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In this paper, we investigate two main schemes for contracting demand-side flexibility by the Distribution System Operator (DSO) at the planning stage: a voluntary demand-side connection agreement where consumers offer their flexibility, i.e., load reduction, to the DSO and a mandatory demand-side connection agreement where the DSO sets the flexibility levels, i.e., load curtailment, to be contracted from residential consumers. A different bilevel equilibrium model is used for each demand connection agreement scheme. In both models, the DSO, in the Upper Level, decides on the flexibility price and network tariffs. Residential consumers react to those signals in the Lower Level. They can be prosumers that invest in solar PV and batteries or passive consumers. The paper answers two regulatory issues. The first is which option to choose for regulators between mandatory and voluntary demand connection agreements. We find that mandatory demand-side connection agreements result in higher welfare gains compared to voluntary ones and a lower price for flexibility. However, such agreements may entail some implementation issues for regulators and different curtailment levels among consumers. This connects with the second issue investigated in this paper on how to implement mandatory demand connection agreements from equity and feasibility perspectives. When introducing a pro-rata constrained mandatory scheme, curtailing consumers equally, we find that welfare levels are still higher than under the voluntary scheme but lower than in the unconstrained mandatory scheme. The difference in welfare and flexibility levels between the two mandatory schemes could represent a potential for a secondary flexibility mechanism, where consumers trade flexibility between themselves.

This paper explores the price setting of demand-side flexibility, modelled as consumers’ voluntary load reduction, in distribution grids. It develops a long-term equilibrium optimization model with a bi-level setting for voluntary demand-side flexibility. In the Upper Level (UL), the Distribution System Operator (DSO) maximizes welfare by deciding the level of network investments and setting the price for demand-side flexibility. The DSO also sets the distribution network tariff in order to recover the network investment and flexibility costs from the Lower Level (LL) consumers. LL’s active residential and commercial consumers react to network tariffs and to the price offered for their flexibility by investing in rooftop solar and batteries and offering a certain volume of demand-side flexibility when requested by the DSO. The passive residential consumers also provide flexibility by decreasing their load, but they do not invest in rooftop solar or batteries. We find that voluntary demand-side flexibility increases welfare and allows significant network investment savings. We also find that the benefits can reach all types of consumers. Besides, it is opportune to apply price differentiation when setting the price for demand-side flexibility, where applicable.

The Spine Toolbox is open-source software for defining, managing, simulating and optimising energy system models. It gives the user the ability to collect, create, organise, and validate model input data, execute a model with selected data and finally archive and visualise results/output data. Spine Toolbox has been designed and developed to support the creation and execution of multivector energy integration models. It conveniently facilitates the linking of models with different scopes, or spatio-temporal resolutions, through the user interface. The models can be organised as a direct acyclic graph and efficiently executed through the embedded workflow management engine. The software helps users to import and manage data, define models and scenarios and orchestrate projects. It supports a self-contained and shareable entity-relationship data structure for storing model parameter values and the associated data. The software is developed using the latest Python environment and supports the execution of plugins. It is shipped in an installation package as a desktop application for different operating systems.

Demand-side flexibility can be incentivised to reduce the need for investment in distribution grids either implicitly or explicitly. Implicit demand-side flexibility is when prosumers react to price signals triggered by network tariffs. Explicit demand-side flexibility is when the DSO can contract flexibility. In this paper, we focus on one contractual arrangement: mandatory curtailment by the DSO for a fixed level of compensation. We develop a long-term bi-level equilibrium model. The upper level (UL) is a regulated DSO deciding on the network investment and/or curtailing consumers for a fixed level of compensation. The lower level (LL) consists of consumers, which can be prosumers or passive consumers. Prosumers can invest in solar PV and battery systems. They react to the network tariffs and to the compensation provided by the DSO for curtailing them. The regulated DSO anticipates the reaction of the consumers when investing in the network and when setting the level of curtailment. Network tariffs are set to recover the network costs and the payments made to consumers that have been curtailed. We find that the economics of explicit demand-side flexibility in distribution grids are positive, and they are more positive when tariffs are cost-reflective. This implies that we cannot avoid redesigning tariffs by using explicit demand flexibility. We also find that setting an appropriate level of compensation is difficult in the presence of prosumers and passive consumers. A level of compensation that is high enough for passive consumers will be gamed by prosumers. ispartof: The economics of explicit demand-side flexibility in distribution grids: the case of mandatory curtailment for a fixed level of compensation status: published

An integral part of the model quality control and quality assurance at the European Commission is a scientific peer-review of models, including those developed by external contractors. The present reports details the outcome of the review of the METIS, which was carried out by an external scientific Review Panel closely following ‘Guidelines for the review of models used in support of EU policies’. The review aimed at verifying and consolidating the scientific credibility of METIS and identifying most promising/relevant areas for a future model development. The report includes also a first reaction from the METIS team, detailing among others how Review Panel’s suggestions will be addressed., JRC.I.2-Foresight, Modelling, Behavioural Insights & Design for Policy

Demand-side flexibility can be incentivised to reduce the need for investment in distribution grids either implicitly or explicitly. Implicit demand-side flexibility is when prosumers react to price signals triggered by network tariffs. Explicit demand-side flexibility is when the DSO can contract flexibility. In this paper, we focus on one contractual arrangement: mandatory curtailment by the DSO for a fixed level of compensation. We develop a long-term bi-level equilibrium model. The upper level (UL) is a regulated DSO deciding on the network investment and/or curtailing consumers for a fixed level of compensation. The lower level (LL) consists of consumers, which can be prosumers or passive consumers. Prosumers can invest in solar PV and battery systems. They react to the network tariffs and to the compensation provided by the DSO for curtailing them. The regulated DSO anticipates the reaction of the consumers when investing in the network and when setting the level of curtailment. Network tariffs are set to recover the network costs and the payments made to consumers that have been curtailed. We find that the economics of explicit demand-side flexibility in distribution grids are positive, and they are more positive when tariffs are cost-reflective. This implies that we cannot avoid redesigning tariffs by using explicit demand flexibility. We also find that setting an appropriate level of compensation is difficult in the presence of prosumers and passive consumers. A level of compensation that is high enough for passive consumers will be gamed by prosumers.

In many countries, distribution grid tariffs are being reformed to adapt to the new realities of an electricity system with distributed energy resources. In Europe, legislative proposals have been made to harmonize these reforms across country borders. Many stakeholders have argued that distribution tariffs are a local affair, while the EU institutions argued that there can be spillovers to other countries, which could justify a more harmonized approach. In this paper, we quantify these spillovers with a simplifed numerical example to give an order of magnitude. We look at different scenarios, and find that the spillovers can be both negative and positive. We also illustrate that the relative size of the countries is an important driver for the signifcance of the effects. To be able to quantify these effects, we developed a long-run market equilibrium model that captures the wholesale market effects of distribution grid tariffs. The problem is formulated as a non-cooperative game involving consumers, generating companies and distribution system operators in a stylized electricity market.

The development of variable renewable energy (VRE) and their massive installation are some of the challenges that the economies of a large part of the world will have to face in the coming years to help ensure a reasonable sustainability of the planet. On the other hand, the advances that have been happening in recent decades in the telecommunications sector and the expansion of the Internet have helped to create a new scenario, giving way to growth opportunities in different economic sectors and new business models. One of the most disruptive tools that has emerged in this new digital ecosystem is what is known as Distributed Ledger Technology (DLT). The opportunity and applicability to strengthen VRE expansion is discussed in this article. To this end, selected case studies and main findings of interviews with experts are discussed. The introduction of blockchain technology, in particular in its use as a platform for smart contracts, offers a great potential, highly valued by experts, in addition to adding other relevant characteristics such as efficiency generated in operational terms and traceability, but also new opportunities for consumers and prosumers to build strong energy communities committed with a sustainable energy transaction. However, among the elements to be improved, we highlight the regulatory uncertainty regarding smart contracts´ security in terms of automatic mechanisms and its legal endorsement for use within the EU; adopting standards to facilitate scalability; and other issues of a more technological nature that should also be improved for a massive implementation in terms of optimizing energy efficiency of the model.

Heat pumps are widely recognized as a key technology to reduce CO2 emissions in the residential building sector, especially when the electricity-generation system is to decarbonize by means of large-scale introduction of renewable electric power generation sources. If heat pumps would be installed in large numbers in the future, the question arises whether all building types show equal benefits and thus should be given the same priority for deployment. This paper aims at answering this question by determining the CO2-abatement cost of installing a heat pump instead of a condensing gas boiler for residential space heating and domestic hot-water production. The electricity system, as well as the building types, are based on a possible future Belgian setting in 2030 with high RES penetration at the electricity-generation side. The added value of this work compared to the current scientific literature lies in the integrated approach, taking both the electricity-generation system and a bottom up building stock model into account. Furthermore, this paper analyzes the possible benefits of active demand response in this framework. The results show that the main drivers for determining the CO2-abatement cost are the renovation level of the building and the type of heat pump installed. For thoroughly insulated buildings, an air coupled heat pump combined with poor heating is the most economic heating system in terms of CO2-abatement cost. Finally, performing active demand response shows clear benefits in reducing costs. Substantial peak shaving can be achieved, making peak capacity at the electricity generation side superfluous, hence lowering the overall CO2-abatement cost. publisher: Elsevier articletitle: -abatement cost of residential heat pumps with active demand response: demand- and supply-side effects journaltitle: Applied Energy articlelink: http://dx.doi.org/10.1016/j.apenergy.2015.07.038 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved. ispartof: Applied Energy vol:156 pages:490-501 status: published

ispartof: 37th IAEE International Conference ispartof: IAEE International Conference location:New York City, USA date:16 Jun - 19 Jun 2014 status: published

The capacity credit is often erroneously considered to be a time-invariant quantity. A multi-year analysis of the incident wind profile of various potential wind sites uncovered that there exist large differences between annual capacity credit figures. The uniformity of these capacity credit figures is found to decrease with diminishing wind time series interval lengths. In recognition of the resulting uncertainty, decision maker risk propensity toward various capacity credit scenarios was investigated by adopting cumulative prospect theory. The methodology proposed in this paper is an extension of the effective load carrying capability method. It enables the quantitative analysis of the attitudes of decision makers with regard to deviations (gains and losses) from the forecasted capacity credit as a result of the uncertainty of the incident wind profile. Here, gains and losses may not be viewed by decision makers as having equal but opposite effects on the appeal of wind power production. Therefore, it is argued that a decision maker will not have a neutral risk propensity toward changes to the outcome of the capacity credit and will discount increases and decreases of the loss of load expectation according to a non-linear preference. In line with the well-known adagium that losses loom larger than gains the value of the capacity credit is found to be lower than its corresponding least squares forecast.

As the share of wind power in the electricity system rises, the limited predictability of wind power generation becomes increasingly critical for operating a reliable electricity system. In most operational & economic models, the wind power forecast error (WPFE) is often assumed to have a Gaussian or so-called B-distribution. However, these distributions are not suited to fully describe the skewed and heavy-tailed character of WPFE data. In this paper, the Lévy a-stable distribution is proposed as an improved description of the WPFE. Based on 6 years of historical wind power data, three forecast scenarios with forecast horizons ranging from 1 to 24 hours are simulated via a persistence approach. The Lévy a-stable distribution models the WPFE better than the Gaussian or so-called B-distribution, especially for short term forecasts. In a case study, an analysis of historical WPFE data showed improvements over the Gaussian and B-distribution between 137 and 567% in terms of cumulative squared residuals. The method presented allows to quantify the probability of a certain error, given a certain wind power forecast. This new statistical description of the WPFE can hold important information for short term economic & operational (reliability) studies in the field of wind power.

International audience

QM-AI-11-002-EN-C (print)/QM-AI-11-002-EN-N (online) THINK Policy Briefs are abbreviated versions of THINK Reports. The EU is subscribing to the international trend of local governments becoming more involved in climate change policy-making and higher levels of government encouraging this trend. With the Covenant of Mayors, the EU has already been successful in voluntarily committing city authorities to reduce their CO2 emissions by at least 20% by 2020. The ambition of the Smart Cities Initiative is to speed up the transition towards local sustainable energy systems. A portfolio of smart cities that represents the population of European cities should be selected, consisting of cities with different energy fundamentals, a different political economy, and different institutional capacities. The cities in this portfolio need to be given the institutional flexibility (human and financial resources) to conceive and manage the implementation of concepts of city smartness, i.e. to lead by example (first level of city smartness: city as a public actor), to govern the actions by the private urban actors (second level of city smartness: city as a local policy maker), and to promote an integrated approach (third level of city smartness: city as a coordinator). To have an impact, the initiative needs to establish a strict performance reporting methodology (currently, city pioneer experiences are difficult to compare or replicate because of a lack of reporting, and pioneers that do report, use very different reporting methodologies), which would allow the creation of a good-practice forum or register. An EU level legislative initiative to require all cities to report about their progress or lack of progress is also recommended to further improve the impact of the initiative. The THINK project (2010-2013) is funded by the European Commission under the Seventh Framework Programme, Strategic Energy Technology Plan. (Call FP7-ENERGY-2009-2, Grant Agreement no: 249736). Coordinator: Prof. Jean-Michel Glachant and Dr. Leonardo Meeus, Florence School of Regulation, Robert Schuman Centre for Advanced Studies, European University Institute

In the transition towards a decarbonized energy system, we need city authorities to lead by example as public actors, to govern the actions of the private urban actors as local policy makers, and to conceive and manage the implementation of an integrated approach as coordinators, which we introduce in this paper as three levels of city smartness. Local governments however have institutional disincentives to act, and if they do act, they are confronted with urban actors that are reluctant to follow. This paper analyzes how city pioneers in Europe have been able to overcome these disincentives thanks to a combination of local circumstances and interventions by higher levels of government. We categorize the state of the art instruments that have been used by higher levels of government into “tambourines”, “carrots”, and “sticks”, and reflect on how the state of the art could be improved.

Gas fired power plants make up the main back-up technology to deal with intermittency induced by massive wind power integration. Therefore, the flexibility needs with respect to unpredictable power generation are actually transferred to the gas market. Applying the well known electric power generation concepts of ‘unit commitment’ and ‘dispatching’ to the gas market, hypothetical electricity generation and gas transmission systems have been modeled to verify, first, the physical impact of wind power forecasting errors on the gas system, and, second, its effect on gas imbalance settlement. We find that increasing unpredictability leads to more expensive physical balancing of the gas system. Cost recovery by means of non-market based settlement faces the problem of defining an appropriate unique penalty that covers the balancing costs and incentivizes shippers. Market based settlement relates the variable imbalance tariffs to the actual system imbalance and thus any factor that strongly impacts on the system state like unpredictability. However, this mechanism raises imbalance settlement tariffs for all unbalanced gas network users, even if the major source of unpredictability is the shipper with the wind power related gas demand; whereas these costs should be borne as much as possible by those effectively causing them.

This article is based on research carried out in the framework of the EUI RSCAS Florence School of Regulation, Florence. This paper presents and motivates the development of a techno-economic education package, consisting of two simulation games, to simulate both the trading and the generation of electricity in a liberalized market. Six attributes (storytelling; players as problem solvers and explorers; feedback; challenges that fit the student characteristics; competition; appropriate graphics and sounds) are relevant in order for simulation games to achieve their learning potentials. These attributes are identified within both developed simulation games.

Loyola de Palacio Programme on Energy Policy If Europe is serious about climate change, it has to reduce its overall greenhouse gas emissions by 80% by 2050, thereby effectively going to a (near-) zero carbon energy and thus, electricity system. The European Climate Foundation, Eurelectric, and the International Energy Agency have consequently published a study elaborating on the final goal of this transition. The studies project scenarios of how such a (near-) zero electricity system would look like and provide recommendations on the policies needed to guide the transition. In this paper, we observe that these studies tell a tale with many similarities. In spite of increased energy efficiency, the electricity demand is projected to increase substantially, with up to 50% from today towards 2050, due to shifts from other sectors towards electricity. This demand will be supplied by a minimum of 40% electricity generation by RES, with the remainder being filled up with nuclear and fossils with CCS. The importance of grid reinforcement, expansion, and planning in this context is emphasized in all three studies. While all three studies further recommend relying on the EU ETS for the transition, the European Climate Foundation and the International Energy Agency consider continuing with targets for RES in combination with a more harmonized EU RES support scheme.

Each semester the THINK project publishes two research reports based on topics proposed by the European Commission QM-31-11-487-EN-C (print) QM-31-11-487-EN-N (online) The THINK project (2010-2013) is funded by the European Commission under the Seventh Framework Programme, Strategic Energy Technology Plan. (Call FP7-ENERGY-2009-2, Grant Agreement no: 249736). Coordinator: Prof. Jean-Michel Glachant and Dr. Leonardo Meeus, Florence School of Regulation, Robert Schuman Centre for Advanced Studies, European University Institute

The creation of the EU ETS led to changes in the merit order of the different plants competing on the electricity grid, and in the fuel-switching opportunities in the UK. This country has the greatest potential for CO2 emissions reduction through fuel-switching within the EU, thanks to its suitable energy mix (39% of coal and 36% of natural gas in 2007). Through the modelling of the UK power system with the E-Simulate model, our central contribution documents that fuel-switching did occur in the UK as a consequence of the EU ETS: 20.1 Mton in 2005, 7.8 Mton in 2006, 0.52 Mton in 2007, and 14.3 Mton in 2008. We assess the relative contribution of different factors (carbon price, fuel prices and load in the power sector) to CO2 emissions abatement by disentangling the impacts coming from the EU ETS, relative fuel prices variation, and with a 'switching band' analysis.

Electricity storage is considered a valuable source of flexibility whose applications cover the whole electricity value chain. However, most of the existing evaluation methods for electricity storage are conceived for only one specific use of the storage, which often leads to the conclusion that the investment on storage does not pay off. We think that the value of storage cannot be properly estimated without taking into account the possibility of aggregating the services that storage can offer to different actors. In this paper, we propose a new business model that allows aggregating multiple revenue streams of electricity storage in a systematic way. The main idea of the business model is to coordinate a series of auctions in which the right to utilize the storage unit is auctioned in different time horizons. The model consists of an optimization module and a coordination mechanism. The former simulates the optimal strategy of a certain actor using the available storage capacities in a certain auction, while the latter ensures non-conflicting uses of storage by actors in different auctions. The functioning of the model is demonstrated by a case study. The results show that a storage unit can achieve a higher return on investment in the manner proposed in the business model.

Florence School of Regulation The European Commission has recently launched the Smart Cities Initiative to demonstrate and disseminate how to foster a quick transition towards local sustainable energy systems. Within this initiative, the three main challenges faced by pioneering cities, are to reduce or modify the demand for energy services, to improve the uptake of energy efficient technologies and to improve the uptake of renewables in the urban environment. We find that enough resources will need to be provided to a significant number of pioneering cities, and propose that the initiative would allocate these resources through project competition, rewarding innovation, ambition and performance, which have been ingredients of success at Member State level.

The binding 20% target for the share of renewables in final energy demand might require up to 35% of the electricity originating from renewable energy sources (RES-E). Wind power is expected to contribute strongly to RES-E deployment. However, wind power output is characterized by a variable profile which is only controllable to a limited extend. When this profile is withdrawn from the original load level, the net load profile is found. This net demand level needs to be covered by conventional power generation in economically optimal way. The load duration methodology determines the optimal number of operating hours of different technologies based on relative cost levels. This methodology can be used as a first approach to find the optimal generation mix. However, the deployment of a larger share of wind power increases variability of the net load profile. The inclusion of ramp limits allows the determination of an optimal generation mix taking variability and technical restrictions in a realistic way into account. Demand and wind power installation projections are used for three regions, illustrating changes in the optimal generation mix. ispartof: IAEE location:Vienna Austria date:8 Sep - 10 Sep 2009 status: published

Portfolio theory has found its way in numerous applications for optimizing the electricity generation mix. Existing models, however, consider typically a single time period and correspondingly do not account properly for actual dispatch constraints and energy sources with a variable output. This paper presents a portfolio theory model that explicitly distinguishes between installed capacity (power) and actual electricity generation (energy). This way, the variability of wind power and the ramp limits of conventional power plants can be correctly taken into account in the investment optimization. The model is written as a quadratically constrained programming problem and used in a case study to optimize the Belgian generation mix. ispartof: IAEE location:Vienna Austria date:8 Sep - 10 Sep 2009 status: published

This paper first qualitatively discusses price and quantity based measures for RES-E deployment as well as CO2 mitigation. A simulation model is further developed to quantitatively discuss the effects of a tradable green certificate system, a tradable CO2 allowance system and a CO2 tax on both RES-E deployment and CO2 mitigation. A three regional model implementation representing the Benelux, France and Germany is used. Significant indirect effects are demonstrated, especially for RES-E supporting measures on the reduction of CO2 emissions. ispartof: pages:177-+ ispartof: 2009 6TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET pages:177-+ ispartof: European Energy Market Conference location:Leuven date:27 May - 29 May 2009 status: published

This paper presents the development of an electricity simulation model taking into account electrical network constraints, applied on the Belgian power system. The base of the model is optimizing an extensive Unit Commitment (UC) problem through the use of Mixed Integer Linear Programming (MILP). Electrical constraints are incorporated through the implementation of a DC load flow. The model encloses the Belgian power system in a 220 – 380 kV high voltage network (i.e., 93 power plants and 106 nodes). The model features the use of pumping storage facilities as well as the inclusion of spinning reserves in a single optimization process. Solution times of the model stay below reasonable values.

An increased environmental awareness has led the energy sector to transition towards a system with a substantial amount of intermittent renewable energy sources (iRES). In order to analyze such transitions and their interaction with policy interventions, energy-system optimization models (ESOMs) have been widely used. Usually these ESOMs are very broad in scope as they encompass different energy sectors in many geographical areas and have a planning horizon that typically spans multiple decades. Because of this, energy system modelers are often confronted with the computational limitations of ESOMs, requiring them to introduce model simplifications regarding the temporal structure, the technical detail, and the spatial resolution included in the model. This dissertation investigates two aspects of ESOMs that are directly affected by the model simplifications mentioned above. First, ensuring generation adequacy is an important feature for a planning model and introducing simplifications can distort both firm capacity requirements of the power system as well as the capacity contributions of different technologies. Second, the way in which cross-border trade potentials with neighboring countries are accounted for can affect domestic investment decisions. Typically, this is done in a highly stylized manner which can lead to technology biases. As such, this dissertation is split up into different objectives. A first objective is to identify the model simplifications that cause generation adequacy problems in the resulting capacity mix. Three simplifications are investigated in this regard, namely (i) the choice of a specific weather year to model the intermittent character of iRES technologies, (ii) the simplification of the temporal representation and (iii) a simplified representation of forced outages of power plants. The results suggest that restricting the temporal representation to only a few representative days has the largest impact on the adequacy of planning model outcomes. This is caused by significant underestimations of peak load situations and inaccurate estimations of the contributions iRES technologies can make during these situations, i.e., the so-called capacity credits. A second objective is to thoroughly investigate the capacity credit of iRES and storage technologies. First, the capacity credit concept is explained and analyzed in its most common interpretation, namely as the reflection of a technology's potential to reduce the peak load. Second, this research provides a different perspective to look at a technology's capacity credit. The perspective taken is that of a value-based capacity credit which reflects a technology's monetary value to the power system. By a series of case studies it is shown that a clear discrepancy exists between the two capacity credit interpretations causing various technology biases to emerge. The third objective is to improve the adequacy awareness of ESOMs. In this regard, two approaches are put forward. The first approach uses a dynamic PRM constraint for which the targeted firm capacity as well as the capacity credits of various technologies are updated in between investment periods. The second approach evaluates an adequacy constraint for carefully selected peak periods, so that the level of detail with which scarcity situations are modeled is increased. The results show that both methods increase the adequacy of the obtained solution regardless of the model's temporal representation. Finally, this dissertation examines the impact of the representation of cross-border trade in ESOMs that are specifically designed for a single country/area. To this end, a planning model for the interconnected Central-Western European power system has been developed. This model is used to compare technology choices and welfare estimates for Belgium when (i) cross-border trade of electricity is ignored and (ii) cross-border trade exchanges are an endogenous part of the planning model. The results indicate that for highly interconnected systems, neglecting cross-border trade or having a highly stylized representation of cross-border exchanges can lead to inaccurate welfare estimates and technology biases. In addition, two methods are explored to better account for transmission exchanges in ESOMs. A first method is based on extending the model's geographical scope and fixing the capacity variables in the neighboring countries in line with pre-designed scenarios for those countries. A second method uses specially tailored import and export curves to represent each country's trade opportunities. The former approach is proven to be an efficient way to accurately capture cross-border trade potentials. Furthermore, the use of import and export curves can shorten the computation time, although the results of the model become less accurate. status: published

Humanity is at the beginning of its third energy revolution, after coal and then oil, we are now moving towards the massive use of renewable energies that we knew since the dawn of time. This change will solve several problems, including climate change, as the concentration of greenhouse gases will stabilise. The success of fossil fuels resides in their storability. Indeed, these energies have been stored in the earth for millions of years and can be extracted whenever humans need energy. Their costs of storage are insignificant compared to their costs of extraction. On the contrary, the main renewable energies are intermittent, i.e. wind and solar energy, and thus cannot be stored. However, these energy can be converted into electricity, heat or another form of energy that can be stored. But storing these energy carriers has a significant cost and thus, redefine the design of the energy system. Hence, the shift from fossil fuels to renewable energies - mainly intermittent - requires a complete rethinking of the energy systemsupply chain from resources to demand while including conversion and storage technologies. In particular, to understand what will be the role of storage technologies in this energy system throughout the energy transition period and after. To address this issue, the thesis addresses the following research question: What is the role of energy storage technologies for the energy transition? Focusing on the role of storage requires the inclusion of its environment, i.e. the energy system. Focusing on the energy transition requires a time horizon fromtoday to about 2050. Thus, answering the research question requires a model that represents the energy systemduring the transition. These has motivated the following five contributions of the thesis. First, it is necessary to review the existing energy system models in the literature. Second, one of these models is improved to account for the complexities of energy storage. This involves, among others, to include different time scales (from hour to year) and to integrate all sectors of the energy system. Third, the model must be extended to the full time horizon, i.e. to enable the definition of the optimal pathway between today’s energy system and the long-termgoal. Fourth, the data for the case study must be collected and documented. And Fifth, as the proposed approach is deterministic (i.e. ‘perfect foresight’), a method for accounting for uncertainty is proposed in order to qualify the results. The contributions as a whole provide a framework for generating several energy transition pathways. By applying it to Belgium, the framework offers an overview of the different strategies to reduce the greenhouse gas emissions, of the uncertain parameters that influence decisionmaking, and of the different pathways to reach the long term-target. The whole-energy system approach enhances synergies among the different sectors, such as the importance of electrification. However, this approach cannot be extended to the whole system due to a limit on electricity production. The model prioritises the sectors that should benefit from electrification, such as low temperature heat. As a consequence, on the contrary to the current trends in the literature, private mobility is mainly supplemented by hydrogen vehicles. The reason is that hydrogen (or methane) is easier to import and store on a massive scale. By optimising several transition pathways, the main trends emerge: energy efficiency, local renewable energy, and finally, import of renewable fuels and electricity. Furthermore, an analysis of the overall transition shows the benefit of anticipating policy decisions such as the ban of technologies that are not compatible with a low emission system. The energy transition will be one of the major challenges of the 21st century. With a profound change in the energy system and an integrated approach across sectors, this ambitious challenge can be overcome. Each country should contribute by maximising its cost-effective production of renewable energy before externalising the problem and unbalancing the efforts of other countries. (FSA - Sciences de l'ingénieur) -- UCL, 2021

The massive introduction of variable and only partly predictable renewable energy sources (such as wind and solar power) in modern power systems poses challenges regarding the limited flexibility of power plants, cost-efficient electricity market operation and the system's reliability. This dissertation presents an operational modeling framework, which is afterwards deployed in case studies for the purpose of gaining new or improved insights on technical and cost-related aspects of electricity generation systems in light of these challenges. At the heart of the developed modeling framework is an operational model of the electricity generation system, called a unit commitment model. This operational model computes the cost-optimal generation schedule for a portfolio of power plants and storage units that respects their technical constraints. Concurrently, the unit commitment model ensures sufficient operating reserves to cope with the uncertainty of intermittent renewable power. By deploying the unit commitment model in combination with a dedicated wind power forecast scenario generation tool, developed for this purpose, the resulting modeling framework is suitable for studying flexible power plant operation, electricity market operations and design changes in systems with high penetrations of intermittent renewable power. This dissertation first addresses the difficulties of managing the variations in renewable power generation and to cope with forecast errors against the backdrop of the limited flexibility of the generation fleet. The impact of the growing share of variable renewables on cycling of thermal power plants is studied. Specific attention is devoted to increased maintenance costs caused by cycling, as they are shown to comprise an important share of the total operational costs, and are significant in comparison to the marginal generation costs. Moreover, the possible trade-off between start-up flexibility and corresponding costs is endogenously included in the scheduling decisions, which leads to cost reductions and reduced cost-optimal operating reserve requirements. Regarding cost-efficient electricity market operation, an important aspect to account for is the significant uncertainty that originates from limitedly predictable intermittent renewables. The case studies in this dissertation demonstrate a considerable cost-saving potential of fixing the final generation schedule closer to the moment of delivery, which is mainly driven by reduced need for operating reserves associated with decreased renewable power uncertainty with shorter forecasting lead times. The cost-benefits are, however, restricted by the flexibility of the generation fleet. Flexibility in the form of operating reserves is required to deal with forecast errors in real time. The benefits of increasing the temporal granularity of operating reserve markets are investigated. The results of a case study show that reserve procurement on a short-term basis (i.e., frequent and dynamic sizing and frequent allocation with high resolution products) leads to considerable total operating cost savings and could mitigate scarcity on operating reserve markets. Furthermore, the studied reserve market design changes facilitate the integration of intermittent renewables. Finally, this dissertation presents a contribution to the field of electricity generation system adequacy assessments. The link between generation adequacy and operational security is studied. The adequacy metrics obtained from operational models that are typically used in today's adequacy studies are shown to represent a worst-case situation and should be interpreted with care. By incorporating state-of-the-art reserve requirements, the contribution of operating reserves to generation adequacy is accurately captured, leading to more accurate adequacy indices than obtained from commonly used methodologies. These findings should encourage power system practitioners to adopt novel operational power system models for adequacy assessments that accurately estimate the contribution of operating reserves to system adequacy. status: published

This dissertation focuses on energy system optimization models (ESOMs). These models are used to generate possible transition pathways of the entire energy system in a single or multiple countries over a time horizon of multiple decades. Experimenting with different transition pathways allows gaining insights into the complexity of the energy system transition and can help in forming a long-term vision of this transition. In addition, these transition pathways can be used to evaluate the adequacy of the current policy framework to achieve a desired transition. As such, these models form valuable tools for policy makers. Due to the large scope of ESOMs, solving these models quickly becomes computationally demanding. To limit the computational cost, ESOMs have historically used a low level of temporal and technical detail to represent the operation of the power system, i.e., intra-annual variations in demand and renewable generation are typically represented by 4-48 so-called time slices and the technical constraints faced by thermal power plants when changing their power output, starting up or shutting down are neglected. However, in the context of an increasing penetration of strongly fluctuating and limitedly predictable renewable energy sources such as wind turbines and solar PV panels, this low level of temporal and technical detail might not be sufficient to grasp the challenges related to integrating these intermittent renewable energy sources (IRES). In this regard, a first objective of this dissertation is to assess the impact of this low level of temporal and technical detail on the results provided by ESOMs. The presented research indicates that both the low level of temporal and technical detail lead to an overestimation of the uptake of IRES, an overestimation of the electricity that can be generated by baseload technologies and an underestimation of the system costs. As the penetration of IRES increases, particularly the low level of temporal detail starts to have a significant impact on the obtained results. This is shown to result from the fact that traditional time-slicing methods lead to smoothing of the variability of IRES. A resulting second objective is to develop improved time-slicing methods. In this regard, two time-slicing methods are proposed which are shown to better capture the variability of IRES without necessitating an increase in the number of time slices. This dissertation focuses in depth on one time-slicing method which relies on representing the different conditions occurring throughout a year via a small number of representative historical periods (e.g., days). The selection of the representative set of historical periods is key for the accuracy of this method. In this regard, a novel, optimization-based, approach to select a representative set of historical periods is developed and benchmarked to state-of-the-art approaches available in the literature. This developed approach is shown to achieve better results than the approaches available in the literature. The significance is that, given that a limited number of time slices can be used, a better selection of representative periods allows improving the results provided by ESOMs. A third objective is to develop methods to tractably account for technical constraints in ESOMs. To this end, reduced formulations of the technical constraints faced by power plants are formulated. The results of a planning model integrating these reduced formulations are compared to the results of a planning model which integrates detailed clustered unit commitment (CUC) constraints for a variety of scenarios and cases. This analysis shows that the developed reduced formulations are sufficiently accurate for long-term planning purposes while reducing computation time by a factor of 5-600 with respect to the model with integrated CUC constraints. However, the research presented in this dissertation also highlights that, due to assumptions which need to be made regarding the cycling capabilities of thermal power plants and the requirements for operating reserves, there is a risk that the incorporated technical constraints are overly and unrealistically restrictive, which can lead to strong overestimations of the system costs and suboptimally low penetration levels of IRES. Recommendations to avoid this potential pitfall are presented. The final part of this dissertation relates to the fact that since the liberalization of the electricity markets, investment decisions in generation capacity are made by private, profit-maximizing, actors. The decisions made by these actors can be influenced by the market design and the policy framework. In this regard, the last objective of this dissertation is to determine to what extent ESOMs can account for specific market designs, policy interventions and behavioral characteristics. An analysis is presented which shows that a number of inherent assumptions are made in optimization models which prevent from representing certain market designs, policy interventions as well as behavioral characteristics. status: published

Electricity generation systems in Europe are undergoing dramatic changes, largely driven by changing energy and climate policies. In this dissertation, three evolutions are dealt with in particular: (1) the deployment of intermittent renewables in electricity systems such as wind and solar photovoltaics, (2) the integration of electricity markets and (3) the mitigation of CO2 emissions from the electricity sector. The focus of this dissertation is on the technical and cost-related aspects of electricity generation systems, taking an operational viewpoint (i.e., no investment decisions are studied in this work). The dissertation presents several case studies inspired by realistic and large-scale electricity systems. Concerning the deployment of intermittent renewables, the impact of variable renewable generation on cycling of conventional power plants is studied, as well as the impact of remote renewables—such as offshore wind—on grid congestions. Regarding the integration of electricity markets, this dissertation investigates the benefits of integrating reserve markets and discusses the flow-based market coupling implemented in Central Western European day-ahead markets. Finally, concerning CO2 emission abatement in the electricity sector, the impact of a CO2 emission price on the generation of electricity is studied, together with the policy interaction between the European emission trading system and the deployment of renewables. A unit commitment model has been developed and is used in this dissertation. This model, referred to as the LUSYM model, is a state-of-the-art deterministic unit commitment model, formulated as a tight and compact mixed-integer program and able to solve large-scale electricity systems within reasonable run times. The model includes power plant constraints, renewables curtailment, load curtailment, storage units, transmission grid constraints and reserve constraints. Benchmarking simulations show that the LUSYM model is competitive or outperforms commercial unit commitment packages in terms of optimality of simulation results and/or run times. All the case studies presented in this dissertation, providing qualitative and quantitative insights, indicate that electricity systems are indeed evolving in the direction envisioned by policy makers (at least when it comes to the deployment of renewables, the integration of electricity markets and the reduction of CO2 emissions). However, this dissertation also demonstrates that changing energy and climate policies pose new challenges to electricity systems, such as maintaining the system balance with a large share of intermittent generation. In this respect, unit commitment modeling plays an important role in evaluating policies and their impact on electricity generation systems, in order to develop effective and efficient policies. status: published

The massive integration of variable and limitedly predictable electricity generation from renewable energy sources (RES) leads to so-called balancing costs (a.o. integration costs). Therefore, system operators are continuously seeking novel sources of operational flexibility and improved methods to size, procure and deploy the associated operational reserves. With these challenges is mind, we develop an operational modeling framework to study the impact of stochastic RES-based generation, novel flexibility providers and demand response on day-to-day electricity generation system operation. This framework consists of a statistical characterization of the uncertainty, which provides input for the generation of scenarios and reserve sizing techniques, which in turn are representations of the uncertainty in so-called unit commitment (UC) models. Historically, these models were typically deterministic in nature, which may make them poorly suited to study and operate power systems with high shares of limitedly predictable RES-based generation. Therefore, the first objective set forth in this dissertation relates to the improvement of existing and the development of novel UC models considering imperfect RES-based electricity generation forecasts, with a focus on the cost-efficiency, reliability and computational effort associated with their solutions. We study and improve the performance of three UC models found in the scientific literature: a deterministic UC model, a stochastic UC model and an improved interval UC model. A deterministic UC model tackles uncertainty by considering reserve constraints, which trigger -- if properly designed -- sufficient scheduled capacity to absorb forecast errors during real-time operation. The cost-efficiency of the deterministic model is improved by (1) the explicit consideration of energy storage-based reserve provision and (2) state-of-the-art probabilistic reserve sizing techniques, based on a novel distributional characterization of the uncertainty on RES-based generation forecasts. Stochastic models on the other hand employ a direct scenario-based representation of the uncertainty, which in theory leads to more cost-efficient UC schedules. The solution stability and bias of the resulting UC schedules have drastically been improved through the development of a dedicated scenario reduction technique. The improved interval UC model attempts to reduce the conservatism of the deterministic UC model by improving the realism of the ramping requirements imposed on the scheduled reserves. Nevertheless, the presented qualitative and quantitative analysis showed that the deterministic and improved interval models yield sub-optimal, overly conservative UC schedules, albeit at a low computational cost. We will attribute this sub-optimal behavior to the inability of these models to account for the expected deployment cost of the scheduled reserves. In contrast, the stochastic UC model results in cost-optimal UC schedules if the selected scenarios are sufficiently representative of the uncertainty at hand, but at an extremely high computational cost. In pursuit of a UC model that combines the cost-optimality of the stochastic model with the computational effort of the deterministic model, we develop two novel UC formulations: a hybrid deterministic-stochastic and a probabilistic UC model. Both formulations include, although through a different approach, an approximation of the expected deployment cost of the scheduled reserves. The hybrid UC model combines a probabilistic reserve requirement and a limited set of scenarios. This model allows approximating the stable solution of the stochastic UC model, but at a computational cost that is an order of magnitude lower than that of the original stochastic problem. The probabilistic model on the other hand is characterized by calculation times similar to that of the deterministic model, but allows for significant cost reductions through the internalization of the reserve sizing problem. The second objective addressed in this dissertation relates to the study of the arbitrage and regulation services an activated demand side may offer. In particular, we study the system value of demand response with electric heating systems. To this end, an integrated model is proposed, which entails the inclusion of a physical demand side model, sufficient to represent the operational flexibility available in limitedly controllable residential electric heating systems, in a probabilistic UC model. In a numerical case study, inspired by the Belgian power system, demand response-based arbitrage and regulation services are shown to contribute significantly to the minimization of the balancing cost associated with imperfect RES-based generation forecasts. The presented models and techniques can be used to assess the impact of uncertainty on reasonably large electric power systems, as illustrated in the last chapter of this dissertation. Independent system operators may use these models to optimize their UC decisions taking into account the uncertainty in their system. The integrated model may lead to adequate estimates of the system value of DR. In addition, demand aggregators may use the presented approach to optimize the scheduling and operation of DR-adherent loads. status: published