Energy modeling for decision making

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A Rising share of Renewable Energy Sources

Today’s trend is to develop intermittent energy sources and to reduce the number of fossil fuel power plants and nuclear facilities. On the international stage, this ambition is driven by prospective scenarios like IRENA’s Global Energy Transition: A Roadmap to 2050 [5]. In France, the “Stratégie Nationale Bas-Carbone” (SNBC) and the energy transition plan, known as PPE [6] mandates that an equivalent of about 25% of the elec-tricity consumption will have to be produced by solar photovoltaic and wind power by 2028.
In this manuscript, we use the adjective intermittent to qualify energy sources such as photovoltaic or wind power. It is important to note that some works like Suchet et al. [7] propose different semantics for the definition of variable or intermittent character. In the context of this thesis, these two terms are used as synonyms.

Renewable energy sources and intermittency

Electricity generation sources can be divided into two categories: dispatchable and in-termittent energy sources. The formers, such as nuclear, coal, or gas power plants, can deliver supply on demand. The second, such as photovoltaic or wind power, are reliant on uncontrollable phenomena like weather cycles.
This manuscript focuses on the study of intermittent sources, especially wind and solar PV. Their production signals are very complex and fluctuate with days, months, and seasons.
Likewise, electricity demand is intermittent and fluctuates due to human needs and activity.
The variability of energy systems involves different time scales related to human rhythms and natural cycles: day and night, weekdays and weekends, summer and winter. These different time scales must all be satisfied and addressed respectively to ensure the balance between production and consumption at any given time.

Residual demand

The residual demand is the difference between electricity demand and intermittent produc-tion. In other words, it is the remaining energy to supply when the production is lower than demand.
Satisfying the residual demand involves technical systems, also called flexibility means. Different methods can provide this required flexibility. First, using a dispatchable electricity supply that follows the demand. Electricity consumption can also be adapted to production. It is known as demand-side management. Eventually, storing electricity when there is excess production to deliver it when needed is a supplementary manner to match electricity demand with supply.

Aim and thesis framework

Nevertheless, the following issue is rarely raised in energy transition discussions. On the one hand, intermittent sources create an enormous need for flexibility — e.g., energy storage — to ensure that electricity supply meets the demand at all times. On the other hand, dispatchable power plants such as gas, nuclear, or hydro-power are by far the cheapest means of flexibility. However, their share tends to be dramatically reduced in the electricity generation mix.
This conflict is at the heart of our investigations: how to ensure the required flexibility while reducing dispatchable power plants’ use? We tackle this question from the other end and use reductio ad absurdum∗ reasoning. The question addressed is “To what extent can the electricity demand be met without using at all the flexibility of dispatchable power plants?”
This concept — dispatchable power plants do not provide any flexibility — is a central assumption to the whole study and highlights the potential of other means of flexibility. It amounts to consider the various strategies to match the energy needs, by dealing with the remaining production fluctuations when the dispatchable resources (baseload production) have been subtracted and kept constant.
This work is not based on any pre-existing energy modeling tools. Everything has been developed from scratch for the purpose of the present study. Energy systems are very complex and modeling their behavior in a detailed and exhaustive way would require to take into account a wide variety of parameters, such as the different energy networks, losses, import-export, and many others. To grasp the essential features and stay focus on the main trends of this complex system, we decided to reduce the number of parameters to a minimum while keeping the principal features: the time-scales and main characteristics of the means considered as potential contributors.
Thus, we use simple technological models. We do not consider any threshold or power ramp that would be a more precise representation of a system — gas turbine, for example — but that would distract us from understanding the basic principles.
Additionally, this work is based on the analysis of seven years of existing electricity production and consumption signals. These time-series are fully known in advance, when optimizing the sizing and operation of the energy system.
It is a bias of our model and a source of costs underestimating: a real system would be oversized to account for electricity production and consumption uncertainty.
For these reasons, we carry out comparative and not absolute analyses which aim to provide orders of magnitude. Our objective is to understand the behavior of the energy system and to highlight its key technological issues. Therefore, we will not try to optimize any electricity generation mix. The technologies considered are the existing ones with their current performances. This work is not a prospective study based on “would be technologies” but an analysis of the relevance and limits of existing solutions.
In summary, we want to provide keys to understanding the implications of the major parameters on the system and highlight the main trends. By focusing on a selected set of parameters and using models with a low level of details, we seek a better understanding of the impact of each decision on the global system.


This work focuses on the French energy system. The conclusions we draw are, therefore, valid for this country only. However, the methodology would remain valid for other case  studies and regions of the world, as long as input data are modified accordingly (weather induced, fluctuation of demand and production, etc).

Scope definition, approach and simplifications

France is modeled as a closed system with no import–export with neighboring countries†. We do not assess the flexibility potential of interconnections. It is a different issue that deserves a proper estimate of associated economic investments and land area usage. Besides, relying on interconnections with neighboring countries with their own energy policy is somehow a “wishful thinking” when dealing with intermittency as a key issue.
The electricity system is modeled with a “copper-plate” approach (i.e., without any grid losses and power limitations). The complementarity between electricity and heat systems is assessed. However, it has not been extended to fuels or other energy vectors due to time constraint.
We assess the potential of different flexibility means to ensure that the energy supply always meets the demand. This analysis is narrowed to a few technological solutions: stor-age of heat or electricity, oversizing of the electricity generation system, or a combination of these.
The energy demand is considered as input data that has to be satisfied. The question is not to know whether we can adapt the load to the supply, but how to make sure that the energy supply always accounts for the demand. Demand-side management, which is not included in the study, is a technical issue, but it also relies on sociological and regulations aspects.
The purpose here is not only to reduce the computational burden or to resolve models with greater temporal resolution. We deliberately use simple technological models, strong assumptions, and a reduced number of parameters to highlight their understanding by varying them in a kind of “sensitivity analysis”.
The limitations and scope of this work are summarized in Figure 1.1.

Analysis standpoint

We define three different metrics to compare several technical solutions and estimate the potential of a “flexibility strategy”. They are the cost in euros, but also the embodied energy and the Global Warming Potential (GWP).
We alternately use euros and embodied energy to evaluate the cost and overall impact of a solution. However, the comparison with the global warming potential could not be presented in this manuscript due to time constraints. The input data that would allow this analysis to be carried out are, nevertheless, provided in the following technology chapter.
The economic investment cost aims to reflect the actual investment required to set up a technology. It is not a market price, but accounts for the system’s life cycle, from raw materials extraction to its set-up. When relevant, operating costs are also included.
The embodied energy and GWP illustrate the environmental impact of a solution because of their physical meaning. Calculated over a cradle-to-gate cycle, the embod-ied energy represents the amount of primary energy required to manufacture a system. Similarly, the GWP emphasizes the Greenhouse Gas (GHG) released during the overall manufacturing process. Gases are weighted relatively to CO2 (e.g., 1 for CO2, larger for CH4). The impact of a manufacturing process is given as the equivalent of CO2 released throughout the whole process. We also consider the potential operating cost — e.g., GHG emisions of a gas turbine operation, for instance.
The embodied energy and the GWP are directly related to physical processes such as heating, cooling, and chemical reactions. Conversely to a market price, these value changes are not related to any economic speculation. Changes are related to physical causes such as resource depletion or process improvements.
To summarize, we have chosen to study two aspects. First, economic investments in euros. Second, in order to go beyond changes in technology prices and focusing on the variability of the energy market — which is not correlated to any price signal (see N. Bouleau [8]) — we also study physical flows. We do it in terms of energy or greenhouse gas emissions. In doing so, we can assess the French energy system’s physical behavior and the mere impacts of a technological choice.
Therefore, in this study, costs may refer to, depending on the context, either Euro, embodied energy or global warming potential. Based on the data available in the literature, we provide costs for each element of the energy system. These data, provided by the literature on Life Cycle Assessment (LCA), are not always exhaustive. This point is discussed in more details in the following technological state of the art Chapter.

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Energy system simplifications – Input data definition

As previously mentioned, technologies are described using simplified models. The next paragraphs define the main parameters of the energy system. Nevertheless, descriptions of all systems, with their own characteristics, are done in the next chapter. What follows only defines the main parameters of our system.
Lifetime: Two physical phenomena can limit the lifetime of a device. It could either be a calendar limitation due to physical aging of the components — for instance, concrete structures last 60 years or more whether it is occupied or not. After this given period — the calendar lifetime — the system reaches its life-end.
It could also be a usage aging, caused by the operation of a system at its maximum power rate. The life end is considered beeing reached once a maximum amount of energy has been delivered. This duration is thus defined as the ratio between the maximum energy delivered and the power installed.
For devices like batteries, the usage lifetime is defined by a maximum number of full cycles that can be performed. It is calculated as the ratio between the maximum energy delivered and the battery’s actual energy capacity.
When both calendar and in-use lifetime describe a technology, it is the first of the two to be reached that defines the system’s end-of-life.
Losses: We account for two kinds of losses in energy storage devices.
First, the efficiency, referred to by the Greek letter η. It is the ratio between the output and the input energy. Those losses can be caused by friction for mechanical devices (Pumped Hydroelectricity Storage, for example) or other dissipation processes in electrochemical reactions.
Second, losses do not only occur during cycling. For some devices — thermal storage in particular —, there is also a self-discharge of the stored energy over time. Even if not used, the state of charge decreases over time, as a leakage.
Capacity factor: Electricity power plants are defined by a nominal power install. It is the maximum power that could be produced by the system. However, for meteorological, technical, and energy market reasons, power plants are not working at their full load. We define the capacity factor (CF) as the ratio between the total energy produced per year over the total energy it would have produced if it had been working at full load. CF = Energy produced over a year
Maximum energy that could have been produced at full load over a year Cost in power A device can be characterized by a cost normalized by a power. It is typically the cost of a power converter, an electricity turbine or a gas burner.
Cost in energy Similarly, a device can also have an investment cost normalized by energy. In the case of energy production, it may be the fuel cost. For storage, it is the cost of the reservoir.

Questions addressed – Synthesis

In some cases, this cost must be added to the investment already made to size the system in power — for hydro storage, for example, the cost of the water reservoir (dam) must be added to the investment for the turbine (power).
Concerning electricity consumption and generation time-series, they are provided by RTE, the French Transmission System Operator (TSO)‡. We use time-series that last 7 years, from 2012 to 2018, with data recorded every 30 minutes.

Questions addressed – Synthesis

To synthesize, this work aims to assess the potential of electricity storage and its comple-mentarity with heat network to handle the variability of PV and wind power deployment. We want to quantify the cost of the service guarantee, i.e. the cost of satisfying the energy demand.
The potential of dispatchable power plants, demand-side management, interconnections as flexibility means, is not evaluated. The electricity or heat grid and their losses are not modeled. We consider the French energy system as a closed system and do not take into account the interconnections and the potential to leverage solar and wind energy in space and time.

Energy transition scenarios – Case studies

There are many scenarios available in the public domain such as ADEME [9], Negawatt [10], NegaTep [11] and others [12, 13]. Each of them has its said and unsaid technical and ideological assumptions, we have decided not to add one to the list and use them as inputs. Analysis of those scenario can be found in the following references [14, 15]. We aim to define a methodology to address the question of flexibility to match intermittency, and to apply it to a few scenarios. They range from the less intermittent — 0%, the variability is the one of the electricity demand — to extreme cases — 100 % PV and 100 % wind power. Although they do not represent realistic scenarios, these two 100 % renewable case studies highlight typical trends in solar and wind cycles. They should therefore be understood as such. The various mixes analyzed are reminded in Figure 1.2. We refer to the share of total energy produced by VRES as the Intermittency penetration rate.
We chose to examine two French energy transition scenarios. The first, known as PPE§, sets targets to 2028 for the electricity production mix [6].The second, Ampère [16], has been developed by the French TSO with a 2035 horizon. It is considered as one of the most plausible scenario. They are presented in Table 1.1, along with the other cases studied in the manuscript — 0 % or 100 % VRES.
Objectives of the PPE and the Ampère scenario are on the scale of the French territory. However, they assume electricity imports and exports as well as changes in our energy consumption patterns¶ — electric vehicles and building thermal renovation, for instance.
We are not taking those aspects into account and only want to analyze the potential of the electricity generation mix. To do so, all scenarios are compared on the same basis:
• The average electricity demand is the same for all scenarios. We chose an average load of 54 GW. It represents the average French electricity demand over the past years (2014 – 2019)[1].
• PPE and Ampère set targets of power capacity installed for four main energy sources: nuclear, Hydroelectricity, PV, and wind power. Shares of other facilities are neglected.
• Using the power installed and the capacity factors of Nuclear, Hydroelectricity, PV, and wind power — see next chapter for their values — we deduce the energy produced by each production mean and calculate its ratio compared with the total electricity production.

Table of contents :

General Introduction 
I Flexibility issues: State of the Art 
1 Framework, scope and hypothesis of this work
I Context
II Aim and thesis framework
III Scope
IV Questions addressed – Synthesis
V Energy transition scenarios – Case studies
VI Research and manuscript organization
2 Energy modeling for decision making — State of the Art 
I Technological state of the art
II Modeling energy systems: different approaches
II Dealing with intermittency: Results 41
3 Storage options and intermittency: Which storage for which time-scale? 
I Questions addressed
II Framework and additional assumptions
III The wavelet decomposition: mathematical presentation
IV Step 1: Assessing the required flexibility
V Step 2: Assessing the sustainability of storage solutions
VI Conclusion
VII Limits of the methodology
VIII Chapter’s highlights
4 Complementarity of storage systems: How do storages work together? 
I Questions addressed
II Framework and assumptions
III Part 1: Single time-scale
IV Part 2: Dual time-scale
V Part 3: Real signals analysis
VI Discussion and Conclusion
VII New elements of understanding compared to the previous chapter
VIII Going further
IX Chapter’s highlights
5 Beyond electricity: assessing the potential of heat energy vector 
I Questions addressed
II Method
III Results
IV Discussion and Conclusion
V Limits and Further research
VI Chapter’s highlights
I Synthesis – Answers to the questions addressed in this Ph.D
II Contribution to the state of the art
III Critical thinking about the limitations of this work
IV Directions for future research
V Mistakes to avoid


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