Identifying Effective Demand Response Program Designs for Residential Customers PDF Download

Author: Julien Gattaciecca
Publisher:
ISBN:
Category : Demand-side management (Electric utilities)
Languages : en
Pages : 75

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Author: Ailin Asadinejad
Publisher:
ISBN:
Category : Electric industries
Languages : en
Pages : 194

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Book Description
The main purpose of this dissertation is to design an appropriate tariff program for residential customers that encourages customers to participate in the system while satisfying market operators and utilities goals. This research investigates three aspects critical for successful programs: tariff designs for DR, impact of renewable on such tariffs, and load elasticity estimates. First, both categories of DR are modeled based on the demand-price elasticity concept and used to design an optimum scheme for achieving the maximum benefit of DR. The objective is to not only reduce costs and improve reliability but also to increase customer acceptance of a DR program by limiting price volatility. A time of use (TOU) program is considered for a PB scheme designed using a monthly peak and off peak tariff. For the IBDR, a novel optimization is proposed that in addition to calculation of an adequate and a reasonable amount of load change for the incentive also finds the best times to request DR. Second, the effect of both DR programs under a high penetration of renewable resources is investigated. LMP variation after renewable expansion is more highly correlated with renewable's intermittent output than the load profile. As a result, a TOU program is difficult to successfully implement; however, analysis shows IBDR can diminish most of the volatile price changes in WECC. To model risk associated with renewable uncertainty, a robust optimization is designed considering market price and elasticity uncertainty. Third, a comprehensive study to estimate residential load elasticity in an IBDR program. A key component in all demand response programs design is elasticity, which implies customer reaction to LSEs offers. Due to limited information, PB elasticity is used in IBDR as well. Customer elasticity is calculated using data from two nationwide surveys and integrated with a detailed residential load model. In addition, IB elasticity is reported at the individual appliance level, which is more effective than one for the aggregate load of the feeder. Considering the importance of HVAC in the aggregate load signal, its elasticity is studied in greater detail and estimated for different customer groupings.

Author: Dan Haas
Publisher: DIANE Publishing
ISBN: 9780756745806
Category : Business & Economics
Languages : en
Pages : 60

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The efficient & reliable functioning of the more than $200 billion electric industry is vital to all Americans. As demonstrated in the 2003 blackout in the Northeast & the 2001 energy crisis in the West, changes in the cost & availability of electricity can have significant impacts on consumers & the national economy. The Fed. Energy Reg'y. Comm. supports using demand-response programs as part of its effort to develop & oversee competitive electricity markets. This report identifies: (1) the types of demand-response programs currently in use; (2) the benefits of these programs; (3) the barriers to their intro. & expansion; & (4) instances where barriers have been overcome. Also examined the fed. govt.'s participation in these programs through the GSA.

Author: Tara L. Becnel
Publisher:
ISBN:
Category : Electric utilities
Languages : en
Pages : 190

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Author: Zhanle Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Demand response (DR) is a recent effort to improve efficiency of the electricity market and the stability of the power system. A successful DR implementation relies on both appropriate policy design and enabling technology. Real-time pricing (RTP) and time of use (TOU) have been identified as two important DR policies to motivate residential customers to participate in DR programs. An efficient residential DR model should implement heterogeneous residential load forecasting, multi-criteria optimization (e.g., objectives for individual homes, utilities and aggregations of them) and intelligent distributed algorithms to evaluate the complex and large-scale power systems. This thesis presents a multi-agent system (MAS) to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HA) and a retailer agent (RA). A heterogeneous load prediction model, a real-time electricity price model and three optimal load control models are developed to associate with the MAS. The load prediction model simulates the benchmark of individual and aggregated load profiles based on statistical information of how people use their appliances including electric vehicles (EV). Each HA has a unique load profile depending on its heterogeneous local configurations. The real-time price prediction model is defined as piecewise linear functions of power and the optimal coefficients are obtained from historical data of real-time loads and electricity prices via the norm approximation approach. The optimal load control models are developed based on dynamic pricing of RTP and TOU. An open-loop optimal load control model under RTP (OL-LCM-RTP) is formulated into a convex programming (CP) problem to minimize electricity payment and waiting time. A HA schedules the controllable loads based on its local information by solving the CP problem; therefore, it only requires a minimum of communication between the HA and the RA. This is greatly useful because the infrastructure for communication is still under development. In addition, the privacy of users is not sacrificed. Simulation results show that the peak-to-average power ratio (PAPR) and the standard deviation of the load profile, and electricity payments are reduced using the proposed mechanism. A close-loop optimal load control model under RTP (CL-LCM-RTP) is developed based on the OL-LCM-RTP by further incorporating feedbacks from RA. A HA solves the CP problem to schedule the controllable loads in a round process using the global load information. The process can be quickly converged in the second round; therefore, it requires limited efforts from the communication and the coordination. It is found that this model can significantly improve the quality of the optimization based on the simulation results. An optimal load control model under TOU (LCM-TOU) is modeled by a linear programming (LP) problem. The objective function is designed to find a trade-off among three factors: 1) the minimum electricity payment; 2) comfort levels with waiting time; and 3) to avoid peak demand rebound. We also evaluate the impacts of the participation levels of TOU programs. The simulation results show a reduced PAPR, standard deviation and the electricity payments from the HAs. The HA, with proposed optimal control mechanisms, can be embedded into a home energy management system (EMS) to make intelligent decisions on behalf of homeowners automatically responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable DR implementation.

Author: Thanappuhettige Nipuna Mihiranga Gomes
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 46

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Book Description
One of the focus areas of the smart-grid initiative is residential level demand response. Literature presents numerous demand response and load control programs. Some of the recent surveys analyze price-responsive demand response optimization, mathematical modeling of demand response, responsive demand forecasting, and communication requirements. Minimal work is done to evaluate and incorporate the impact of such programs on the grid. The real-time demand response program should benefit both utility and the consumers in an optimized manner. Most demand response schemes in the literature fail to identify the benefit to the distribution system. Only a few of publications in the literature indicate that the proposed demand response programs could benefit the utility. Of that, only a handful of work shows and verifies the actual benefits. One of the works, which evaluated the grid impact, focuses on individual appliances and their contribution to voltage drop mitigation. Regardless of the benefit to the consumers, the utility will not be interested in those programs if they do not provide a considerable benefit to them. This has limited the distribution system operators from identifying the worth of demand response and launch programs which are beneficial to both consumers and distribution operators. This thesis addresses the value of demand response programs to the grid. The first part of this thesis identifies the benefit of the demand response programs available in the literature. Next, the thesis presents an approach to incorporate utility focused demand response benefits into distribution system operation. This is done by maintaining distribution level requirements such as minimal deviations in nodal voltage and power factor. A modified AC distribution power-flow method is proposed along with the demand response as a constraint. The demand response constraint is developed using the first part. The outcome of this work can be used by utilities to evaluate the benefits of demand response programs

Author: K.S.K Weranga
Publisher: Springer Science & Business Media
ISBN: 9814451827
Category : Technology & Engineering
Languages : en
Pages : 146

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Book Description
Taking into account the present day trends and the requirements, this Brief focuses on smart metering of electricity for next generation energy efficiency and conservation. The contents include discussions on the smart metering concepts and existing technologies and systems as well as design and implementation of smart metering schemes together with detailed examples.

Author: Clark W. Gellings
Publisher: CRC Press
ISBN: 1000355314
Category : Business & Economics
Languages : en
Pages : 182

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Book Description
The power system has often been cited as the greatest and most complex machine ever built, yet it is predominantly a mechanical system. Technologies and intelligent systems are now available that can significantly enhance the overall functionality of power distribution and make it ready to meet the needs of the 21st century. This book explains how sensors, communications technologies, computational ability, control, and feedback mechanisms can be effectively combined to create this new, continually adjusting "smart grid" system. It provides an understanding of both IntelliGridSM architecture and EnergyPortSM as well as how to integrate intelligent systems to achieve the goals of reliability, cost containment, energy efficiency in power production and delivery, and end-use energy efficiency.

Author: Bonnie Wylie Pratt
Publisher:
ISBN:
Category : Renewable energy
Languages : en
Pages : 352

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Book Description
Innovation in the electric sector has the potential to drive job growth, decrease environmental impacts, reduce rate payer costs, and increase reliability and resiliency. However, the traditional electric system was built to deliver a controlled flow of energy from a centralized location with maximum reliability and minimum cost. As both customer expectations and generation technologies change, new avenues for grid innovation are being explored. Residential customers, commercial and industrial clients, and electric utilities must all find a way to balance goals for decarbonization and social justice with maintaining a least cost, reliable power grid. Grounded in Geel's energy system transition framework, this dissertation explores how each of these three stakeholder groups is navigating the transition to renewables. The first study tests the idea that residential customers will be more inclined to change their behavior when altruistically contributing to a greater goal. Renewed Darwinian theory was explored to question the exclusive use of financial incentives in demand response programs, with evidence that enabling altruism may influence electricity demand even more effectively than traditional financial incentives. A difference in differences approach was designed to test the impact of the Burlington Electric Department's Defeat the Peak program on residential energy use where the incentive was a group donation to a local charity. Results suggest utility savings of over $12 in energy supply costs for every $1 they invested in the program. Financial levers, however, can be quite effective in influencing electricity demand, and may result in cost-shifting from high to low demand consumers. The second study focused on rate design for commercial and industrial customers through an analysis of the utility demand charge. For over a century the demand charge has been a primary means to recover total cost-of-service including fixed, embedded, and overhead costs. Under the current system, most small commercial and residential customers do not receive a strong direct price signal to invest in storage, load shifting, or renewables. Larger commercial and industrial customers exercise some measure of control over their loads to reduce demand charges, but with only modest benefit or value to the system as a whole. The system costs are then redistributed to all customer classes, potentially falling disproportionately on low demand customers. To investigate, a regression analysis was conducted with cost and market characteristics from 447 US electric utilities. Results suggest that demand charges predict a significant degree of variability in residential pricing, confirming suspected cost shifting. Redesigning the demand charge could open up new markets for renewable energy entrepreneurs and lower grid costs and customer rates, supporting goals of decarbonization while also achieving reliable least-cost power. In the third study, an iterative approach was employed to understand why some utilities lean into the energy system transition while others take a more conservative stance. A database of 170 US electric utilities was constructed including a qualitative assessment of Integrated Resource Plans for renewability orientation. Institutional resource-based theory was utilized to take a striated approach to understanding firm heterogeneity, identifying factors at the individual manager level, firm level, and external environment that can influence a utility's energy supply characteristics. Independent variables in a simultaneous regression analysis included CEO gender and tenure at the individual level, ownership structure and firm age at the firm level, and the impact of policies and state rurality at the inter-firm level. Results indicate that a significant amount of a utility's commitment to the renewable energy transition can be predicted based on these firm characteristics.

Author: Brandon Jeffrey Johnson
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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With their large impact on the power system and widespread distribution, residential loads provide vast resources that if utilized correctly have the potential to help reduce both electricity cost and demand throughout the day. Previous research in this area has been primarily focused on building more energy efficient homes and improving the efficiencies of appliances and lighting technologies. Far less attention has been given to the ability of residential loads to provide various demand response services. Residential loads with demand response capabilities have the potential to be very useful in both peak shifting and regulation applications, and could be utilized in the future to help maintain power system stability and security. Before this can become a reality, however, the effect residential loads providing demand response services can have on the power system must be understood. One method for determining the overall impact residential demand response can have on the power system is through modeling. In this thesis, the development of a dynamic simulation tool capable of predicting residential power demand on a one-second time scale is discussed. To produce the most accurate results, a bottom-up modeling approach is utilized in which the characteristics of the household, its individual loads, and the behavior of its occupants are modeled. Using this technique, the contribution of each residential load towards the total aggregate demand of the residential sector can be identified. Occupant behavior models are developed using data collected in the American Time Use Survey to create a statistically accurate representation of how occupants interact with major residential loads. These models are simulated using a Markov Chain Monte Carlo method, and predict occupant behavior based on the time of the day and day of the week. To predict residential power demand, dynamic models of the most common residential loads are developed and used in conjunction with these occupant behavior models and environmental input data. Finally, several demand response strategies are applied to this simulation tool to quantify the potential impact residential demand response programs can have on the power system and illustrate the importance of understanding their overall effects.