Abstract
Nowadays, the world is facing energy crisis and environmental issues. This is why the energy demand is increasing in different energy sections. The buildings as a large energy consumer are critical to face with these issues. To overcome these challenges, the conventional active buildings are moving toward the active building. Demand flexibility and self-generation are two characteristics of the active building. However, the main feature of such emerging buildings is related to their flexibility demand. Demand flexibility in active buildings is enabled by demand response programs. The change in energy consumption pattern by residents of buildings is the aim of implementing such programs. Demand response programs are designed and managed by aggregators in retail markets. In addition, the enabling technologies for implementing such programs are provided by aggregators. Therefore, the aggregators are important for develo** acting buildings. Accordingly, in this chapter, the role of aggregators in demand flexibility of active buildings is outlined. Firstly, the concept of aggregators and retail electricity markets are presented. Then, the benefits, barriers, motivators, and challenges of demand response programs are discussed. Moreover, the existing demand response programs and enabling technologies for implementing such programs are described in this chapter.
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References
Ackermann, T., Andersson, G., & Söder, L. (2001). Distributed generation: A definition. Electric Power Systems Research, 57(3), 195–204. https://doi.org/10.1016/S0378-7796(01)00101-8
Aghaei, J., & Alizadeh, M. I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, 64–72. https://doi.org/10.1016/j.rser.2012.09.019
Albadi, M. H., & El-Saadany, E. F. (2007). Demand response in electricity markets: An overview. In 2007 IEEE power engineering society general meeting, PES, pp. 1–5. https://doi.org/10.1109/PES.2007.385728
Albadi, M. H., & El-Saadany, E. F. (2008). A summary of demand response in electricity markets. Electric Power Systems Research, 78(11), 1989–1996. https://doi.org/10.1016/j.epsr.2008.04.002
Algarni, A. A. S., & Bhattacharya, K. (2009). A generic operations framework for discos in retail electricity markets. IEEE Transactions on Power Systems, 24(1), 356–367. https://doi.org/10.1109/TPWRS.2008.2007001
Arens, E. A., Auslander, D., Culler, D., & Al, E. (2006). Demand response enabling technology development. UC Berkeley Controls and Information Technology. https://doi.org/10.11436/mssj.15.250
“Assessment of Demand Response and Advanced Metering”. (2006). Staff report Federal Energy Regulatory Commission (FERC).
Babar, M., Nguyen, P. H., Cuk, V., & Kamphuis, I. G. (2015). The development of demand elasticity model for demand response in the retail market environment. In 2015 IEEE Eindhoven PowerTech, PowerTech 2015. https://doi.org/10.1109/PTC.2015.7232789
Bae, M., Kim, H., Kim, E., Chung, A. Y., Kim, H., & Roh, J. H. (2014). Toward electricity retail competition: Survey and case study on technical infrastructure for advanced electricity market system. Applied Energy, 133, 252–273. https://doi.org/10.1016/j.apenergy.2014.07.044
Balijepalli, V. S. K. M., Pradhan, V., Khaparde, S. A., & Shereef, R. M. (2011). Review of demand response under smart grid paradigm. In 2011 IEEE PES International conference on innovative smart grid technologies-India, ISGT India 2011, pp. 236–243. https://doi.org/10.1109/ISET-India.2011.6145388
Barreto, C., Mojica-Nava, E., & Quijano, N. (2013). Design of mechanisms for demand response programs. In Proceedings of the IEEE conference on decision and control, pp. 1828–1833. https://doi.org/10.1109/CDC.2013.6760148
Bayat, M., Sheshyekani, K., Hamzeh, M., & Rezazadeh, A. (2016). Coordination of distributed energy resources and demand response for voltage and frequency support of MV microgrids. IEEE Transactions on Power Systems, 31(2), 1506–1516. https://doi.org/10.1109/TPWRS.2015.2434938
Bayer, B. (2015). Current practice and thinking with integrating demand response for power system flexibility in the electricity markets in the USA and Germany. Current Sustainable/Renewable Energy Reports, 2(2), 55–62. https://doi.org/10.1007/s40518-015-0028-7
Boogert, A., & Dupont, D. (2008). When supply meets demand: The case of hourly spot electricity prices. IEEE Transactions on Power Systems, 23(2), 389–398. https://doi.org/10.1109/TPWRS.2008.920731
Bradley, P., Leach, M., & Torriti, J. (2013). A review of the costs and benefits of demand response for electricity in the UK. Energy Policy, 52, 312–327. https://doi.org/10.1016/j.enpol.2012.09.039
Bulut, M. B., Odlare, M., Stigson, P., Wallin, F., & Vassileva, I. (2016). Active buildings in smart grids – Exploring the views of the Swedish energy and buildings sectors. Energy and Buildings, 117, 185–198. https://doi.org/10.1016/j.enbuild.2016.02.017
Burger, S., & Chaves-ávila, J. P. (2016). The value of aggregators in electricity systems. In C. Batlle, & I. J. Pérez-Arriaga (Eds,), MIT Center for Energy and Environmental Policy Research, 2016, vol. CEEPR WP 2, no. January.
Burger, S., Chaves-Ávila, J. P., Batlle, C., & Pérez-Arriaga, I. J. (2017). A review of the value of aggregators in electricity systems. Renewable and Sustainable Energy Reviews, 77(February), 395–405. https://doi.org/10.1016/j.rser.2017.04.014
Cao, J., Yang, B., Chen, C., & Guan, X. (2012). Optimal demand response using mechanism design in the smart grid. In Chinese Control Conference, CCC, pp. 2520–2525.
Cappers, P., MacDonald, J., Goldman, C., & Ma, O. (2013). An assessment of market and policy barriers for demand response providing ancillary services in U.S. electricity markets. Energy Policy, 62, 1031–1039. https://doi.org/10.1016/j.enpol.2013.08.003
Caves, D., Eakin, K., & Faruqui, A. (2000). Mitigating price spikes in wholesale markets through market-based pricing in retail markets. Electricity Journal, 13(3), 13–23. https://doi.org/10.1016/S1040-6190(00)00092-0
Chakravorty, D., Chaudhuri, B., & Hui, S. Y. R. (2017). Rapid frequency response from smart loads in Great Britain power system. IEEE Transactions on Smart Grid, 8(5), 2160–2169. https://doi.org/10.1109/TSG.2016.2517409
Chen, T., Pourbabak, H., Liang, Z., & Su, W. (2017). An integrated eVoucher mechanism for flexible loads in real-time retail electricity market. IEEE Access, 5(c), 2101–2110. https://doi.org/10.1109/ACCESS.2017.2659704
Chen, T., Alsafasfeh, Q., Pourbabak, H., & Su, W. (2018a). The next-generation U.S. retail electricity market with customers and prosumers-A bibliographical survey. Energies, 11(1). https://doi.org/10.3390/en11010008
Chen, Y., Xu, P., Gu, J., Schmidt, F., & Li, W. (2018b). Measures to improve energy demand flexibility in buildings for demand response (DR): A review. Energy and Buildings, 177, 125–139. https://doi.org/10.1016/j.enbuild.2018.08.003
Chowdhury, S., Chowdhury, S. P., & Crossley, P. (2009). Microgrids and active distribution networks.
Conchado, A., & Linares, P. (2012). The economic impact of demand-response programs on power systems. A survey of the state of the art. Handbook of Networks in Power Systems, 1, 281–301. https://doi.org/10.1007/978-3-642-23193-3_11
Conchado, A., Linares, P., Lago, O., & Santamaría, A. (2016). An estimation of the economic and environmental benefits of a demand-response electricity program for Spain. Sustainable Production and Consumption, 8(March), 108–119. https://doi.org/10.1016/j.spc.2016.09.004
Council of European Energy Regulators. (2011). Advice on the take-off of a demand response electricity market with smart meters.
Deng, R., Yang, Z., Chow, M. Y., & Chen, J. (2015). A survey on demand response in smart grids: Mathematical models and approaches. IEEE Transactions on Industrial Informatics, 11(3), 570–582. https://doi.org/10.1109/TII.2015.2414719
Ding, Y. M., Hong, S. H., & Li, X. H. (2014). A demand response energy management scheme for industrial facilities in smart grid. IEEE Transactions on Industrial Informatics, 10(4), 2257–2269. https://doi.org/10.1109/TII.2014.2330995
Downward, A., Young, D., & Zakeri, G. (2016). Electricity retail contracting under risk-aversion. European Journal of Operational Research, 251(3), 846–859. https://doi.org/10.1016/j.ejor.2015.11.040
Early detection of turn-to-turn faults in power transformer winding: An experimental study (2019). In 2019 international aegean conference on electrical machines and power electronics (ACEMP) & 2019 international conference on optimization of electrical and electronic equipment (OPTIM). https://doi.org/10.1109/acemp-optim44294.2019.9007169
Eid, C., Koliou, E., Valles, M., Reneses, J., & Hakvoort, R. (2016). Time-based pricing and electricity demand response: Existing barriers and next steps. Utilities Policy, 40, 15–25. https://doi.org/10.1016/j.jup.2016.04.001
Eissa, M. M. (2011). Demand side management program evaluation based on industrial and commercial field data. Energy Policy, 39(10), 5961–5969. https://doi.org/10.1016/j.enpol.2011.06.057
El-Khattam, W., & Salama, M. M. A. (2004). Distributed generation technologies, definitions and benefits. Electric Power Systems Research, 71(2), 119–128. https://doi.org/10.1016/j.epsr.2004.01.006
Eric, C., Fredrich, K., & Andy, T. (2012). Maximizing the value of demand response. The Electricity Journal, 25(7), 6–16. https://doi.org/10.1016/j.tej.2012.08.004
Flores, M., & Waddams Price, C. (2013). Consumer behaviour in the British retail electricity market. Centre for Competition Policy. no. 10.
Fotouhi Ghazvini, M. A., Faria, P., Ramos, S., Morais, H., & Vale, Z. (2015). Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market. Energy, 82, 786–799. https://doi.org/10.1016/j.energy.2015.01.090
Gao, D. C., & Sun, Y. (2016). A GA-based coordinated demand response control for building group level peak demand limiting with benefits to grid power balance. Energy and Buildings, 110, 31–40. https://doi.org/10.1016/j.enbuild.2015.10.039
Ghatikar, G. (2014). Demand response opportunities and enabling technologies for data centers: Findings from field studies. Lawrence Berkeley National Laboratory.
Ghazvini, M. A. F., Ramos, S., Soares, J., Vale, Z., & Castro, R. (2016). Toward retail competition in the Portuguese electricity market. In International conference on the European energy market, EEM, vol. 2016-July. https://doi.org/10.1109/EEM.2016.7521209
Gilbraith, N., & Powers, S. E. (2013). Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City. Energy Policy, 59, 459–469. https://doi.org/10.1016/j.enpol.2013.03.056
Gkatzikis, L., Koutsopoulos, I., & Salonidis, T. (2013). The role of aggregators in smart grid demand response markets. IEEE Journal on Selected Areas in Communications, 31(7), 1247–1257. https://doi.org/10.1109/JSAC.2013.130708
Goel, L., Qiuwei, W., & Peng, W. (2006). Reliability enhancement of a deregulated power system considering demand response. In 2006 IEEE power engineering society general meeting, PES, pp. 1–6. https://doi.org/10.1109/pes.2006.1708965
Golmohamadi, H., Keypour, R., Hassanpour, A., & Davoudi, M. (2015). Optimization of green energy portfolio in retail market using stochastic programming. In 2015 North American power symposium, NAPS 2015. https://doi.org/10.1109/NAPS.2015.7335233
Golmohamadi, H., Keypour, R., Bak-Jensen, B., & Pillai, J. R. (2019). A multi-agent based optimization of residential and industrial demand response aggregators. International Journal of Electrical Power and Energy Systems, 107(December 2018), 472–485. https://doi.org/10.1016/j.ijepes.2018.12.020
Good, N., Ellis, K. A., & Mancarella, P. (2017). Review and classification of barriers and enablers of demand response in the smart grid. Renewable and Sustainable Energy Reviews, 72(January 2016), 57–72. https://doi.org/10.1016/j.rser.2017.01.043
Guille, C., & Gross, G. (2009). A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy, 37(11), 4379–4390. https://doi.org/10.1016/j.enpol.2009.05.053
Guo, P., Li, V. O. K., & Lam, J. C. K. (2017). Smart demand response in China: Challenges and drivers. Energy Policy, 107(December 2015), 1–10. https://doi.org/10.1016/j.enpol.2017.04.019
Gyamfi, S., & Krumdieck, S. (2011). Price, environment and security: Exploring multi-modal motivation in voluntary residential peak demand response. Energy Policy, 39(5), 2993–3004. https://doi.org/10.1016/j.enpol.2011.03.012
Haider, H. T., See, O. H., & Elmenreich, W. (2016). A review of residential demand response of smart grid. Renewable and Sustainable Energy Reviews, 59, 166–178. https://doi.org/10.1016/j.rser.2016.01.016
He, J., Cai, L., Cheng, P., & Fan, J. (2015). Optimal investment for retail company in electricity market. IEEE Transactions on Industrial Informatics, 11(5), 1210–1219. https://doi.org/10.1109/TII.2015.2475215
Ikäheimo, J., Evens, C., & Kärkkäinen, S. (2010). DER Aggregator business: The Finnish case. Technical Research Centre of Finland (VTT).
Ilieva, I., Bremdal, B., Ottesen, S. Ø., Rajasekharan, J., & Olivella-Rosell, P. (2016). Design characteristics of a smart grid dominated local market. CIRED Workshop – Helsinki, 2016.
Imani, M. H., Ghadi, M. J., Ghavidel, S., & Li, L. (2018). Demand response modeling in microgrid operation: A review and application for incentive-based and time-based programs. Renewable and Sustainable Energy Reviews, 94(June), 486–499. https://doi.org/10.1016/j.rser.2018.06.017
Ivanov, C., Getachew, L., Fenrick, S. A., & Vittetoe, B. (2013). Enabling technologies and energy savings: The case of EnergyWise smart meter pilot of Connexus energy. Utilities Policy, 26, 76–84. https://doi.org/10.1016/j.jup.2012.10.001
Javadi, M., Marzband, M., Akorede, M. F., Godina, R., Al-Sumaiti, A. S., & Pouresmaeil, E. (2018). A centralized smart decision-making hierarchical interactive architecture for multiple home microgrids in retail electricity market. Energies, 11(11), 1–22. https://doi.org/10.3390/en11113144
Joung, M., & Kim, J. (2013). Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability. Applied Energy, 101, 441–448. https://doi.org/10.1016/j.apenergy.2012.05.009
Kharrati, S., Kazemi, M., & Ehsan, M. (2016). Equilibria in the competitive retail electricity market considering uncertainty and risk management. Energy, 106, 315–328. https://doi.org/10.1016/j.energy.2016.03.069
Khezri, R., Oshnoei, A., Hagh, M. T., & Muyeen, S. M. (2018). Coordination of heat pumps, electric vehicles and AGC for efficient LFC in a smart hybrid power system via SCA-based optimized FOPID controllers. Energies, 11(2). https://doi.org/10.3390/en11020420
Kim, J. H., & Shcherbakova, A. (2011). Common failures of demand response. Energy, 36(2), 873–880. https://doi.org/10.1016/j.energy.2010.12.027
Kim, H., & Thottan, M. (2011). A two-stage market model for microgrid power transactions via aggregators. Bell Labs Technical Journal, 16(3), 101–107. https://doi.org/10.1002/bltj.20524
Kim, S., Mowakeaa, R., Kim, S. J., & Lim, H. (2020). Building energy management for demand response using kernel lifelong learning. IEEE Access, 8, 82131–82141. https://doi.org/10.1109/ACCESS.2020.2991110
Kuleshov, D., Viljainen, S., Annala, S., & Gore, O. (2012). Russian electricity sector reform: Challenges to retail competition. Utilities Policy, 23, 40–49. https://doi.org/10.1016/j.jup.2012.05.001
Leerbeck, K., Bacher, P., Junker, R. G., Tveit, A., Corradi, O., & Madsen, H. (2020). Control of heat pumps with CO2 emission intensity forecasts. Energies, 13(11), 1–19. https://doi.org/10.3390/en13112851
Lehto, E. (2011). Electricity prices in the Finnish retail market. Energy Policy, 39(4), 2179–2192. https://doi.org/10.1016/j.enpol.2011.02.007
Liang, Z., Bian, D., Zhang, X., Shi, D., Diao, R., & Wang, Z. (2019). Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market. Applied Energy, 236, 916–926. https://doi.org/10.1016/j.apenergy.2018.12.048
Mansour-Saatloo, A., Moradzadeh, A., Mohammadi-Ivatloo, B., Ahmadian, A., & Elkamel, A. (2020). Machine learning based PEVs load extraction and analysis. Electronics (Switzerland), 9(7), 1–15. https://doi.org/10.3390/electronics9071150
Martinez, V. J., & Rudnick, H. (2012). Design of demand response programs in emerging countries. In 2012 IEEE international conference on power system technology, POWERCON 2012, pp. 1–6. https://doi.org/10.1109/PowerCon.2012.6401387
Marzband, M., Javadi, M., Domínguez-García, J. L., & Moghaddam, M. M. (2016). Non-cooperative game theory based energy management systems for energy district in the retail market considering DER uncertainties. IET Generation, Transmission and Distribution, 10(12), 2999–3009. https://doi.org/10.1049/iet-gtd.2016.0024
Marzband, M., Javadi, M., Pourmousavi, S. A., & Lightbody, G. (2018). An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory. Electric Power Systems Research, 157, 187–199. https://doi.org/10.1016/j.epsr.2017.12.024
Mathieu, J. L., Haring, T., Ledyard, J. O., & Andersson, G. (2013). Residential Demand Response program design: Engineering and economic perspectives. In International conference on the European energy market, EEM. https://doi.org/10.1109/EEM.2013.6607296
Mehrtash, M., Capitanescu, F., Heiselberg, P. K., Gibon, T., & Bertrand, A. (2020). An enhanced optimal PV and battery sizing model for zero energy buildings considering environmental impacts. IEEE Transactions on Industry Applications, 56(6), 6846–6856. https://doi.org/10.1109/TIA.2020.3022742
Mhanna, S., Verbič, G., & Chapman, A. C. (2014). Towards a realistic implementation of mechanism design in demand response aggregation. In Proceedings – 2014 power systems computation conference, PSCC 2014. https://doi.org/10.1109/PSCC.2014.7038379
Mirzaei, M. A., Yazdankhah, A. S., & Mohammadi-Ivatloo, B. (2018). Integration of demand response and hydrogen storage system in security constrained unit commitment with high penetration of wind energy. In 26th Iranian conference on electrical engineering, ICEE 2018, pp. 1203–1208. https://doi.org/10.1109/ICEE.2018.8472631
Mirzaei, M. A., Sadeghi Yazdankhah, A., & Mohammadi-Ivatloo, B. (2019). Stochastic security-constrained operation of wind and hydrogen energy storage systems integrated with price-based demand response. International Journal of Hydrogen Energy, 44(27), 14217–14227. https://doi.org/10.1016/j.ijhydene.2018.12.054
Mirzaei, M. A., et al. (2020). An integrated energy hub system based on power-to-gas and compressed air energy storage technologies in presence of multiple shiftable loads. IET Generation, Transmission & Distribution. https://doi.org/10.1049/iet-gtd.2019.1163
Momber, I., Wogrin, S., & Gomez San Roman, T. (2016). Retail pricing: A bilevel program for PEV aggregator decisions using indirect load control. IEEE Transactions on Power Systems, 31(1), 464–473. https://doi.org/10.1109/TPWRS.2014.2379637
Moradzadeh, A., & Pourhossein, K. (2019a). Short circuit location in transformer winding using deep learning of its frequency responses. In Proceedings 2019 international aegean conference on electrical machines and power electronics, ACEMP 2019 and 2019 international conference on optimization of electrical and electronic equipment, OPTIM 2019, pp. 268–273. https://doi.org/10.1109/ACEMP-OPTIM44294.2019.9007176
Moradzadeh, A., & Pourhossein, K. (2019b). Application of support vector machines to locate minor short circuits in transformer windings. In 2019 54th international universities power engineering conference, UPEC 2019 – Proceedings, pp. 1–6. https://doi.org/10.1109/UPEC.2019.8893542
Moradzadeh, A., & Pourhossein, K. (2020). PCA-assisted location of small short circuit in transformer winding. In 2020 28th Iranian conference on electrical engineering, ICEE 2020, pp. 1–6. https://doi.org/10.1109/ICEE50131.2020.9260815
Moradzadeh, A., Sadeghian, O., Pourhossein, K., Mohammadi-Ivatloo, B., & Anvari-Moghaddam, A. (2020a). Improving residential load disaggregation for sustainable development of energy via principal component analysis. Sustainability (Switzerland), 12(8), 3158. https://doi.org/10.3390/SU12083158
Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., & Anvari-Moghaddam, A. (2020b). Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Applied Sciences (Switzerland), 10(11), 3829. https://doi.org/10.3390/app10113829
Moradzadeh, A., Zakeri, S., Shoaran, M., Mohammadi-Ivatloo, B., & Mohammadi, F. (2020c). Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms. Sustainability (Switzerland), 12(17), 7076. https://doi.org/10.3390/su12177076
Moradzadeh, A., Zeinal-Kheiri, S., Mohammadi-Ivatloo, B., Abapour, M., & Anvari-Moghaddam, A. (2020d). Support vector machine-assisted improvement residential load disaggregation. In 2020 28th Iranian conference on electrical engineering (ICEE), pp. 1–6. https://doi.org/10.1109/ICEE50131.2020.9260869
Moradzadeh, A., Mohammadi-Ivatloo, B., Abapour, M., Anvari-Moghaddam, A., Gholami Farkoush, S., & Rhee, S.-B. (2021a). A practical solution based on convolutional neural network for non-intrusive load monitoring. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02720-6
Moradzadeh, A., Moayyed, H., Zakeri, S., Mohammadi-Ivatloo, B., & Aguiar, A. P. (2021b). Deep learning-assisted short-term load forecasting for sustainable management of energy in microgrid. Inventions, 6(1), 15. https://doi.org/10.3390/inventions6010015
Moradzaeh, A., & Khaffafi, K. (2018). Comparison and evaluation of the performance of various types of neural networks for planning issues related to optimal management of charging and discharging electric cars in intelligent power grids. Emerging Science Journal, 1(4). https://doi.org/10.28991/ijse-01123
Naing, W. O., & Miranda, V. (2005). Multi-energy retail market simulation with intelligent agents. In 2005 IEEE Russia Power Tech, PowerTech. https://doi.org/10.1109/PTC.2005.4524755
Nguyen, D. T., Negnevitsky, M., De Groot, M., & Wang, C. (2009). Demand response in the retail market: Benefits and challenges. In AUPEC’09 – 19th Australasian universities power engineering conference: Sustainable energy technologies and systems, pp. 1–6.
Nikolic, D., Negnevitsky, M., De Groot, M., Gamble, S., Forbes, J., & Ross, M. (2016). Fast demand response as an enabling technology for high renewable energy penetration in isolated power systems. CIGRE Session, 46.
Nolan, S., & O’Malley, M. (2015). Challenges and barriers to demand response deployment and evaluation. Applied Energy, 152, 1–10. https://doi.org/10.1016/j.apenergy.2015.04.083
Oconnell, N., Pinson, P., Madsen, H., & Omalley, M. (2014). Benefits and challenges of electrical demand response: A critical review. Renewable and Sustainable Energy Reviews, 39, 686–699. https://doi.org/10.1016/j.rser.2014.07.098
Oshnoei, A. (2017). Application of IPSO and fuzzy logic methods in electrical vehicles for efficient frequency control of multi-area power systems. In 2017 Iranian conference on electrical engineering (ICEE), pp. 1349–1354.
Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Informatics, 7(3), 381–388. https://doi.org/10.1109/TII.2011.2158841
Papavasiliou, A., & Oren, S. S. (2014). Large-scale integration of deferrable demand and renewable energy sources. IEEE Transactions on Power Systems, 29(1), 489–499. https://doi.org/10.1109/TPWRS.2013.2238644
Parvania, M., Fotuhi-Firuzabad, M., & Shahidehpour, M. (2012). Demand response participation in wholesale energy markets. In IEEE power and energy society general meeting, pp. 1–4. https://doi.org/10.1109/PESGM.2012.6344591
Paterakis, N. G., Erdinç, O., & Catalão, J. P. S. (2017). An overview of demand response: Key-elements and international experience. Renewable and Sustainable Energy Reviews, 69(July 2016), 871–891. https://doi.org/10.1016/j.rser.2016.11.167
Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., & D’haeseleer, W. (2005). Distributed generation: Definition, benefits and issues. Energy Policy, 33(6), 787–798. https://doi.org/10.1016/j.enpol.2003.10.004
Ponds, K. T., Arefi, A., Sayigh, A., & Ledwich, G. (2018). Aggregator of demand response for renewable integration and customer engagement: Strengths, weaknesses, opportunities, and threats, 11(Energies, 9). https://doi.org/10.3390/en11092391
Rabiee, A., Soroudi, A., Mohammadi-Ivatloo, B., & Parniani, M. (2014). Corrective voltage control scheme considering demand response and stochastic wind power. IEEE Transactions on Power Systems, 29(6), 2965–2973. https://doi.org/10.1109/TPWRS.2014.2316018
Rafinia, A., Moshtagh, J., & Rezaei, N. (2020). Towards an enhanced power system sustainability: An MILP under-frequency load shedding scheme considering demand response resources. Sustainable Cities and Society, 102168. https://doi.org/10.1016/j.scs.2020.102168
Rahimi, F., & Ipakchi, A. (2010). Demand response as a market resource under the smart grid paradigm. IEEE Transactions on Smart Grid, 1(1), 82–88. https://doi.org/10.1109/TSG.2010.2045906
Rahman, A., Srikumar, V., & Smith, A. D. (2018). Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Applied Energy, 212, 372–385. https://doi.org/10.1016/j.apenergy.2017.12.051
Razmara, M., Bharati, G. R., Hanover, D., Shahbakhti, M., Paudyal, S., & Robinett, R. D. (2017). Building-to-grid predictive power flow control for demand response and demand flexibility programs. Applied Energy, 203, 128–141. https://doi.org/10.1016/j.apenergy.2017.06.040
Sadeghian, O., Nazari-Heris, M., Abapour, M., Taheri, S. S., & Zare, K. (2019a). Improving reliability of distribution networks using plug-in electric vehicles and demand response. Journal of Modern Power Systems and Clean Energy, 7(5), 1189–1199. https://doi.org/10.1007/s40565-019-0523-8
Sadeghian, O., Shotorbani, A. M., & Mohammadi-Ivatloo, B. (2019b). Generation maintenance scheduling in virtual power plants. IET Generation, Transmission and Distribution, 13(12), 2584–2596. https://doi.org/10.1049/iet-gtd.2018.6751
Sadeghian, O., Oshnoei, A., Nikkhah, S., & Mohammadi-Ivatloo, B. (2019c). Multi-objective optimisation of generation maintenance scheduling in restructured power systems based on global criterion method. IET Smart Grid, 2(2), 203–213. https://doi.org/10.1049/iet-stg.2018.0140
Sadeghian, O., Mohammadpour Shotorbani, A., & Mohammadi-Ivatloo, B. (2019d). Risk-based stochastic short-term maintenance scheduling of GenCos in an oligopolistic electricity market considering the long-term plan. Electric Power Systems Research, 175, 105908. https://doi.org/10.1016/j.epsr.2019.105908
Sadeghian, O., Oshnoei, A., Khezri, R., & Muyeen, S. M. (2020a). Risk-constrained stochastic optimal allocation of energy storage system in virtual power plants. Journal of Energy Storage, 31(January), 101732. https://doi.org/10.1016/j.est.2020.101732
Sadeghian, O., Oshnoei, A., Kheradmandi, M., Khezri, R., & Mohammadi-Ivatloo, B. (2020b). A robust data clustering method for probabilistic load flow in wind integrated radial distribution networks. International Journal of Electrical Power and Energy Systems, 115, 105392. https://doi.org/10.1016/j.ijepes.2019.105392
Sadeghian, O., Oshnoei, A., Kheradmandi, M., & Mohammadi-ivatloo, B. (2020c). Optimal placement of multi-period-based switched capacitor in radial distribution systems. Computers and Electrical Engineering, 1–25.
Sadeghian, O., Moradzadeh, A., Mohammadi-Ivatloo, B., Mohammadi-Ivatloo, B., Abapour, M., & Marquez, F. P. G. (2020d). Generation units maintenance in combined heat and power integrated systems using the mixed integer quadratic programming approach. Energies, 13(11), 2840. https://doi.org/10.3390/en13112840
Sadineni, S. B., & Boehm, R. F. (2012). Measurements and simulations for peak electrical load reduction in cooling dominated climate. Energy, 37(1), 689–697. https://doi.org/10.1016/j.energy.2011.10.026
Safdarian, A., Fotuhi-Firuzabad, M., & Lehtonen, M. (2014a). A distributed algorithm for managing residential demand response in smart grids. IEEE Transactions on Industrial Informatics, 10(4), 2385–2393. https://doi.org/10.1109/TII.2014.2316639
Safdarian, A., Degefa, M. Z., Lehtonen, M., & Fotuhi-Firuzabad, M. (2014b). Distribution network reliability improvements in presence of demand response. IET Generation, Transmission and Distribution, 8(12), 2027–2035. https://doi.org/10.1049/iet-gtd.2013.0815
Safdarian, A., Fotuhi-Firuzabad, M., & Lehtonen, M. (2016). Benefits of demand response on operation of distribution networks: A case study. IEEE Systems Journal, 10(1), 189–197. https://doi.org/10.1109/JSYST.2013.2297792
Santinelli, G., Beillan, V., Monteverdi, I., Jalmain, I., Decorme, R., & Tatibouet, M. (2016). ‘Good causes’ crowdfunding as a driver for behavioural change in demand response scenarios. In 4th European conference on behaviour and energy efficiency (Behave 2016).
Sekizaki, S., Nishizaki, I., & Hayashida, T. (2016). Electricity retail market model with flexible price settings and elastic price-based demand responses by consumers in distribution network. International Journal of Electrical Power and Energy Systems, 81, 371–386. https://doi.org/10.1016/j.ijepes.2016.02.029
Shafie-Khah, M., et al. (2016). Optimal behavior of electric vehicle parking lots as demand response aggregation agents. IEEE Transactions on Smart Grid, 7(6), 2654–2665. https://doi.org/10.1109/TSG.2015.2496796
Sharma, A., & Sharma, H. (2019). Demand side response: Drivers, challenges, and opportunities. In International conference on advancements in computing & management (ICACM-2019), pp. 754–759. https://doi.org/10.2139/ssrn.3462969
Shin, K. J., & Managi, S. (2017). Liberalization of a retail electricity market: Consumer satisfaction and household switching behavior in Japan. Energy Policy, 110(January), 675–685. https://doi.org/10.1016/j.enpol.2017.07.048
Siano, P. (2014). Demand response and smart grids – A survey. Renewable and Sustainable Energy Reviews, 30, 461–478. https://doi.org/10.1016/j.rser.2013.10.022
Siano, P., & Sarno, D. (2016). Assessing the benefits of residential demand response in a real time distribution energy market. Applied Energy, 161, 533–551. https://doi.org/10.1016/j.apenergy.2015.10.017
Smith, K., & Hledik, R. (2011). Drivers of Demand response adoption: Past, present, and future. Institute for Building Efficiency.
Spees, K., & Lave, L. B. (2007). Demand response and electricity market efficiency. The Electricity Journal, 20(3), 69–85. https://doi.org/10.1016/j.tej.2007.01.006
Stavrakas, V., & Flamos, A. (2020). A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector. Energy Conversion and Management, 205(August 2019), 112339. https://doi.org/10.1016/j.enconman.2019.112339
Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030
Su, X. (2015). Have customers benefited from electricity retail competition? Journal of Regulatory Economics, 47(2), 146–182. https://doi.org/10.1007/s11149-014-9263-x
Su, C. L., & Kirschen, D. (2009). Quantifying the effect of demand response on electricity markets. IEEE Transactions on Power Systems, 24(3), 1199–1207. https://doi.org/10.1109/TPWRS.2009.2023259
Swadley, A., & Yücel, M. (2011). Did residential electricity rates fall after retail competition? A dynamic panel analysis. Energy Policy, 39(12), 7702–7711. https://doi.org/10.1016/j.enpol.2011.09.014
Teimourzadeh, H., Moradzadeh, A., Shoaran, M., Mohammadi-Ivatloo, B., & Razzaghi, R. (2021). High impedance single-phase faults diagnosis in transmission lines via deep reinforcement learning of transfer functions. IEEE Access, 9, 15796–15809. https://doi.org/10.1109/ACCESS.2021.3051411
Torstensson, D., & Wallin, F. (2015). Potential and barriers for demand response at household customers. Energy Procedia, 75, 1189–1196. https://doi.org/10.1016/j.egypro.2015.07.570
U. department of Energy. (2005). Benefits of demand response in electricity markets and recommendations for achieving them. In A report to The United States Congress. https://doi.org/10.1503/cmaj.1070122
Vahidinasab, V., Ardalan, C., Mohammadi-Ivatloo, B., Giaouris, D., & Walker, S. L. (2021). Active building as an energy system: Concept, challenges, and outlook. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2021.3073087
Vallés, M., Reneses, J., Cossent, R., & Frías, P. (2016). Regulatory and market barriers to the realization of demand response in electricity distribution networks: A European perspective. Electric Power Systems Research, 140, 689–698. https://doi.org/10.1016/j.epsr.2016.04.026
Van Dievel, P., De Vos, K., & Belmans, R. (2014). Demand response in electricity distribution grids: Regulatory framework and barriers. In International conference on the European energy market, EEM. https://doi.org/10.1109/EEM.2014.6861286
Vand, B., Martin, K., Jokisalo, J., Kosonen, R., & Hast, A. (2020). Demand response potential of district heating and ventilation in an educational office building. Science and Technology for the Built Environment, 26(3), 304–319. https://doi.org/10.1080/23744731.2019.1693207
Verbong, G. P. J., Beemsterboer, S., & Sengers, F. (2013). Smart grids or smart users? Involving users in develo** a low carbon electricity economy. Energy Policy, 52, 117–125. https://doi.org/10.1016/j.enpol.2012.05.003
Wang, Y., Pordanjani, I. R., & Xu, W. (2011). An event-driven demand response scheme for power system security enhancement. IEEE Transactions on Smart Grid, 2(1), 23–29. https://doi.org/10.1109/TSG.2011.2105287
Warren, P. (2014). A review of demand-side management policy in the UK. Renewable and Sustainable Energy Reviews, 29, 941–951. https://doi.org/10.1016/j.rser.2013.09.009
Yamaguchi, Y., et al. (2020). An integrated approach of estimating demand response flexibility of domestic laundry appliances based on household heterogeneity and activities. Energy Policy, 142(March), 111467. https://doi.org/10.1016/j.enpol.2020.111467
Yan, X., Ozturk, Y., Hu, Z., & Song, Y. (2018). A review on price-driven residential demand response. Renewable and Sustainable Energy Reviews, 96(August), 411–419. https://doi.org/10.1016/j.rser.2018.08.003
Yang, Y. (2014). Understanding household switching behavior in the retail electricity market. Energy Policy, 69, 406–414. https://doi.org/10.1016/j.enpol.2014.03.009
Yao, M., Hu, Z., Zhang, N., Duan, W., & Zhang, J. (2015). Low-carbon benefits analysis of energy-intensive industrial demand response resources for ancillary services. Journal of Modern Power Systems and Clean Energy, 3(1), 131–138. https://doi.org/10.1007/s40565-015-0102-6
Yoon, A. Y., Kang, H. K., & Moon, S. (2020). Optimal price based demand response of HVAC systems in commercial buildings considering peak load reduction. Energies, 13(4). https://doi.org/10.3390/en13040862
Yu, J., Zhou, J. Z., Yang, J. J., Wu, W., Fu, B., & Liao, R. T. (2004). Agent-based retail electricity market: Modeling and analysis. In Proceedings of 2004 international conference on machine learning and cybernetics, Vol. 1, no. August, pp. 95–100. https://doi.org/10.1109/icmlc.2004.1380618
Zakariazadeh, A., Homaee, O., Jadid, S., & Siano, P. (2014). A new approach for real time voltage control using demand response in an automated distribution system. Applied Energy, 117, 157–166. https://doi.org/10.1016/j.apenergy.2013.12.004
Zhang, Q., & Li, J. (2012). Demand response in electricity markets: A review. In 9th international conference on the european energy market, EEM 12. https://doi.org/10.1109/EEM.2012.6254817
Zhang, X., Hug, G., Kolter, Z., & Harjunkoski, I. (2015). Industrial demand response by steel plants with spinning reserve provision. In 2015 North American power symposium, NAPS 2015. https://doi.org/10.1109/NAPS.2015.7335115
Zipperer, A., et al. (2013). Electric energy management in the smart home: Perspectives on enabling technologies and consumer behavior. Proceedings of the IEEE, 101(11), 2397–2408. https://doi.org/10.1109/JPROC.2013.2270172
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Sadeghian, O., Moradzadeh, A., Mohammadi-Ivatloo, B., Vahidinasab, V. (2022). Active Buildings Demand Response: Provision and Aggregation. In: Vahidinasab, V., Mohammadi-Ivatloo, B. (eds) Active Building Energy Systems. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-79742-3_14
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