Energy storage
The storage of energy is bounded by various constraints. First, the constraints that highly influence the available flexibility of a storage unit are the operational ones. Technical characteristics strongly influence the available flexibility of storing electricity: “Speed, reaction, ramping up or down of
energy consumption is for equipment like batteries a determining factor…”[Aggregator 1]. These
kind of technical constraints are connected with each other and other time- and volume constraints. These constraints differ per battery and are application-dependent, but certain technical constraints go up for every storage unit: “…certain equipment can only be switched on once per 15 minutes and for
certain equipment it sometimes takes half an hour before it really starts to store more or less energy, those are restrictions to the flexibility”[Aggregator 1].A battery has a certain volume or capacity
(KWh) and can be (dis)charged with a certain speed (KW). These factors together determine the time it takes to (dis)charge a battery and how much flexibility is available in that flexibility source: “An
average plugin with a 3.7KW charging point and a battery of 10-12KWh is full within 3 hours”[Aggregator 3]. These temporal constraints can be of high importance for a business: “Especially the fact that a battery needs to be charged at a certain time is a limiting factor. Dependent on the time period that can be a bump in your business model.”[Aggregator 2].
However, although a diverse set of techniques exist that can store energy in times of surplus and release energy in times of shortage, results show that energy storage is not a financial viable flexibility option. The aggregators whom currently are involved in energy storage are able to do so by subsidies or investments of large energy suppliers. However, without financial support it seems unwise to invest in providing flexibility using energy storage: “Storing energy on a large scale is way too expensive
and just not feasible. Not for our customers, and also not for us. Electricity is too cheap at the moment to invest in storage”[DSO’]. And also aggregators agree: “We don’t store electricity in batteries. It is just not viable because of the purchase costs of a battery, they are very expensive”[Aggregator 2].
4.3.2. Demand side management
The constraints that set boundaries to the available flexibility of DSM do not differ much from the constraints that set boundaries to the available flexibility of energy storage. The available flexibility of DSM is also limited by operational constraints. At the moment DSM is mostly applied at large refrigerators, boilers, pumping stations and other industrial installations. These sorts of equipment have all sort of technical constraints: “If you look at cooling that the temperature limits within which
you must stay are constraining. You can only additionally cool, or not cool, for a limited amount of time because at some point it just becomes too hot or too cold”[Aggregator 1]. DSM is often
concerned with cooling or heating and therefore temperature plays an important role: “Should it be
back at a certain period of time at a certain temperature?[Aggregator 2]. Herein, the temporal
constraints again are connected to the operational constraints. Just as for storage, time is an essential factor to keep in mind: “At the same time you should also think about the power consumption and
efficiency of the process. In the beginning, the efficiency of a process is usually lower than when the process is running for several minutes”[Aggregator 2].
Furthermore, financial constraints are also of importance for DSM, but very dependent on the business model. Every aggregator has its own fixed and variable costs: “With us, demand management is
limited by what we can offer as a proposition to our riders”[Aggregator 3]. For other aggregators
their cost base relies on the number and types of equipment they manage: “Each time you switch on
equipment there are fixed start-up costs”[Aggregator 1]. Also, for some aggregators, dependent on
the way they try to unlock flexibility, the behavior of their customers can also be a constraining factor:
- 19 - management is identified as a constraining factor. An aggregator should not interfere with certain systems or equipment too often: “Systems can only cope with a certain frequency of management.
Refrigeration compressors cannot continuously be switched on or off, otherwise they break. And you cannot switch on or off a pumping station every 5 minutes”[Aggregator 1].
4.4. Additional findings regarding factors that limit ESF
During interviews with the different organizations it became soon clear that at the moment there are two factors which strongly limit the development of flexibility services in the energy system; the current market structure and laws and regulations.
The current set up of the energy market in The Netherlands makes it hard for small organizations to enter the market: “Without agreements with a major energy supplier it is very difficult. You cannot do
business with someone unless you have an agreement with his or her supplier”[Aggregator 1]. The
current energy market is old and based on adjusting supply to demand which makes it hard for aggregators who aim to unlock flexibility in the market and offer services with that flexibility: “It is
hard for small organizations to enter the market without support from a large established organization”[Aggregator 3]. And the market structure is not only a limiting factor for aggregators
but also for DSO’s, which criticize the pricing structure of the market: “You have a peak in your
energy use once a year and then you pay the whole year an amount according to that peak. Then there is no incentive for consumers to change their consuming behavior”[DSO]. One could think this just
would be an incentive for consumers to avoid those peaks and save money. However, there is no incentive for consumers to alter their behavior once their peak is established and their payment is determined for the rest of the year. Also, DSO’s currently receive grants which they use to invest in their power grid, also recognized as a limiting factor: “DSO’s rather invest in or expand their power
grid than giving aggregators the opportunity to solve their problems with flex services”[Aggregator 3]. It is a safer choice for a DSO to invest in their power grid and make sure they are capable of
dealing with demand during peak periods. At the moment DSO is not convinced that an aggregator is reliable enough and really can guarantee what they promise. Also, the cost base of the DSO’s are regulated which makes it very hard for DSO’s to purchase services from aggregators: “It is difficult
for us to start using services of aggregators because we ca not pass on those costs to the rates that we set for our own clients”.
Linked to this restricting market structure are outdated laws and regulations. As well as for DSO’s as aggregators current legislation limits their ability to invest in or offer flexibility services: “At the
moment, legislation is mainly the factor that bounds us in providing space in the power grid for customer to deal flexible with that space”[DSO]. For aggregators is the balance responsibility party
(BRP) also an important role. The BRP ensures that demand and supply of electricity for its portfolio at any time of the day are optimally aligned. In most cases the energy supplier takes on the role of BRP on behalf of the customer.
4.5. Measuring ESF
During interviews with the different cases it became clear that in the current energy system there are no organizations that have modelled flexibility to such a detailed level as this study strives to. Especially for DSO’s and TSO’s there is not really an incentive to develop such a model since it is not their core business. However, the aggregators are strongly involved in developing models to analyze flexibility. Different types of data play an important role in developing a model for energy system flexibility and applying that model in practice for the operations management of flexibility services. First, in order to develop a model of flexibility, historical data is of importance: “Based on a lot of
- 20 -
measuring flexibility”[Aggregator 1]. Then, for the operations management of flexibility services,
real-time data is required for measuring flexibility: “In our office we continuously measure the
frequency of the power grid. Based on that data our software makes decisions.”[Aggregator 3]. Not
only aggregators benefit from measuring flexibility: “We look at our transmission curve, how much
electricity does get in the area and how much goes out…”[DSO]. By combining that information with
current consumption data of their customers it provides information about how much flexibility (in MW) is available in the grid.
Next to that, various technical data such as ramping rates and process efficiency needs to be incorporated in a model in order to measure flexibility: “Eventually can all flexibility options be
traced back to a model with bandwidths, and all kinds of characteristics restrict that model, the operational barriers.”[Aggregator 2]. Information about the process that provides flexibility is
essential: “…what kinds of processes do I have and which parameters do we add to these
processes?”[Aggregator 2]. Also for DSO’s it is useful to know how flexible their customers are: “How quick can they ramp up or down, switch on or off to ensure that they can proper manage their energy…”[DSO].
4.6. Summary
The data collection and analysis resulted in some interesting findings. Results show that energy system flexibility can be defined among various dimensions, of which the technical and commercial are most relevant. The technical dimension is mainly concerned with adapting production and consumption with the aim to maintain a balance in the power grid. The commercial dimension is involved with utilizing the technical capabilities of the power grid to develop services. Furthermore, the behavioral dimension can play an important role, but only when the unlocked flexibility is a result of consumers exhibiting different behavior.
For developing, offering and managing flexibility services aggregators have an interest in obtaining:
insights in the exact amount of available flexibility at any moment in time of a flexibility source;
insights in the technical requirements/constraints for unlocking flexibility from a flexibility source;
insights in the costs of unlocking flexibility from a flexibility source;
insights in the pricing structures of flexibility services.
Furthermore, for consumers of flexibility services it is essential that an aggregator can guarantee supply, availability and affordability of their service.
The sources that provide flexibility for flexibility services are subject to several constraints. Results show that for energy storage and DSM practices different sorts of operational constraint like ramping rates and minimum up/down time limit the available flexibility of those sources. Furthermore, several other non-technical constraints play an important role. Financial factors can be constraining for both flexibility sources but especially for energy storage as a result of the high capital costs. During data collection it also became clear that currently the market structure and laws and regulations do not really support stakeholders in the energy system to get involved with flexibility services.
- 21 -
5. Discussion
This section reflects the results from the previous chapter against the working paper of van der Burg & Wortmann (2017) about energy system flexibility. The main focus is to identify conflicting points and similarities between the results of the interviews and what is described in the working paper. Also, shortcomings of the model will be depicted and possible additions and improvements to the model will be proposed.
5.1. General flexibility options & dimensions of flexibility
In order to operationalize flexibility it is essential to define and operationalize the flexibility options that the four sources of flexibility provide. The working paper proposes two general flexibility options, from the perspective of the grid, as presented in figure 5:
Infeed flexibility; with the ability to increase or subsequently decrease energy infeed to the grid.
Outfeed flexibility; with the ability to increase or subsequently decrease energy outfeed to the grid.
Not surprisingly, the two flexibility options are in line with the results since it is essential to maintain a balance in the power grid, which can only be realized by adjusting infeed and outfeed to the power grid. Based on these two general flexibility options the following measurable dimensions are proposed in the working paper, which allow for a formal way of expressing available and provided flexibility:
The first dimension is infeed- or outfeed rate (MW), explained as speed. This explanation could be clarified more precise, since speed is a very common concept and could be interpreted in different ways. A more comprehensive explanation may be ‘amount of energy per unit of time supplied/consumed’. The second dimension, ramp rate up/down (MW/h), is explained as acceleration/deceleration. This could also be explained in a more comprehensive way, for instance as ‘the increase/decrease of the amount of energy per unit of time supplied/consumed’ or ‘the speed at which the input or output can be adjusted’.
The third proposed dimension is volume or time. These are two different dimensions; volume is measured in MWh and time in hours. Both dimensions give important insights in flexibility. For instance, it is interesting to have insights into the available infeed volume (MWh) during a certain time
General Flexibility dimensions
A) Infeed flexibility (I. increase infeed & II. decrease infeed):
Dimension Explanation Unit
Infeed rate Speed (MW) or (MJ/h) Ramp rate up/ down Acceleration/ Deceleration (MW/h) or (MJ/h/ h) Volume or Time Sustain (MWh) or (MJ) or (h)
B) Outfeed flexibility (III. increase infeed & VI. decrease infeed):
Dimension Explanation Unit
Outfeed rate Speed (MW) or (MJ/h) Ramp rate up/ down Acceleration/ Deceleration (MW/h) or (MJ/h/ h) Volume or Time Sustain (MWh) or (MJ) or (h)
Table 3. Dimensions of operational flexibility Figure 5. General flexibility
- 22 - period. Time is also an interesting dimension to include since for flexibility service it is important to have insights in the starting time and the duration of the service. However, visualizing four dimensions in one metric is complex and it is desirable to have a metric that is fairly easy to understand and use by practice.
According to the technical dimension of flexibility of TSO (F. ‘t Hoen, personal communication, December 19, 2016), flexibility is:
1. the capability to adapt power generation or consumption (MW) 2. at a certain moment,
3. over a specific period of time (h), 4. at a certain speed (MW per second),
5. at a certain location or within a certain area,
6. and thus to inject or withdraw energy (MWh) in or from the power system.
This definition also incorporates other factors like location which is also recognized by Eid et al. ( 2015). The impact of a source of flexibility is bounded by the geographic reach of that source. It makes a lot of difference whether it is possible to deploy a source of flexibility at the level of a building, district, or national (Donker et al., 2015). For instance, the performance of a solar panel is dependent on the geographical location, i.e. latitude, longitude and season (Deshmukh & Deshmukh, 2008). However, for operationalizing flexibility it is hard to include these kinds of factors. In the definition of TSO the general flexibility dimensions can be identified. A balance in the power grid can be realized by altering the infeed/outfeed rate (MW) and ramping that rate up or down (MW/h) over a specific period of time (h), bounded by various constraints dependent on the flexibility source, like volume (MWh). Four different units (MW, MW/h, MWh, h) or dimensions can be recognized after analyzing different definitions of energy system flexibility. Hence, the operationalization of flexibility will be comprehensive by including both volume and time as separate dimensions since they both give important insights in flexibility.
5.2. Relation flexibility options and sources of flexibility
The working paper illustrates how the flexibility options apply to the different sources of flexibility and how the different sources of flexibility relate to each other in terms of the general flexibility options, as displayed in table 4.
Relation flexibility options and sources of flexibility
Flexibility options:
Sources of flexibility
Storage Conversion Supply Side Management Demand Side Management
I. Increase in feed Ramp up discharging Ramp up conversion Ramp up production
II. Decrease in feed Ramp down discharging Ramp down conversion Ramp down/ curtail production
III. Increase out feed Ramp up charging Ramp up conversion Increase consumption
IV. Decrease out feed Ramp down charging Ramp down conversion Decrease consumption Table 4. Overview sources of flexibility and flexibility options
- 23 - should not be included as a source of operational flexibility. However, it should definitely be kept in mind as a flexibility source since interconnection can reduce the risk of supply shortages in some parts of Europe for instance.
5.3. Constraints that set boundaries on available flexibility
As mentioned in the results section will this paper mainly address the constraints that set boundaries to the available flexibility of energy storage and DSM.
5.3.1. Energy storage
The available flexibility is bounded by:
1. Technical constraints: - Maximum charge rate (MW) - Maximum discharge rate (MW) - Maximum ramp rate up (MW/h) - Maximum ramp rate down (MW/h) - Maximum storage capacity (MWh) 2. Time constraints: - Storage disconnected at time T 3. Time & volume constraints: - Storage full/empty at time T
Table 5. Constraints that set boundaries to the available flexibility of energy storage
Table 5 displays the constraints that set boundaries to the available flexibility of energy storage identified in the working paper. Results are in line with the identified constraints, however additional constraints could be considered.
An additional technical constraint should be storage losses (MW). There is no perfect storage. Efficiency rates and storage losses prove to be more negative in practice. Not all storage technologies can meet the technical requirements for certain applications. Results show that it is important to take the efficiency of the energy storage unit in mind. For instance, a large-scale storage system as a pumped hydro plant has an efficiency between 70% and 80%. The energy that is lost during the process cannot be neglected. Also, a storage unit requires energy to charge itself at each cycle and that accounts for energy loss. As illustrated in figure 6, there are charging efficiency losses, ‘leakage’ loses and discharging efficiency losses which should be taken into account (Bruce et al., 2015).
Second, some other additional technical constraints should be added. In order for a storage unit to stay within its own secure parameters the minimum up/down time (h) should be taken into account. Some storage units have to stay operational for a minimal amount of time before they can be switched off. And vice versa, some also have to be switched off for a minimal amount of time before they can be switched on. Furthermore, the start-up time (h) and shut-down time (h) are factors which influence the available flexibility of energy storage. The start-up time is the time between the decision to switch on a storage unit and the moment its power input/output reaches the required level. Then, shut-down time is the time between the decision to switch off a storage unit and the moment its power input/output reaches zero. Although the minimum up/down time and start-up/shut-down time are expressed using
- 24 - time units they should be categorized as technical constraints and not as time constraints. For instance, the start-up time of a storage unit is a result of the technical restrictions of that unit.
Furthermore, there are several other factors that do not directly limit the available operational flexibility of energy storage but have been recognized as limiting factors for the deployment of energy storage and definitely should be considered. Results show that storage of electricity, in most cases, is not a financially viable option. The investment costs as well as the variable costs are too high for most applications. Next to that is the uncertain future of the energy market, which is subject to a transition, an obstacle which makes long-term investments risky. Furthermore, the accessibility to the market is a limiting factor. Deploying storage for the primary reserve capacity of TSO is possible via weekly auctions. However, legislation only allows balance responsible parties (BRP) to be active on this market. As a result it is not possible for local energy initiatives to enter the market.
5.3.2. Demand side management
The available flexibility is bounded by:
1. Technical constraints: - Maximum consumption rate (MW) - Minimum consumption rate (MW) - Maximum consumption volume (MWh) - Minimum consumption volume (MWh) 2. Time constraints: - Flexible demand disconnects/stops at
time T
3. Time & volume constraints: - Consumed volume X (MWh) at time T with consumption rate Y (MW)
Table 6. Constraints that set boundaries to the available flexibility of demand side management
Table 6 displays the constraints that set boundaries to the available flexibility of DSM identified in the working paper. Results showed that DSM practices are also subject to another constraint, frequency of
management. It is not possible to constantly steer demand, it can only be done a limited number of
times. There is a limit on the number of starts of certain equipment for a given period of time. For instance, refrigeration compressors cannot continuously be switched on and off otherwise they break. Certain equipment can only be switched on once a quarter. Therefore, the minimum up/down time (h) is also an important constraint for DSM practices as a measure of the maximum frequency that equipment can be committed as a flexible resource. Accordingly, it is not possible to steer demand in quick succession. For instance, when an aggregator manages a pumping station they set a set point, they do not really switch it on or off. They set the internal parameters so that the system operates within its secure limits. When the set point for instance is set very high, it still may take half an hour for certain equipment before they really start using more energy, which certainly could be recognized as a constraint of the available operational flexibility. So, the start-up time (h) and shut-down time (h) are also of importance for DSM practices. Just as for energy storage can the proposed additional constraints be labeled as technical constraints and not as time constraint for the same reasons.
- 25 - Furthermore, consumers and aggregators can only enter the market when they are represented by their energy supplier, whom is a BRP on the market. But energy suppliers have no interest in selling the flexibility of their customers because the current pricing structure does not allow consumers to settle for their flexibility. Legislation obliges consumers to pay a fixed price for their energy consumption based on their consumption profiles. If a flexible pricing structure would be applied it would allow consumers to be rewarded for their flexibility. This would create an incentive for consumers to exhibit different behavior. So policy and market issues also play an important role.
5.4. Expressing available flexibility
The following descriptions are all originated from the working paper of van der Burg & Wortmann (2017) and are based on the case of one flexibility option, energy storage. Appendix E illustrates all required parameters, variables and constants that are needed for modelling flexibility in the case of energy storage. Appendix F displays the proposed formulas of the working paper including technical, time and volume constraints meaning that the storage disconnects at time Tend and has to be full at time Tend.
The available flexibility is specified in the working paper per general flexibility option (i.e. infeed or outfeed flexibility), and over the three dimensions: MW (decrease & increase), MW/h (up & down), and MWh, as represented in table 7.
Dimensions of available flexibility Unit Description
Infeed
flexibility Available infeed rate +
(t)
(MW) The potential amount, expressed in MW, the infeed rate can increase at a specific moment in time
Available infeed rate – (t)
(MW) The potential amount, expressed in MW, the infeed rate can decrease at a specific moment in time
Available infeed ramp up (t)
(MW/h) The potential amount, expressed in MW/h, the ramp up rate can increase at a specific moment in time
Available infeed ramp down (t)
(MW/h) The potential amount, expressed in MW/h, the ramp down rate can increase at a specific moment in time
Available infeed volume
(t)
(MWh) The potential amount, expressed in MWh, can be fed into the grid at a specific moment in time
Outfeed flexibility
Available outfeed rate +
(t)
(MW) The potential amount, expressed in MW, the outfeed rate can increase at a specific moment in time
Available outfeed rate –
(t)
(MW) The potential amount, expressed in MW, the outfeed rate can decrease at a specific moment in time
Available outfeed ramp up (t)
(MW/h) The potential amount, expressed in MW/h, the ramp up rate can increase at a specific moment in time
Available outfeed ramp down (t)
(MW/h) The potential amount, expressed in MW/h, the ramp down rate can increase at a specific moment in time
Available outfeed volume (t)
(MWh) The potential amount, expressed in MWh, can be fed out of the grid at a specific moment in time
Table 7. Overview of available flexibility options
