In a world where calls for climate action grow louder by the day, the role of renewable energy and energy efficient generation sources has become extremely important. One such energy efficient resource that can increase the penetration of renewable energy into the grid is the Combined Heat and Power technology. Combined Heat and Power (CHP) plants produce useful thermal and electrical power output from a single input fuel source and are widely used in the industrial and commercial sectors for reliable on-site power production. However, several unfavorable policies combined with inconsistent regulations have discouraged investments in this technology and reduced participation of such facilities in grid operations. The potential benefits that could be offered by this technology are numerous - improving grid resiliency during emergencies, deferring transmission system updates and reducing toxic emissions, to name a few. With increased share of renewable energy sources in the generation mix, there is a pressing need for reliable base generation that can meet the grid requirements without contributing negatively to the environment. Since CHP units are good candidates to help achieve this two-fold requirement, it is important to understand the present barriers to their deployment and grid involvement. In this thesis work, we explore some of these challenges and propose suitable grid integration technology as well as market participation approaches for better involvement of distributed CHP units in the industrial and commercial sectors. / Master of Science / Combined Heat and Power is a generation technology which uses a single fuel source to produce two useful outputs - electric power and thermal energy - by capturing and reusing the exhaust steam by-product. These generating units have much higher efficiencies than conventional power plants, lower fuel emissions and have been a popular choice among several industries and commercial buildings with a need for uninterrupted heat and power. With increasing calls for climate action and large scale deployment of renewable based energy generation sources, there is a higher need for reliable base-line generation which can handle the fluctuations and uncertainty of such renewables. This need can be met by CHP units owing to their geographic distribution and their high operating duration. CHPs also provide a myriad of other benefits for the grid operators and environmental benefits, compared to the conventional generators. However, unfavorable and inconsistent regulatory procedures have discouraged these facility owners from actively engaging in providing grid services. Therefore, it is imperative to look into some of the existing policies and understand where the changes and incentives need to be made. In this work, we look into methods that can ease CHP integration from a technological and an economic point of view, with the aim of encouraging grid operators and CHP owners to be more active participants.

abstract: The grand transition of electric grids from conventional fossil fuel resources to intermittent bulk renewable resources and distributed energy resources (DERs) has initiated a paradigm shift in power system operation. Distributed energy resources (i.e. rooftop solar photovoltaic, battery storage, electric vehicles, and demand response), communication infrastructures, and smart measurement devices provide the opportunity for electric utility customers to play an active role in power system operation and even benefit financially from this opportunity. However, new operational challenges have been introduced due to the intrinsic characteristics of DERs such as intermittency of renewable resources, distributed nature of these resources, variety of DERs technologies and human-in-the-loop effect. Demand response (DR) is one of DERs and is highly influenced by human-in-the-loop effect. A data-driven based analysis is implemented to analyze and reveal the customers price responsiveness, and human-in-the-loop effect. The results confirm the critical impact of demographic characteristics of customers on their interaction with smart grid and their quality of service (QoS). The proposed framework is also applicable to other types of DERs. A chance-constraint based second-order-cone programming AC optimal power flow (SOCP-ACOPF) is utilized to dispatch DERs in distribution grid with knowing customers price responsiveness and energy output distribution. The simulation shows that the reliability of distribution gird can be improved by using chance-constraint. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

Indiana University-Purdue University Indianapolis (IUPUI) / Electric demand charge is a large portion (usually 40%) of electric bill in residential, commercial, and manufacturing sectors. This charge is based on the greatest of all demands that have occurred during a month recorded by utility provider for an end-user. During the past several years, electric demand forecasting have been broadly studied by utilities on account of the fact that it has a crucial impact on planning resources to provide consumers reliable power at all time; on the other hand, not many studies have been conducted on consumer side. In this thesis, a novel Maximum Daily Demand (MDD) forecasting method, called Adaptive-Rate-of-Change (ARC), is proposed by analysing real-time demand trend data and incorporating moving average calculations as well as rate of change formularization to develop a forecasting tool which can be applied on either utility or consumer sides. ARC algorithm is implemented on two different real case studies to develop very short-term load forecasting (VSTLF), short-term load forecasting (STLF), and medium-term load forecasting (MTLF). The Chi-square test is used to validate the forecasting results. The results of the test reveal that the ARC algorithm is 84% successful in forecasting maximum daily demands in a period of 72 days with the P-value equals to 0.0301. Demand charge is also estimated to be saved by $8,056 (345.6 kW) for the first year for case study I (a die casting company) by using ARC algorithm. Following that, a new Maximum Demand Management (MDM) method is proposed to provide electric consumers a complete package. The proposed MDM method broadens the electric consumer understanding of how MDD is sensitive to the temperature, production, occupancy, and different sub-systems. The MDM method are applied on two different real case studies to calculate sensitivities by using linear regression models. In all linear regression models, R-squareds calculated as 0.9037, 0.8987, and 0.8197 which indicate very good fits between fitted values and observed values. The results of proposed demand forecasting and management methods can be very helpful and beneficial in decision making for demand management and demand response program.

This thesis examines the possibilities and limitations of connectingoffice buildings to demand response (DR) programs, with emphasis on the effect it has on indoor climate. Heating, ventilation and airconditioning (AC) systems was used as sources to scale power, and thereby contribute with power to capacity markets related to the electrical grid. By simulations performed in MatLab, as well as livetests in one of Vasakronan’s buildings, this work contributes with knowledge of DR in office buildings in the Swedish environment, which to date have been lacking in literature. All systems considered in thisreport has potential to contribute with power to DR programs. Heating and AC systems has greater power saving potential than the ventilation system. However, ventilation also holds potential as a source of power savings and is applicable to a larger portion of the building stock.Indoor air carbon dioxide level is affected to varying degrees depending on the extent of power reduction, where a reduction up to 90 % of ventilation is possible without exceeding approved limits. Reduction of ventilation caused the highest rise in carbon dioxide levels during mid- day and especially during the winter. Flexibility is key for the control system, especially when considering future climate challenges andtrends. DR strategies should include control of all systems both during the active DR time but also when returning to normal activity to reduce the risk of compensation from other systems.

In recent years, the electrification of technology that is traditionally powered by fossil fuels has become a popular means to reduce greenhouse gases (GHG). Although the intentions are well founded, the strain on the electrical grid is seldom taken into consideration. When there is increased load on the grid, it is typically met by fossil fuel peaking power plants or additional fossil fuel infrastructure. Depending on the electrical generation technology deployed and the power plant efficiency, electrification can result in an increase in GHG emissions. To make better informed decisions for GHG reductions, policy makers and engineers are in need of smart energy systems, such as the Integrated Community Energy and Harvesting (ICE-Harvest) system. ICE-Harvest systems work with and can respond to changes on the electrical grid, providing demand response. The system creates electrical demand when renewable generation sources are available, reduces demand when fossil fuel generation is present, and can offset centralized generation using distributed combined heat and power resources. In this thesis, steps to design a micro-thermal network (MTN) for the ICE-Harvest system are outlined and different operational strategies are explored that respond to grid behaviour in real time. How fast the thermal network reacts to grid level variations is defined as the response time. The physical response of the thermal network is a temperature set point change. A design map was developed presenting multiple parameters that contribute to the response time, the trade-offs between them, and the corresponding temperature difference achievable. Through developing models in the equation-based object-oriented software Dymola, the viability for real time temperature set point changes in micro-thermal networks was explored. The MTN and the energy transfer stations (ETSs) that transfer energy between the thermal network and the buildings have been modeled. Yearly system simulations were conducted to analyze the corresponding performance of the MTN in terms of electrical requirements and overall GHG emissions. An operational range of the system was presented demonstrating the flexibility of the ICE-Harvest system. The simulation results have identified the ICE-Harvest system as a viable means to provide demand response to the grid and to reduce GHG emissions. Future work and recommendations will be made to improve the response of the system and further reduce electrical consumption and GHG emissions. / Thesis / Master of Science in Mechanical Engineering (MSME)

Yes / Energy consumers oftentimes suffer some element of discomfort associated with the implementation of demand response programs as they aim to follow a suggested energy consumption profile generated from scheduling algorithms for the purpose of optimizing grid performance. This is because people naturally do not like to be told what to do or when to use their appliances. Although advances in renewable energy have made the consumer to also become energy supplier, who can actively cash in at times of the day when energy cost is high to either sell excess energy generated or consume it internally if required, thereby nullifying the adverse effect of this discomfort. But a majority of consumers still rely wholly on the supply from the grid. This impact on users' comfort who are active participants in demand response programs was investigated and ways to minimizing load scheduling discomfort was sought in order to encourage user participation.

High renewable energy penetration is a goal for many countries to increase energy security and reduce carbon emissions from conventional power plants. Wind energy is one of leading sources among different renewable resources. However, high wind energy penetration in the system brings new challenges to the electric power system due to its variable and stochastic nature, and non-correlation between wind and load profiles. The term non-correlation is used in this research refers to the fact that wind or any other renewable generation, which is nature driven, does not follow the load like conventional power plants. Wind spill is a challenge to utilities with high wind energy penetration levels. This occurs from situations mentioned above and the fact that wind generation sometimes exceeds the servable load minus must-run generation. In these cases there is no option but to curtail non-usable wind generation. This dissertation presents grid-scale energy storage and demand response options as an optimization problem to minimize spilled wind energy. Even after managing this spilled wind energy, there is still a challenge in a system with high wind energy penetration coming from wind power forecast error. Wind power forecast error is handled by having more back-up energy and spilling the non-usable wind power. This research offers a way to use the grid-scale energy storage units to mitigate impacts of wind power forecast error by. A signal processing method is proposed to decompose the fluctuating wind power forecast error signal, based on the fact that each energy storage or conventional unit is more efficient to operate within specific cycling regimes. Finally, an algorithm is proposed schedule energy storage for mitigating both impacts. / Ph. D.

Electricity production from renewable energy resources such as wind energy and photovoltaics is variable. Integration of these intermittent resources into the electricity system leads to new challenges in how to manage imbalance between supply and demand on the grid. One way to meet these challenges is to develop so-called smart grid solutions. One idea, called demand response, is to adjust the amount or timing of energy consumption, e.g. by control of household appliances, to provide flexibility that could be used to balance the grid. In aggregate, when applied to many units across the system, large volumes of energy could be made available when needed and this grid flexibility can be used as a product on the electricity regulation market. Despite the potential benefits, the number of demand response bids is currently low. The aim of this thesis is to identify barriers in the Swedish regulation market, and togive Sweden's transmission system operator, Svenska kraftnät, recommendations on how to facilitate implementations of the technique. This has been done throughliterature studies and a wide range of interviews with people within the electricity market sector. The results indicate that a combination of several elements in the complex energy system impede the introduction of demand response. The main issues are related to market regulations and profitability difficulties. The authors recommend that Svenska kraftnät lowers the minimum bid size in all of the Swedish bidding areas, and adjusts the balance responsibility agreement and the system of balancing settlement.

The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.