Download or read book Control and Communication for Demand Response with Thermostatically Controlled Loads written by Kai Ma and published by Springer Nature. This book was released on 2022-12-11 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on control and communication for demand response with thermostatically controlled loads. This is achieved by providing in-depth study on a number of major topics such as load control, optimization strategies, communication network model, resource allocation methods, system design, implementation, and performance evaluation. Two major cost modeling methods are established in detail, which are cost modeling based on Taguchi Loss Function and cost modeling based on regulation errors. The comprehensive and systematic treatment of issues in optimization strategies and resource allocation for demand response are one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in control and communication. The book can benefit researchers, engineers, and graduate students in fields of control theory, automation, communication engineering and economics, etc.

Download or read book Demand Response from Thermostatically Controlled Loads written by Vincenzo Trovato and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Quantifying Flexibility of Residential Thermostatically Controlled Loads for Demand Response written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy written by Meng Song and published by Springer Nature. This book was released on 2021-11-30 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of renewable energy enhances the sustainability of power systems, but the inherent variability also poses great challenges to the planning and operation of large power grids. The corresponding electric power deficiencies can be compensated by fast ramping generators and energy storage devices. However, frequent ramp up/down power adjustments can increase the operation and the maintenance cost of generators. Moreover, storage devices are regarded as costly alternatives. Demand response (DR) and transactive energy can address this problem owing to its attractive and versatile capability for balancing the supply-demand, improving energy efficiency, and enhancing system resilience. Distributed resources are the typical participants of DR and transactive energy programs, which greatly contribute to keep the supply and demand in a balance. Thermostatically controlled loads (TCLs) (i.e., air conditioners, water heaters, and refrigerators) represent an example of distributed resources, the ratio of which to the total power consumption in developed countries is up to 30%–40%. Providing tremendous potentials in adjustable power consumption, TCLs have attracted major interests in DR and transactive energy opportunities. It has highlighted the advantages of TCLs in responding to uncertainties in power systems. This book provides an insight of TCLs as typical distributed resources in smart grids for demand response and transactive energy to address the imbalance between supply and demand problems in power systems. The key points on analysis of uncertainty parameters, aggregated control models, battery modelling, multi-time scale control, transactive control and robust restoration of TCLs are all included. These are the research points of smart grids and deserve much attention. We believe this book will offer the related researcher a better understanding on the integration of distributed resources into smart grid for demand response and transactive energy. And it will be helpful to address the problems in practical projects.

Download or read book Ensemble of Thermostatically Controlled Loads written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Thermostatically Controlled Loads (TCL), e.g. air-conditioners and heaters, are by far the most wide-spread consumers of electricity. Normally the devices are calibrated to provide the so-called bang-bang control of temperature - changing from on to off , and vice versa, depending on temperature. Aggregation of a large group of similar devices into a statistical ensemble is considered, where the devices operate following the same dynamics subject to stochastic perturbations and randomized, Poisson on/off switching policy. We analyze, using theoretical and computational tools of statistical physics, how the ensemble relaxes to a stationary distribution and establish relation between the re- laxation and statistics of the probability flux, associated with devices' cycling in the mixed (discrete, switch on/off , and continuous, temperature) phase space. This allowed us to derive and analyze spec- trum of the non-equilibrium (detailed balance broken) statistical system. and uncover how switching policy affects oscillatory trend and speed of the relaxation. Relaxation of the ensemble is of a practical interest because it describes how the ensemble recovers from significant perturbations, e.g. forceful temporary switching o aimed at utilizing flexibility of the ensemble in providing "demand response" services relieving consumption temporarily to balance larger power grid. We discuss how the statistical analysis can guide further development of the emerging demand response technology.

Download or read book Swarm Behavior to Mitigate Rebound in Air Conditioning Demand Response Events written by Jason Yasuto Kuwada and published by . This book was released on 2019 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Thermostatically Controlled Loads (TCLs) have shown great potential for Demand Response (DR) events. However, it has been commonly seen that DR events using TCLs may cause demand rebound, especially in homogeneous populations. To further explore the potential for DR events, as well as the negative effects, a stability and resilience analysis were performed on multiple populations and verified with agent based modeling simulations. At the core of this study is an added thermostat criterion created from the combination of a proportional gain and the average compressor operating state of neighboring TCLs. Where DR events in TCLs are commonly controlled by set point manipulation, the modified thermostat behavior proposed in this study alters the effective dead band of each individual TCL. Previous work has shown the effectiveness of the proposed behavior to mitigating the demand rebound. By adding the average operating state of neighboring TCLs and a proportional gain, the systems feedback is changed, opening the possibilities to creating an unstable response. Stability limit are found from linearized systems, differing in delay schemes and connection architecture. The stability analysis was verified through agent-based modeling simulations on MATLAB. The linearization assumption was tested by simulating the systems while altering the parameters of population size and thermostat dead band. Resilience of several systems, differing in connection architecture, is computed and compared to results of a simulated denial of service attack on the system. Resilience for each architecture was calculated using the algebraic connectivity of the graph. The simulated attack is completed by removing the TCLs ability to communicate with in the agent based model. The stability analysis showed the effect of the gain value on the performance of the system and that the stability limit was directly affected by the effective deadband. As the deadband size was increased, the predicted results found from the analysis aligned with simulations of the system. Contrarily the resilience analysis was validated by simulations with smaller deadband sizes. Simulations of cyber-attacks also showed optimal attacks based on operating state of thermostats, as well as locations within the population."--Boise State University ScholarWorks.

Download or read book Modeling Analysis and Control of Demand Response Resources written by and published by . This book was released on 2012 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy (DOE) as ?a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized? [29]. DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility. This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C&I facilities, to help building managers understand building energy consumption and ‘ask the right questions’ to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, id est, a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, in general, baseline model error is large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment. Emerging DR paradigms focus on faster timescale DR. Here, we investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. Specifically, we compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We develop Markov Chain models to describe the temperature state evolution of heterogeneous populations of TCLs, and use Kalman filtering for both state and joint parameter/state estimation. We present a look-ahead proportional controller to broadcast control signals to all TCLs, which always remain in their temperature dead-band. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26–9.3% of steady state aggregated power consumption. Results depend upon the information available for system identification, state estimation, and control. We find that, depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all. We also estimate the size of the TCL potential resource; potential revenue from participation in markets; and break-even costs associated with deploying DR-enabling technologies. We find that current TCL energy storage capacity in California is 8–11 GWh, wi...

Download or read book Advances in Energy System Optimization written by Valentin Bertsch and published by Springer. This book was released on 2017-03-16 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers presented in this volume address diverse challenges in energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids and from theoretical considerations to data provision concerns and applied case studies. The International Symposium on Energy System Optimization (ISESO) was held on November 9th and 10th 2015 at the Heidelberg Institute for Theoretical Studies (HITS) and was organized by HITS, Heidelberg University and Karlsruhe Institute of Technology.

Download or read book Modeling Analysis and Control of Demand Response Resources written by Johanna L. Mathieu and published by . This book was released on 2012 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy as à̀ tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized." DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility. This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C & I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C & I facilities, to help building managers understand building energy consumption and àsk the right questions' to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, i.e., a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C & I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C & I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, in general, baseline model error is large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. In some cases, however, aggregations of C & I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment. Emerging DR paradigms focus on faster timescale DR. Here, we investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. Specifically, we compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We develop Markov Chain models to describe the temperature state evolution of heterogeneous populations of TCLs, and use Kalman filtering for both state and joint parameter/state estimation. We present a look-ahead proportional controller to broadcast control signals to all TCLs, which always remain in their temperature dead-band. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26-9.3% of steady state aggregated power consumption. Results depend upon the information available for system identification, state estimation, and control. We find that, depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all. We also estimate the size of the TCL potential resource; potential revenue from participation in markets; and break-even costs associated with deploying DR-enabling technologies. We find that current TCL energy storage capacity in California is 8-11 GWh, with refrigerators contributing the most. Annual revenues from participation in regulation vary from $10 to $220 per TCL per year depending upon the type of TCL and climate zone, while load following and arbitrage revenues are more modest at $2 to $35 per TCL per year. These results lead to a number of policy recommendations that will make it easier to engage residential loads in fast timescale DR.

Download or read book Frequency Control Via Demand Response in Smart Grid written by Farid Farmani and published by . This book was released on 2018 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to have a reliable microgrid (MG) system, we need to keep the frequency within an acceptable range. However, due to disturbances in a MG system (such as a sudden load change), it can experience major or minor deviations in frequency, which needs to be reduced within seconds to provide the system stability. In order to maintain the balance between energy supply and demand, traditionally, generation side controllers are utilized to stabilize the power system frequency. These systems add high operational cost, which is not desired for power system operators. With the introduction of smart grid, more and more renewable energy sources are to be used in the power system. The intermittent behavior of these energy resources, as well as high operation cost of conventional controllers, has led to research for new alternatives. In a smart grid environment, demand response (DR) programs can be considered as a promising alternative to the conventional controllers, to e ciently contribute to the frequency regulation by switching responsive loads on or o . DR programs can reduce the amount of energy reserve required and, hence, are more cost efficient. Moreover, they can act very fast and can provide a wide range of operation time from a few seconds to several minutes. Thermostatically controlled loads (TCLs) are proper candidates to participate in frequency regulation programs. However, individual TCLs do not have a noticeable impact on frequency due to small size. They should be aggregated in order to have a considerable effect on frequency. Nevertheless, there are still many challenges which should be addressed in order to make use of TCLs for frequency control in smart grid. In this regard, proper aggregated load models and control algorithms for TCLs contributing to this service need to be investigated. In this thesis, we present an aggregation model for TCLs and a control strategy to coordinate power provided from DR participants with that of generation side of the MG to keep system frequency within its desired range. For the aggregation model considered in this study, a state space model is used to take into account the interdependency of TCLs' temperature participating in DR programs. The model groups TCLs into clusters, each controlled by an aggregator. A minimum off/on period is considered for individual TCLs to avoid frequent switching of these devices. A control strategy is presented to control frequency by coordinating the generation and demand side regulation service providers. Computer simulation results show that the proposed aggregation model and control strategy can effectively control frequency under various case studies.

Download or read book Controller for Thermostatically Controlled Loads written by and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A system and method of controlling aggregated thermostatically controlled appliances (TCAs) for demand response is disclosed. A targeted load profile is formulated and a forecasted load profile is generated. The TCAs within an "on" or "off" control group are prioritized based on their operating temperatures. The "on" or "off" status of the TCAs is determined. Command signals are sent to turn on or turn off the TCAs.

Download or read book Demand Response in Smart Grids written by Pengwei Du and published by Springer. This book was released on 2019-07-02 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first of its kind to comprehensively describe the principles of demand response. This allows consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage in response to the grid reliability need, time-based rates or other forms of financial incentives. The main contents of the book include modeling of demand response resources, incentive design, scheduling and dispatch algorithms, and impacts on grid operation and planning. Through case studies and illustrative examples, the authors highlight and compare the advantages, disadvantages and benefits that demand response can have on grid operations and electricity market efficiency. First book of its kind to introduce the principles of demand response; Combines theory with real-world applications useful for both professionals and academic researchers; Covers demand response in the context of power system applications.

Download or read book Residential Demand Response on Thermostatically Controlled Appliances for Handling Generation Failures and Optimizing Consumer Costs in Smart Grids written by Kevin Madsen and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Large scale Demand Response of Thermostatic Loads written by Luminița Cristiana Totu and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Reinforcement Learning Characterization of Thermostatic Control for HVAC Demand Response and Experimentation Framework for Simulated Building Energy Control written by Christopher J. Eubel and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The U.S. electrical grid is in a transformation from centralized generation sources and unidirectional flow of power, to distributed networks of utility-scale and on-site renewable generation, energy storage, and flexible demand. As the electrical grid adopts more intermittent renewable energy sources, the challenges to maintaining grid stability and meeting electricity demand will only increase. The variable generation of intermittent sources combined with the existing variations in daily and seasonal electricity demand could create situations where maintaining sufficient capacity and managing distribution is often infeasible. With renewable energy aside, the grid can still struggle to meet and manage peak loads, often resorting to quick-acting, dirty “peaker” plants to compensate for supply. These peak loads are not only a challenge for supply, but also require infrastructure to be sized for such capacity. Demand-side management, or demand response, incorporate the objectives and incentives for consumers to manage their own electricity demand throughout the day so as to reduce peak loads and support grid stability. The incentives for demand response participation are often provide through the dynamic pricing of electricity. By targeting cheaper prices throughout the day, consumers can minimize their energy expense while simultaneously satisfying demand response objectives. However, this coordinated use of electricity requires flexible loads, and heating, ventilation, and air conditioning systems is one such load of particular interest. Thermal inertia of buildings and favorable weather conditions allow for its flexible use, and its energy intensiveness and rising usage around the world make it an important load to consider. Although, coordinating such loads as to maintain comfortable indoor climate and satisfy demand response objectives is not so easily done, and it is a contradictory task. In this thesis we employ a deep reinforcement learning approach to thermostatic control of HVAC to maintain thermal comfort and maximize demand response participation. We utilize EnergyPlus building energy simulation as a testbed for experimentation of reinforcement learning control. However, we see that for a number of reasons this problem and environment is challenging for the reinforcement learning framework. We address and characterize these challenges encountered from experimentation. We also present a reinforcement learning framework that utilizes a native tool of EnergyPlus which allows the implementation of custom control on running simulations. This framework allows reinforcement learning researchers and practitioners to easily interface with any configured EnergyPlus building model for the experimentation of building energy control. This platform, along with the characterization of reinforcement learning in this environment, provide a baseline for accelerating further research in this space of building energy control for dynamically priced demand response participation.

Download or read book Insights Into the Behavior of Heterogeneous Thermostatically Controlled Loads and Battery Packs written by Donald Docimo and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation analyzes and determines fundamental insights into the behavior of heterogeneous populations of two types of energy storage devices: (i) thermostatically controlled loads (TCLs) harnessed for demand response and (ii) battery cells in a battery pack. Determined through reduced-order models, the insights are intended to facilitate control of these devices. The selection of these devices comes from two similarities. First, populations of these devices are utilized for filtering differences between power demanded and power supplied. Control of power demanded by TCLs is a proposed method to integrate renewable resources into the grid without the need to shed excess supplied energy or disrupt grid frequency. Likewise, stationary battery packs are implemented for renewable resource integration, and packs are used in partially or fully electric vehicles to match the power requirements.Second, beyond the application connection there exists a fundamental connection: the aggregate behavior of both systems is inherently different from the average unit behavior. As the literature states, despite the fact that individual TCL power dynamics are oscillatory, heterogeneous populations of TCLs exhibit damped aggregate power dynamics. This damping phenomenon is beneficial to control methods attempting to match power demand to supply. As this damping introduced by heterogeneity changes the nature of the aggregate dynamics, there is a focus on developing low-order aggregate power dynamic models that incorporate heterogeneity. Similarly, studies and experiments in the battery literature show that pack capacity degradation is not captured by the average battery cell. The battery pack capacity and other pack state of health (SOH) parameters are unfavorably influenced by the unhealthiest cells within the pack, impacting pack control through the SOH parameter-determined current and voltage limits. Furthermore, studies show that heterogeneity found within the cells influences the degradation of other cells within the pack, with and without cell charge balancing strategies.Though the literature discusses these aggregate behavior dynamics, there are still fundamental insights missing for both systems. Studies do exist that attempt to determine a relationship between TCL heterogeneity and the damped aggregated dynamics, but there is a lack of a concrete study relating how heterogeneity of multiple parameters impacts the aggregate dynamics. There are studies that attempt to determine how heterogeneity of battery cells impact aggregate pack SOH, but there is a lack of insights determined from analyzing a model specifically developed to capture heterogeneity dynamics. This dissertation proposes the solution to both of these problems through the development of reduced-order models that are built with the explicit purpose of determining the insights missing from the literature. For the TCL problem, a model of an individual TCL is reformulated and a stochastic parameter integral is applied to develop a reduced-order aggregate power demand model. A set of second-order linear time-varying (LTV) differential equations characterize this model, with the time-varying damping ratios describing the strength of the damping observed in the aggregate power demand. This model, shown to be accurate for low-order approximations, furnishes several insights, including: (i) Only heterogeneity in the TCL power demands characteristic frequency creates damping in the aggregate response. Other heterogeneities, such as in the demands duty cycle, do not. (ii) Low levels of heterogeneity in the characteristic frequencies also create a beating phenomenon in the aggregate power dynamics. (iii) The damping ratios depend on the dominant characteristic frequencies and decay over time. These insights lead to understanding useful for system design through intelligent selection of TCL populations to meet operator needs. The damping ratios lend themselves to better-informed controller design, potentially allowing more aggressive demand response controllers without inducing instability.For the battery pack problem, a framework is developed to determine insights into the behavior of heterogeneity within a pack for different balancing strategies. An electrochemical cell model is reduced to describe heterogeneity between cells through an LTV model for two different studies. In the first study, charge and capacity heterogeneity are considered, and insights lead to the knowledge that voltage balancing algorithms do not remove capacity differences within the lifespan of the pack. A novel control algorithm is developed from these insights, which balances not voltage, but rather charge and capacity, and is shown to increase pack lifespan by up to 9.2%. In the second study, charge and temperature heterogeneity are considered, and the tradeoffs between balancing these are formulated. It is discovered that temperature heterogeneity removal can be assisted by control of an average current through two cells using entropic and ohmic effects, and a model predictive control (MPC) algorithm is developed to show this.Through this dissertation, an understanding is gained regarding how the aggregate response of heterogeneous groups of energy storage devices is inherently different than individual device response. For both the TCL demand response problem and the battery pack problem, the insights coupled with reduced-order models are intended to facilitate the development of accurate and efficient control methods.

Download or read book Advances in Energy Systems written by Peter D. Lund and published by John Wiley & Sons. This book was released on 2019-02-06 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to a multi-disciplinary approach that includes perspectives from noted experts in the energy and utilities fields Advances in Energy Systems offers a stellar collection of articles selected from the acclaimed journal Wiley Interdisciplinary Review: Energy and Environment. The journalcovers all aspects of energy policy, science and technology, environmental and climate change. The book covers a wide range of relevant issues related to the systemic changes for large-scale integration of renewable energy as part of the on-going energy transition. The book addresses smart energy systems technologies, flexibility measures, recent changes in the marketplace and current policies. With contributions from a list of internationally renowned experts, the book deals with the hot topic of systems integration for future energy systems and energy transition. This important resource: Contains contributions from noted experts in the field Covers a broad range of topics on the topic of renewable energy Explores the technical impacts of high shares of wind and solar power Offers a review of international smart-grid policies Includes information on wireless power transmission Presents an authoritative view of micro-grids Contains a wealth of other relevant topics Written forenergy planners, energy market professionals and technology developers, Advances in Energy Systems is an essential guide with contributions from an international panel of experts that addresses the most recent smart energy technologies.