Abstract
With the latest smart technologies in the electricity sector, the consumers of electricity got the opportunity to reduce their electricity consumption cost by participating in the demand response programs offered by the utility companies. In this paper, a model of energy management system is introduced for the energy scheduling at home. Residential automatic smart appliances of general use are selected for energy scheduling. The energy controlling device in the EMS model receives the real time electricity price signals from the utility company and schedule the appliances according to the user requirements in such a way so that the electricity consumption cost could be minimized. The appliances are scheduled under real time pricing combined with inclined block rate pricing scheme so that the peak to average ratio of power could be maintained in the satisfactory range. This helps the utility companies in maintaining the system reliability. For the solution of the scheduling problem, particle swarm optimization algorithm is used due to its effectiveness and easy implementation. Finally, the results have been compared and verified against the results achieved by genetic algorithm.
References
[1] Choi H, Lee JH, Hong SH. Implementation and evaluation of the apparatus for intelligent energy management to apply to the smart grid at home. IEEE International Instrumentation and Measurement Technology Conference, Binjiang, 2011:1–5.10.1109/IMTC.2011.5944215Search in Google Scholar
[2] STAFF REPORT. Demand Response & Advanced Metering. FERC, 2006 (https://www.ferc.gov/legal/staff-reports/demand-response.pdf).Search in Google Scholar
[3] Peretto L. The role of measurements in the smart grid era. IEEE Instrum Meas Mag. 2010; 13:22–5.10.1109/MIM.2010.5475163Search in Google Scholar
[4] Stoll P, Brandt N, Nordström L. Including dynamic CO2 intensity with demand response. Energy Policy. 2014;65:490–500.10.1016/j.enpol.2013.10.044Search in Google Scholar
[5] Roscoe AJ, Ault G. Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response. IET Renew Power Gener. 2010;4:369–82.10.1049/iet-rpg.2009.0212Search in Google Scholar
[6] Kakran S, Chanana S. Smart operations of smart grids integrated with distributed generation: A review. Renewable Sustainable Energy Rev. 2018;81:524–35.10.1016/j.rser.2017.07.045Search in Google Scholar
[7] Rotger-Griful S, Jacobsen RH, Nguyen D, Sørensen G. Demand response potential of ventilation systems in residential buildings. Energy Build. 2016;121:1–10.10.1016/j.enbuild.2016.03.061Search in Google Scholar
[8] Knudsen MD, Petersen S. Demand response potential of model predictive control of space heating based on price and carbon dioxide intensity signals. Energy Build. 2016;125:196–204.10.1016/j.enbuild.2016.04.053Search in Google Scholar
[9] Tsui KM, Chan SC. Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans Smart Grid. 2012;3:1812–21.10.1109/TSG.2012.2218835Search in Google Scholar
[10] Alizadeh M, Chang TH, Scaglione A, Chen C, Kishore S. The emergence of deferrable energy requests and a greener future: what stands in the way? 5th International Symposium on Communications, Control and Signal Processing, IEEE, Rome, Italy, 2012:1–6.10.1109/ISCCSP.2012.6217838Search in Google Scholar
[11] Schulke A, Bauknecht J, Haussler J. Power demand shifting with smart consumers: A platform for power grid friendly consumption control strategies. First IEEE International Conference on Smart Grid Communications. 2010:437–4210.1109/SMARTGRID.2010.5622081Search in Google Scholar
[12] Li Ping Q, Zhang YJ, Jianwei H, Yuan W. Demand response management via real-time electricity price control in smart grids. IEEE J Sel Areas Commun. 2013;31:1268–80.10.1109/JSAC.2013.130710Search in Google Scholar
[13] Missaoui R, Joumaa H, Ploix S, Bacha S. Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build. 2014;71:155–67.10.1016/j.enbuild.2013.12.018Search in Google Scholar
[14] Huang YT, Tian HJ, Wang L. Demand response for home energy management system. Int J Electr Power Energy Syst. 2015;73:448–55.10.1016/j.ijepes.2015.05.032Search in Google Scholar
[15] Jain A, Srivastava S. Price responsive demand management of an industrial buyer in day-ahead electricity market. Int J Emerg Electr Power Syst. 2017;18:1.10.1515/ijeeps-2015-0204Search in Google Scholar
[16] Chen Z, Wu L, Fu Y. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans Smart Grid. 2012;3:1822–31.10.1109/TSG.2012.2212729Search in Google Scholar
[17] Rastegar M, Fotuhi Firozabad M, Zareipour H. Home energy management incorporating operational priority of appliances. Electr Power Energy Syst. 2016;74:286–92.10.1016/j.ijepes.2015.07.035Search in Google Scholar
[18] Restegar M, Firuzabad MF, Aminifar F. Load commitment in a smart home. Appl Energy. 2012;96:45–54.10.1016/j.apenergy.2012.01.056Search in Google Scholar
[19] Pipattanasomporn M, Kuzlu M, Rahman S. An algorithm for intelligent home energy management and demand response analysis. IEEE Trans Smart Grid. 2012;3:2166–73.10.1109/TSG.2012.2201182Search in Google Scholar
[20] Fadlullah ZM, Quan DM, Kato N, Gtes: SI. An optimized game-theoretic demand-side management scheme for smart grid. IEEE Syst J. 2014;8:588–97.10.1109/JSYST.2013.2260934Search in Google Scholar
[21] Du P, Lu N. Appliance commitment for household load scheduling. IEEE Trans Smart Grid. 2011;2:411–19.10.1109/TSG.2011.2140344Search in Google Scholar
[22] Pedrasa MA, Spooner TD, MacGill IF. Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans Smart Grid. 2010;1:134–43.10.1109/TSG.2010.2053053Search in Google Scholar
[23] Mohsenian-Rad AH, Wong VW, Jatskevich J, Schober R. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. Innovative Smart Grid Technol, Gothenburg 2010;1–6.10.1109/ISGT.2010.5434752Search in Google Scholar
[24] Mohsenian-Rad AH, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid. 2010;1:120–33.10.1109/TSG.2010.2055903Search in Google Scholar
[25] Haider HT, See OH, Elmenreich W. A review of residential demand response of smart grid. Renewable Sustainable Energy Rev. 2016;59:166–78.10.1016/j.rser.2016.01.016Search in Google Scholar
[26] Roy T, Das A, Ni Z. Optimization in load scheduling of a residential community using dynamic pricing. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, 2017:1–5.10.1109/ISGT.2017.8086087Search in Google Scholar
[27] Elyas SH, Sadeghian H, Alwan HO, Wang Z. Optimized household demand management with local solar PV generation. North American Power Symposium (NAPS), Morgantown, WV, 2017:1–6.10.1109/NAPS.2017.8107411Search in Google Scholar
[28] Wang C, Zhou Y, Jiao B, Wang Y, Liu W, Wang D. Robust optimization for load scheduling of a smart home with photovoltaic system. Energy Convers Manage. 2015;102:247–57.10.1016/j.enconman.2015.01.053Search in Google Scholar
[29] Ratnam EL, Weller SR, Kellett CM. Scheduling residential battery storage with solar PV: assessing the benefits of net metering. Appl Energy. 2015;155:881–91.10.1016/j.apenergy.2015.06.061Search in Google Scholar
[30] Kakran S, Chanana S. An energy scheduling method for multiple users of residential community connected to the grid and wind energy source. Build Serv Eng Res Technol. 2017;39:295–309.10.1177/0143624417734536Search in Google Scholar
[31] Umetani S, Fukushima Y, Morita H. A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system. Omega. 2017;67:115–22.10.1016/j.omega.2016.04.005Search in Google Scholar
[32] Haurie A, Andrey C. The economics of electricity dynamic pricing and demand response programs, 2013. [Online]. Available: www.ordecsys.com/fr/system/files/shared/TOU-Premier Rapport.pdf.Search in Google Scholar
[33] Zhao Z, Lee WC, Shin Y, Song KB. An optimal power scheduling method for demand response in home energy management system. IEEE Trans Smart Grid. 2013;4:1391–400.10.1109/TSG.2013.2251018Search in Google Scholar
[34] Inclining block rate in British Columbia Hydro Co Aug. [Online]. Available: http://www.bchydro.com/youraccount/con-tent/resi-dential_rates.jsp.Search in Google Scholar
[35] Real-time pricing for residential customers. Ameren Illinois Power Co. [Online]. Available: https://www.ameren.com/account/retail-energy.Search in Google Scholar
© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Prospectives for the Use of Li-Ion Batteries in Hybrid Stand-Alone Power Sources
- Development of an Over-Temperature Supervising System of Switch Cabinet Based on Gas Sensing Technology
- Robust Investment for Demand Response in a Distribution Network considering Wind Power and Load Demand Uncertainties
- Reduction of Electric Field Stress on the Surface Contour and at the Triple Junction in UHVAC GIS by Spacer Design Optimization
- Optimal Energy Scheduling Method under Load Shaping Demand Response Program in a Home Energy Management System
- Sequence Component-Based Improved Passive Islanding Detection Method for Distribution System with Distributed Generations
- Optimal Switching Angle Scheme for a Cascaded H Bridge Inverter using Pigeon Inspired Optimization
- A Novel System and Experimental Verification for Locating Partial Discharge in Gas Insulated Switchgears
- A Comprehensive Induction Machine Model for Multi-Phase Power Flow Studies – Application to Industrial Power Systems and Wind Farms
- A Simplified Indirect Technique for the Measurement of Mechanical Power in Three-Phase Asynchronous Motors
- Three-Phase Grid Connected Bi-Directional Charging System to Control Active and Reactive Power with Harmonic Compensation
Articles in the same Issue
- Prospectives for the Use of Li-Ion Batteries in Hybrid Stand-Alone Power Sources
- Development of an Over-Temperature Supervising System of Switch Cabinet Based on Gas Sensing Technology
- Robust Investment for Demand Response in a Distribution Network considering Wind Power and Load Demand Uncertainties
- Reduction of Electric Field Stress on the Surface Contour and at the Triple Junction in UHVAC GIS by Spacer Design Optimization
- Optimal Energy Scheduling Method under Load Shaping Demand Response Program in a Home Energy Management System
- Sequence Component-Based Improved Passive Islanding Detection Method for Distribution System with Distributed Generations
- Optimal Switching Angle Scheme for a Cascaded H Bridge Inverter using Pigeon Inspired Optimization
- A Novel System and Experimental Verification for Locating Partial Discharge in Gas Insulated Switchgears
- A Comprehensive Induction Machine Model for Multi-Phase Power Flow Studies – Application to Industrial Power Systems and Wind Farms
- A Simplified Indirect Technique for the Measurement of Mechanical Power in Three-Phase Asynchronous Motors
- Three-Phase Grid Connected Bi-Directional Charging System to Control Active and Reactive Power with Harmonic Compensation