Abstract
With the reformation of electric power market and the development of smart grid technology, smart residential community, a new residential demand side entity, tends to play an important role in demand response program. This paper presents a day-ahead demand response scheduling model for the novel residential community considering the impact of various residential load uncertainties. In this paper, each residential load with uncertainties is firstly modeled using Copula, and the random scenarios are generated by Monte Carlo simulation. Secondly, the residential loads are classified into different categories according to various demand response strategies, and an optimal scheduling scheme for residential loads and distributed generation is modeled. Finally, the generated scenarios are integrated into the scheduling model to form the complete optimal residential demand stochastic scheduling scheme. The presented model can reduce the cost of user’s electricity consumption and decrease the peak load, load peak-valley difference, and the energy consumption of residential load profile without bringing discomfort to the users, through which residential community can participate in demand response efficiently. Besides, this model can also provide support for the decision of electricity pricing strategies under power market development.
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Rieger A, Thummert R, Fridgen G, Kahlen M, Ketter W (2016) Estimating the benefits of cooperation in a residential microgrid: a data-driven approach. Appl Energy 180:130–141
Liu Y, Yuen C, Ul Hassan N, Huang S, Yu R, Xie S (2015) Electricity cost minimization for a microgrid with distributed energy resource under different information availability. IEEE Trans Ind Electron 62(4):2571–2583
Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renew Sustain Energy Rev 59:166–178
Khodaei A, Shahidehpou M, Choi J (2013) Optimal hourly scheduling of community-aggregated electricity consumption. J Electr Eng Technol 8(6):1251–1260
Qu Z, Qu N, Liu Y, Yin X, Qu C, Wang W, Han J (2018) Multi-objective optimization model of electricity behavior considering the combination of household appliance correlation and comfort. J Electr Eng Technol 13(5):1821–1830
Mohsenian-Rad AH, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1(2):120–133
Sortomme E, El-Sharkawi MA (2011) Optimal charging strategies for unidirectional vehicle-to-grid. IEEE Trans Smart Grid 2(1):119–126
Ma K, Yao T, Yang J, Guan X (2016) Residential power scheduling for demand response in smart grid. Int J Electr Power Energy Syst 78:320–325
Chen C, Wang J, Kishore S (2014) A distributed direct load control approach for large scale residential demand response. IEEE Trans Power Syst 29(5):2219–2228
Rastegar M, Fotuhi-Firuzabad M (2015) Load management in a residential energy hub with renewable distributed energy resources. Energy Build 107:234–242
Brahman F, Honarmand M, Jadid S (2015) Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system. Energy Build 90:65–75
Jia L, Tong L (2012) Optimal pricing for residential demand response: a stochastic optimization approach. In: 2012 50th annual Allerton conference on communication, control, and computing, pp 1879–1884
Aghajani GR, Shayanfar HA, Shayeghi H (2017) Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy 126:622–637
Zakariazadeh A, Jadid S, Siano P (2014) Smart microgrid energy and reserve scheduling with demand response using stochastic optimization. Int J Electr Power Energy Syst 63:523–533
Tasdighi M, Ghasemi H, Rahimi-Kian A (2013) Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling. IEEE Trans Smart Grid 1:1–9
Ikeda S, Ooka R (2016) A new optimization strategy for the operating schedule of energy systems under uncertainty of renewable energy sources and demand changes. Energy Build 125:75–85
Ottesen SO, Tomasgard A (2015) A stochastic model for scheduling energy flexibility in buildings. Energy 88:364–376
Liu Y, Guo B, Zhang T, Wang R, Zhang Y (2016) Model predictive control-based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies. IET Gener Transm Distrib 10(10):2367–2378
Zhang Y, Zhang T, Wang R, Liu Y, Guo B (2015) Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts. Sol Energy 122:1052–1065
Li H, Zang C, Zeng P, Yu H, Li Z, Ni F (2016) Optimal home energy management integrating random PV and appliances based on stochastic programming. In: Proceedings of the 28th Chinese control and decision conference, CCDC 2016, pp 429–434
Good N, Karangelos E, Navarro-Espinosa A, Mancarella P (2015) Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users’ discomfort. IEEE Trans Smart Grid 6(5):2333–2342
Wang J, Li Y, Zhou Y (2016) Interval number optimization for household load scheduling with uncertainty. Energy Build 130:613–624
Shin JS, Bae IS, Kim JO (2018) Impact of user convenience on appliance scheduling of a home energy management system. J Electr Eng Technol 13(1):68–77
Wang C, Zhou Y, Wu J, Wang J, Zhang Y, Wang D (2015) Robust-index method for household load scheduling considering uncertainties of customer behavior. IEEE Trans Smart Grid 6(4):1806–1818
Bina MT, Ahmadi D (2015) Stochastic modeling for the next day domestic demand response applications. IEEE Trans Power Syst 30(6):2880–2893
Nelsan RB (2006) An introduction to copulas, 2nd edn. Springer, New York
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London
Sahebi MM, Duki EA, Kia M, Soroudi A, Ehsan M (2012) Simultanous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts. Gener Transm Distrib IET 6(7):605–611
Paterakis NG, Erdin O, Bakirtzis AG, Catalo JPS (2015) Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies. IEEE Trans Ind Inform 11(6):1509–1519
Mathieu JL (2013) Modeling, analysis, and control of demand response resources. Ph.D. dissertation, Lawrence Berkeley National Laboratory, University of California, Berkeley
Sabziparvar A, Tabari H (2010) The estimated average daily soil temperature at a few examples of climate using weather data. J Soil Water Sci Isfehan Univ Technol 14(52):125–138
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This work is supported by National Key Research and Development Program of China (2016YFB0901100) and National Nature Science Foundation of China (51577061).
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Nan, S., Zhou, M., Li, G. et al. Optimal Scheduling Approach on Smart Residential Community Considering Residential Load Uncertainties. J. Electr. Eng. Technol. 14, 613–625 (2019). https://doi.org/10.1007/s42835-019-00094-0
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DOI: https://doi.org/10.1007/s42835-019-00094-0