This thesis stems from the interest of Holmen group to investigate the opportunitiesavailable for large electricity consumers in the Swedish primary reserve markets.The study performed focuses on one of Holmen's paper mill and it aims at identifyinga load inside the production process that is suitable for providing frequency containmentservices for the grid. The evaluation of the mill's consumption prole and the technicalrequirements of the reserve market led to the identication of the electric boiler coupledwith a steam accumulator as the most appropriate load.Five case study simulating the participation of the mill to dierent energy and reservemarkets have been evaluated. For each case a linear optimization problem has beenformulated. The rst simulation represents the current practice of the mill in relation tothe energy purchased on the spot market (following it will be also referred as referencecase). The second case study (II c.s.) integrates the use of the steam accumulator asa tool to perform thermal load shifting. In the third case study (III c.s.) the mill ismodelled to bid on the spot and primary reserve market by oering some capacity ofthe electric boiler. The last two case studies (IV and V c.s.) recalls the rst and lastpreviously mentioned, but also include the possibility of having energy imbalance. Thismeans that the imbalance settlement operated by eSett will produce an additional costor prot for the mill.The last three problem formulations fall under the denition of stochastic problems,since two random variable are present, namely: average hourly frequency value andimbalance settlement price. The uncertainty of the variables is represented throughscenarios.The outcome derived from the combination of the results for the winter and summercases shows that each strategy brings an economic saving when compared to the referencecase (I c.s.). The less interesting strategies are the ones that do not involve the reservemarket, leading to about 0.03% (II c.s.) and 0.06% (IV c.s.) of saving on the overallyearly energy cost. Contrariwise, by oering FCR-N capacity, the cost of electricitycan be cut by 5.15% (III c.s.) and 6.69% (V c.s.), respectively considering and notconsidering the imbalance settlement. / Avhandlingen har sitt ursprung i skogsindustrikoncernen Holmens intresse att undersökamöjligheten för stora elförbrukare att delta på den svenska primär-reservmarknaden. Studien som utförts fokuserar på ett av Holmens pappersbruk och syftar till att identifiera en elektrisk process som, inom bruksgränserna, är lämplig för att tillhandahålla frekvensregleringstjänster till det nationella nätet. En utvärdering av brukets elförbrukning samt de tekniska krav som ställs på reservmarknaden ledde till att en elektrisk panna med tillkopplad ångackumulator identifierades som mest lämplig.Fem budstrategier som simulerar brukets deltagande till olika energioch reservmarknader har presenterats. För varje strategi är ett linjärt optimeringsproblem formulerat. Den första strategin visar på nuvarande sätt bruket köper elektricitet på spotmarknaden. Den andra strategin integrerar användning av ångackumulatorn som ett verktyg för att utföra termisk lastskiftning. I den tredje modelleras deltagande också på primärreservmarknaden genom att erbjuda en viss kapacitet hos elpannan. De två sista strategierna baseras på den första och tredje, men tillåter i tillägg obalanser vilket innebär en extra kostnad eller möjlig intjäning för bruket.De tre sista problemformuleringarna faller under definitionen stokastiska problem, eftersom två slumpmässiga variabler är närvarande, nämligen: genomsnittligt timfrekvensvärde och priset för obalans. Osäkerheten för variablerna representeras genom scenarier.Resultatet visar att varje strategi ger en ekonomisk besparing jämfört med refer-ensfallet (strategi ett). De mindre intressanta strategierna är de som inte involverarreservmarknaden, vilka endast leder till ca 0,03% och 0,06% minskning av den totalaårliga energikostnaden. Däremot, genom att erbjuda FCR-N-kapacitet kan kostnaden för el minskas med 6,69% och 5,15% beroende s eller ej.

Electric power systems are undergoing a paradigm shift where an increasing number of variable renewable energy resources such as wind and solar power are being introduced to all levels of existing power grids. At the same time consumers are gaining a more active role where self energy production and home automation solutions are no longer uncommon. This challenges traditional power systems which were designed to serve as a centralized top-down solution for providing electricity to consumers. Demand response has risen as a promising solution to cope with some of the challenges that this shift is creating. In this thesis, control and scheduling studies using demand response, and consumer load models adapted to environments similar to Sweden are proposed and evaluated. The studies use model predictive control approaches for the purpose of providing ancillary and financial services to electricity market actors using thermal flexibility from detached houses. The approaches are evaluated on use-cases using data from Sweden for the purpose of reducing power imbalances of a balance responsible player and congestion management for a system operator. Simulations show promising results for reducing power imbalances by up to 30% and managing daily congestion of 5-19 MW using demand response. Moreover, a consumer load model of an office building is proposed using a gray-box modeling approach combining physical understanding of buildings with empirical data. Furthermore, the proposed consumer load model along with a similar model for detached houses are packaged and made freely available as MATLAB applications for other researchers and stakeholders working with demand response. The applications allow the user to generate synthetic electricity load profiles for heterogeneous populations of detached houses and office buildings down to 1-min resolution. The aim of this thesis has been to summarize and discuss the main highlights of the included articles. The interested reader is encouraged to investigate further details in the second part of the thesis as they provide a more comprehensive account of the studies and models proposed. / <p>QC 20171011</p>

Demand response (DR) has been widely documented as a potential solution for severalchallenges the electrical power system is facing, such as the integration of intermittentrenewable electricity generation and maintaining system reliability undera rapid, global electrification. While l ots of r esearch has been done i nto differentmarket designs and tariffing methods, less work is available on the implications ofdemand response on power grid operation, especially for the low voltage side. Thepurpose of this thesis is to estimate the impact of a demand response program on thepower losses in the low-voltage distribution network.The thesis will also contributeto the, currently limited, knowledge base on practical implementation of demandresponse by evaluating the outcome of a real-life DR pilot project. This pilot is partof smart cities development project ’Stockholm Royal Seaport’ (SRS) in the east ofStockholm.The study compared the consumption behaviour of around 400 reference consumerswith a group of 154 DR enabled apartments, that are provided with an hourly varyingelectricity tariff. The goal was to evaluate what percentage of daily consumptionis being shifted from peak to off-peak hours by the active consumers in responseto the price signal, using hourly metering data collected between the 1st of Januaryand the 22nd of March 2017. During this period, grid measurements were also collectedfrom the SRS smart grid and used to estimate the technical power losses inthe low-voltage distribution network. By combining the daily load shift of the DRconsumers and the observed daily power loss fraction in the grid, an estimation wasmade of the impact of the demand response on the grid losses. A simulation modelwas also proposed, and used to simulate the effect of load shift on losses in a givengrid situation.It was found that the DR apartments overall exhibit a load shift of 2.8% of dailyelectricity consumption towards peak hours, and have a lower average load factor(0.57 versus 0.62 for the reference group). This could either mean that the pricesignal does not sufficiently manage to change load behaviour, or that the referencegroup was not representative. However, a strong variation in average load shift wasobserved amongst the individual DR apartments, ranging from -16% (shift towardspeak hours) to 7%. Especially the most electricity consuming apartments showedpositive load shifts. No direct influence of the load shift on the level of grid losseswas found. This could be due to a too small amount of DR consumers in the grid orconfounding factors such as variations in power factor and load size. To circumventthis problem, the simulation model was used to calculate loss reductions for severalpossible reference consumer groups and their possible reactions to a price signal. Itwas found that in the SRS project, the potential for loss reductions is limited becausethe reference group are already ’good’ consumers. The maximum loss reductionwould be around 4%. For grids with severe peak consumption however, optimalloss reductions from load shifting up to 25% were found.The key take-away is that, while the technical potential for loss reduction is considerablein grids with strong peak loads, more research is needed to identify incentivesthat effectively manage to make households change their consumption behaviour.More work should also be done to find methods that can correctly evaluate loadshifts. / Efterfrågeflexibilitet (DR) har i stor utsträckning setts som en möjlig lösning för flerautmaningar som elsystemet står inför, till exempel integration av intermittent förnybarelproduktion och för att upprätthålla tillförlitligheten i elsystem under en snabb, globalelektrifiering. Medan mycket forskning har gjorts i olika marknadslösningar ochtariffsystem är mindre arbete tillgängligt om konsekvenserna av efterfrågeflexibilitetpå elnätet, speciellt för lågspänningssidan. Syftet med detta examensarbete är attuppskatta inverkan av ett efterfrågeflexibilitetprogram på förluster ilågspänningsdistributionsnätet. Rapporten kommer också att bidra till den förnärvarande begränsade kunskapsbasen om praktisk genomförande avefterfrågeflexibilitet genom att utvärdera resultatet av ett verkligt DR-pilotprojekt.Denna pilot är en del av ett utvecklingsprojekt för smarta städer "Stockholm RoyalSeaport" (SRS) i östra delen av Stockholm.Studien jämförde konsumtionsbeteendet hos cirka 400 referenskonsumenter med engrupp av 154 DR-aktiverade lägenheter, som är försedda med ett varierande timprisför el. Målet var att utvärdera vilken procentandel av daglig förbrukning de aktivakonsumenterna flyttar från höglasttimmar till låglasttimmar som svar på prissignalen.Studien är baserad på timmätningsdata samlad mellan den 1:a januari och den 22:amars 2017. Under denna period samlades också mätdata från elnätet in och dessa datahar använts för att uppskatta de tekniska förlusterna i lågspänningsdistributionsnätet.Genom att kombinera den dagliga lastförflyttningen av DR konsumenterna och denobserverade dagliga effektförlustfraktionen i nätet gjordes en uppskattning av effektenav efterfrågeflexibilitetet på nätförlusterna. En simuleringsmodell föreslogs också, ochanvändes för att simulera effekten av lastförflyttning på förluster i en given situationför nätet.Det konstaterades att DR-lägenheterna totalt sett uppvisar en lastförflyttning på 2,8 %av det dagliga elförbrukning mot höglasttimmar, och har en lägre genomsnittliglastfaktor (0,57 mot 0,62 för referensgruppen). Detta kan antingen betyda attprissignalen inte lyckas tillräckligt med att ändra förbrukningsbeteende eller attreferensgruppen inte var representativ. En stark variation i genomsnitt lastförflyttninghar emellertid observerats bland de enskilda DR-lägenheterna, från -16 % (flyttningtill höglasttimmar) till 7%. Speciellt de mest elförbrukande lägenheterna visadepositiva lastförflyttningar. Inget direkt inflytande av lastflyttning på nätförlusternahittades. Detta kan bero på en för liten mängd DR-konsumenter i nätet eller andrafaktorer som variationer i effektfaktor och belastningsstorlek. För att kringgå dettaproblem användes simuleringsmodellen för att beräkna förlustreduktioner för fleramöjliga referenskonsumentgrupper och deras eventuella reaktioner på en prissignal.Det konstaterades att potentialen för förlustreduktioner är begränsad i SRS-projekteteftersom referensgruppen är redan "bra" konsumenter. Den maximalaförlustreduktionen skulle vara omkring 4 %. För nät med hög topplast hittadesoptimala förlustreduktioner från lastförflyttning upp till 25 %. Den viktigasteslutsatsen är att medan den tekniska potentialen för förlustreduktion är stor i nät medhög topplast så krävs det mer forskning för att identifiera incitament som effektivtlyckas få hushållen att förändra sitt konsumtionsbeteende. Mer arbete bör också görasför att hitta metoder som korrekt kan utvärdera lastförflyttningar.

The power system is a complex entity with unique plant designs, control systems, and market strategies. For many years, engineers have developed advanced technology to keep the grid efficient and balanced. With the rise of renewable sources, some new technology and programs must be developed to keep the quality of the power system. Unlike traditional power plants, renewable energy is highly dependent on environmental factors, such as sunlight and wind, meaning the generation depends on an unpredictable source of fuel. As the grid moves to more sustainable sources, the power market faces a growing challenge of less control over the forecasted supply offered by each renewable plant. This uncertainty creates a high need to develop alternative methods to ensure the power supply always meets demand. With diminishing control over our generation, one potential solution has been to explore demand response initiatives. Demand response focuses on the engagement of consumers to reduce the electricity demand, facilitating sub-hourly efforts on the supply side. This paper will analyze the effect of demand response efforts on the participants and provide insights into potential benefits and challenges associated with implementing demand response strategies. The findings of the studies will contribute to a better understanding on the compensation structure of current Direct Load Control programs and the level of participation required for it to be effectively integrated into the power system, promoting a more reliable and sustainable future.

No description available.

Annual building renovation rates in Europe currently stand at 0.4 to 1.2%. In order for Europe to meetits energy efficiency targets a “renovation wave” will have to be triggered that will, at least double, the current rates (“A European Green Deal | European Commission” 2019). It is clear, in the “Clean Energy Package for All Europeans”, that the European Commission regards Energy Performance Contracting (EPC) as a key tool to boost the aforementioned “renovation wave”. This is a renovation model in which the client shares the performance and financial risk of the energy efficiency renovation with the Energy Service (ESCO), responsible for designing, implementing, and operating the project during its lifetime. This is a model that has not seen the expected uptake in Europe its potential suggested, due to a set of, already well identified, regulatory, market, financial and social barriers. This project proposes an innovative EPC model – the Integrated Benefits Model – that aims at tackling some of the current barriers and envisions what the future of energy consumption in buildings can be. This model was tested in a real case study and was shown to reduce the project’s payback time by 16% when compared to a traditional energy efficiency renovation. This increases the attractiveness of energy retrofits among building owners. To address some of the remaining barriers, a set of recommendations to stakeholders was drafted, in order to facilitate a wider adoption of EPCs (and in particular the Integrated Benefits Model) across the whole value chain. / Byggnadsrenoveringsgraden ligger för närvarande på 0,4 till 1,2%. För att Europa ska kunna uppnå sina energieffektivitetsmål måste en ”renoveringsvåg” utlösas som åtminstone kommer att fördubbla den uvarande siffrorona (“A European Green Deal | European Commission” 2019). Det är tydligt i satsningen "Ren energi för alla européer" att Europeiska kommissionen ser Energy Performance Contracting (EPC) som ett nyckelverktyg för att utlösa den ovannämnda "renoveringsvågen". Detta är en renoveringsmodell där kunden delar prestanda och finansiell risk för energieffektivitetsrenoveringen med ett s.k. Energy Service Company (ESCO), som ansvarar för att utforma, implementera och driva projektet under dess livstid. Detta är dock en modell som inte har utvecklats som väntat i Europa trots sin potential. Skälet till detta är på grund av en uppsättning väl identifierade reglerande, marknadsmässiga, finansiella och sociala hinder. Detta projekt föreslår en innovativ EPC-modell - Integrated Benefits Model - som syftar till att ta itu med några av de nuvarande hindren. Denna modell testades i en riktig fallstudie och visade sig minska projektets återbetalningstid med 16% jämfört med en traditionell energieffektivitetsrenovering. Detta ökar attraktiviteten för energieffektiviseringsåtgärder bland byggnadsägare. För att ta itu med några av de återstående hindren har en uppsättning rekommendationer utarbetades till intressenter för att möjliggöra EPC:er (och särskilt den integrerade förmånsmodellen) över hela värdekedjan.

Yes / This paper proposes a probabilistic method for active distribution networks planning with integration of demand response. Uncertainties related to solar irradiance, load demand and future load growth are modelled by probability density functions. The method simultaneously minimizes the total operational cost and total energy losses of the lines from the point of view of distribution network operators with integration of demand response over the planning horizon considering active management schemes including coordinated voltage control and adaptive power factor control. Monte Carlo simulation method is employed to use the generated probability density functions and the weighting factor method is used to solve the multi-objective optimization problem. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system.

In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. / Ph. D.

The efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings. / Ph. D.

Among many smart grid technologies, demand response (DR) is gaining increasing popularity. Many utility companies provide a variety of programs to encourage DR participation. Under these circumstances, various building energy management (BEM) systems have emerged to facilitate the building control during a DR event. Nonetheless, due to the cost and return on investment, these solutions mainly target homes and large commercial buildings, leaving aside small- and medium-sized commercial buildings (SMCB). SMCB, however, accounts for 90% of commercial buildings in the US, and offer great potential of load reduction during peak hours. With the advent of Internet-of-Things (IoT) devices and technologies, low cost smart building solutions have become possible for the SMCB; nonetheless, related intelligent algorithms are not widely available. This dissertation work investigates automated building control algorithms, tailored for the SMCB, to realize automatic device control during DR events. To be specific, a control framework for Air-Conditioning (AC) units' coordination is proposed. The goal of such framework is to reduce the aggregated AC power consumption while maintaining the thermal comfort inside a building during DR events. To achieve this goal, three major components of the framework were studied: building thermal property modeling, AC power consumption modeling and control algorithms design. Firstly, to consider occupants' thermal comfort, a reverse thermal model was designed to predict the indoor temperature of thermal zones under different AC control signals. The model was trained with supervised learning using coarse-grained temperature data recorded by smart thermostats; thus, it requires no lengthy configuration as a forward model does. The cost efficiency and plug-and-play feature of the model make it appropriate for SMCB. Secondly, a power disaggregation algorithm is proposed to model the power-outdoor temperature relationship of multiple AC units, using data from a single power meter and thermostats. Finally, algorithms based on mixed integer linear programming (MILP) and reinforcement learning (RL) were devised to coordinate multiple AC units in a building during a DR event. Integrated with the thermal model and AC power consumption model, these algorithms minimize occupants' thermal discomfort while restricting the aggregated AC power consumption below the DR limit. The efficiency of these control algorithms was tested, which demonstrate that they can generate AC control schedule in short notice (5 minutes) ahead of a DR event. Verification and validation of the proposed framework was conducted in both simulation and actual building environments. In addition, though the framework is designed for SMCBs, it can also be applied to large homes with multiple AC units to coordinate. This work is expected to give an insight into the BEM sector, helping the popularization of implementing DR in buildings. The research findings from this dissertation work shows the validity of the proposed algorithms, which can be used in BEM systems and cloud-based smart thermostats to exploit the untapped DR resource in SMCB. / PHD / For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.