An Exploratory Analysis of the Dutch Electricity System in ...An Exploratory Analysis of the Dutch Electricity System in Transition Jan H. Kwakkel, Gönenç Yücel Delft University

An Exploratory Analysis of the Dutch Electricity System in Transition

Jan H. Kwakkel, Gönenç Yücel Delft University of Technology, Faculty of Technology, Policy and Management, Delft, the Netherlands

Abstract--Recent contextual developments constitute a

backdrop of change for the Dutch electricity system. Institutional change driven by liberalization, changing economic competitiveness of the dominant fuels, new technologies, and changing end-user preferences regarding electricity supply are some examples of these developments. This paper explores plausible transition trajectories in the face of these developments given technological uncertainty about investment and operating costs, and fuel efficiency of various alternative technologies; political uncertainty about future CO2 abetment policies such as emission trading; and socio-economic uncertainty about fuel prices, investment decisions of suppliers, and load curves. Various alternative developments for these uncertainties are specified. The consequences of each of these alternative developments is assessed using an agent-based model of the Dutch electricity system. The outputs are analyzed using various data-mining and data visualization techniques in order to reveal arch-typical transition trajectories and their conditions for occurring. Policy recommendations are derived from this. The results indicate that most transition trajectories point towards a future energy supply system that is reliant on clean coal and gas. Under the explored uncertainties, only rarely a transition to renewables occurs. The various sustainable energy support programs appear to be ineffective in steering the energy supply system towards a more sustainable mode of functioning across the various uncertainties.

I. INTRODUCTION

The Dutch electricity systems have been going through a significant institutional transformation since 1998 [1]. This transformation will influence the way the infrastructure will evolve over time [2]. It is an open question exactly how the infrastructure will evolve over time, because of the long delays typical to large-scale socio-technical systems. However, a survey of the current generator park reveals that roughly 20% of the park is expected to be decommissioned in the coming decade. Thus, there is a window of opportunity to significantly alter the generation portfolio. There are various forces that affect the decision-making by generator companies with respect to their investment decisions. Among others, technological developments in generator technology, fluctuations in fuel prices, environmental policies by governments, and potential shifts in end-user preferences, play an important role. The co-existence of aforementioned developments makes the current era a climate in which a transition may emerge. However, whether the system will shift to a newly emerging trajectory, or not is not apparent. The development trajectory will be shaped by the internal dynamics of the system under the influence of the aforementioned contextual developments. This dynamic puzzle constitutes the main motivation behind this study,

which aims to develop insights about plausible development paths for the Dutch electricity system.

In order to develop insight into plausible development paths for the Dutch electricity system, one approach would be to try and reduce the uncertainty about the various forces that affect the investment decisions. This is implicitly what is done through various forecasting and foresight techniques such as forecasting, trend extrapolation, Delphi studies, and scenario approaches [3]. In this paper, we follow the opposite approach: we explicate the major uncertainties and assess their influence on the development path. An agent based model of the Dutch energy system is presented and its behavior across 13 different uncertainties is explored. In this way, interaction affects between these uncertainties are explicitly accounted for. Moreover, the emphasis is on the real issue of interest, the emerging pathway, and not on factors that could affect this issue.

The structure of this paper is as follows. Section 2 provides more background on the method. Section 3 introduces the Dutch energy market and the agent based model. Section 4 presents the results of the analysis of the major uncertainties on the future pathway of the Dutch electricity system. Section 5 present the conclusions.

II. EXPLORATORY MODELING AND ANALYSIS

Most models are intended to be predictive and use consolidative modeling techniques, in which known facts are consolidated into a single ’best estimate’ model. The consolidated model is subsequently used to predict system behavior [4, 5]. In such uses, the model is assumed to be an accurate representation of that portion of the real world being analyzed. However, the consolidative approach is valid only when there is sufficient knowledge at the appropriate level and of adequate quality available that is, only when we are able to validate the model in a strict empirical sense. We can validate models only if the situation is observable and measurable, the underlying structure is constant over time, and the phenomenon permits the collection of sufficient data [5]. Unfortunately, for many systems of interest, these conditions are not met. This may be due to a variety of factors, including the infeasibility of critical experiments, impossibility of accurate measurements or observations, immaturity of theory, or nonlinearity of system behavior, but is fundamentally a matter of not knowing enough to make predictions [5, 6]. Many scientists have realized this. Some claim “the forecast is always wrong” [7]; others say such predictive models are “bad” [4, 5], “wrong” [8], or “useless”

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2011 Proceedings of PICMET '11: Technology Management In The Energy-Smart World (PICMET)

[9], which raises the question whether models can be used at all in decision-making about such systems [see e.g. 3, 10, 11]. Such decision-making problems are known as decision-making problems under deep uncertainty or severe uncertainty [12-14]. Decision-making under deep uncertainty is defined as situations in which decisionmakers do not know or cannot agree on a system model, the prior probabilities for the uncertain parameters of the system model, and/or how to value the outcomes [15].

In case of decision-making under deep uncertainty, the potential for using a consolidative modeling approach is limited. However, even if the consolidative modeling approach cannot be used, there is normally a wealth of information, knowledge, and data available that can be used to inform decision-making. Exploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems [16, 17]. EMA specifies multiple models that are consistent with the available information. Instead of building a single model and treating it as a reliable representation of the information, an ensemble of models is created and the implications of these models are explored. A single model run drawn from this set of models is not a prediction. Rather, it provides a computational experiment that reveals how the world would behave if the assumptions any particular model makes about the various uncertainties were correct. By conducting many such computational experiments, one can explore the implications of the various assumptions. EMA aims at offering support for exploring this set of models across the range of plausible parameter values and drawing valid inferences from this exploration [16, 17]. From analyzing the results of this series of experiments, analysts can draw valid inferences that can be used for decision-making, without falling into the pitfall of trying to predict that which is unpredictable.

The basic steps in EMA are: (1) conceptualize the policy problem, (2) specify the uncertainties relevant for policy analysis, (3) develop a fast and simple model of the system of interest, (4) design and perform computational experiments, (5) explore and display the outcomes of the computational experiments to reveal useful patterns of system behavior, (6) make policy recommendations [17].

EMA is a new, innovative research approach to supporting policymaking under deep uncertainty. EMA is not focused narrowly on optimizing a (complex) system to accomplish a particular goal or answer a specific question, but can be used to address ’beyond what if’ questions, such as “Under what circumstances would this policy option do well? Under what circumstances would it likely fail?” It is exceptionally valuable in stimulating ’out of the box’ thinking and supporting the development of adaptive plans. EMA has been applied to a variety of decision-making problems. It has been applied to climate change problems in an effort to identify policy options that are on the one hand

acceptable to a wide variety of countries, depending on their state of development and their belief about climate change, and on the other hand that are robust across a wide variety of different plausible future climate change developments [12]. Another EMA application is from the field of energy generation. Agusdinata [17] studied how CO2 emissions could be reduced in the Dutch household sector. The Dutch national government wants to reduce carbon emissions from Dutch households by 80% in 2050 compared to 1990. The extent to which that goal can be realized depends on a wide range of factors and actors. Agusdinata [17] analyzed the results of 100.000 model runs using a Classification and Regression Tree (CART). A third area in which EMA has been applied is transport planning. Agusdinata [17] reports a case study related to intelligent speed limiters. Currently, research is ongoing into EMA for Airport Strategic Planning.

III. THE DUTCH ENERGY SYSTEM IN TRANSITION

The Dutch electricity systems have been going through a significant institutional transformation since 1998 [1]. Although this does not have a direct influence on the physical infrastructure, it altered the ownership and management scheme of this physical infrastructure. Briefly, the whole change process resembles a gradual shift from a system operated by a central operator overseeing the system for cost minimization, to a system of independent actors operating their infrastructure for individual profit maximization. This shift will influence the way the infrastructure will evolve over time [2]. However, due to long delays typical to large-scale socio-technical systems, the impacts of this transformation on the infrastructure’s evolution cannot be immediate. Hence, despite the time passed since this transformation, its impact will be more apparent in the following years. Furthermore, a detailed survey of the generator park reveals the fact that an important portion (around 20%) of conventional plants is expected to be decommissioned during the following decade. This creates room for an important change in the generation portfolio. Technological developments in generation technologies, and fluctuations in the fuel prices make it difficult to foresee the developments during such an important new investment phase. Moreover, increasing environmental concerns both at national policy and end-user preferences level also play an important role in the possible development trajectories. The co-existence of aforementioned developments makes the current era a climate in which a transition may emerge. However, whether the system will shift to a newly emerging trajectory, or not is not apparent. The development trajectory will be shaped by the internal dynamics of the system under the influence of the aforementioned contextual developments. This dynamic puzzle constitutes the main motivation behind this study, which aims to develop insights about plausible development paths for the Dutch electricity system.

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A. A multi-agent model for the Dutch electricity system: ElectTrans ElectTrans is an agent-based simulation model, which

explicitly focuses on multiple actor groups within the electricity system (e.g. generation companies, households, industrial users, the state as a regulator, etc.). The description of the model is structured around the major aspects of the system; i.e. demand, and supply. B. End-users and demand dynamics

Four groups of end-users are represented in the model, which are industrial users, commercial users, horti-/agricultural users, and households. It is possible to name two major supply options for all actor groups, i.e. using electricity supplied via central generation, and adoption of distributed generation options [18] for self-generation. There are two grid-based options in the model: gray electricity and green electricity1. Various distributed generation options are also available, such as wind turbines and gas engine CHPs.

Actors’ decisions about supply options are not just based on cost figures, but they are multi-dimensional decisions where environmental and social issues also play a role. Representation of such a decision process in the model is of primary importance. Referring to the decision analysis field [19, 20], in order to formulate such a decision process a preference structure, which specifies the relevant importance of the assessment criteria, is used. In the preference structure implemented, the actor groups consider four criteria; cost of supply, price volatility, environmental performance, and in-group familiarity. Among these criteria, environmental performance refers to direct CO2 emissions caused per kWh electricity generated by the evaluated options, and familiarity refers to the diffusion of the option within the actor group to which the actor belongs. The weights of these criteria for an actor specify the individual preference structure of that actor, which is the main factor that differentiates the decisions. For example, an actor giving more importance to familiarity characterizes a less innovative user, who prefers to comply with the historical choices of the group it belongs to. On the other hand, another actor weighing familiarity less is more inclined towards opting for innovative options; hence represents a more innovative profile. This preference structure is coupled with a variant of the logit function [21, 22] in determining the shift of end-user demand among electricity supply options.

C. Generation companies and central supply system

Generation companies are mainly responsible for short-term operation, as well as long-term management of their generator park. The short-term operation involves unit commitment decisions, and price bidding in the electricity

1 Electricity generated from renewable sources such as wind, solar, biomass,

etc.

market, which are directly related to load dispatching to take place in the market. Regarding operational behavior, generation companies are assumed to be acting non-strategically: firstly, they do not withhold generation capacity for strategic reasons [23]. Secondly, there is no inter-actor cooperation in price bidding to increase the prices in order to increase overall revenues. The long-term decisions are related to capacity investment and decommissioning. Decommissioning decisions are mainly based on expected lifetime of the technology used in a generation unit. A unit at the end of it is lifetime, and/or an old unit making loss can be decommissioned. Generation companies’ expansion decisions are mainly driven by profit expectations, and are dependent on forecasts about fuel prices, demand, active generation capacity connected to the grid, and feasible investment options.

The forecasts of the generation companies regarding the first three determinants of investment decisions are based on companies’ perceived information about the market. The companies utilize an adaptive trend estimation and univariate trend extrapolation heuristic to develop their future estimates regarding these key variables.

The forecasts of the generation companies regarding market conditions are used to evaluate available investment options. Expected cost and revenue figures are calculated for each option, and combining these figures with investment and fixed O&M costs, the company comes up with a return on investment (ROI) estimate for each option [24]. The option with the highest ROI is selected for investment. If the highest ROI is less than expected ROI of the agent, which is 15% in the base case, the agent does not make any investment for capacity expansion. While conducting the aforementioned feasibility analysis, agents rely on information they have about technical and economical properties of available options. Actors are not assumed to possess perfect information about these attributes, and learn about improvements in the actual properties of options with a perception delay.

The generation unit options that are available for investment in the model are initialized according to common commercial sizes [25, 26]. As in the case of end-user options, generation options’ technical and economical properties develop over time in the model. The rate of this development is proportional to the gap between the current level of the attribute and the plausible future level. In other words, development rate is defined to be proportional to room-for-development. Current and plausible future values for technical (e.g. electrical efficiency, availability) and economic attributes (e.g. investment cost, fixed O&M cost) of options are determined based on recent studies on the issue [25, 27-33].

A more detailed description of the ElectTrans can be found in [34].

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IV. RESULTS A. Uncertainties

Table 1 presents an overview of the uncertainties that are explored in the EMA study. In total, 13 uncertainties are explored across the specified range. Most of uncertainties are multiplier factors that will be used to alter the base value of the corresponding parameters. For example, assume the investment cost of wind turbine is 100, and the Wind Investment Cost Factor is 0.8; then the model will be initialized with an investment cost of 100x0.8=80. To be more precise, it is not the initial investment cost we are altering, but the expected future investment cost towards which the option evolves during the time horizon of the simulation. B. Experimental design

The model is implemented in Java using the agent based simulation library Repast 3.1. Through a Python to Java bridge called jpype, the model is executed from Python. Python is an open source high level programming language. Extensive open source libraries for scientific computing, jointly known as scipy, are readily available. Using these libraries, a latin hypercube sample across the specified uncertainties is generated consisting of a total of 15.000 cases. Given our stated interest in understanding system behavior, a uniform distribution is assumed for all uncertainties. By assuming a uniform distribution, an

effective sampling of the space of possible parameterizations of the model takes place. The assumed uniform distribution should not be interpreted as a laplacian prior. The agent based model generates output files with time series data for various outcomes, including installed generation capacity, realized generation, and price. These output files are parsed inside the Python core and stored for further analysis. C. Analysis of results

Figure 1 shows a performance envelope for five outcome indicators. - Total generation: Total amount of electric energy

generated - Total fossil: Total amount of electric energy generated

using fossil fuels as the energy source - Total non-fossil: Total amount of electric energy

generated using non-fossil fuels as the energy source (renewables and nuclear)

- Capacity: Total installed power generation capacity - Price: Average price of electricity on the central grid

The figure shows the upper and lower bounds that are encountered across the 15.000 runs. The figure also shows the distribution of outcomes at the end of the runtime. It appears from these results, that there is a limited development of non-fossil generation, suggesting that under most uncertainties a transition towards more sustainable generation does not take place.

TABLE 1: THE UNCERTAINTIES AND THEIR RANGES Key Description Base Range Ccs Investment Cost Factor Multiplier factor to alter the future investment cost of carbon capture-based

generation options 1.0 0.6-1.25

Ccs operational Cost Factor Multiplier factor to alter the future variable operating costs of carbon capture-based generation options

1.0 0.6-1.25

Year in which CCSS becomes available

The year after which the CCS technology will be commercially available for all generation companies

2025 2015-2040

Wind Investment Cost Factor

Multiplier factor to alter the future investment cost of wind-based generation options

1.0 0.75-1.15

Biomass Investment Cost Factor

Multiplier factor to alter the future investment cost of biomass-based generation options

1.0 0.6-1.25

Biomass operational Cost Factor

Multiplier factor to alter the future variable operating costs of biomass-based generation options

1.0 0.6-1.25

Coal Price Increase Percentage

Yearly fractional increase in coal prices 0.0065 0.002-0.015

Gas Price Increase Percentage

Yearly fractional increase in natural gas prices 0.015 0.002-0.03

Demand Growth Fraction Yearly fractional change in the demand of end users 0.015 0-0.03 Load Slope Change Fraction Yearly fractional change in the slope of the load-duration curve 0 -0.01-0.01 Planning Horizon of the generation companies

Upper bound for the planning horizon of the generation companies. (Planning horizon for each generation company is randomly initialized using a uniform distribution with a lower (i.e. 5 years) and an upper bound)

8 6-12

Mean return on investment of generation companies

Average expected return-on-investment for the generation companies 0.2 0.1-0.25

Carbon Price Case Id of the carbon price scenario. 5 carbon price time series have been specified. This integer indicates which time series is used in a particular model instance.

1 1-5

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Figure 1: Performance envelopes and distribution of end states for five outcome indicators.

Figure 2: Development of fraction fossil generation and average price over time.

To further investigate the results, 3d plots such as those

shown in Figure 2 and Figure 3 have been generated. These provide insight into how two outcomes developed together over time. Figure 2 shows the development of the fraction of

fossil fuel-based generation and the average price over time. Figure 3 shows the development of the fraction of fossil fuel-based generation and the fraction of central generation over time.

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Figure 3: Development of fraction fossil generation and fraction central generation over time.

Figure 4: Scatter plot of providers expected return on investment and providers planning horizon, colors

indicate the end value of fossil fuel based generation.

Having explored the results of the runs, the next step is to

provide insight into the mapping of the uncertainties to the results. That is, what results are to be expended given some uncertain conditions. Figure 4 and Figure 5 are examples of some simple analysis using scatter plots. Figure 4 shows a scatterplot of the generation companies’ expected return on investment and their planning horizon. Each points corresponds to a single run. The runs are color coded based

on the end value of fossil fuel-based generation, using a standard heat color map (blue is low, red is high). No pattern is apparent in this plot, suggesting that either these two uncertainties have limited influence on the end value of fossil fuel based generation or that this influence is obscured by the other 11 uncertainties that are also taken into account. The same conclusion can also be drawn from Figure 5.

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Figure 5: Scatter plot of load slope and relative change in fuel prices, color indicates percentage of coal-based generation

To provide insight into how the various uncertainties

jointly determine outcomes, a classification tree was made based on the results. Classification trees are a frequently employed data mining technique. They are used to predict class membership based on a set of attributes. In the context of this paper, we used the 13 uncertainties as attributes. As class we used the terminal value for the fraction of fossil fuel-based generation. This terminal value was split. If it was lower or equal to 0.6, it is coded as 0, else it is codes as 1. Figure 6 shows a classification tree that results from this analysis. The tree was generated using the open source orange data mining package. This is a C++ library with

python bindings to many useful data mining and machine learning algorithms.

From Figure 6, we can deduce which combinations of values for the various uncertainties lead to which results. For example, the extent to which a transition occurs is critically depended on the gas price. If the gas price is very low, between 0.015 and 0.018, the only real hope for a more sustainable generation is a high carbon price (left most branch in Figure 6). Looking at the tree, we also conclude that under most developments, the future generation of energy will not be very sustainable. That is, in most cases, the fraction of fossil based generation in the final year is higher than 0.6.

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Figure 6: Classification tree of fraction fossil-based generation

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V. CONCLUSIONS

This paper started from the observation that in the coming years, roughly 20% of the Dutch energy generation capacity would be decommissioned. Coupled with the institutional changes, it is an open question how the Dutch energy system will evolve. By exploring the behavior of the system across a wide range of uncertainties, insight into this evolution can be generated. It appears that a gradual but relatively small change will take place. Irrespective of how the various uncertainties will enfold in the future, a gradual shift towards more decentralized generation and slightly more sustainable generation will take place. However, it appears highly unlikely that a dramatic transition towards more sustainable modes of generation will take place in the coming years.

The results suggest that if the Dutch government want to stimulate a significant shift towards more sustainable modes of generation, the current policies are insufficient. Moreover, even the introduction of a carbon tax, for example through a European emission trading system appears to be insufficient. Only in combination with high fuel prices a high carbon tax could be effective. This implies that additional policies might be necessary. The developed model and the presented approach could serve as a technique for supporting the design of such policies. Another avenue of further research is that the current model looks at the Netherlands in isolation. However, there are experts that suggest that in the future the Netherlands might become a major exported of energy. Currently, the international trade of energy is not explicitly considered in the model. Modifying the model in such a way that allows for the explicit exploration of the impacts on energy trade for the future path of the Dutch energy system is an important avenue for research.

This paper focused on the Dutch energy system. However, this case also served as a testbed for EMA. Further research into this method is also necessary. In particular the effective representation of the results and the mapping of inputs to outputs is needed. The presented classification tree is one step towards this. More sophisticated techniques need to be explored. Furthermore, research into classifying and clustering time series data that results from the model runs is needed. Such classification and clustering can help in identifying the typical paths or modes of behavior the system can exhibit.

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An Exploratory Analysis of the Dutch Electricity System in ...An Exploratory Analysis of the Dutch Electricity System in Transition Jan H. Kwakkel, Gönenç Yücel Delft University