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.

Along with the growth of electricity demand and the penetration of intermittent renewable energy sources, electric power distribution networks will face more and more stress conditions, especially as electric vehicles (EVs) take a greater share in the personal automobile market. This may cause potential transformer overloads, feeder congestions, and undue circuit failures. Demand response (DR) is gaining attention as it can potentially relieve system stress conditions through load management. DR can possibly defer or avoid construction of large-scale power generation and transmission infrastructures by improving the electric utility load factor. This dissertation proposes to develop a planning tool for electric utilities that can provide an insight into the implementation of demand response at the end-user level. The proposed planning tool comprises control algorithms and a simulation platform that are designed to intelligently manage end-use loads to make the EV penetration transparent to an electric power distribution network. The proposed planning tool computes the demand response amount necessary at the circuit/substation level to alleviate the stress condition due to the penetration of EVs. Then, the demand response amount is allocated to the end-user as a basis for appliance scheduling and control. To accomplish the dissertation objective, electrical loads of both residential and commercial customers, as well as EV fleets, are modeled, validated, and aggregated with their control algorithms proposed at the appliance level. A multi-layer demand response model is developed that takes into account both concerns from utilities for load reduction and concerns from consumers for convenience and privacy. An analytic hierarchy process (AHP)-based approach is put forward taking into consideration opinions from all stakeholders in order to determine the priority and importance of various consumer groups. The proposed demand response strategy takes into consideration dynamic priorities of the load based on the consumers' real-time needs. Consumer comfort indices are introduced to measure the impact of demand response on consumers' life style. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration. Research findings from this work indicate that the proposed demand response strategy can fulfill the task of peak demand reduction with different EV penetration levels while maintaining consumer comfort levels. The study shows that the higher number of EVs in the distribution circuit will result in the higher DR impacts on consumers' comfort. This indicates that when EV numbers exceed a certain threshold in an area, other measures besides demand response will have to be taken into account to tackle the peak demand growth. The proposed planning tool is expected to provide an insight into the implementation of demand response at the end-user level. It can be used to estimate demand response potentials and the benefit of implementing demand response at different DR penetration levels within a distribution circuit. The planning tool can be used by a utility to design proper incentives and encourage consumers to participate in DR programs. At the same time, the simulation results will give a better understanding of the DR impact on scheduling of electric appliances. / 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.

An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR. Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR. Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers' heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms. One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers' comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result. Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR. / PHD / The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than the load, the match can be restored either by increasing the generation or by decreasing the load. Often, during system stress conditions, it is cheaper to decrease certain loads than to increase generation, and this method of achieving power balance is called demand response (DR). Residential sector consumes 37% of the total U.S. electricity consumption and is largely unexplored for demand response purpose, so the focus of the dissertation is on providing solutions to enable residential houses to provide demand response services. This dissertation presents two broad solutions. The first is a set of efficient algorithms that intelligently controls the customers’ heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid while keeping their comfort in mind. The second solution is a simulation software that can help evaluate and experiment with different residential demand response algorithms. The first algorithm is for reducing the collective power consumption of an aggregation of residential HVAC, whereas the second algorithm is for making the collective power follow a signal sent by the grid operators. It is shown that the algorithms presented can intelligently control the HVAC devices such that DR services can be provided to the grid while ensuring that the temperatures of the houses remain within comfortable range. The algorithms can enable demand response service providers to tap into the residential demand response market and earn revenue, while the simulation software can be valuable for future research in this area. The simulation software is simple to use and is designed with extensibility in mind, so adding new features is easy. The software is shown to work well for studying residential building control for demand response purpose and can be a useful tool for future research in residential DR.

Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.