Demand response programs in smart grids – survey

  • Abstract
  • Keywords
  • References
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  • Abstract

    The smart grid in this century has an essential role in changing the philosophy of the electrical power engineering. In the past, the generation must be equal to the demand under any situation but with the introduction of the non-conventional grids everything is changed, and the customers should consume energy in the same amount to what already generated from the generation units. The tool to achieve all that is the demand response (DR) strategy. DR can alter the consumption pattern of the consumers to make it flatting instead of the sharp curves that lead to additional costs coming from the increasing generating in the periods of the peaks in the load curve. In this paper, the demand response programs listed and discussed with an indication to the documents that deal with each type.



  • Keywords

    Smart Grid; Demand Response; Demand Side Management; Load Scheduling; Smart Metering and Bidding Strategy.

  • References

      [1] M. Brenna et al., “Challenges in energy systems for the smart-cities of the future,” in 2012 IEEE International Energy Conference and Exhibition, ENERGYCON 2012, 2012, pp. 755–762.

      [2] M. Brenna, M. C. Falvo, F. Foiadelli, L. Martirano, and D. Poli, “Sustainable Energy Microsystem (SEM): Preliminary energy analysis,” in 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012, 2012, pp. 1–6.

      [3] J. N. Bharothu, M. Sridhar, and R. S. Rao, “A Literature Survey Report On Smart Grid Technologies,” pp. 1–8, 2014.

      [4] M. C. Falvo, L. Martirano, D. Sbordone, and E. Bocci, “Technologies for Smart Grids : a brief review,” 2013.

      [5] H. Farhangi, “The path of the smart grid,” IEEE Power Energy Mag., vol. 8, no. 1, pp. 18–28, 2010.

      [6] Q. Yang, “Satellite based “Power Utility Intranet” for smart management of electric distribution networks: The AuRA-NMS case study,” in 2012 IEEE International Conference on Communications (ICC), 2012, pp. 2822–2826.

      [7] M. H. Albadi and E. F. El-Saadany, “A summary of demand response in electricity markets,” Electric Power Systems Research, vol. 78, no. 11. pp. 1989–1996, 2008.

      [8] P. Warren, “A review of demand-side management policy in the UK,” Renew. Sustain. Energy Rev., vol. 29, pp. 941–951, 2014.

      [9] S. Wang, X. Xue, and C. Yan, “Building power demand response methods toward smart grid,” HVAC R Res., vol. 20, no. 6, pp. 665–687, 2014.

      [10] N. Good, K. A. Ellis, and P. Mancarella, “Review and classi fi cation of barriers and enablers of demand response in the smart grid,” Renew. Sustain. Energy Rev., vol. 72, no. January 2016, pp. 57–72, 2017.

      [11] U. D. of Energy, “Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them - A Report to the United States Congress Pursant to Section 1252 of the Energy Policy Act of 2005,” United States Congr. Purs. To Sect. 1252 Energy Policy Act 2005, no. February, p. 122, 2006.

      [12] K. Kostková, Ľ. Omelina, P. Kyčina, and P. Jamrich, “An introduction to load management,” Electr. Power Syst. Res., vol. 95, pp. 184–191, 2013.

      [13] A. Barbato, A. Capone, L. Chen, F. Martignon, and S. Paris, “A distributed demand-side management framework for the smart grid,” Comput. Commun., vol. 57, no. November, pp. 13–24, 2015.

      [14] M. Muratori, B. A. Schuelke-Leech, and G. Rizzoni, “Role of residential demand response in modern electricity markets,” Renew. Sustain. Energy Rev., vol. 33, pp. 546–553, 2014.

      [15] M. Hinnells, “Technologies to achieve demand reduction and microgeneration in buildings,” Energy Policy, vol. 36, no. 12, pp. 4427–4433, 2008.

      [16] C. Delmastro, E. Lavagno, and G. Mutani, “Chinese residential energy demand: Scenarios to 2030 and policies implication,” Energy Build., vol. 89, pp. 49–60, 2015.

      [17] A. Zahedi, “A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid,” Renew. Sustain. Energy Rev., vol. 15, no. 9, pp. 4775–4779, 2011.

      [18] O. Zhang, S. Yu, and P. Liu, “Development mode for renewable energy power in China: Electricity pool and distributed generation units,” Renew. Sustain. Energy Rev., vol. 44, pp. 657–668, 2015.

      [19] K. S. Reddy, M. Kumar, T. K. Mallick, H. Sharon, and S. Lokeswaran, “A review of Integration, Control, Communication and Metering (ICCM) of renewable energy based smart grid,” Renew. Sustain. Energy Rev., vol. 38, pp. 180–192, 2014.

      [20] A. Zyadin, P. Halder, T. Kähkönen, and A. Puhakka, “Challenges to renewable energy: A bulletin of perceptions from international academic arena,” Renew. Energy, vol. 69, pp. 82–88, 2014.

      [21] H. T. Haider, O. H. See, and W. Elmenreich, “A review of residential demand response of smart grid,” Renewable and Sustainable Energy Reviews, vol. 59. Elsevier, pp. 166–178, 2016.

      [22] Z. Jin, K. Chongqing, and L. Kai, “Demand side management in China,” IEEE PES Gen. Meet., pp. 1–4, 2010.

      [23] A. H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid,” IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 320–331, Dec. 2010.

      [24] B. Ramanathan and V. Vittal, “A framework for evaluation of advanced direct load control with minimum disruption,” IEEE Trans. Power Syst., vol. 23, no. 4, pp. 1681–1688, Nov. 2008.

      [25] H. S. Oh and R. J. Thomas, “Demand-side bidding agents: Modeling and simulation,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1050–1056, 2008.

      [26] M. Marwan, F. Kamel, and W. Xiang, “Mitigation of electricity price/demand using demand side response smart grid model,” Apr. 2011.

      [27] D. T. Nguyen, “Demand response for domestic and small business consumers: A new challenge,” Ieee Pes T&D 2010, pp. 1–7, 2010.

      [28] P. Palensky and D. Dietrich, “Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads,” IEEE Trans. Ind. Informatics, vol. 7, no. 3, pp. 1641–1653, 2011.

      [29] Z. Fan et al., “Smart Grid Communications : Overview of Research Activities,” Signal Processing, vol. 15, pp. 1–18, 2012.

      [30] M. M. Eissa, “Demand side management program evaluation based on industrial and commercial field data,” Energy Policy, vol. 39, no. 10, pp. 5961–5969, 2011.

      [31] “Benefits of Demand Response in Electricity Markets and Recommendations for Achieving them, Report to the United States Congress.,” US Dep. Energy, 2006.

      [32] J. Z. et al. Chiu, Albert, Ali Ipakchi, Angela Chuang, Bin Qiu, D. Brooks, E. Koch, “Framework for integrated demand response (DR) and distributed energy resources (DER) models,” NAESB & UCAIug, 2009.

      [33] P. Khajavi, H. Monsef, and H. Abniki, “Load profile reformation through demand response programs using Smart Grid,” 2010 Mod. Electr. Power Syst., no. Dlc, pp. 1–6, 2010.

      [34] V. S. K. M. Balijepalli, V. Pradhan, S. A. Khaparde, and R. M. Shereef, “Review of demand response under smart grid paradigm,” 2011 IEEE PES Int. Conf. Innov. Smart Grid Technol. ISGT India 2011, pp. 236–243, 2011.

      [35] kema, “Integrating Increased Dispatchable Demand Response and Dynamic Price Response into,” 2011.

      [36] M. Al Essa, “Demand Response Design of Domestic Heat Pumps,” Designs, vol. 2, no. 1, p. 1, 2017.

      [37] L. Zhou, J. J. P. C. Rodrigues, and L. M. Oliveira, “QoE-driven power scheduling in smart grid: Architecture, strategy, and methodology,” IEEE Commun. Mag., vol. 50, no. 5, pp. 136–141, 2012.

      [38] M. Alizadeh, A. Scaglione, and R. J. Thomas, “From packet to power switching: Digital direct load scheduling,” IEEE J. Sel. Areas Commun., vol. 30, no. 6, pp. 1027–1036, 2012.

      [39] H. Hildmann and F. Saffre, “Influence of variable supply and load flexibility on Demand-Side Management,” 2011 8th Int. Conf. Eur. Energy Mark. EEM 11, no. May, pp. 63–68, 2011.

      [40] P. Palensky and G. Zucker, “Demand response with functional buildings using simplified process models,” IECON 2011-37th …, pp. 3230–3235, 2011.

      [41] W. Shi and V. W. S. Wong, “Real-time vehicle-to-grid control algorithm under price uncertainty,” 2011 IEEE Int. Conf. Smart Grid Commun. SmartGridComm 2011, pp. 261–266, 2011.

      [42] H. Yano, K. Kudo, T. Ikegami, H. Iguchi, K. Kataoka, and K. Ogimoto, “A novel charging-time control method for numerous EVs based on a period weighted prescheduling for power supply and demand balancing,” 2012 IEEE PES Innov. Smart Grid Technol. ISGT 2012, pp. 1–6, 2012.

      [43] N. Lu and Y. Zhang, “Design considerations of a centralized load controller using thermostatically controlled appliances for continuous regulation reserves,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 914–921, 2013.

      [44] A. Mehrizi-Sani and R. Iravani, “Potential-function based control of a microgrid in islanded and grid-connected modes,” IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1883–1891, 2010.

      [45] B. V. Solanki, A. Raghurajan, K. Bhattacharya, and C. A. Canizares, “Including Smart Loads for Optimal Demand Response in Integrated Energy Management Systems for Isolated Microgrids,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1739–1748, 2017.

      [46] M. Latifi, A. Khalili, A. Rastegarnia, and S. Sanei, “Fully Distributed Demand Response Using the Adaptive Diffusion-Stackelberg Algorithm,” IEEE Trans. Ind. Informatics, vol. 13, no. 5, pp. 2291–2301, 2017.

      [47] Z. Tan, P. Yang, and A. Nehorai, “An optimal and distributed demand response strategy with electric vehicles in the smart grid,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 861–869, 2014.

      [48] Q. Dong, L. Yu, W. Song, J. Yang, Y. Wu, and J. Qi, “Fast distributed demand response algorithm in smart grid,” IEEE/CAA J. Autom. Sin., vol. 4, no. 2, pp. 280–296, 2017.

      [49] M. H. Yaghmaee Moghaddam, A. Leon-Garcia, and M. Moghaddassian, “On the Performance of Distributed and Cloud-Based Demand Response in Smart Grid,” IEEE Trans. Smart Grid, vol. 3053, no. c, pp. 1–1, 2017.

      [50] T. V. Vu, C. S. Edrington, and R. Hovsapian, “Distributed demand response considering line loss for distributed renewable energy systems,” in 2017 IEEE Power & Energy Society General Meeting, 2017, pp. 1–5.

      [51] S. Tang and Y. Xu, “Distributed control of multi-zone commercial buildings for demand response,” in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017, no. 51507193, pp. 1–5.

      [52] J. Han and M. A. Piette, “Solutions for summer electric power shortages : Demand Response and its applications in air conditioning and refrigerating systems,” Refrig. Air Cond. Electr. Power Mach., vol. 29, no. 1, pp. 1–4, 2008.

      [53] J. Aghaei and M. I. Alizadeh, “Demand response in smart electricity grids equipped with renewable energy sources: A review,” Renew. Sustain. Energy Rev., vol. 18, pp. 64–72, 2013.

      [54] M. Doostizadeh and H. Ghasemi, “A day-ahead electricity pricing model based on smart metering and demand-side management,” Energy, vol. 46, no. 1, pp. 221–230, 2012.

      [55] S. Gyamfi, S. Krumdieck, and T. Urmee, “Residential peak electricity demand response - Highlights of some behavioural issues,” Renew. Sustain. Energy Rev., vol. 25, pp. 71–77, 2013.

      [56] O. Hafez, “Time of use prices considering smart meters and their future implementation in Saudi Arabia smart grid,” in 2017 Saudi Arabia Smart Grid Conference, SASG 2017, 2018, pp. 1–5.

      [57] M. S. Ahmed, F. el Bendary, and H. M. M. Moustafa, “Comparative study between: time of use and real-time pricing using fuzzy technique,” CIRED - Open Access Proc. J., vol. 2017, no. 1, pp. 2641–2644, 2017.

      [58] N. Vermaak, N. Gurusinghe, T. Ariyarathna, and R. Gouws, “Data logger and companion application for time-of-use electricity,” 2018 IEEE Int. Conf. Ind. Electron. Sustain. Energy Syst., pp. 465–470, 2018.

      [59] A. Pasban-gajan and M. Fotuhi-firuzabad, “Optimal Scheduling of Renewable-Based Energy Hubs Considering Time-of-Use Pricing Scheme,” 2017.

      [60] Q. Zhou, W. Guan, and W. Sun, “Impact of demand response contracts on load forecasting in a smart grid environment,” IEEE Power Energy Soc. Gen. Meet., pp. 1–4, 2012.

      [61] K. Boonchuay and S. Chaitusaney, “Optimal critical peak pricing scheme with consideration of marginal generation cost,” ECTI-CON 2017 - 2017 14th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol., no. 5, pp. 226–229, 2017.

      [62] M. Naz, Z. I. Id, and N. Javaid, “Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes,” pp. 1–25.

      [63] L. Huang, S. Xu, X. Wang, X. Huo, and H. Zheng, “Dynamic optimized decision model of smart utilization for typical public building employing critical peak pricing,” in 2016 China International Conference on Electricity Distribution (CICED), 2016, pp. 1–5.

      [64] J. Andruszkiewicz, J. Lorenc, A. Michalski, and W. Borowiak, “Opportunities of demand flexibility bidding in result of critical peak pricing implementation for low voltage customers in Polish power system,” in 2016 13th International Conference on the European Energy Market (EEM), 2016, pp. 1–5.

      [65] Ming Zhou, Gengyin Li, and Yucan Yin, “Dynamic decision model of critical peak pricing considering electric vehicles’ charging load,” in International Conference on Renewable Power Generation (RPG 2015), 2015, p. 6 .-6 .

      [66] Rocky Mountain Institute, “Demand Response: An Introduction – Overview of Programs, Technologies, & Lessons Learned,” p. 46, 2006.

      [67] H. Aalami, M. Moghaddam, and G. Yousefi, “Modeling and prioritizing demand response programs in power markets SNPD 2016,” Electr. Power Syst. Res., 2010.

      [68] Y. Liang, L. He, X. Cao, and Z. J. Shen, “Stochastic control for smart grid users with flexible demand,” IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 2296–2308, Dec. 2013.

      [69] K. V. Nagarajan, “Benefit-Cost Methodology for Evaluating Peak-Load Pricing System Implementation for Electricity Rates,” IEEE Trans. Syst. Man. Cybern., vol. 10, no. 9, pp. 541–547, 1980.

      [70] V. Grimm, L. Schewe, M. Schmidt, and G. Zöttl, “Uniqueness of market equilibrium on a network: A peak-load pricing approach,” Eur. J. Oper. Res., vol. 261, no. 3, pp. 971–983, Sep. 2017.

      [71] S. Mohajeryami, M. Doostan, and A. Asadinejad, “An investigation of the relationship between accuracy of customer baseline calculation and efficiency of peak time rebate program,” in 2016 IEEE Power and Energy Conference at Illinois, PECI 2016, 2016, pp. 1–8.

      [72] J. Vuelvas and F. Ruiz, “Demand response: Understanding the rational behavior of consumers in a Peak Time Rebate Program,” in 2015 IEEE 2nd Colombian Conference on Automatic Control, CCAC 2015 - Conference Proceedings, 2015, pp. 1–6.

      [73] S. Mohajeryami, P. Schwarz, and P. T. Baboli, “Including the behavioral aspects of customers in demand response model: Real time pricing versus peak time rebate,” in 2015 North American Power Symposium, NAPS 2015, 2015, pp. 1–6.

      [74] J. Vuelvas and F. Ruiz, “Rational consumer decisions in a peak time rebate program,” Electr. Power Syst. Res., vol. 143, pp. 533–543, Feb. 2017.

      [75] S. Mohajeryami, M. Doostan, and P. Schwarz, “The impact of Customer Baseline Load (CBL) calculation methods on Peak Time Rebate program offered to residential customers,” Electr. Power Syst. Res., vol. 137, pp. 59–65, Aug. 2016.

      [76] P. Samadi, H. Mohsenian-Rad, R. Schober, and V. W. S. Wong, “Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1170–1180, Sep. 2012.

      [77] P. Samadi, R. Schober, and V. W. S. Wong, “Optimal energy consumption scheduling using mechanism design for the future smart grid,” in 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2011, pp. 369–374.

      [78] P. G. Sessa, N. Walton, and M. Kamgarpour, “Exploring the Vickrey-Clarke-Groves Mechanism for Electricity Markets,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 189–194, Jul. 2017.

      [79] E. Nekouei, T. Alpcan, and D. Chattopadhyay, “Game-theoretic frameworks for demand response in electricity markets,” IEEE Trans. Smart Grid, vol. 6, no. 2, pp. 748–758, Mar. 2015.

      [80] M. N. Ullah, N. Javaid, I. Khan, A. Mahmood, and M. U. Farooq, “Residential energy consumption controlling techniques to enable autonomous demand side management in future smart grid communications,” in Proceedings - 2013 8th International Conference on Broadband, Wireless Computing, Communication and Applications, BWCCA 2013, 2013, pp. 545–550.

      [81] M. R. Rao, J. Kuri, and T. V. Prabhakar, “Towards optimal load management with day ahead pricing,” in 2015 7th International Conference on Communication Systems and Networks, COMSNETS 2015 - Proceedings, 2015, pp. 1–8.

      [82] T. C. Chiu, C. W. Pai, Y. Y. Shih, and A. C. Pang, “Optimal day-ahead pricing with renewable energy for smart grid,” in 2014 IEEE International Conference on Communications Workshops, ICC 2014, 2014, pp. 472–476.

      [83] N. G. Paterakis, O. Erdinç, A. G. Bakirtzis, and J. P. S. Catalão, “Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies,” IEEE Trans. Ind. Informatics, vol. 11, no. 6, pp. 1509–1519, Dec. 2015.

      [84] A. Ghafar et al., “GreyWolf Optimization Technique for HEMS Using Day Ahead Pricing Scheme,” Springer, Cham, 2018, pp. 25–36.

      [85] [85] T. C. Chiu, Y. Y. Shih, A. C. Pang, and C. W. Pai, “Optimized Day-Ahead Pricing with Renewable Energy Demand-Side Management for Smart Grids,” IEEE Internet Things J., vol. 4, no. 2, pp. 374–383, Apr. 2017.

      [86] Y. Aoki, H. Ito, C. Ninagawa, and J. Morikawa, “Smart grid real-time pricing optimization control with simulated annealing algorithm for office building air-conditioning facilities,” in 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 1308–1313.

      [87] V. P. Ramavarapu, R. Sowers, and R. S. Sreenivas, “A smart power outlet for electric devices that can benefit from Real-Time Pricing,” in 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), 2017, pp. 11–17.

      [88] M. U. Qureshi, A. Girault, M. Mauger, and S. Grijalva, “Implementation of home energy management system with optimal load scheduling based on real-time electricity pricing models,” in 2017 IEEE 7th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), 2017, vol. 2017–Septe, pp. 134–139.

      [89] H. B. da Silva and L. P. Santiago, “On the trade-off between real-time pricing and the social acceptability costs of demand response,” Renewable and Sustainable Energy Reviews, vol. 81. Pergamon, pp. 1513–1521, 01-Jan-2018.

      [90] J. Zethmayr and D. Kolata, “The costs and benefits of real-time pricing: An empirical investigation into consumer bills using hourly energy data and prices,” Electr. J., vol. 31, no. 2, pp. 50–57, Mar. 2018.

      [91] R. Alishov, M. Spähn, and R. Witzmann, “Co-Simulation architecture for centralised direct load control in smart grid,” in IET Conference Publications, 2016, vol. 2016, no. CP686, p. 25 (4 .)-25 (4 .).

      [92] D. W. Caves, P. Hanser, J. A. Herriges, and R. J. Windle, “Load impact of interruptible and curtailable rate programs: Evidence from ten utilities,” IEEE Trans. Power Syst., vol. 3, no. 4, pp. 1757–1763, 1988.

      [93] M. Shafie-khah et al., “Optimal Demand Response Programs for improving the efficiency of day-ahead electricity markets using a multi attribute decision making approach,” in 2016 IEEE International Energy Conference (ENERGYCON), 2016, pp. 1–6.

      [94] H. A. Aalami, J. Khodaei, and M. Fard, “Economical and technical evaluation of implementation mandatory demand response programs on Iranian power system,” in Proceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011, 2011, pp. 271–276.

      [95] M. M. Sahebi, E. A. Duki, M. Kia, A. Soroudi, and M. Ehsan, “Simultanous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts,” IET Gener. Transm. Distrib., vol. 6, no. 7, p. 605, 2012.

      [96] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Demand response modeling considering Interruptible/Curtailable loads and capacity market programs,” Appl. Energy, vol. 87, no. 1, pp. 243–250, Jan. 2010.

      [97] M. H. Imani, M. Y. Talouki, P. Niknejad, and K. Yousefpour, “Running direct load control demand response program in microgrid by considering optimal position of storage unit,” 2018 IEEE Texas Power Energy Conf. (TPEC), Texas Power Energy Conf. (TPEC), 2018 IEEE, pp. 1–6, Feb. 2018.

      [98] N. Piovesan, M. Miozzo, and P. Dini, “Optimal direct load control of renewable powered small cells: Performance evaluation and bounds,” in 2018 IEEE Wireless Communications and Networking Conference (WCNC), 2018, pp. 1–6.

      [99] I. S. Bayram, O. Alrawi, H. Al-Naimi, and M. Koç, “Direct load control of air conditioners in Qatar: An empirical study,” in 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017, 2017, vol. 2017–Janua, pp. 1007–1012.

      [100] C. V. Chandran, M. Basu, and K. Sunderland, “Comparative study between direct load control and fuzzy logic control based demand response,” in Proceedings - 2016 51st International Universities Power Engineering Conference, UPEC 2016, 2017, vol. 2017–Janua, pp. 1–6.

      [101] S. Cheng, Z. Liu, T. Huang, X. Jiang, and R.-J. Wai, “Optimal planning of hybrid energy generation system in the microgrid considering direct load control,” in 2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2017, pp. 1–5.

      [102] O. Erdinc, A. Tascikaraoglu, N. G. Paterakis, and J. P. S. Catalao, “An energy credit based incentive mechanism for the direct load control of residential HVAC systems incorporation in day-ahead planning,” in 2017 IEEE Manchester PowerTech, Powertech 2017, 2017, pp. 1–6.

      [103] L. Hucheng, W. Xinwei, Y. Yubo, S. Wei, and G. Yanfeng, “Simulation and analysis of electric water heater load regulation model based on direct load control,” in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017, pp. 1–5.

      [104] H. Saitoh et al., “Hybrid real-time simulator for evaluation of direct load control to assist power system control,” in 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2017, pp. 1–5.

      [105] T. Strasser et al., “A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems,” IEEE Transactions on Industrial Electronics, vol. 62, no. 4. pp. 2424–2438, Apr-2015.

      [106] Y. C. Li and S. H. Hong, “Real-Time Demand Bidding for Energy Management in Discrete Manufacturing Facilities,” IEEE Trans. Ind. Electron., vol. 64, no. 1, pp. 739–749, Jan. 2017.

      [107] M. S. Rahman, A. Basu, S. Kiyomoto, and M. Z. A. Bhuiyan, “Privacy-friendly secure bidding for smart grid demand-response,” Inf. Sci. (Ny)., vol. 379, pp. 229–240, Feb. 2017.

      [108] T. S. Garcia, M. Shafie-Khah, G. J. Osório, and J. P. S. Catalão, “Optimal bidding strategy of responsive demands in a new decentralized market-based scheme,” in Conference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017, 2017, pp. 1–5.

      [109] M. Babar, P. H. Nguyen, V. Cuk, I. G. Kamphuis, and W. L. Kling, “Complex bid model and strategy for dispatchable loads in real time market-based demand response,” in IEEE PES Innovative Smart Grid Technologies Conference Europe, 2015, vol. 2015–Janua, no. January, pp. 1–5.

      [110] P. Tarasak, C. C. Chai, Y. S. Kwok, and S. W. Oh, “Demand bidding program and its application in hotel energy management,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 821–828, Mar. 2014.

      [111] Y. Liu, J. T. Holzer, and M. C. Ferris, “Extending the bidding format to promote demand response,” Energy Policy, vol. 86, pp. 82–92, Nov. 2015.

      [112] C. W. Gellings, Saving Energy and Reducing CO2 Emissions with Electricity, First edit. USA: The Fairmont Press,Inc., 2013.

      [113] A. Zakariazadeh, O. Homaee, S. Jadid, and P. Siano, “A new approach for real time voltage control using demand response in an automated distribution system,” Appl. Energy, vol. 117, pp. 157–166, Mar. 2014.

      [114] C. Pham, N. H. Tran, S. Ren, C. Seon Hong, K. K. Nguyen, and M. Cheriet, “A distributed approach to emergency demand response in geo-distributed mixed-use buildings,” J. Build. Eng., vol. 19, pp. 506–518, Jun. 2018.

      [115] M. Hosseini Imani, P. Niknejad, and M. R. Barzegaran, “The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation,” Int. J. Electr. Power Energy Syst., vol. 96, pp. 114–125, Mar. 2018.

      [116] J. Aghaei, M. I. Alizadeh, P. Siano, and A. Heidari, “Contribution of emergency demand response programs in power system reliability,” Energy, vol. 103, pp. 688–696, May 2016.

      [117] Y. Dong, X. Xie, K. Wang, B. Zhou, and Q. Jiang, “An Emergency-Demand-Response Based under Speed Load Shedding Scheme to Improve Short-Term Voltage Stability,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3726–3735, Sep. 2017.

      [118] P. Khajavi, H. Abniki, and A. B. Arani, “The role of incentive based demand response programs in smart grid,” in 2011 10th International Conference on Environment and Electrical Engineering, EEEIC.EU 2011 - Conference Proceedings, 2011, pp. 1–4.

      [119] M. Alipour, K. Zare, and B. Mohammadi-Ivatloo, “Short-term scheduling of combined heat and power generation units in the presence of demand response programs,” Energy, vol. 71, pp. 289–301, Jul. 2014.

      [120] P. Palensky and D. Dietrich, “Demand side management: Demand response, intelligent energy systems, and smart loads,” IEEE Trans. Ind. Informatics, vol. 7, no. 3, pp. 381–388, Aug. 2011.

      [121] H. Guo, Q. Chen, Q. Xia, J. Zhang, M. Li, and P. Zou, “Evaluating the impacts of VPPs on the joint energy and ancillary service markets equilibrium,” in IEEE Power and Energy Society General Meeting, 2018, vol. 2018–Janua, pp. 1–5.

      [122] J. Yang et al., “A noval bidding strategy of electric vehicles participation in ancillary service market,” in 2017 4th International Conference on Systems and Informatics, ICSAI 2017, 2018, vol. 2018–Janua, pp. 306–311.

      [123] N. Natale, F. Pilo, G. Pisano, and G. G. Soma, “Distribution system participation to the Italian ancillary service market,” in AEIT 2016 - International Annual Conference: Sustainable Development in the Mediterranean Area, Energy and ICT Networks of the Future, 2017, pp. 1–6.

      [124] M. P. Moghaddam, A. Abdollahi, and M. Rashidinejad, “Flexible demand response programs modeling in competitive electricity markets,” Appl. Energy, vol. 88, no. 9, pp. 3257–3269, Sep. 2011.

      [125] J. S. Vardakas, N. Zorba, and C. V Verikoukis, “A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms,” IEEE Commun. Surv. Tutorials, vol. 17, no. 1, pp. 152–178, 2015.

      [126] A. A. Desta, H. Badis, and L. George, “Demand Response Scheduling in Production Lines Constrained by Available Power,” in 2018 IEEE International Conference on Communications Workshops (ICC Workshops), 2018, pp. 1–6.

      [127] K. Ma, T. Yao, J. Yang, and X. Guan, “Residential power scheduling for demand response in smart grid,” Int. J. Electr. Power Energy Syst., vol. 78, pp. 320–325, Jun. 2016.

      [128] J. S. Vardakas, N. Zorba, and C. V. Verikoukis, “Performance evaluation of power demand scheduling scenarios in a smart grid environment,” Appl. Energy, vol. 142, pp. 164–178, Mar. 2015.

      [129] A. Ranjan, P. Khargonekar, and S. Sahni, “Offline preemptive scheduling of power demands to minimize peak power in smart grids,” in Proceedings - International Symposium on Computers and Communications, 2014, pp. 1–6.

      [130] K. M. Tsui and S. C. Chan, “Demand response optimization for smart home scheduling under real-time pricing,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1812–1821, Dec. 2012.

      [131] N. Kumaraguruparan, H. Sivaramakrishnan, and S. S. Sapatnekar, “Residential task scheduling under dynamic pricing using the multiple knapsack method,” in 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012, 2012, pp. 1–6.

      [132] I. Koutsopoulos and L. Tassiulas, “Optimal control policies for power demand scheduling in the smart grid,” IEEE J. Sel. Areas Commun., vol. 30, no. 6, pp. 1049–1060, Jul. 2012.

      [133] Q. Dong, L. Yu, W. Z. Song, L. Tong, and S. Tang, “Distributed demand and response algorithm for optimizing social-welfare in smart grid,” in Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012, 2012, pp. 1228–1239.

      [134] Y. F. Du, L. Jiang, C. Duan, Y. Z. Li, and J. S. Smith, “Energy Consumption Scheduling of HVAC Considering Weather Forecast Error Through the Distributionally Robust Approach,” IEEE Trans. Ind. Informatics, vol. 14, no. 3, pp. 846–857, Mar. 2018.

      [135] K. Bocian, M. Ganzha, and M. Paprzycki, “Agent system for energy consumption scheduling in an intelligent neighborhood — Preliminary considerations,” in 2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall), 2017, pp. 1–8.

      [136] T. Assaf, A. H. Osman, M. S. Hassan, and H. Mir, “Fair and efficient energy consumption scheduling algorithm using tabu search for future smart grids,” IET Gener. Transm. Distrib., Sep. 2017.

      [137] T. Yanan, D. Srinivasan, and A. Trivedi, “Multi-objective Optimal Energy Consumption Scheduling in Smart Grids,” in 2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 2017, pp. 93–98.

      [138] T. Assaf, A. H. Osman, and M. Hassan, “Fair Autonomous Energy Consumption Scheduling Based on Game-Theoretic Approach for the Future Smart Grid,” in 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation (UKSim), 2016, pp. 235–239.

      [139] P. Khadgi, L. Bai, G. Evans, and Q. P. Zheng, “A simulation model with multi-attribute utility functions for energy consumption scheduling in a smart grid,” Energy Syst., vol. 6, no. 4, pp. 533–550, Nov. 2015.

      [140] H. Chen, Y. Li, R. H. Y. Louie, and B. Vucetic, “Autonomous demand side management based on energy consumption scheduling and instantaneous load billing: An aggregative game approach,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1744–1754, Jul. 2014.






Article ID: 23797
DOI: 10.14419/ijet.v7i4.23797

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