Optimal scheduling of home appliances using a demand response model considering the residents welfare

Document Type : Power Article


Department of Electrical Engineering, Shahreza Campus, University of Isfahan, Iran


The implementation of residential demand response has been a serious challenge for power system operators. The advent of smart grids has provided required communication infrastructures to exchange online signals with smart homes. In this paper, a new model for residential demand response is presented. The model divides the household loads in three categories as uncontrollable, controllable with thermostat, and controllable without thermostats. The proposed demand response model is a mixed real-time pricing (RTP) and incentive-based program capable of reflecting hourly prices while motivating residents to participate in the requested demand side management scheme. The proposed model was validated for a residential complex including 50 smart homes using GAMS software. Results verify the efficiency of the model to shed the peak load and shift the starting time of home appliances appropriately. A sensitivity analysis was conducted to investigate the impact of welfare violation cost and incentive rate on numerical results as well.


Main Subjects

[1] X. Wu, W. Cao, D. Wang, M. Ding, L. Yu, and Y. Nakanishi, “Demand Response Model Based on Improved Pareto Optimum Considering Seasonal Electricity Prices for Dongfushan Island,” Renewable Energy, vol. 164, 2021, pp. 926–936.
[2] R. Sharifi, A. Anvari-Moghaddam, S. Hamid Fathi, J. M. Guerrero, and V. Vahidinasab, “An Optimal Market-Oriented Demand Response Model for Price-Responsive Residential Consumers,” Energy Efficiency, vol. 12, no. 3, 2019, pp. 803–815.
[3] S. Pal, S. Thakur, R. Kumar, and B. K. Panigrahi, “A Strategical Game Theoretic Based Demand Response Model for Residential Consumers in a Fair Environment,”International Journal of Electrical Power & Energy Systems, vol. 97, 2018, pp. 201–210.
[4] M. Alipour, K. Zare, H. Seyedi, and M. Jalali, “Real-Time Price-Based Demand Response Model for Combined Heat and Power Systems,” Energy, vol. 168, 2019, pp. 1119–1127.
[5] J. R. Vázquez-Canteli and Z. Nagy, “Reinforcement Learning for Demand Response: A Review of Algorithms and Modeling Techniques,” Applied Energy, vol. 235, 2019, pp. 1072–1089.
[6] Z. Tan, S. Yang, H. Lin, G. De, L. Ju, and F. Zhou, “Multi-Scenario Operation Optimization Model for Park Integrated Energy System Based on Multi-Energy Demand Response,” Sustainable Cities and Society, vol. 53, 2020, p. 101973.
[7] X. Yan, Y. Ozturk, Z. Hu, and Y. Song, “A Review on Price-Driven Residential Demand Response,” Renewable and Sustainable Energy Reviews, vol. 96, 2018, pp. 411–419.
[8] B. Parrish, P. Heptonstall, R. Gross, and B. K. Sovacool, “A Systematic Review of Motivations, Enablers and Barriers for Consumer Engagement with Residential Demand Response,” Energy Policy, vol. 138, 2020, p. 111221.
[9] R. Lu, S. H. Hong, and M. Yu, “Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network,” IEEE Transaction on Smart Grid, vol. 10, no. 6, 2019, pp. 6629–6639.
[10]  S. J. Darby, “Demand Response and Smart Technology in Theory and Practice: Customer Experiences and System Actors,” Energy Policy, vol. 143, 2020, p. 111573.
[11]  H. M. Ruzbahani, A. Rahimnejad, and H. Karimipour, “Smart Households Demand Response Management with Micro Grid,” in IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019, pp. 1–5.
[12]  Y. Susowake, H. Masrur, T. Yabiku, T. Senjyu, A. Motin Howlader, M. Abdel-Akher,and  M.Hemeida.A, “A Multi-Objective Optimization Approach Towards a Proposed Smart Apartment with Demand-Response in Japan,” Energies, vol. 13, no. 1, 2020, p. 127.
[13]  Y. Liu, L. Xiao, G. Yao, and S. Bu, “Pricing-Based Demand Response for a Smart Home with Various Types of Household Appliances Considering Customer Satisfaction,” IEEE Access, vol. 7, 2019, pp. 86463–86472.
[14]  C. Gorria, J. Jimeno, I. Laresgoiti, M. Lezaun, and N. Ruiz, “Forecasting Flexibility in Electricity Demand with Price/Consumption Volume Signals,” Electric Power System Research, vol. 95, 2013, pp. 200–205.
[15]  J. Zhao, S. Kucuksari, E. Mazhari, and Y. J. Son, “Integrated Analysis of High-Penetration PV and PHEV with Energy Storage and Demand Response,” Applied Energy, vol. 112, 2013, pp. 35–51.
[16]  A. R. Jordehi, “Optimisation of Demand Response in Electric Power Systems, A Review,” Renewable and Sustainable Energy Reviews, vol. 103, 2019, pp. 308–319.
[17]  N. Gilbraith and S. E. Powers, “Residential Demand Response Reduces Air Pollutant Emissions on Peak Electricity Demand Days in New York City,” Energy Policy, vol. 59, 2013, pp. 459–469.
[18]  M. Rastegar, M. Fotuhi-Firuzabad, and F. Aminifar, “Load Commitment in a Smart Home,” Applied Energy, vol. 96, 2012, pp. 45–54.
[19] سید محمدباقر ساداتی، جمال مشتاق و میعادرضا شفیعی­خواه، "تاثیر خودروهای الکتریکی و برنامه پاسخ‌گویی بار بر بهره‌برداری بهینه از شبکه‌ی توزیع در چهارچوب یک مدل دو سطحی جدید"، نشریه مدل­سازی در مهندسی، دوره 16، شماره54، پاییز 1397، صفحه 53-68.
[20] Asadinejad A, Tomsovic K., ''Optimal Use of Incentive and Price Based Demand Response to Reduce Costs and Price Volatility'', Electric Power Systems Research., vol.144, Mar. 2017, pp. 215-23.
[21]  Xu B, Wang J, Guo M, Lu J, Li G, Han L., "A Hybrid Demand Response Mechanism Based on Real-Time Incentive and Real-Time Pricing'', Energy. vol. 231, Sep. 2021, pp. 120940.
[22] Shakeri, M.; Amin, N.; Pasupuleti, J.; Mehbodniya, A.; Asim, N.; Tiong, S.K.; Low, F.W.; Yaw, C.T.; Samsudin, N.A.; Rokonuzzaman, M.; et al., "An Autonomous Home Energy Management System Using Dynamic Priority Strategy in Conventional Homes." Energies, vol. 13, no. 13, 2020, pp. 3312
[23] Inoue, M.; Higuma, T.; Ito, Y.; Kushiro, N.; Kubota, H. "Network Architecture for Home Energy Management System." IEEE Transactions on Consumer Electronics, vol. 49, 2003, pp. 606–613
[24] Digital Illumination Interface Alliance. Standards. 2018. Available online: https://www.dali-alliance.org/dali/ (accessed on 25 July 2022).
[25] Lilakiatsakun, W.; Seneviratne, A. Wireless Home Networks Based on a Hierarchical Bluetooth Scatternet Architecture. In Proceedings of the Ninth IEEE International Conference on Networks, ICON 2001, Bangkok, Thailand, 10–11 October 2001; pp. 481–485.
[26] Tozlu, S.; Senel, M.; Mao, W.; Keshavarzian, "A. Wi-Fi Enabled Sensors for Internet of Things: A Practical Approach." IEEE Communications Magazine, vol. 50, 2012, pp. 134–143
[27] Khalid R, Javaid N, Rahim MH, Aslam S, Sher A. "Fuzzy Energy Management Controller and Scheduler for Smart Homes." Sustainable Computing: Informatics and Systems. vol. 21, Mar. 2019, pp. 103-18.
[28] Ahmed MS, Mohamed A, Khatib T, Shareef H, Homod RZ, Abd Ali J., "Real Time Optimal Schedule Controller for Home Energy Management System Using New Binary Backtracking Search Algorithm." Energy and Buildings. vol. 138, Mar.  2017; pp. 215-27.
[29] Aslam S, Iqbal Z, Javaid N, Khan ZA, Aurangzeb K, Haider SI. Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes. Energies. Dec. 2017; vol. 10, no. 12, pp. 2065.
[30]  Javaid N, Ullah I, Akbar M, Iqbal Z, Khan FA, Alrajeh N, Alabed MS. An Intelligent Load Management System with Renewable Energy Integration for Smart Homes. IEEE Access. vol. 14, no. 5, June. 2017, pp. 13587-600.
[31] Yahia Z, Pradhan A. Multi-Objective Optimization of Household Appliance Scheduling Problem Considering Consumer Preference and Peak Load Reduction. Sustainable Cities and Society. vol. 55, Apr. 2020, pp. 102058.
[32] Fayaz M, Kim D. Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic. Energies. Jan. 2018, vol. 11, no. 1, pp. 161.
[33] Kou X, Du Y, Li F, Pulgar-Painemal H, Zandi H, Dong J, Olama MM. Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response. IEEE Open Access Journal of Power and Energy. vol. 8, May. 2021, pp. 186-97.
[34] Wan Y, Qin J, Yu X, Yang T, Kang Y. Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach. IEEE/CAA Journal of Automatica Sinica. vol. 9, no. 1, Oct. 2021, pp. 123-34.
[35] Essiet IO, Sun Y, Wang Z. Optimized Energy Consumption Model for Smart Home Using Improved Differential Evolution Algorithm. Energy. vol. 172, Apr. 2019, pp. 354-65.
[36] Jindal A, Bhambhu BS, Singh M, Kumar N, Naik K. A Heuristic-Based Appliance Scheduling Scheme for Smart Homes. IEEE Transactions on Industrial Informatics. vol. 16, no. 5, Apr. 2019, pp. 3242-55.
[37] Monfared HJ, Ghasemi A, Loni A, Marzband M. A Hybrid Price-Based Demand Response Program for the Residential Micro-Grid. Energy. vol. 185, Oct. 2019, pp. 274-85.
[38] نیلوفر گرامی و احمد قاسمی،"مدل‌سازی مصرف انرژی در فرآیندهای تولید واحدهای صنعتی بر مبنای روش تقریب خطی تکه‌ای با هدف اجرای برنامه‌های پاسخ‌گویی بار و مشارکت در بازار انرژی و خدمات جانبی"، نشریه مدل­سازی در مهندسی، دوره 17، شماره59، زمستان 1398، صفحه 179-193.
[39] ابراهیم اکبری، رحمت­اله هوشمند، مهدی قلی­پور و معین پرستگاری، "استراتژی پیشنهاددهی بهینه مشارکت یکپارچه ذخیره‌ساز هوای فشرده و تکنولوژی برق به گاز در بازار روز پیش تحت رویکرد ترکیبی تصادفی-مقاوم"، نشریه مدل­سازی در مهندسی، دوره 10، شماره 2، تابستان 1399، صفحه 2-13.
[40]  C. W. Gellings, The Smart Grid: Enabling Energy Efficiency and Demand Response, River Publishers, 2020.
[41] Meeraus A, Bussieck M, Jagla JH, Nelissen F, Westermann L. GAMS. Cambridge, MA: Scientific Press; 1988.
[42]  Historical & Forecast Weather Data.” https://www.weatheranalytics.com/products/atlas/ (accessed Mar. 14, 2022).
[43]  P. Sánchez-Martín, G. Sánchez, and G. Morales-España, “Direct Load Control Decision Model for Aggregated EV Charging Points,” IEEE Transactions on Power Systems, vol. 27, no. 3, 2012, pp. 1577–1584.