Distributed generators (DGs) are proposed as a possible solution to supply economic and reliable electricity to customers. It is adapted to overcome the challenges that are characterized by centralized generation such as transmission and distribution losses, high cost of fossil fuels and environmental damage. This work presents the basic principles of integrating renewable DGs in low voltage distribution networks and particularly focuses on the operation of DG installations and their impacts on active and reactive power. In this thesis, a novel technique that applies the stochastic approach for the operation of distribution networks with considering active network management (ANM) schemes and demand response (DR) within a joint active and reactive distribution market environment is proposed. The projected model is maximized based on social welfare (SW) using market-based joint active and reactive optimal power flow (OPF). The intermittent behaviour of renewable sources (such as solar irradiance and wind speed) and the load demands are modelled through Scenario-Tree technique. The distributed network frame is recast using mixed-integer linear programming (MILP) that is solved by using the GAMS software and then the obtained results are being analysed and discussed. In addition, the impact of wind and solar power penetration on the active and reactive distribution locational prices (D-LMPs) within the distribution market environment is explored in terms of the maximization of SW considering the uncertainty related to solar irradiance, wind speed and load demands. Finally, a realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method. Results show that ANM schemes and DR integration lead to an increase in the social welfare and total dispatched active and reactive power and consequently decrease in active and reactive D-LMPs. / Ministry of Higher Education and Scientific Research - Iraq / The selected author's publications, the published versions of which were attached at the end of the thesis, have been removed due to copyright.

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented. / Libyan government

Smart metering and precise measurement of energy consumption levels have brought more detailed information and interest on the actual load profile of a house which continues to improve consumer-retailer relationships. Participation in demand response (DR) programs is one of these relationships but studies have shown that there are considerable impacts resulting to some level of discomfort on consumers as they aim to follow a suggested load profile. This research therefore investigates the impact on consumers while participating in DR programs by evaluating various perspectives that includes:  Modelling the causes discomfort during participation in DR programs;  Evaluation of user participation capabilities in DR programs;  Identification of schedulable and non-schedulable loads and opportunities;  Application of load scheduling mechanism which caters for specific user concerns.  Investigation towards ensuring a secure and robust system design. The key source of information that enhances this work is obtained from data on historical user behavior which can be stored within a smart controller installed in the home and optimised using genetic algorithm implemented on MATLAB. Results show that user participation in DR programs can be improved and effectively managed if the challenges facing home owners are adequately understood. This is the key contribution of this work whereby load schedules created are specifically tailored to meet the need of the users hence minimizing the impact of discomfort experienced due to participation in DR programs. Finally as part of the test for robustness of the system design in order to prevent or minimize the impact of any event of a successful cyber-attack on the load or price profiles, this work includes means to managing any such attacks thereby mitigating the impact of such attacks on users who participate in demand response programs. Solutions to these attacks are also proffered with the aim of increasing robustness of the grid by being sufficiently proactive.

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The Swedish electricity system is transitioning due to the establishment of climate policy goals and trends related to technology and demographics. The transition has resulted in an increased demand for electricity. The increased demand for electricity in combination with lack of forecasts, planning and coordination between actors in the electricity sector has led to the occurrence of grid congestion. Extending the network is time-consuming and requires substantial investments. Instead, an alternative is to utilise the available grid capacity more efficiently by implementing flexibility solutions. Flexibility can be achieved by implementing incentives such as grid tariffs. This solution has recently gained much attention in Sweden, but it is not apparent how grid tariffs should be designed. The purpose of this thesis is to investigate how distribution grid tariffs could be designed to incentivise different actors to contribute to flexibility in a way that results in an efficient use of the electrical grid. A qualitative study was performed, collecting empirical data through semistructured interviews with actors in the Swedish electricity market. The aim is that the results from this thesis will act as a basis for DSOs planning to design grid tariffs with the purpose to utilise the grid more efficiently. The findings present a ToU capacity charge with off-peak periods that are free of charge as the preferable main price signal in the tariff to achieve efficient utilisation of the grid. It is further argued that other structural elements can complement the ToU capacity charge. A small fixed charge could be added in order to contribute to the cost reflectiveness of the grid tariff. A small energy charge could be incorporated in order to provide consumer with incentives to be flexible below the current metered maximum power and strengthen the signal from the ToU capacity charge. A small energy charge can avert difficulties related to providing incentives below the current metered maximum, as it still can provide some incentives to be flexible, or strengthen the signal from the ToU capacity charge. Further, the energy charge can ensure sustainability if customers respond well to a ToU capacity charge and to compensate solar PV customers. Furthermore, recommendations to further enable the grid tariffs potential to provide price signals include shifting the focus of the revenue cap from CapEx to OpEx and exploring the hampering signals of the energy tax as well as contradicting price signals from the wholesale electricity price. / Det svenska elsystemet genomgår en förändring till följd av införandet av klimatmål och trender relaterade till teknik och demografi. Denna förändring har resulterat i ett ökat effektbehov. Ett ökat effektbehov i kombination med bristande prognostisering, planering och samordning mellan aktörer inom elsektorn har lett till uppkomsten av kapacitetsbrist. Att bygga ut elnätet är tar tid och kräver större investeringar. Ett alternativ är att istället utnyttja det befintliga elnätet mer effektivt genom att implementera flexibilitetslösningar. Flexibilitet kan uppnås genom att införa incitament i form av elnätstariffer. Denna lösning har nyligen fått mycket uppmärksamhet i Sverige, men det är inte klart inte hur dessa elnätstariffer ska utformas. Syftet med detta arbete är att undersöka hur distributionsnätets tariffer kan utformas för att stimulera olika aktörer att bidra med flexibilitet på ett sätt som resulterar i en effektiv användning av det befintliga elnätet. En kvalitativ studie genomfördes där empiriska data samlades in genom semistrukturerade intervjuer med aktörer på den svenska elmarknaden. Syftet är att resultaten från detta arbete ska fungera som ett underlag för nätägare som planerar att utforma elnätstariffer med syftet att utnyttja nätet mer effektivt. Resultaten visar att en ToU-effektavgift med gratis off-peak perioder bör vara den huvudsakliga prissignalen i en elnätstariff som ämnar att utnyttja det befintliga nätet mer effektivt. Det visar även att andra strukturella element kan komplettera ToU-effektavgiften. En mindre fast avgift kan adderas i syfte att göra elnätstariffen mer kostnadsriktig. En mindre energiavgift kan införas för att ge kunder incitament att vara flexibla även under den nuvarande uppmätta maximala effekten och stärka signalen från ToU-effektavgiften. Vidare kan energiavgiften säkerställa tillräckliga intäkter för nätägaren om kunderna svarar bra på en ToU-effektavgift och för att kompensera kunder med solceller. Ytterligare rekommendationer för att möjliggöra prissignaler genom elnätstariffer inkluderar att skifta fokus på intäktsramen från CapEx till OpEx och utforska de hämmande prissignalerna från energiskatten och de motstridiga prissignalerna från elhandelspriset.

The Pecan Street Project (PSP) is a public-private initiative that seeks to establish the City of Austin and its electric utility, Austin Energy (AE), as leaders in developing the electric utility system of the future and clean energy economy. The four main components of the project are to: 1) develop a local, public-private consortium dedicated to research and development of clean energy technologies and distributed power generation; 2) open the city’s electric grid to act as a lab to test emerging clean energy technologies; 3) develop a new business model to ensure AE’s continued profitability; and 4) show the world how the new business and systems model can work. This report provides a case study of PSP and describes an analytical approach for evaluating projects, programs, and policies proposed by PSP working groups to develop a cleaner, more efficient electric system. This report includes a history of the project, discusses opportunities and challenges identified by PSP, and evaluates the potential economic, environmental, system, and other impacts of different project ideas through a technical analysis. This report concludes with a series of recommendations to PSP and identifies policy implications for the City of Austin, AE, other policymakers, and other electric utilities. / text

Increased use of variable energy and integration of electricity markets in the European Union have led to new challenges when balancing supply and demand in the grid. Load management is a possible way to manage these challenges by adjusting electricity consumption in order to balance the power system. Household appliances can be used for this purpose, for example by providing automatic frequency control. The aim of this master thesis is to investigate the potential of load management from private households and to examine how the power system is affected by a more flexible electricity consumption. The thesis focuses on fridges as automatic frequency control reserves and the ability of fridges to balance the power system. Attributes such as capacity, activation time, persistence and the effect of load reconnection has been modelled in Simulink, Matlab. Complementary interviews with actors from the electricity industry have been done to summarize their insights on demand response. The results indicate that fridges can be used as a part of the automatic frequency controlled reserve FCR-N, based on assumptions made in this study. However, current requirements imposed on balancing resources are not fully met by the fridges. In order for household appliances to provide the needed flexibility, some requirements might need to be rephrased. The results also show that the capacity for providing balancing services differs for positive and negative imbalances, as consumption can be reduced with 30 MW and increased with 66 MW. When controlling the fridges by moving the reference temperature, there is a risk that the appliances will synchronize with each other, i.e. reconnect or disconnect at the same time, resulting in frequency oscillations. It is therefore important that load management is designed to avoid this kind of behavior.

Increased use of variable energy and integration of electricity markets in the European Union have led to new challenges when balancing supply and demand in the grid. Load management is a possible way to manage these challenges by adjusting electricity consumption in order to balance the power system. Household appliances can be used for this purpose, for example by providing automatic frequency control. The aim of this master thesis is to investigate the potential of load management from private households and to examine how the power system is affected by a more flexible electricity consumption. The thesis focuses on fridges as automatic frequency control reserves and the ability of fridges to balance the power system. Attributes such as capacity, activation time, persistence and the effect of load reconnection has been modelled in Simulink, Matlab. Complementary interviews with actors from the electricity industry have been done to summarize their insights on demand response. The results indicate that fridges can be used as a part of the automatic frequency controlled reserve FCR-N, based on assumptions made in this study. However, current requirements imposed on balancing resources are not fully met by the fridges. In order for household appliances to provide the needed flexibility, some requirements might need to be rephrased. The results also show that the capacity for providing balancing services differs for positive and negative imbalances, as consumption can be reduced with 30 MW and increased with 66 MW. When controlling the fridges by moving the reference temperature, there is a risk that the appliances will synchronize with each other, i.e. reconnect or disconnect at the same time, resulting in frequency oscillations. It is therefore important that load management is designed to avoid this kind of behavior.

Conventionally, to support varying power demand, the utility company must prepare to supply more electricity than actually needed, which causes inefficiency and waste. With the increasing penetration of renewable energy which is intermittent and stochastic, how to balance the power generation and demand becomes even more challenging. Demand response, which reschedules part of the elastic load in users' side, is a promising technology to increase power generation efficiency and reduce costs. However, how to coordinate all the distributed heterogeneous elastic loads efficiently is a major challenge and sparks numerous research efforts. In this thesis, we investigate different methods to provide demand response and improve power grid efficiency. First, we consider how to schedule the charging process of all the Plugged-in Hybrid Electrical Vehicles (PHEVs) so that demand peaks caused by PHEV charging are flattened. Existing solutions are either centralized which may not be scalable, or decentralized based on real-time pricing (RTP) which may not be applicable immediately for many markets. Our proposed PHEV charging approach does not need complicated, centralized control and can be executed online in a distributed manner. In addition, we extend our approach and apply it to the distribution grid to solve the bus congestion and voltage drop problems by controlling the access probability of PHEVs. One of the advantages of our algorithm is that it does not need accurate predictions on base load and future users' behaviors. Furthermore, it is deployable even when the grid size is large. Different from PHEVs, whose future arrivals are hard to predict, there is another category of elastic load, such as Heating Ventilation and Air-Conditioning (HVAC) systems, whose future status can be predicted based on the current status and control actions. How to minimize the power generation cost using this kind of elastic load is also an interesting topic to the power companies. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, and to guarantee user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Then, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize the user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation. Both randomized PHEV charging and HVAC control algorithms discussed above belong to direct or centralized load shaping, which has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In the last part of this thesis, we investigate indirect load shaping in a distributed manner. On one hand, we aim to reduce the users' energy cost by investigating how to fully utilize the battery pack and the water tank for the Combined Heat and Power (CHP) systems. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions can be obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. On the other hand, based on the users' reaction model, we propose an algorithm, with a time complexity of O(log n), to determine the RTP for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services. / Graduate