Literatura académica sobre el tema "Aggregate residential load"
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Artículos de revistas sobre el tema "Aggregate residential load"
Zhou, Xiao, Jing Shi, Yuejin Tang, Yuanyuan Li, Shujian Li y Kang Gong. "Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response". Energies 12, n.º 4 (20 de febrero de 2019): 683. http://dx.doi.org/10.3390/en12040683.
Texto completoNashrullah, Erwin y 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.
Texto completoDjokic, Sasa Z. y Igor Papic. "Smart Grid Implementation of Demand Side Management and Micro-Generation". International Journal of Energy Optimization and Engineering 1, n.º 2 (abril de 2012): 1–19. http://dx.doi.org/10.4018/ijeoe.2012040101.
Texto completoAfzaal, Muhammad Umar, Intisar Ali Sajjad, Muhammad Faisal Nadeem Khan, Shaikh Saaqib Haroon, Salman Amin, Rui Bo y 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, n.º 3 (7 de octubre de 2020): 4491–503. http://dx.doi.org/10.3233/jifs-200462.
Texto completoLindberg, K. B., S. J. Bakker y I. Sartori. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts". Utilities Policy 58 (junio de 2019): 63–88. http://dx.doi.org/10.1016/j.jup.2019.03.004.
Texto completoRoth, Jonathan, Jayashree Chadalawada, Rishee K. Jain y Clayton Miller. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification". Energies 14, n.º 5 (8 de marzo de 2021): 1481. http://dx.doi.org/10.3390/en14051481.
Texto completoAhajjam, Mohamed Aymane, Daniel Bonilla Licea, Mounir Ghogho y Abdellatif Kobbane. "IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings". Sensors 20, n.º 4 (14 de febrero de 2020): 1048. http://dx.doi.org/10.3390/s20041048.
Texto completoYousefi, Ali, Waiching Tang, Mehrnoush Khavarian, Cheng Fang y Shanyong Wang. "Thermal and Mechanical Properties of Cement Mortar Composite Containing Recycled Expanded Glass Aggregate and Nano Titanium Dioxide". Applied Sciences 10, n.º 7 (26 de marzo de 2020): 2246. http://dx.doi.org/10.3390/app10072246.
Texto completoOlama, Mohammed, Teja Kuruganti, James Nutaro y Jin Dong. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid". Energies 11, n.º 7 (16 de julio de 2018): 1852. http://dx.doi.org/10.3390/en11071852.
Texto completoKapustin, Fedor y Vladimir A. Belyakov. "Application of Modified Peat Aggregate for Lightweight Concrete". Solid State Phenomena 309 (agosto de 2020): 120–25. http://dx.doi.org/10.4028/www.scientific.net/ssp.309.120.
Texto completoTesis sobre el tema "Aggregate residential load"
SAJJAD, MALIK INTISAR ALI. "Characterisation and Flexibility Assessment of Aggregate Electrical Demand". Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2594365.
Texto completoHasan, Mehedi. "Aggregator-Assisted Residential Participation in Demand Response Program". Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/32546.
Texto completoMaster of Science
Adhikari, Rajendra. "Algorithms and Simulation Framework for Residential Demand Response". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87585.
Texto completoPHD
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.
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.
Texto completoWorldwide 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
Actas de conferencias sobre el tema "Aggregate residential load"
Sajjad, Intisar A., Gianfranco Chicco y Roberto Napoli. "Demand flexibility time intervals for aggregate residential load patterns". En 2015 IEEE Eindhoven PowerTech. IEEE, 2015. http://dx.doi.org/10.1109/ptc.2015.7232760.
Texto completoLedva, Gregory S., Sarah Peterson y Johanna L. Mathieu. "Benchmarking of Aggregate Residential Load Models Used for Demand Response". En 2018 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2018. http://dx.doi.org/10.1109/pesgm.2018.8585847.
Texto completoGuo, Z., J. Meyer, N. Al-Shibli, X. Xiao, S. Djokic, A. Collin, R. Langella, A. Testa, I. Papic y A. Blanco. "Aggregate Harmonic Load Models of Residential Customers. Part 1: Time-Domain Models". En 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 2019. http://dx.doi.org/10.1109/isgteurope.2019.8905621.
Texto completoGuo, Z., J. Meyer, N. Al-Shibli, X. Xiao, S. Djokic, A. Collin, R. Langella, A. Testa, I. Papic y A. Blanco. "Aggregate Harmonic Load Models of Residential Customers. Part 2: Frequency-Domain Models". En 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 2019. http://dx.doi.org/10.1109/isgteurope.2019.8905746.
Texto completoCollin, A. J., I. Hernando-Gil, J. L. Acosta y S. Z. Djokic. "An 11 kV steady state residential aggregate load model. Part 1: Aggregation methodology". En 2011 IEEE PES PowerTech - Trondheim. IEEE, 2011. http://dx.doi.org/10.1109/ptc.2011.6019381.
Texto completoCollin, A. J., J. L. Acosta, I. Hernando-Gil y S. Z. Djokic. "An 11 kV steady state residential aggregate load model. Part 2: Microgeneration and demand-side management". En 2011 IEEE PES PowerTech - Trondheim. IEEE, 2011. http://dx.doi.org/10.1109/ptc.2011.6019384.
Texto completoZufferey, Thierry, Damiano Toffanin, Diren Toprak, Andreas Ulbig y Gabriela Hug. "Generating Stochastic Residential Load Profiles from Smart Meter Data for an Optimal Power Matching at an Aggregate Level". En 2018 Power Systems Computation Conference (PSCC). IEEE, 2018. http://dx.doi.org/10.23919/pscc.2018.8442470.
Texto completoSchwartz, Ryan y John F. Gardner. "Emergent Behavior in a Population of Thermostatically Controlled Loads With Peer-to-Peer Communication". En ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10456.
Texto completoCamporeale, Sergio Mario, Bernardo Fortunato, Marco Torresi, Flavia Turi, Antonio Marco Pantaleo y Achille Pellerano. "Part Load Performance and Operating Strategies of a Natural Gas–Biomass Dual Fuelled Microturbine for CHP Generation". En ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27109.
Texto completoRibarov, Lubomir A. y David S. Liscinsky. "Microgrid Viability for Small-Scale Cooling, Heating, and Power". En ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50045.
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