Energy management in microgrids considering the demand response in the presence of distributed generation resources on the IoT platform

References

• Al-Fuqaha, A., M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. 2015. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials 17:2347–31. doi:10.1109/COMST.2015.2444095.
   Web of Science ®Google Scholar
• Asimakopoulou, G.-E., A.-L. Dimeas, and N.-D. Hatziargyriou. 2013. Leader-follower strategiesfor energy management of multi-microgrids. IEEE Transactions on Smart Grid 4:1909–16. doi:10.1109/TSG.2013.2256941.
   Web of Science ®Google Scholar
• Bagdadee, A.-H., L. Zhang, and S.-H. Remus. 2020. A brief review of the IoT-based energy management system in the smart industry. Artificial Intelligence and Evolutionary Computations in Engineering Systems 1056:443–59. doi:10.1007/978-981-15-0199-9_38.
   Google Scholar
• Bahramara, S., and H. Golpîra. 2018. Robust optimization of micro-grids operation problem in the presence of electric vehicles. Sustainable Cities and Society 37:388–95. doi:10.1016/j.scs.2017.11.039.
   Web of Science ®Google Scholar
• Bastani, M., H. Damgacioglu, and N. Celik. 2018. A δ-constraint multi-objective optimization framework for operation planning of smart grids. Sustainable Cities and Society 38:21–30. doi:10.1016/j.scs.2017.12.006.
   Web of Science ®Google Scholar
• Bezdek, J.-C. 1981. Pattern recognition with fuzzy objective function algorithms. New York: Springer.
   Google Scholar
• Bui, V.-H., H. A, and H.-M. Kim. 2018. A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response. IEEE Transactions on Smart Grid 9:1323–33. doi:10.1109/TSG.2016.2585671.
   Web of Science ®Google Scholar
• Bui, V.-H., A. Hussain, and H.-M. Kim. 2016. Demand bidding and real-time pricing-based optimal operation of multi-microgrids. International Journal of Smart Home 10:193–208. doi:10.14257/ijsh.2016.10.4.18.
   Google Scholar
• Cecati, C., C. Citro, and P. Siano. 2011. Combined operations of renewable energy systems and responsive demand in a smart grid. IEEE Transactions on Sustainable Energy 2:468–76. doi:10.1109/TSTE.2011.2161624.
   Web of Science ®Google Scholar
• Chamandoust, H., S. Bahramara, and G. Derakhshan. 2020c. Day-ahead scheduling problem of smart micro-grid with high penetration of wind energy and demand side management strategies. Sustainable Energy Technologies and Assessments 40:100747. doi:10.1016/j.seta.2020.100747.
   Web of Science ®Google Scholar
• Chamandoust, H., G. Derakhshan, and S. Bahramara. 2020a. Multi-objective performance of smart hybrid energy system with Multi-optimal participation of customers in day-ahead energy market. Energy and Buildings 216:109964. doi:10.1016/j.enbuild.2020.109964.
   Web of Science ®Google Scholar
• Chamandoust, H., G. Derakhshan, S.-M. Hakimi, and S. Bahramara. 2019. Tri-objective optimal scheduling of smart energy hub system with schedulable loads. Journal of Cleaner Production 236:117584. doi:10.1016/j.jclepro.2019.07.059.
   Web of Science ®Google Scholar
• Chamandoust, H., G. Derakhshan, S.-M. Hakimi, and S. Bahramara. 2020b. Multi-objectives optimal scheduling in smart energy hub system with electrical and thermal responsive loads. Environmental and Climate Technologies 24:209–32. doi:10.2478/rtuect-2020-0013.
   Web of Science ®Google Scholar
• Chamandoust, H., G. Derakhshan, S.-M. Hakimi, and S. Bahramara. 2020d. Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources. Journal of Energy Storage 27:101112. doi:10.1016/j.est.2019.101112.
   Web of Science ®Google Scholar
• Choi, S., S. Park, D.-J. Kang, S.-J. Han, and H.-M. Kim. 2011. A microgrid energy management system for inducing optimal demand response. IEEE International Conference on Smart Grid Communications (Smart GridComm), Brussels, Belgium. doi:10.1109/SmartGridComm.2011.6102317.
   Google Scholar
• Debnath, K., and L. Goel. 1995. Power system planning - a reliability perspective. Electric Power Systems Research 34:179–85. doi:10.1016/0378-7796(95)00976-X.
   Web of Science ®Google Scholar
• Elmouatamid, A., Y. Naitmalek, M. Bakhouya, R. Ouladsine, N. Elkamoun, K. Zine-Dine, and M. Khaidar. 2019. An energy management platform for micro-grid systems using Internet of Things and Big-data technologies. Journal of Systems and Control Engineering 233:904–17. doi:10.1177/0959651819856251.
   Web of Science ®Google Scholar
• Esmaeili, M., M. Sedighizadeh, and M. Esmaili. 2016. Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty. Energy 103:86–99. doi:10.1016/j.energy.2016.02.152.
   Web of Science ®Google Scholar
• Firouzmakan, P., R.-A. Hooshmand, M. Bornapourb, and A. Khodabakhshian. 2019. A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs. Renewable and Sustainable Energy Reviews 108:355–68. doi:10.1016/j.rser.2019.04.001.
   Web of Science ®Google Scholar
• Frank, A., R. Asuncion, and M. Frank. 2019. Smart optimization of energy consumption using IoT. 2nd IEEE Middle East and North Africa Communications Conference (MENACOMM), Manama, Bahrain. doi: 10.1109/MENACOMM46666.2019.8988549.
   Google Scholar
• Geoffrion, A.-M. 1972. Generalized benders decomposition. Journal of Optimization Theory and Applications 10:237–60. doi:10.1007/BF00934810.
   Google Scholar
• Ghasemi, A., and M. Enayatzare. 2018. Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response. Renewable Energy 123:460–74. doi:10.1016/j.renene.2018.02.072.
   Web of Science ®Google Scholar
• Gregoratti, D., and J. Matamoros. 2015. Distributed energy trading: The multiple microgrid case. IEEE Transactions on Industrial Electronics 62:2551–59. doi:10.1109/TIE.2014.2352592.
   Web of Science ®Google Scholar
• Guoping, Z., W. Weijun, and M. Longbo. 2018. An overview of microgrid planning and design method. IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China. doi: 10.1109/IAEAC.2018.8577763.
   Google Scholar
• Heinzelman, W.-B., A.-P. Chandrakasan, and H. Balakrishnan. 2002. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications 1:660–70. doi:10.1109/TWC.2002.804190.
   Web of Science ®Google Scholar
• Huang, J., Y. Hong, Z. Zhao, and Y. Yuan. 2017. An energy-efficient multi-hop routing protocol based on grid clustering for wireless sensor networks. Cluster Computing 20:3071–83. doi:10.1007/s10586-017-0993-2.
   Web of Science ®Google Scholar
• Huang, G.-H., Y.-T. Niu, Q.-G. Lin, -X.-X. Zhang, and Y.-P. Yang. 2011. An interval-parameter chance-constraint mixed-integer programming for energy systems planning under uncertainty. Energy Sources, Part B: Economics, Planning, and Policy 6:192–205. doi:10.1080/15567240903485931.
   Web of Science ®Google Scholar
• Hussain, A., I.-S. Choi, Y.-H. Im, and H.-M. Kim. 2019. Optimal operation of greenhouses in microgrids perspective. IEEE Transactions on Smart Grid 10:3474–85. doi:10.1109/TSG.2018.2828942.
   Web of Science ®Google Scholar
• Igualada, L., C. Corchero, M. Cruz-Zambrano, and F.-J. Heredia. 2014. Optimal energy management for a residential microgrid including a vehicle-to-grid system. IEEE Transactions on Smart Grid 5:2163–72. doi:10.1109/TSG.2014.2318836.
   Web of Science ®Google Scholar
• Ilic, M.-D. 2007. From hierarchical to open access electric power systems. Proceedings of the IEEE USA 95:1060–84. doi: 10.1109/JPROC.2007.894711.
   Google Scholar
• Jiang, Q., M. Xue, and G. Geng. 2013. Energy management of microgrid in grid-connected and stand-alone modes. IEEE Transactions on Power Systems 28:3380–89. doi:10.1109/TPWRS.2013.2244104.
   Web of Science ®Google Scholar
• Katiraei, F., and M.-R. Iravani. 2006. Power management strategies for a microgrid with multiple distributed generation units. IEEE Transactions on Power Systems 21:1821–31. doi:10.1109/TPWRS.2006.879260.
   Web of Science ®Google Scholar
• Katiraei, F., R. Iravani, N. Hatziargyriou, and A. Dimeas. 2008. Microgrids management. IEEE Power Energy Magazine 6:54–65. doi:10.1109/MPE.2008.918702.
   Web of Science ®Google Scholar
• Khajenasiri, I., A. Estebsari, M. Verhelst, and G. Gielen. 2017. A review on Internet of Things solutions for intelligent energy control in buildings for smart city applications. Energy Procedia 111:770–79. doi:10.1016/j.egypro.2017.03.239.
   Google Scholar
• Khavari, F., A. Badri, and A. Zangeneh. 2020. Energy management in multi-microgrids considering point of common coupling constraint. International Journal of Electrical Power and Energy Systems 115:105465. doi:10.1016/j.ijepes.2019.105465.
   Web of Science ®Google Scholar
• Khodaei, A. 2013. Microgrid optimal scheduling with multi-period islanding constraints. IEEE Transactions on Power Systems 29:1383–92. doi:10.1109/TPWRS.2013.2290006.
   Web of Science ®Google Scholar
• Khodaei, A. 2014. Resiliency-oriented microgrid optimal scheduling. IEEE Transactions on Smart Grid 5:1584–91. doi:10.1109/TSG.2014.2311465.
   Web of Science ®Google Scholar
• Khodaei, A. 2015. Provisional microgrids. IEEE Transactions on Smart Grid 6:1107–15. doi:10.1109/TSG.2014.2358885.
   Web of Science ®Google Scholar
• Leonori, S., M. Paschero, F. Massimo, F. Mascioli, and A. Rizzi. 2020. Optimization strategies for Microgrid energy management systems by genetic algorithms. Applied Soft Computing 86:105903. doi:10.1016/j.asoc.2019.105903.
   Web of Science ®Google Scholar
• Li, Z., M. Shahidehpour, and X. Liu. 2018. Cyber-secure decentralized energy management for IoT-enabled active distribution network. Journal of Modern Power Systems and Clean Energy 6:900–17. doi:10.1007/s40565-018-0425-1.
   Web of Science ®Google Scholar
• Lin, Q.-G., G.-H. Huang, B. Bass, Y.-F. Huang, and X.-D. Zhang. 2011. DESPU: Dynamic optimization for energy systems planning under uncertainty. Energy Sources, Part B: Economics, Planning, and Policy 6:321–38. doi:10.1080/15567240802592134.
   Web of Science ®Google Scholar
• Morales, J.-M., and J. Perez-Ruiz. 2007. Point estimate schemes to solve the probabilistic power flow. IEEE Transactions on Power Systems 22:1594–601. doi:10.1109/TPWRS.2007.907515.
   Web of Science ®Google Scholar
• Mpelogianni, V., G. Kosmas, and -P.-P. Groumpos. 2019. Modeling a microgrid using fuzzy cognitive maps. Communications in Computer and Information Science 1083:334–43. doi:10.1007/978-3-030-29743-5_27.
   Google Scholar
• Nikmehr, N., and S.-N. Ravadanegh. 2016. Reliability evaluation of multi-microgrids considering optimal operation of small-scale energy zones under load-generation uncertainties. International Journal of Electrical Power & Energy Systems 78:80–87. doi:10.1016/j.ijepes.2015.11.094.
   Web of Science ®Google Scholar
• Olivares, D.-E., C.-A. Cañizares, and M. Kazerani. 2014. A centralized energy management system for isolated microgrids. IEEE Transactions on Smart Grid 5:1864–75. doi:10.1109/TSG.2013.2294187.
   Web of Science ®Google Scholar
• Olivares, D.-E., A.-C. Claudio, and M. Kazerani. 2011. A centralized optimal energy management system for microgrids. IEEE Power and Energy Society General Detroit, MI, USA Meeting.1–6. doi: 10.1109/PES.2011.6039527.
   Google Scholar
• Pant, M., B. Dey, and S. Nandi. 2015. A multi-hop routing protocol for wireless sensor network based on grid clustering. Applications and Innovations in Mobile Computing (AIMoC), Kolkata, India, IEEE. doi: 10.1109/AIMOC.2015.7083842.
   Google Scholar
• Patil, H. K., and T. M. Chen. 2017. Chapter 18 - Wireless sensor network security: The Internet of things. Computer and Information Security Handbook (Third Edition) 317–37. doi:10.1016/B978-0-12-803843-7.00018-1.
   Google Scholar
• Pawar, P., M. TarunKumar, and P. Vittal K. 2020. An IoT based intelligent smart energy management system with accurate forecasting and load strategy for renewable generation. Measurement 152:107187. doi:10.1016/j.measurement.2019.107187.
   Web of Science ®Google Scholar
• Pawar, P., and P. Vittal K. 2019. Design and development of advanced smart energy management system integrated with IoT framework in smart grid environment. Journal of Energy Storage 25:100846. doi:10.1016/j.est.2019.100846.
   Web of Science ®Google Scholar
• Pourmousavi, S.-A., M.-H. Nehrir, C.-M. Colson, and C. Wang. 2010. Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization. IEEE Transactions on Sustainable Energy 1:193–201. doi:10.1109/TSTE.2010.2061881.
   Web of Science ®Google Scholar
• Rahim, S., Z. Iqbal, N. Shaheen, Z. Ali Khan, U. Qasim, S. Ahmed Khan, and N. Javaid. 2016. Ant colony optimization based energy management controller for smart grid. IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, Switzerland. doi: 10.1109/AINA.2016.163.
   Google Scholar
• Ruan, D., and J. Huang. 2019. A PSO-based uneven dynamic clustering multi-hop routing protocol for wireless sensor networks. Sensors 19:1835. doi:10.3390/s19081835.
   PubMedGoogle Scholar
• Said, O., Z. AL-Makhadmeh, and A. Tolba. 2020. EMS: An energy management scheme for green IoT environments. IEEE Access 8:44983–98. doi:10.1109/ACCESS.2020.2976641.
   Google Scholar
• Sajwan, M., D. Gosain, and A.-K. Sharma. 2018. CAMP: Cluster aided multi-path routing protocol for wireless sensor networks. Wireless Networks 25:2603–20. doi:10.1007/s11276-018-1689-0.
   Web of Science ®Google Scholar
• Saleem, Y., N. Crespi, M.-H. Rehmani, and R. Copeland. 2019. Internet of things-aided smart grid: Technologies, architectures, applications, prototypes, and future research directions. IEEE Access 7:62962–3003. doi:10.1109/ACCESS.2019.2913984.
   Web of Science ®Google Scholar
• Samanta, H., A. Bhattacharjee, M. Pramanik, A. Das, K. Das Bhattacharya, and H. Saha. 2020. Internet of things based smart energy management in a vanadium redox flow battery storage integrated bio-solar microgrid. Journal of Energy Storage 32. doi:10.1016/j.est.2020.101967.
   PubMed Web of Science ®Google Scholar
• Sanseverino, E.-R., M.-L. Di Silvester, M.-G. Ippolito, A. De Paola, and G. Lo Re. 2011. An execution monitoring and replanning approach for optimal energy management in microgrids. Energy 36:3429–36. doi:10.1016/j.energy.2011.03.047.
   Web of Science ®Google Scholar
• Shahidehpour, M., and Y. Fu. 2005. Tutorial: Benders decomposition in restructure power systems. Illinois institute of technology.
   Google Scholar
• Siano, P., C. Cecati, H. Yu, and J. Kolbusz. 2012. Real time operation of smart grids via FCN networks and optimal power flow. IEEE Transactions on Industrial Informatics 8:944–52. doi:10.1109/TII.2012.2205391.
   Web of Science ®Google Scholar
• Singh, B., and D.-K. Lobiyal. 2012. A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-centric Computing and Information Sciences 2. doi:10.1186/2192-1962-2-13.
   Google Scholar
• Sohrabitabar, V., M.-A. Jirdehi, and R. Hemmati. 2017. Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option. Energy 118:827–39. doi:10.1016/j.energy.2016.10.113.
   Web of Science ®Google Scholar
• Song, N.-O., J.-H. Lee, H.-M. Kim, Y.-H. Im, and J.-Y. Lee. 2015. Optimal energy management of multi-microgrids with sequentially coordinated operations. Energies 8:8371–90. doi:10.3390/en8088371.
   Web of Science ®Google Scholar
• Srinivasa Rao, P.-C., P.-K. Jana, and H. Banka. 2016. A particle swarm optimization-based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks 23:2005–20. doi:10.1007/s11276-016-1270-7.
   Web of Science ®Google Scholar
• Su, W., and J. Wang. 2012. Energy management systems in microgrid operations. The Electricity Journal 25:45–60. doi:10.1016/j.tej.2012.09.010.
   Google Scholar
• Tsikalakis, A.-G., and N.-D. Hatziargyriou. 2008. Centralized control for optimizing microgrids operation. IEEE Transactions on Energy Conversion 23:241–48. doi:10.1109/TEC.2007.914686.
   Web of Science ®Google Scholar
• Wang, Z., B. Chen, J. Wang, -M.-M. Begovic, and C. Chen. 2015. Coordinated energy management of networked microgrids in distribution systems. IEEE Transactions on Smart Grid 6:45–53. doi:10.1109/TSG.2014.2329846.
   Web of Science ®Google Scholar
• Wang, Y., Y. Huang, Y. Wang, M. Zeng, F. Li, Y. Wang, and Y. Zhang. 2018. Energy management of smart micro-grid with response loads and distributed generation considering demand response. Journal of Cleaner Production 197:1069–83. doi:10.1016/j.jclepro.2018.06.271.
   Web of Science ®Google Scholar
• Wang, C., X. Li, Y. Liu, and H. Wang. 2014. The research on development direction and points in IoT in China power grid. International Conference on Information Science, Electronics and Electrical Engineering, Sapporo, Japan. doi:10.1109/InfoSEEE.2014.6948106.
   Google Scholar
• Wang, Z., Y. Liu, Z. Ma, X. Liu, and J. Ma. 2020. LiPSG: lightweight privacy-preserving Q-learning based energy management for the IoT-enable smart grid. IEEE Internet of Things Journal 7:3935–47. doi:10.1109/JIOT.2020.2968631.
   Web of Science ®Google Scholar
• Wang, Z., X. Qin, and B. Liu. 2018.An energy-efficient clustering routing algorithm for WSN-assisted IoT. IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, IEEE. doi: 10.1109/WCNC.2018.8377171.
   Google Scholar
• Wei, M., S. Ho Hong, and M. Alam. 2016. An IoT-based energy-management platform for industrial facilities. Applied Energy 164:607–19. doi:10.1016/j.apenergy.2015.11.107.
   Web of Science ®Google Scholar
• Wu, X., X. Hu, S. Moura, X. Yin, and V. Pickert. 2016. Stochastic control of smart home energy management with plug in electric vehicle battery energy storage and photovoltaic array. Journal of Power Sources 333:203–12. doi:10.1016/j.jpowsour.2016.09.157.
   Web of Science ®Google Scholar
• Wu, X., X. Wang, and C. Qu. 2014. A hierarchical framework for generation scheduling of microgrids. IEEE Transactions on Power Delivery 29:2448–57. doi:10.1109/TPWRD.2014.2360064.
   Web of Science ®Google Scholar
• Xia, H., R.-H. Zhang, J. Yu, and Z.-K. Pan. 2016. Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks. International Journal of Wireless Information Networks 23:141–50. doi:10.1007/s10776-016-0304-5.
   Google Scholar
• Yagcitekin, B., O. Guler, and U. Cali. 2012. An energy management strategy for a wind power plant based on energy storage and wind power prediction. Energy Sources, Part B: Economics, Planning, and Policy 7:152–61. doi:10.1080/15567249.2011.601702.
   Web of Science ®Google Scholar
• Zakariazadeh, A., S. Jadid, and P. Siano. 2014. Economic-environmental energy and reserve scheduling of smart distribution systems: A multi objective mathematical programming approach. Energy Conversion and Management 78:151–64. doi:10.1016/j.enconman.2013.10.051.
   Web of Science ®Google Scholar
• Zhao, B., Y. Shi, X. Dong, W. Luan, and J. Bornemann. 2014. Short term operation scheduling in renewable-powered microgrids: A duality-based approach. IEEE Transaction on Sustainable Energy 5:209–17. doi:10.1109/TSTE.2013.2279837.
   Web of Science ®Google Scholar
• Zhao, B., Y. Shi, X. Dong, W. Luan, and J. Bornemann. 2014. Short term operation scheduling in renewable-powered microgrids: A duality-based approach. IEEE Transaction on Sustainable Energy 5:209–17. doi:10.1109/TSTE.2013.2279837.
   Web of Science ®Google Scholar