Academic literature on the topic 'Aggregate residential load'
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Journal articles on the topic "Aggregate residential load"
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Zhou, Xiao, Jing Shi, Yuejin Tang, Yuanyuan Li, Shujian Li, and Kang Gong. "Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response." Energies 12, no. 4 (February 20, 2019): 683. http://dx.doi.org/10.3390/en12040683.
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The improvement of intelligent appliances provides the basis for the demand response (DR) of residential loads. Thermostatically controlled loads (TCLs) are one of the most important DR resources and are characterized by a large load and a high degree of control. Due to its distribution characteristic, the aggregation of TCLs and their control are key issues in implementing the load control for the DR. In this study, we focus on air conditioning loads as an example of TCLs and propose a simple and transferable aggregate model by establishing a virtual house model, which accurately captures the aggregate flexibility. The deviation of the aggregate model is analyzed for the model evaluation. An air conditioning DR control scheme is proposed based on the aggregate model; it has the advantage of simple implementation and convenient control for the individual units. Simulations are performed in Gridlab-D to evaluate the accuracy and effectiveness of the proposed model and control method.
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Nashrullah, Erwin, and Abdul Halim. "Polynomial Load Model Development for Analysing Residential Electric Energy Use Behaviour." MATEC Web of Conferences 218 (2018): 01007. http://dx.doi.org/10.1051/matecconf/201821801007.
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Analysing and simulating the dynamic behaviour of home power system as a part of community-based energy system needs load model of either aggregate or dis-aggregate power use. Moreover, in the context of home energy efficiency, development of specific and accurate residential load model can help system designer to develop a tool for reducing energy consumption effectively. In this paper, a new method for developing two types of residential polynomial load model is presented. In the research, computation technique of model parameters is provided based on median filter and least square estimation and implemented by MATLAB. We use AMPDs data set, which have 1-minute data sampling, to show the effectiveness of proposed method. After simulation is carried out, the performance evaluation of model is provided through exploring root mean-squared error between original data and model output. From simulation results, it could be concluded that proposed model is enough for helping system designer to analyse home power energy use.
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Djokic, Sasa Z., and Igor Papic. "Smart Grid Implementation of Demand Side Management and Micro-Generation." International Journal of Energy Optimization and Engineering 1, no. 2 (April 2012): 1–19. http://dx.doi.org/10.4018/ijeoe.2012040101.
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This paper analyses the influence and effects of demand side management (DSM) and micro-generation (MG) on the operation of future “smart grids.” Using the residential load sector with PV and wind-based MG as an example, the paper introduces a general methodology allowing to identify demand-manageable portion of the load in the aggregate demand, as well as to fully correlate variable power outputs of MG with the changes in load demands, including specific DSM actions and schemes. The presented analysis is illustrated using a detailed model of a typical UK LV/MV residential network.
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Afzaal, Muhammad Umar, Intisar Ali Sajjad, Muhammad Faisal Nadeem Khan, Shaikh Saaqib Haroon, Salman Amin, Rui Bo, and Waqas ur Rehman. "Inter-temporal characterization of aggregate residential demand based on Weibull distribution and generalized regression neural networks for scenario generations." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4491–503. http://dx.doi.org/10.3233/jifs-200462.
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The characterization of electrical demand patterns for aggregated customers is considered as an important aspect for system operators or electrical load aggregators to analyze their behavior. The variation in electrical demand among two consecutive time intervals is dependent on various factors such as, lifestyle of customers, weather conditions, type and time of use of appliances and ambient temperature. This paper proposes an improved methodology for probabilistic characterization of aggregate demand while considering different demand aggregation levels and averaging time step durations. At first, a probabilistic model based on Weibull distribution combined with generalized regression neural networks (GRNN) is developed to extract the inter-temporal behavior of demand variations and, then, this information is used to regenerate aggregate demand patterns. Average Mean Absolute Percentage Error (AMAPE) is used as a statistical indicator to assess the accuracy and effectiveness of proposed probabilistic modeling approach. The results have demonstrated that the performance of proposed approach is better in comparison with an existing Beta distribution-based method to characterize aggregate electrical demand patterns.
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Lindberg, K. B., S. J. Bakker, and I. Sartori. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts." Utilities Policy 58 (June 2019): 63–88. http://dx.doi.org/10.1016/j.jup.2019.03.004.
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Roth, Jonathan, Jayashree Chadalawada, Rishee K. Jain, and Clayton Miller. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification." Energies 14, no. 5 (March 8, 2021): 1481. http://dx.doi.org/10.3390/en14051481.
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As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use.
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Ahajjam, Mohamed Aymane, Daniel Bonilla Licea, Mounir Ghogho, and Abdellatif Kobbane. "IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings." Sensors 20, no. 4 (February 14, 2020): 1048. http://dx.doi.org/10.3390/s20041048.
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Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.
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Yousefi, Ali, Waiching Tang, Mehrnoush Khavarian, Cheng Fang, and Shanyong Wang. "Thermal and Mechanical Properties of Cement Mortar Composite Containing Recycled Expanded Glass Aggregate and Nano Titanium Dioxide." Applied Sciences 10, no. 7 (March 26, 2020): 2246. http://dx.doi.org/10.3390/app10072246.
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One of the growing concerns in the construction industry is energy consumption and energy efficiency in residential buildings. Moreover, management of non-degradable solid glass wastes is becoming a critical issue worldwide. Accordingly, incorporation of recycled expanded glass aggregates (EGA) as a substitution for natural fine aggregate in cement composites would be a sustainable solution in terms of energy consumption in the buildings and waste management. This experimental research aims to investigate the effects of EGA on fresh and hardened properties and thermal insulating performance of cement mortar. To enhance the mechanical properties and water resistance of the EGA-mortar, nano titanium dioxide (nTiO2) was used as nanofillers. The results showed an increase in workability and water absorption of the EGA-mortar. In addition, a significant decrease in bulk density and compressive strength observed by incorporating EGA into the cement mortar. The EGA-mortar exhibited a low heat transfer rate and excellent thermal insulation property. Furthermore, inclusion of nTiO2 increased compressive strength and water resistance of EGA-mortar, however, their heat transfer rate was increased. The results demonstrated that EGA-mortar can be integrated into the building envelop or non-load bearing elements such as wall partition as a thermal resistance to reduce the energy consumption in residential buildings.
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Olama, Mohammed, Teja Kuruganti, James Nutaro, and Jin Dong. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid." Energies 11, no. 7 (July 16, 2018): 1852. http://dx.doi.org/10.3390/en11071852.
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Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.
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Kapustin, Fedor, and Vladimir A. Belyakov. "Application of Modified Peat Aggregate for Lightweight Concrete." Solid State Phenomena 309 (August 2020): 120–25. http://dx.doi.org/10.4028/www.scientific.net/ssp.309.120.
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The scientific article "Application of Modified Peat Aggregate for Lightweight Concrete" presents the results of studies of the properties of a new composite material for use in enclosing structures of residential and public buildings. Physical and mechanical characteristics of possible aggregates of local production for this type of concrete affecting its operational properties are considered. The prospects of using fly ash as an additive improving the characteristics of polystyrene concrete with the addition of modified peat have been established. The analysis was made and the optimal compositions for obtaining lightweight concrete based on peat and polystyrene foam were selected. The desorption properties of lightweight concrete important for its effective operation as a wall material were tested. It was found that the use of new types of surfactants can improve the water wettability of peat particles and polystyrene granules, thereby reducing the water-cement ratio and improving the compressive strength of the material. Possible efficiency of application of this type of concrete for use in enclosing structures of buildings and constructions under construction in seismic regions of Russia is considered. The presence of damping effect manifested in the material due to the presence of polystyrene granules in the perception of a certain level of load, which is important for the work of concrete under seismic influences, was experimentally established.
Dissertations / Theses on the topic "Aggregate residential load"
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SAJJAD, MALIK INTISAR ALI. "Characterisation and Flexibility Assessment of Aggregate Electrical Demand." Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2594365.
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The renewable energy sources (RES) are intermittent in their nature and their integration in electric power grid has introduced the mismatch between supply and demand. This mismatch can be leveled by using the flexibilities from the supply and the demand side.
The demand side in a power system has key importance in the evolving context of the energy systems. Electrical load patterns that represent the consumption level are affected by different types of uncertainties associated with customer’s behavior and with keeping acceptable comfort level. The resulting aggregated load pattern indicates the system response that may be more or less flexible in different periods of time. The distribution system operator in a microgrid is responsible for its secure and economic operation. Enhancing the knowledge on the aggregated behavior of these customers is particularly important for the distribution system operator, also with the aim of determining the potential flexibility of the demand and setting up the economic terms of the electricity provision to the customers. Extra charges due to high energy demand and contract violation penalties can be avoided using demand side flexibility. Demand side flexibility has many benefits in normal as well as emergency conditions like less cost and quick response.
The study of aggregate residential demand for flexibility measures is important due to the diverse energy usage behavior of individual residents and conceptually, its availability all around the year for load management. Exploitation of possible flexibilities of the group of residential customer’s behavior is considered as an important option to promote demand response programs and to achieve greater energy savings.
As far as the residential sector is concerned, a reasonable work can be found in the literature to assess the flexibility for the individual appliances, the aggregation of selected appliances. However, little work is found on the aggregation of residential units. Also, despite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. There is a need to develop a methodology to extract flexibility information from aggregate electricity consumption behavior of the residents and develop useful flexibility indices for the aggregate residential loads. For this purpose, the first action required is to augment availability of information about the characteristics of aggregate electricity demand. The analysis of aggregate demand patterns is carried out by considering the demand pattern data representing the average power determined from the energy referring to a given time step duration.
This thesis contains a comprehensive statistical analysis to investigate the effect of time step duration and aggregation level on load variation profile. Then the customer behavior about the change is demand is modeled using the binomial probability distribution. This model has led towards some novel definitions of flexibility indices. A new method based on the Beta probability distribution has been developed to generate the time coupled aggregate residential demand patterns, whose evolution depends on the uncertainties associated with the customer’s behavior. The outcome of this research work has also led towards defining the role of customers in microgrid application. For this purpose, a structure of the business model for a smart (mini) grid is proposed. The data sets used for all kind of analysis are generated for the different aggregations of the extra-urban residential customers using a bottom-up approach.
The tools presented in this research work can be helpful for a system operator or an aggregator to assess demand side flexibilities, manage resources and efficiently use demand response programs. The findings of this work are also supportive to determine the metering structure for a microgrid application in which, by using current ICT technologies, it is possible to decide a compromise solution between the aggregation level and time step duration for smart metering. On the other hand, the research findings also led to the conclusion that the flexibility level for the individual residential customers is not so high to give economic benefits that make it attractive to participate in DR programs. From the studies, it seems that the problem is not with the technical aspects but with the current business model of the smart grids. For the future extension of this work, a framework of a new smart business model for smart (mini) grids, centric to customers, is presented. It is expected that the developments using the proposed background of the business model can lead towards a different era in the development of the power systems with the new wave of research; as new tools are required to embed economic and social considerations in planning the proposed architecture.
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Hasan, Mehedi. "Aggregator-Assisted Residential Participation in Demand Response Program." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/32546.
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The demand for electricity of a particular location can vary significantly based on season, ambient temperature, time of the day etc. High demand can result in very high wholesale price of electricity. The reason for this is very short operating duration of peaking power plants which require large capital investments to establish. Those power plants remain idle for most of the time of a year except for some peak demand periods during hot summer days. This process is inherently inefficient but it is necessary to meet the uninterrupted power supply criterion. With the advantage of new technologies, demand response can be a preferable alternative, where peak reduction can be obtained during the short durations of peak demand by controlling loads. Some controllable loads are with thermal inertia and some loads are deferrable for a short duration without making any significant impact on usersâ lifestyle and comfort. Demand response can help to attain supply - demand balance without completely depending on expensive peaking power plants.
In this research work, an incentive-based model is considered to determine the potential of peak demand reduction due to the participation of residential customers in a demand response program. Electric water heating and air-conditioning are two largest residential loads. In this work, hot water preheating and air-conditioning pre-cooling techniques are investigated with the help of developed mathematical models to find out demand response potentials of those loads. The developed water heater model is validated by comparing results of two test-case simulations with the expected outcomes. Additional energy loss possibility associated with water preheating is also investigated using the developed energy loss model. The preheating temperature set-point is mathematically determined to obtain maximum demand reduction by keeping thermal loss to a minimal level. Case studies are performed for 15 summer days to investigate the demand response potential of water preheating. Similarly, demand response potential associated with pre-cooling operation of air-conditioning is also investigated with the help of the developed mathematical model. The required temperature set-point modification is determined mathematically and validated with the help of known outdoor temperature profiles. Case studies are performed for 15 summer days to demonstrate effectiveness of this procedure. On the other hand, total load and demand response potential of a single house is usually too small to participate in an incentive-based demand response program. Thus, the scope of combining several houses together under a single platform is also investigated in this work. Monte Carlo procedure-based simulations are performed to get an insight about the best and the worst case demand response outcomes of a cluster of houses. In case of electrical water heater control, aggregate demand response potential of 25 houses is determined. Similarly, in case of air-conditioning control (pre-cooling), approximate values of maximum, minimum and mean demand reduction amounts are determined for a cluster of 25 houses. Expected increase in indoor temperature of a house is calculated. Afterwards, the air-conditioning demand scheduling algorithm is developed to keep aggregate air-conditioning power demand to a minimal level during a demand response event. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.Master of Science
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Adhikari, Rajendra. "Algorithms and Simulation Framework for Residential Demand Response." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87585.
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An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR.
Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR.
Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers� heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms.
One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers� comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result.
Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR.PHD
The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than the load, the match can be restored either by increasing the generation or by decreasing the load. Often, during system stress conditions, it is cheaper to decrease certain loads than to increase generation, and this method of achieving power balance is called demand response (DR). Residential sector consumes 37% of the total U.S. electricity consumption and is largely unexplored for demand response purpose, so the focus of the dissertation is on providing solutions to enable residential houses to provide demand response services. This dissertation presents two broad solutions. The first is a set of efficient algorithms that intelligently controls the customers’ heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid while keeping their comfort in mind. The second solution is a simulation software that can help evaluate and experiment with different residential demand response algorithms. The first algorithm is for reducing the collective power consumption of an aggregation of residential HVAC, whereas the second algorithm is for making the collective power follow a signal sent by the grid operators. It is shown that the algorithms presented can intelligently control the HVAC devices such that DR services can be provided to the grid while ensuring that the temperatures of the houses remain within comfortable range. The algorithms can enable demand response service providers to tap into the residential demand response market and earn revenue, while the simulation software can be valuable for future research in this area. The simulation software is simple to use and is designed with extensibility in mind, so adding new features is easy. The software is shown to work well for studying residential building control for demand response purpose and can be a useful tool for future research in residential DR.
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Lee, Seungman. "Optimization and Simulation Based Cost-Benefit Analysis on a Residential Demand Response : Applications to the French and South Korean Demand Response Mechanisms." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED054.
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À cause de la préoccupation mondiale sur les émissions de CO2, le changement climatique et la transition énergétique, nous faisons plus d'attention à la maîtrise de la demande d'électricité. En particulier, avec l'effacement de consommation électrique, nous pouvons profiter de plusieurs avantages, comme l'augmentation de l'efficacité de l'ensemble du marché de l'électricité, la sécurité d'approvisionnement d'électricité renforcée, et l'investissement plus efficace et souhaitable ainsi que l'avantage de l'environnement et le soutien aux énergies renouvelables. En Europe, la France a démarré le mécanisme de NEBEF à la fin de 2013, et la Corée du Sud a lancé le programme de l'effacement de consommation électrique basé sur le marché fin 2014. Parmi un certain nombre de questions et d’hypothèses que nous devons prendre en compte en termes de l'effacement, l'estimation de la courbe de référence est l'un des éléments les plus importants et les plus fondamentaux. Dans cette recherche, sur la base du profil de consommation redimensionné pour un ménage moyen, plusieurs méthodes d'estimation de la courbe de référence sont établies et examinées à la fois pour les mécanismes de l'effacement français et coréen. Cette investigation sur les méthodes de l'estimation pourrait contribuer à la recherche d'une méthode d'estimation meilleure et plus précise qui augmentera les motivations pour les participants. Avec les courbes de référence estimées, les analyses coûts-bénéfices ont été réalisées, elles-mêmes utilisées dans l'analyse décisionnelle pour les participants. Pour réaliser les analyses coûts-bénéfices, un modèle mathématique simple utilisant l'algèbre linéaire est créé et modifié afin de bien représenter les paramètres de chaque mécanisme de l'effacement. Ce modèle nous permet une compréhension intuitive et claire des mécanismes. Ce modèle générique peut être utilisé pour différents pays et secteurs, résidentiel, commercial et industriel, avec quelques modifications de modèle. La simulation de Monte Carlo est utilisée afin de refléter la nature stochastique de la réalité, et l'optimisation est également utilisée pour représenter et comprendre la rationalité des participants, et pour fournir des explications microéconomiques sur les comportements des participants. Afin de dégager des implications significatives pour une meilleure architecture du marché de l'effacement, plusieurs analyses de sensibilité sont effectuées sur les éléments clés du modèle pour les mécanismesWorldwide concern on CO2 emissions, climate change, and the energy transition made us to pay more attention to Demand-side Management (DSM). In particular, with Demand Response (DR), we could expect several benefits, such as increased efficiency of the entire electricity market, enhanced security of electricity supply by reducing peak demand, and more efficient and desirable investment as well as the environmental advantage and the support for renewable energy sources. In Europe, France launched the NEBEF mechanism at the end of 2013, and South Korea inaugurated the market-based DR program at the end of 2014. Among a number of economic issues and assumptions that we need to take into consideration for DR, Customer Baseline Load (CBL) estimation is one of the most important and fundamental elements. In this research, based on the re-scaled load profile for an average household, several CBL estimation methods are established and examined thoroughly both for Korean and French DR mechanisms. This investigation on CBL estimation methods could contribute to searching for a better and accurate CBL estimation method that will increase the motivations for DR participants. With those estimated CBLs, the Cost-Benefit Analyses (CBAs) are conducted which, in turn, are utilized in the Decision-making Analysis for DR participants. For the CBAs, a simple mathematical model using linear algebra is set up and modified in order to well represent for each DR mechanism's parameters. With this model, it is expected to provide intuitive and clear understanding on DR mechanisms. This generic DR model can be used for different countries and sectors (e.g. residential, commercial, and industrial) with a few model modifications. The Monte Carlo simulation is used to reflect the stochastic nature of the reality and the optimization is also used to represent and understand the rationality of the DR participants, and to provide micro-economic explanations on DR participants' behaviours. In order to draw some meaningful implications for a better DR market design several Sensitivity Analyses (SAs) are conducted on the key elements of the model for DR mechanisms
Conference papers on the topic "Aggregate residential load"
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Sajjad, Intisar A., Gianfranco Chicco, and Roberto Napoli. "Demand flexibility time intervals for aggregate residential load patterns." In 2015 IEEE Eindhoven PowerTech. IEEE, 2015. http://dx.doi.org/10.1109/ptc.2015.7232760.
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Ledva, Gregory S., Sarah Peterson, and Johanna L. Mathieu. "Benchmarking of Aggregate Residential Load Models Used for Demand Response." In 2018 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2018. http://dx.doi.org/10.1109/pesgm.2018.8585847.
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Guo, Z., J. Meyer, N. Al-Shibli, X. Xiao, S. Djokic, A. Collin, R. Langella, A. Testa, I. Papic, and A. Blanco. "Aggregate Harmonic Load Models of Residential Customers. Part 1: Time-Domain Models." In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 2019. http://dx.doi.org/10.1109/isgteurope.2019.8905621.
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Guo, Z., J. Meyer, N. Al-Shibli, X. Xiao, S. Djokic, A. Collin, R. Langella, A. Testa, I. Papic, and A. Blanco. "Aggregate Harmonic Load Models of Residential Customers. Part 2: Frequency-Domain Models." In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 2019. http://dx.doi.org/10.1109/isgteurope.2019.8905746.
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Collin, A. J., I. Hernando-Gil, J. L. Acosta, and S. Z. Djokic. "An 11 kV steady state residential aggregate load model. Part 1: Aggregation methodology." In 2011 IEEE PES PowerTech - Trondheim. IEEE, 2011. http://dx.doi.org/10.1109/ptc.2011.6019381.
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Collin, A. J., J. L. Acosta, I. Hernando-Gil, and S. Z. Djokic. "An 11 kV steady state residential aggregate load model. Part 2: Microgeneration and demand-side management." In 2011 IEEE PES PowerTech - Trondheim. IEEE, 2011. http://dx.doi.org/10.1109/ptc.2011.6019384.
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Zufferey, Thierry, Damiano Toffanin, Diren Toprak, Andreas Ulbig, and Gabriela Hug. "Generating Stochastic Residential Load Profiles from Smart Meter Data for an Optimal Power Matching at an Aggregate Level." In 2018 Power Systems Computation Conference (PSCC). IEEE, 2018. http://dx.doi.org/10.23919/pscc.2018.8442470.
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Schwartz, Ryan, and John F. Gardner. "Emergent Behavior in a Population of Thermostatically Controlled Loads With Peer-to-Peer Communication." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10456.
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Abstract Thermostatically controlled loads (TCLs) are often considered as a possible resource for demand response (DR) events. However, it is well understood that coordinated control of a large population of previously un-coordinated TCLs may result in load synchronization that results in higher peaks and large uncontrolled swings in aggregate load. In this paper we use agent based modeling to simulate a number of residential air conditioning loads and allow each to communicate a limited amount of information with their nearest neighbors. As a result, we document emergent behavior of this large scale, distributed and nonlinear system. Using the techniques described here, the population of TCLs experienced up to a 30% reduction in peak demand following the DR event. This behavior is shown to be beneficial to the goals of balancing the grid and integrating increasing penetration of variable generators.
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Camporeale, Sergio Mario, Bernardo Fortunato, Marco Torresi, Flavia Turi, Antonio Marco Pantaleo, and Achille Pellerano. "Part Load Performance and Operating Strategies of a Natural Gas–Biomass Dual Fuelled Microturbine for CHP Generation." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27109.
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The focus of this paper is on the part load performance of a small scale (100 kWe) combined heat and power (CHP) plant fired by natural gas and solid biomass to serve a residential energy demand. The plant is based on a modified regenerative micro gas turbine (MGT), where compressed air exiting from recuperator is externally heated by the hot gases produced in a biomass furnace; then the air is conveyed to combustion chamber where a conventional internal combustion with natural gas takes place, reaching the maximum cycle temperature allowed by the turbine blades. The hot gas expands in the turbine and then feeds the recuperator, while the biomass combustion flue gases are used for pre-heating the combustion air that feeds the furnace. The part load efficiency is examined considering a single shaft layout of the gas turbine and variable speed regulation. In this layout, the turbine shaft is connected to a high speed electric generator and a frequency converter is used to adjust the frequency of the produced electric power. The results show that the variable rotational speed operation allows high the part load efficiency, mainly due to maximum cycle temperature that can be kept about constant. Different biomass/natural gas energy input ratios are also modelled, in order to assess the trade-offs between: (i) lower energy conversion efficiency and higher investment cost when increasing the biomass input rate; (ii) higher primary energy savings and revenues from feed-in tariff available for biomass electricity fed into the grid. The strategies of baseload (BL), heat driven (HD) and electricity driven (ED) plant operation are compared, for an aggregate of residential end-users in cold, average and mild climate conditions.
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Ribarov, Lubomir A., and David S. Liscinsky. "Microgrid Viability for Small-Scale Cooling, Heating, and Power." In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50045.
Full textAbstract:
Cooling, Heating, and Power (CHP) energy systems provide higher fuel efficiency than conventional systems, resulting in reduced emissions and other environmental benefits. Until recently the focus of CHP system development has been primarily on medium-scale commercial applications in a limited number of market segments where clear value propositions lead to short term payback. Small-scale integrated CHP systems that show promise of achieving economic viability through significant improvements in fuel utilization have received increased attention lately. In this paper the economic potential is quantified for small-scale (micro-grid) integrated CHP systems suitable for groups of buildings with aggregate electric loads in the 15 kW–120 kW range. Technologies are evaluated for community building groups (CBGs) consisting of aggregation of pure residential entities and combined residential and light commercial entities. Emphasis is on determination of the minimum load size (i.e. the smallest electric and thermal load for a given CBG that is supplied with electric, heating, cooling power from a CHP) for which a micro-grid CHP system is both technically and economically viable. In this paper, the operation of the CHP system is parallel with the public utility grid at all times, i.e. the grid is interconnected. Evaluations of CHP technology options using simulation studies in a “three-dimensional” space (CHP technology option, CBG load aggregation, and geographical location in the USA) were evaluated based on comparisons of net present value (NPV). The simulations indicated that as electric load increases, the viability of the CHP system (independent of the system’s size) becomes more favorable. Exceeding a system runtime (utilization) of 70% was shown to pass the break-even line in the NPV analysis. Finally, geographic location was found to have a relatively weak effect on the reported trends. These results suggest that micro grid CHP systems have the potential to be economically viable with relative independence of geographic location if adequately sized to match the load requirements.