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The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated. / Doctor of Philosophy / The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.

As we are moving towards a future carbon-neutralsociety, development of residential microgrids attracts much attentionaround the world with its efficient utilization of renewableenergy. A residential microgrid is a small power system fora house, which consists of a solar photovoltaic (PV) source,a battery storage, residential loads, and an interface to thegrid. In this paper, a hybrid AC-DC microgrid is proposed,studied and simulated in Matlab/Simulink. A coordinated controlstrategy is developed so that the PV converter is controlledto maximize its power generation, the battery converter iscontrolled to stabilize the system with the battery state of chargeconstraints, and an interlinking converter is controlled to decidethe connection/disconnection and the power flow with the grid.The simulation results show the effectiveness of the proposedsolution under various operating conditions. / I det här pappret föreslås, studeras ochsimuleras ett hybrid-anpassat lokalt självförsörjande elnät iSimulink och Matlab. Solpaneler utgör den distribueradeförnyelsebara energikällan i nätet. Panelerna styrs med enMPPT-algoritm för att maximera kraftgenereringen. Batterietsladdningstillstånd används i det designade batterilagringssystemetför att garantera lång livstid och för att fatta beslut omladdning och urladdning. Kraftöverföring mellan ACoch DCnätverk sker via en dubbelriktad omvandlare. Det konstrueradehybridnätet fungerar självständigt samt vid sammankopplingtill huvudnätet. Ett koordinerat kontrollsystem implementerasför att möjliggöra kommunikationen mellan lokalnätets olikadelar. Resultaten från simuleringstestet visar att det föreslagnanätet uppfyller stabilitetskrav och god funktion under varierandedriftstillstånd. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Emerging integration of renewable energy has prompted a wide range of research on the use of energy storage to compensate for the added uncertainty that accompanies these resources. In the Electric Reliability Council of Texas (ERCOT), compressed air energy storage (CAES) has drawn particular attention because Texas has suitable geology and also lacks appropriate resources and locations for pumped hydroelectric storage (PHS). While there have been studies on incorporation of renewable energy, utilization of energy storage, and dispatch optimization, this is the first body of work to integrate all these subjects along with the proven ability to recreate historical dispatch and price conditions. To quantify the operational behavior, economic feasibility, and environmental impacts of CAES, this work utilized sophisticated unit commitment and dispatch (UC&D) models that determine the least-cost dispatch for meeting a set of grid and generator constraints. This work first addressed the ability of these models to recreate historical dispatch and price conditions through a calibration analysis that incorporated major model improvements such as capacity availability and sophisticated treatment of combined heat and power (CHP) plants. These additions appreciably improved the consistency of the model results when compared to historical ERCOT conditions. An initial UC&D model was used to investigate the impacts on the dispatch of a future high wind generation scenario with the potential to utilize numerous CAES facilities. For all future natural gas prices considered, the addition of CAES led to reduced use of high marginal cost generator types, increased use of base-load generator types, and average reductions in the total operating costs of 3.7 million dollars per week. Additional analyses demonstrated the importance of allowing CAES to participate in all available energy and ancillary services (AS) markets and that a reduction in future thermal capacity would increase the use of CAES. A second UC&D model, which incorporated advanced features like variable marginal heat rates, was used to analyze the influence of future wind generation variability on the dispatch and resulting environmental impacts. This analysis revealed that higher amounts of wind variability led to an increase in the daily net load ramping requirements which resulted in less use of coal and nuclear generators in favor of faster ramping units along with reductions in emissions and water use. The changes to the net load also resulted in increased volatility of the energy and AS prices between daily minimum and maximum levels. These impacts were also found to increase with compounding intensity as higher levels of wind variability were reached. Lastly, the advanced UC&D model was also used to evaluate the operational behavior and potential economic feasibility of a first entrant conventional or adiabatic CAES system. Both storage systems were found to operate in a single mode that enabled very high utilization of their capacity indicating both systems have highly desirable characteristics. The results suggest that there is a positive case for the investment in a first entrant CAES facility in the ERCOT market. / text

This Master´s thesis deals with problematics related to the connection of new decentralized power sources into electrical power grid. Due to advantageous legislative support of these new, especially photovoltaic power sources, a massive amount of these sources have been connected into the power grid between years 2009 and 2010. For theoretical understanding of processes during a steady-state, the initial parts of this paper are focused on a procedure which solves steady-state on every power line mentioned. When we speak of decentralized power sources connection, it is necessary to mention the connected legislative. National distribution grid operators in collaboration with national regulatory commission have decided on a legislative document Rules of distribution grid operation, which puts a set of demands and requirements on applicants wishing to connect a new power source to the grid. The text of this thesis is focused mainly on demands required after the latest change in 1/2010. Practical part of this work deals with verification of new power source influence on a related power grid and meeting the legislatively required demands. The most important demands are voltage change due to new power source operation and its transfer to other voltage levels, higher harmonics injection, power output fluctuation and last, not least, changes in load flow directions. For reasons previously mentioned an analysis is made and possibilities of reducing or removing of these influences are introduced. To achieve these goals, two computer programs, Siemens Sinaut Spectrum and NetCalc are used.