More renewable energy leads to higher energy imbalances in the Swedish electric power system. In the same time, the grid capacity is almost reached in some regions which requires an extension of the current grids or a reduction of the power consumption. Demand response could be a key factor for both stabilizing the energy balances and reducing the grid congestion. The aim with this thesis is to analyze the potential incomes that demand response from heat pumps can generate for the balance responsibility parties and the grid operators and evaluate how it would affect the end-consumers.   The investigated local grid that contains of 174 multi-family buildings with heat pumps could reduce its highest peak power with 2,9 MW. This peak power reduction generated a cost reduction of 483 000 SEK per year or 2800 SEK per building per year in reduced penalty fees and power subscription fees. The mFRR market and the power reserve market were determined to be the most suitable markets for using demand response from heat pumps on for the balance responsibility party in the electricity price region SE3. SE3 consists of 10146 multi-family buildings with heat pumps. The mFRR market generated an average income of 2 699 000 SEK per winter season whereas the power reserve market generated a yearly administrative compensation of 1 133 000 SEK per season and 104 000 SEK per call-off. It is important that end-consumers obtain demand-based tariffs or hourly based tariffs to enable a cost reduction from the control system.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-04-13T18:29:42Z No. of bitstreams: 1 rodrigovitalmenta.pdf: 1050020 bytes, checksum: 0a0b9fed57688fe227860197468177c5 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-04-24T03:23:52Z (GMT) No. of bitstreams: 1 rodrigovitalmenta.pdf: 1050020 bytes, checksum: 0a0b9fed57688fe227860197468177c5 (MD5) / Made available in DSpace on 2016-04-24T03:23:52Z (GMT). No. of bitstreams: 1 rodrigovitalmenta.pdf: 1050020 bytes, checksum: 0a0b9fed57688fe227860197468177c5 (MD5) Previous issue date: 2015-09-28 / Este trabalho propõe uma metodologia para cálculo da tarifa variável de energia elétrica considerando o ambiente de redes inteligentes (“Smart Grids”). Este problema, conhecido como “Resposta de Demanda” (RD) ou “Tarifa Dinâmica” (TD), permite aos consumidores, que até então pagam um preço fixo para energia, participarem ativamente do mercado de energia tendo em vista que a diferença de preço durante as horas do dia induz a redução de consumo nos horários de ponta e aumento em outros horários. A formulação proposta para cálculo da tarifa é baseada em programação não linear onde a rede elétrica é considerada juntamente com os limites operativos. A resposta do programa informa o valor da tarifa durante o dia de tal forma que tanto a empresa distribuidora quanto os consumidores ganham com o novo modelo de tarifa (Modelo Ganha-Ganha/MGG). A metodologia proposta foi testada em sistemas de distribuição conhecidos da literatura. Os resultados mostram que o processo proposto neste trabalho é promissor para aplicação em Smart Grids. / This work proposes a methodology for calculating the variable energy tariff considering the environment of Smart Grids. This problem has been known as "Demand Response" (RD) or "Dynamic Rate" (TD) and it allows consumers, which have been paying a fixed price for energy, to participate in the energy market. The price difference during the day can induce lower consumption at peak times as well as high consumption at other times. The proposed approach for the optimal tariff calculation is based on nonlinear programming where the network is considered. The consumers are represented by using a relationship between energy price and consumption. The proposed optimization problem leads to optimal energy price to obtain a Win-Win strategy for both the Distribution company and consumers. The proposed methodology is tested in known distribution systems of literature and the results show that it is promising for application in Smart Grid system.

Cette thèse propose un concept d'optimisation de la consommation d'énergie dans les maisons intelligentes basées sur la gestion de la demande qui repose sur l'utilisation de système d e gestion de l'énergie à la maison (HEMS) qui est en mesure de contrôler les appareils ménagers. L'avantage de ce concept est l'optimisation de la consommation d'énergie sans réduire les utilisateurs vivant confort. Un mécanisme adaptatif pour une croissance intelligente système de gestion de l'énergie de la maison qui a composé des algorithmes qui régissent l'utilisation des différents types de charges par ordre de priorité pré-sélectionné dans la maison intelligente est proposé. En outre, une méthode pourl'optimisation de la puissance générée à partir d'un hybride de systèmes d'énergie renouvelables (HRES) afin d'obtenir la demande de charge. particules technique d'optimisation essaim (PSO) est utilisé comme l'optimisation algorithme de recherche en raison de ses avantages par rapport à d'autres techniques pour réduire le coût moyen actualisé de l'énergie (LCE) avec une plage acceptable de la production en tenant compte des pertes entre la production et la demande. Le problème est défini et la fonction objective est introduite en tenant compte des valeurs de remise en forme de sensibilité dans le processus d’essaim de particules. La structure de l'algorithme a été construite en utilisant un logiciel MATLAB et Arduino 1.0.5 du logiciel.Ce travail atteint le but de réduire la charge de l'électricité et la coupure du rapport pic-moyenne (PAR). / This thesis proposes a concept of power consumption optimization in smart homes based on demand side management that reposes on using Home Energy Management System (HEMS) that is able to control home appliances. The advantage of the concept is optimizing power consumption without reducing the users living comfort. An adaptive mechanism for smart home energy management system which composed of algorithms that govern the use of different types of loads in order of pre-selected priority in smart home is proposed. In addition a method for the optimization of the power generated from a Hybrid Renewable Energy Systems (HRES) in order to achieve the load demand. Particle Swarm Optimization Technique (PSO) is used as optimization searching algorithm due to its advantages over other techniques for reducing the Levelized Cost of Energy (LCE) with an acceptable range of the production taking into consideration the losses between production and demand sides. The problem is defined and the objective function is introduced taking into consideration fitness values sensitivity in particle swarm process. The algorithm structure was built using MATLAB software and Arduino 1.0.5 Software. This work achieves the purpose of reducing electricity expense and clipping the Peak-toAverage Ratio (PAR). The experimental setup for the smart meter implementing HEMS is built relying on the Arduino Mega 2560 board as a main controller and a web application of URL http://www.smarthome-em.com to interface with the proposed smart meter using the Arduino WIFI Shield.

The increasing levels of renewable generation and the electrification of transport and heating as parts of the movement towards low-carbon energy systems to cope with climate change will place significant challenges on the electricity system to facilitate the way towards future low carbon energy systems in a cost effective way and ensure secure power delivery. New solutions and higher levels of flexibility are required than currently exist in order to reduce the integration costs of low carbon generation and demand technologies. Price-based demand response in residential sector is considered as one of these potential solutions. However, a certain level of automation is required to reduce both the uncertainty in the consumer response and the complexity for consumers to react to the price signal. This thesis presents a comprehensive and general residential optimization-based Automated Demand Response (ADR). The modelling of home appliances has been extensively developed to include all the classifications proposed in the literature, namely, deferrable and thermal in addition to new groups of critical and fully curtailable loads. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer’s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user’s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer’s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different case studies show the effectiveness of the proposed controller to minimize the household’s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks. This thesis also proposes a two-stage distribution-planning framework to assess the benefits of the proposed ADR models in response to a location-specific time of use Distribution Use of Systems Charge (DUoSC) on the required investments to connect future low-carbon technologies. The network investments and the satisfaction of consumers in terms of energy curtailment are both quantified. The first stage aims to generate location-specific time of use price signals for all users in the network, which represents their contributions in future network investments due to congestion and security constraints. The second stage relates to a group of ADR controllers at residential premises that aims to minimise the daily energy payment whilst maximising consumer comfort in response to the corresponding price signal produced from the first stage.

<div> <div> <div> <p>Real-time pricing (RTP) of electricity for consumers has long been argued to be crucial for realizing the many envisioned benefits of demand flexibility in a smart grid. However, many details of how to actually implement a RTP scheme are still under debate. Since most of the organized wholesale electricity markets in the US implement a two-settlement mechanism, with day-ahead electricity price forecasts guiding financial and physical transactions in the next day and real-time ex post prices settling any real-time imbalances, it is a natural idea to let consumers respond to the day-ahead prices in real-time. However, if such an idea is not controlled properly, the inherent closed-loop operation may lead consumers to all respond in the same fashion, causing large swings of real-time demand and prices, which may jeopardize system stability and increase consumers’ financial risks. </p><p><br></p> <p>To overcome the potential uncertainties and undesired demand peak caused by “selfish” behaviors by individual consumers under RTP, in this research, we develop a fully decentralized price-driven demand response (DR) approach under game- theoretical frameworks. In game theory, agents usually make decisions based on their belief about competitors’ states, which needs to maintain a large amount of knowledge and thus can be intractable and implausible for a large population. Instead, we propose using regret-based learning in games by focusing on each agent’s own history and utility received. We study two learning mechanisms: bandit learning with incomplete information feedback, and low regret learning with full information feedback. With the learning in games, we establish performance guarantees for each individual agent (i.e., regret minimization) and the overall system (i.e., bounds on price of anarchy).</p><p><br></p></div></div></div><div><div><div> <p>In addition to the game-theoretical framework for price-driven demand response, we also apply such a framework for peer-to-peer energy trading auctions. The market- based approach can better incentivize the development of distributed energy resources (DERs) on demand side. However, the complexity of double-sided auctions in an energy market and agents’ bounded rationality may invalidate many well-established theories in auction design, and consequently, hinder market development. To address these issues, we propose an automated bidding framework based on multi-armed bandit learning through repeated auctions, and is aimed to minimize each bidder’s cumulative regret. We also use such a framework to compare market outcomes of three different auction designs. </p> </div> </div> </div>

Smarta elnät nämns ofta som ett sätt att hantera ökad elektrifiering av transporter och industri och en växande andel väderberoende elproduktion. Ett syfte med det här examensarbetet är att studera möjliga följder för lokalnätägaren Sala-Heby Energi Elnät AB, om småhuskunder använder smart styrning av elvärmesystem för att sänka sina elnätsfakturor. Med rådande tariffmodell betalar småhusägaren för den gångna månadens tre högsta timmedeleffekter kl. 07 till 19 helgfria vardagar. Hur nätägaren påverkas av styrning, är en central fråga för projektet Auto-Flex, som startade i januari 2021. Uppsatsens litteraturstudie pekar på att efterfrågeflexibilitet kan ge olika följder för elnätet och för elmarknadens parter, beroende på vilka incitament som används för att skapa ett flexibelt beteende. Med efterfrågeflexibilitet avses här kunders förmåga att flytta eller minska sitt lastuttag från elnätet. I det här examensarbetet utförs beräkningar i Excel för att undersöka följderna av laststyrning och analysen utgår från historiska elmätardata från ca 140 anonymiserade hushållskunder samt från data över effektuttag från regionnätet. Beräkningarna visar att styrning som gynnar kunden ekonomiskt blir en förlustaffärför Sala-Heby Energi Elnät AB, trots sänkt effektuttag från regionnätet. Det gäller, i de flesta fall, även när extra styrning läggs till under timmar då effektuttaget från regionnätet är högt. Resultaten bygger på förenklade beräkningar, där ingen hänsyn tagits till hur effektsänkning av elvärmesystem samspelar med väderfaktorer eller med styrningens varaktighet. Samma effektsänkning har antagits vid varje styrtillfälle och för alla hushåll. Ett andra syfte med det här examensarbetet är att undersöka hur huvudaktörerna i projektet Auto-Flex ser på säkerhetsfrågor i samband utvecklingen mot ett mer IT-beroende elnät. Därför genomfördes två semistrukturerade intervjuer, med integritet, leveranssäkerhet och affärsmodeller som teman. Intervjupersonerna lyfte inga allvarliga hot kopplade till projektet Auto-Flex. Samtidigt kunde de se teoretiska risker med storskalig smart laststyrning i kapacitetssvaga elnät. Mer forskning behövs om smarta elnät och hållbarhet. Kopplingen mellan incitament till flexibilitet och flexibilitetens inverkan på elnätet, hur sårbarheter kopplade till informationsteknik påverkar elnätets leveranssäkerhet och hur smartteknik står sig miljömässigt i förhållande till nätutbyggnad är tre intressanta områden. / By facilitating demand side management, smart grids are expected to smooth the way for a transition to cleaner electric energy. This bachelor’s thesis aims to analyse the consequences for a distribution system operator (DSO) of direct load control,which is set to minimize the consumer’s bill for power transmission. This is also a central theme in the recently initiated Auto-Flex smart grid project, with main actors DSO Sala-Heby Energi Elnät AB and tech company Ngenic AB. The included study of scientific articles points out that the impact of demand response on electric grids is largely determined by incentives used to harvest demand side flexibility. In this thesis, the consequences of direct load control are examined by means of simplified calculations in Excel, analysing electric meter data from approximately 140 anonymous customers, in addition to power supply data for the township connection to the regional distribution grid. If customers with electric heating systems would install load control equipment to lower their power transmission bills, the local DSO would experience reduced revenues. The reduction in revenues would not be offset economically by curbed peak power transmission from the regional grid, according to the executed calculations. Even if extra load control was added in peak days, the net economic result for the local DSO would still be negative in most of the studied cases. Individual characteristics of heating systems and buildings have not been accounted for in this study, neither has the correlation between load reduction, outdoor temperature and load control duration. A second aim of this thesis is to examine attitudes of the main actors in the Auto-Flex project on confidentiality, reliability and demand side management business models in relation to the development of smart grids. Through semi-structured interviews, it was revealed that neither chief executive officer of Ngenic AB, Björn Berg, nor chief grid officer of Sala-Heby Energi Elnät AB, Per-Erik Johansson, see any severe threats against customer confidentiality, nor against power reliability, when implementing direct load control within the project. However, it was pointed out that an electric grid with very low physical capacity could become vulnerable to load control failures. Further examination of the connection between business models, power reliability, and cyber security are crucial to ensure socially, economically, and environmentally sustainable smart grids.

Elektrifieringen av bilflottan sker i rasande takt. Även andra samhällssektorers efterfrågan på el väntas öka drastiskt under kommande decennier, vilket i kombination med en växande andel intermittenta energikällor trappar upp påfrestningarna på elnätet och ställer krav på anpassningar. En föreslagen dellösning till kraftsystemets kommande utmaningar är att utnyttja efterfrågeflexibiliteten i laddande elbilar genom att en aggregator styr ett stort antal elbilsladdare och säljer den sammanlagda kapaciteten på till exempel Svenska kraftnäts stödtjänstmarknader.  För att avgöra hur mycket flexibilitet som elbilsladdning kan bidra med behöver aggregatorn upprätta prognoser över hur mycket effekt som mest sannolikt finns tillgänglig vid en viss tidpunkt – en punktprognos – men också en uppskattning av vilken effektnivå man kan vara nästan säker på att utfallet överstiger – en kvantilprognos. I den här studien har en undersökning gjorts av hur prognosfelet förändras om gruppen av aggregerade elbilsladdare ökas, och hur mycket en aggregator på så sätt kan sänka sina marginaler vid försäljning av efterfrågeflexibiliteten för att med säkerhet kunna uppfylla sitt bud. Det gjordes genom att kvantifiera flexibiliteten för 1 000 destinationsladdare belägna vid huvudsakligen arbetsplatser, och genom att skala upp och ner datamängden genom slumpmässiga urval. För dessa grupper gjordes sedan probabilistiska prognoser av flexibiliteten med en rullande medelvärdes- och en ARIMA-modell. Utifrån prognoserna simulerades slutligen potentiella intäkter om aggregatorn skulle använda den flexibla kapaciteten för uppreglering till stödtjänsten FCR-D upp, vilket är en frekvensreserv som aktiveras vid störningar av nätfrekvensen.  Resultaten visar att en tiodubbling av antalet aggregerade elbilsladdare mer än halverar det relativa prognosfelet. De båda prognosmodellerna visade sig ha jämförbar precision, vilket talar för att använda sig av den rullande medelvärdesmetoden på grund av dess lägre komplexitet. Den ökade säkerheten i prognosen resulterade dessutom i högre intäkter per laddare.  De genomsnittliga intäkterna av att leverera flexibilitet från 1 000 aggregerade elbilsladdare till FCR-D uppgick till 6 900 kr per månad, eller 0,8 kr per session – siffror som troligen hade varit högre utan coronapandemins ökade hemarbete. En 99-procentig konfidensgrad för kvantilprognosen resulterade i en säkerhetsmarginal med varierande storlek, som i genomsnitt var runt 90 procent för 100 laddpunkter, 60 procent för 1 000 laddpunkter samt 30 procent för 10 000 laddpunkter. Mest flexibilitet fanns tillgänglig under vardagsförmiddagar då ungefär 600 kW fanns tillgängligt som mest för 1 000 laddpunkter.  Att döma av tio års nätfrekvensdata är den sammanlagda sannolikheten för att över 50 procent aktivering av FCR-D-budet skulle sammanfalla med att utfallet för den tillgängliga kapaciteten är en-på-hundra-låg i princip obefintlig – en gång på drygt 511 år. Att aggregatorn lägger sina bud utifrån en 99-procentig konfidensgrad kan alltså anses säkert. / The electrification of the car fleet is taking place at a frenetic pace. Additionally, demand for electricity from other sectors of the Swedish society is expected to grow considerably in the coming decades, which in combination with an increasing proportion of intermittent energy sources puts increasing pressure on the electrical grid and prompts a need to adapt to these changes. A proposed solution to part of the power system's upcoming challenges is to utilize the flexibility available from charging electric vehicles (EVs) by letting an aggregator control a large number of EV chargers and sell the extra capacity to, for example, Svenska kraftnät's balancing markets. To quantify how much flexibility charging EVs can contribute with, the aggregator needs to make forecasts of how much power that is most likely available at a given time – a point forecast – but also an estimate of what power level the aggregator almost certainly will exceed – a quantile forecast. In this study, an investigation has been made of how the forecast error changes if the amount of aggregated EV chargers is increased, and how much an aggregator can lower their margins when selling the flexibility to be able to deliver according to the bid with certainty. This was done by quantifying the flexibility of 1000 EV chargers located at mainly workplaces, and by scaling up and down the data through random sampling. For these groups, probabilistic forecasts of the flexibility were then made with a moving average forecast as well as an ARIMA model. Based on the forecasts, potential revenues were finally simulated for the case where the aggregator uses the available flexibility for up-regulation to the balancing market FCR-D up, which is a frequency containment reserve that is activated in the event of disturbances. The results show that a tenfold increase in the number of aggregated EV chargers more than halves the forecast error. The two forecast models proved to have comparable precision, which suggests that the moving average forecast is recommended due to its lower complexity compared to the ARIMA model. The increased precision in the forecasts also resulted in higher revenues per charger. The average income from delivering flexibility from 1000 aggregated electric car chargers to FCR-D amounted to SEK 6900 per month, or SEK 0.8 per session – figures that would probably have been higher without the corona pandemic's increased share of work done from home. A 99 percent confidence level for the quantile forecast resulted in a safety margin of varying size, which on average was around 90 percent for 100 chargers, 60 percent for 1000 chargers and 30 percent for 10,000 chargers. Most flexibility was shown to be available on weekday mornings when approximately 600 kW was available at most for 1000 chargers. By examining frequency data for the Nordic power grid from the past ten years, the joint probability that a more than 50 percent activation of the FCR-D bid would coincide with the outcome for the available capacity being one-in-a-hundred-low, was concluded to be nearly non-existent – likely only once in about 511 years. For the aggregator to place bids based on a 99 percent confidence level can thus be considered safe.

This thesis discusses the operational coordination of electrical appliances and devices in a smart home. At present, the diminishing volume of fossil fuels and the increasing pressure to use renewable sources of energy necessitate the integration of such volatile sources into electrical grids. This process, however, results in higher energy costs, and the consumers are thus more willing to change their behaviour to either reduce the expenses or maintain them at a reasonable level. One of the relatively few customer-oriented options to optimise energy costs consists in the demand – response principle, which utilises external information to minimise energy consumption during high price periods. Assuming the constantly changing conditions in electrical grids, and thus also the varying demands, it is vital to provide for automatic optimisation excluding the need of user intervention. The thesis presents a method which, after being implemented into the control member, will facilitate the optimal use of appliances and devices within a smart home. As the behaviour considered optimal from the perspective of demand - response is often inconsistent with the consumer‘s requirements for comfortable use of the appliances, the proposed technique offers a compromise through enabling the consumer to select the appropriate strategy. Five universal optimisation models are designed within the thesis; these models facilitate description of common home appliances and local electricity sources. The core of the method lies in formulating and optimising a mixed integer quadratic problem (MIQP). The optimisation task yields an operational schedule for the individual appliances, and this scheme considers the energy costs, the working cycle of the appliance, the user’s demands, the system restrictions and/or other input data. Furthermore, the author extends the above-discussed general technique, enabling it to adopt robust behaviour. The method then secures the preset strategy even during a marked change of the input conditions, and its robustness is a viable precondition for the overall applicability of the technique in the real control member.

L’essor des technologies renouvelable à apport variable pose des nombreuses difficultés dans le fonctionnement du système électrique. Ce système doit garantir l’équilibre offre-demande à tout moment, ainsi que d’assurer des hauts niveaux de fiabilité du service. Donc, la variabilité accroît les besoins de flexibilité et des services système. Ils existent plusieurs options capables de fournir ceux services, dont : le renforcement des interconnections, le pilotage intelligent de la demande, le renforcement des capacités de réponse rapide des unités de production, mais aussi, le mis en œuvre des technologies de stockage de l’électricité. Cependant, les marchés électriques actuels sont basés sur la rémunération de l’énergie. Donc, la valorisation intégrale des services qui peut fournir le stockage semble difficile, ce qui restreint le « business case » des options de flexibilité.Cette thèse s’inscrit autour des propos suivants : (1) modéliser et évaluer les interrelations entre variabilité, besoins de flexibilité et objectifs de décarbonation du parc électrique, (2) analyser le rôle, ainsi que la valeur, des différents technologies du stockage à travers le cas Français aux horizons 2020, 2030 et 2050, et (3) discuter sur les aspects de régulation de la flexibilité, ainsi que proposer des politique énergétiques concrètes permettant la réussite des objectifs de transition énergétique et de décarbonation du mix électrique français. / The increasing variability of electricity production in Europe, which is mainly due to the intermittent production of renewables such as wind and photovoltaic (VRE), will require significant efforts to reconcile demand and supply at all times. Thus, increasing shares of variability imply increasing amounts of system services. In addition to upgraded interconnections, demand-side management (DSM) and dispatchable backup capacity, electric energy storage (EES) technologies will have a major role to play in this context.However, due to the peculiar price formation mechanism prevailing in energy-only electricity markets, the commercial case for EES is being eroded by the very forces that create the need for its increased deployment at the system level. The private incentives of EES are thus diminishing while its social value, which is determined by the multiple system services these technologies can supply, is increasing.This thesis sets out to (1) model and assess the interplays between variability, flexibility needs and decarbonization objectives, (2) analyze the role and the value of EES technologies in view of the French official objectives by 2020, 2030 and 2050, and (3) discuss regulatory aspects, and propose a set of energy policies allowing to succeed in the energy transition and decarbonization goals.

Dans l’industrie électrique, le progrès technologique apporté par les réseaux intelligents vient défier l’idée selon laquelle les consommateurs ne pourraient pas réagir aux prix des marchés de gros. L’intégration des Effacements de Demande (ED) dans le système électrique se heurte néanmoins à la question de leur efficacité économique. Cette thèse évalue la valeur économique des ED en s’appuyant sur un modèle de marché de l’énergie sous incertitude permettant de calculer les profits d’un agrégateur, par classe de consommateur et d’usage final. Le modèle appartient à la classe des problèmes linéaires stochastiques à plusieurs périodes. Sa résolution s’appuie sur Stochastic Dual Dynamic Programming. Il apparaît qu’en France, les secteurs rentables sont le load-shedding industriel et le load-shifting du ciment et du papier. Le load-shifting du chauffage électrique n’est pas profitable pour le tertiaire et le résidentiel. De plus, la valeur capacitaire des ED est déterminante. Dans l’ensemble, les ED deviennent viables mais le développement de leur potentiel semble conditionné à une baisse des coûts fixes dans les technologies de réseau intelligent. / In liberalised power markets the inability of consumers to adapt their demand in accordance to wholesale prices is increasingly challenged. Nowadays technical progress within the smart grid industry constitutes promising changes for the integration of end-users into the power system, but the deployment of Demand Response (DR) still faces the challenge of its economic viability. This thesis aims to assess the economic value of DR. We rely on an energy-only market model under uncertainty in order to quantify the revenues of DR aggregators, classified by category of consumers and end-uses of electricity. The model is formulated as a multi-stage stochastic linear problem and solved by Stochastic Dual Dynamic Programming. It appears that in France, industrial load-shedding and load-shifting of cement, paper, and pulp are profitable. For residential and tertiary consumers, load-shifting of electric heating is not profitable. We also show that the capacity value of DR is crucial. Overall, results show that DR is beginning to become economically attractive, but that fixed costs of smart grid technologies still need to come down further to fully develop its potential.