Skip to main content
Top

2022 | OriginalPaper | Chapter

10. Impact of Demand Response Programs on the Operation of Power and Gas Systems

Authors : Mohammad Mehdi Davary, Mohammad Taghi Ameli, Hossein Ameli

Published in: Whole Energy Systems

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Because of increasing attention to environmental pollution globally, renewable energies such as wind and solar in the electricity supply have risen drastically. On the other hand, due to uncertainty in the amount of renewable energy production and the role of flexible gas power plants in compensating for the possible shortage of production of renewable power plants, both energy carriers’ consumption needs have a direct impact on each other. Therefore, to ensure efficient energy supply and optimal operation of electricity and gas networks, integrated network operation is necessary. Also, because of unbalanced consumption and the need for high investment to supply energy only for limited hours and low efficiency of equipment, demand response (DR) is more important as an effective method for reducing the cost and management of congestion. In this chapter, the types of DR and its linear and nonlinear modeling in the electricity and gas networks during the simultaneous operation of the gas and electricity networks are investigated. Finally, in order to investigate the effect of DR implementation on the simultaneous operation of electricity and gas networks in an integrated test system, DR with non-demand response was compared.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Zantye, M. S., Arora, A., & Hasan, M. F. (2019). Operational power plant scheduling with flexible carbon capture: A multistage stochastic optimization approach. Computers & Chemical Engineering, 130, 106544.CrossRef Zantye, M. S., Arora, A., & Hasan, M. F. (2019). Operational power plant scheduling with flexible carbon capture: A multistage stochastic optimization approach. Computers & Chemical Engineering, 130, 106544.CrossRef
2.
go back to reference Ameli, H., Ameli, M. T., & Hosseinian, S. H. (2017a). Multi-stage frequency control of a microgrid in the presence of renewable energy units. Electric Power Components and Systems, 45(2), 159–170.CrossRef Ameli, H., Ameli, M. T., & Hosseinian, S. H. (2017a). Multi-stage frequency control of a microgrid in the presence of renewable energy units. Electric Power Components and Systems, 45(2), 159–170.CrossRef
3.
go back to reference Moshari, A., Yousefi, G. R., Ebrahimi, A., & Haghbin, S. (2010). Demand-side behavior in grid environment. In IEEE innovative smart grid technologies, conference on 2010. Moshari, A., Yousefi, G. R., Ebrahimi, A., & Haghbin, S. (2010). Demand-side behavior in grid environment. In IEEE innovative smart grid technologies, conference on 2010.
4.
go back to reference Smith, V., & Kiesling, L. (2015, August). A market-based model for ISO-sponsored demand response programs. A white paper prepared for the multi-client study. Smith, V., & Kiesling, L. (2015, August). A market-based model for ISO-sponsored demand response programs. A white paper prepared for the multi-client study.
5.
go back to reference Siano, P. (2014). Demand response and smart grids-A survey. Renewable and Sustainable Energy Reviews, 30, 461–478.CrossRef Siano, P. (2014). Demand response and smart grids-A survey. Renewable and Sustainable Energy Reviews, 30, 461–478.CrossRef
6.
go back to reference Sun, J., Palade, V., Wu, X. J., Fang, W., & Wang, Z. (2014, February). Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization. IEEE Transactions on Industrial Informatics, 10(1), 222–231.CrossRef Sun, J., Palade, V., Wu, X. J., Fang, W., & Wang, Z. (2014, February). Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization. IEEE Transactions on Industrial Informatics, 10(1), 222–231.CrossRef
7.
go back to reference Mansour-Saatloo, A., Agabalaye-Rahvar, M., Mirzaei, M. A., Mohammadi-Ivatloo, B., Abapour, M., & Zare, K. (2020). Robust scheduling of hydrogen based smart micro energy hub with integrated demand response. Journal of Cleaner Production, 267, 122041.CrossRef Mansour-Saatloo, A., Agabalaye-Rahvar, M., Mirzaei, M. A., Mohammadi-Ivatloo, B., Abapour, M., & Zare, K. (2020). Robust scheduling of hydrogen based smart micro energy hub with integrated demand response. Journal of Cleaner Production, 267, 122041.CrossRef
8.
go back to reference Qadrdan, M., et al. (2017). Efficacy of options to address balancing challenges: Integrated gas and electricity perspectives. Applied Energy, 190, 181–190.CrossRef Qadrdan, M., et al. (2017). Efficacy of options to address balancing challenges: Integrated gas and electricity perspectives. Applied Energy, 190, 181–190.CrossRef
9.
go back to reference Liu, C., Shahidehpour, M., & Wang, J. (2011). Coordinated scheduling of electricity and natural gas infrastructures with a transient model for natural gas flow. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21(2), 025102.CrossRef Liu, C., Shahidehpour, M., & Wang, J. (2011). Coordinated scheduling of electricity and natural gas infrastructures with a transient model for natural gas flow. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21(2), 025102.CrossRef
10.
go back to reference Alabdulwahab, A., et al. (2015). Stochastic security-constrained scheduling of coordinated electricity and natural gas infrastructures. IEEE Systems Journal, 11(3), 1674–1683.CrossRef Alabdulwahab, A., et al. (2015). Stochastic security-constrained scheduling of coordinated electricity and natural gas infrastructures. IEEE Systems Journal, 11(3), 1674–1683.CrossRef
11.
go back to reference Zhang, X., et al. (2016). Hourly electricity demand response in the stochastic day-ahead scheduling of coordinated electricity and natural gas networks. IEEE Transactions on Power Systems, 31(1), 592–601.CrossRef Zhang, X., et al. (2016). Hourly electricity demand response in the stochastic day-ahead scheduling of coordinated electricity and natural gas networks. IEEE Transactions on Power Systems, 31(1), 592–601.CrossRef
12.
go back to reference He, C., et al. (2017). Robust co-optimization scheduling of electricity and natural gas systems via ADMM. IEEE Transactions on Sustainable Energy, 8(2), 658–670.CrossRef He, C., et al. (2017). Robust co-optimization scheduling of electricity and natural gas systems via ADMM. IEEE Transactions on Sustainable Energy, 8(2), 658–670.CrossRef
13.
go back to reference Ahmadi, A., Nezhad, A. E., & Hredzak, B. (2019). Security-constrained unit commitment in presence of lithium-ion battery storage units using information-gap decision theory. IEEE Transactions on Industrial Informatics, 15(1), 148–157.CrossRef Ahmadi, A., Nezhad, A. E., & Hredzak, B. (2019). Security-constrained unit commitment in presence of lithium-ion battery storage units using information-gap decision theory. IEEE Transactions on Industrial Informatics, 15(1), 148–157.CrossRef
14.
go back to reference Ringkjøb, H. K., Haugan, P. M., & Solbrekke, I. M. (2018). A review of modelling tools for energy and electricity systems with large shares of variable renewables. Renewable and Sustainable Energy Reviews, 96, 440–459.CrossRef Ringkjøb, H. K., Haugan, P. M., & Solbrekke, I. M. (2018). A review of modelling tools for energy and electricity systems with large shares of variable renewables. Renewable and Sustainable Energy Reviews, 96, 440–459.CrossRef
15.
go back to reference Ameli, H., Qadrdan, M., & Strbac, G. (2017). Value of gas network infrastructure flexibility in supporting cost effective operation of power systems. Applied Energy, 202, 571–580.CrossRef Ameli, H., Qadrdan, M., & Strbac, G. (2017). Value of gas network infrastructure flexibility in supporting cost effective operation of power systems. Applied Energy, 202, 571–580.CrossRef
16.
go back to reference Joung, M., & Kim, J. (2016). Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability. Applied Energy, 101, 441–448.CrossRef Joung, M., & Kim, J. (2016). Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability. Applied Energy, 101, 441–448.CrossRef
17.
go back to reference Ameli, H., Qadrdan, M., & Strbac, G. (2019). Coordinated operation strategies for natural gas and power systems in presence of gas-related flexibilities. Energy Systems Integration, 1(1), 313. Ameli, H., Qadrdan, M., & Strbac, G. (2019). Coordinated operation strategies for natural gas and power systems in presence of gas-related flexibilities. Energy Systems Integration, 1(1), 313.
18.
go back to reference Bai, L., et al. (2016). Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty. Applied Energy, 167, 270–279.CrossRef Bai, L., et al. (2016). Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty. Applied Energy, 167, 270–279.CrossRef
19.
go back to reference Chuan, H., et al. (2017). Robust coordination of interdependent electricity and natural gas systems in day-ahead scheduling for facilitating volatile renewable generations via power to-gas technology. Journal of Modern Power Systems and Clean Energy, 5(3), 375–388.CrossRef Chuan, H., et al. (2017). Robust coordination of interdependent electricity and natural gas systems in day-ahead scheduling for facilitating volatile renewable generations via power to-gas technology. Journal of Modern Power Systems and Clean Energy, 5(3), 375–388.CrossRef
21.
go back to reference Li, X. H., & Ho Hong, S. (2014). User-expected price-based demand response algorithm for a home to-grid system. Energy, 64, 437–449.CrossRef Li, X. H., & Ho Hong, S. (2014). User-expected price-based demand response algorithm for a home to-grid system. Energy, 64, 437–449.CrossRef
22.
go back to reference Aghaei, J., & Alizadeh, M. I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 64–72. Aghaei, J., & Alizadeh, M. I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 64–72.
23.
go back to reference Mazidi, M., Zakariazadeh, A., Jadid, S., & Siano, P. (2017). Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Conversion and Management, 86, 1118–1127.CrossRef Mazidi, M., Zakariazadeh, A., Jadid, S., & Siano, P. (2017). Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Conversion and Management, 86, 1118–1127.CrossRef
24.
go back to reference Zakariazadeh, A., Jadid, S., & Siano, P. (2017). Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs. Electrical Power and Energy Systems, 218–225. Zakariazadeh, A., Jadid, S., & Siano, P. (2017). Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs. Electrical Power and Energy Systems, 218–225.
25.
go back to reference Parsa Moghaddam, M., Abdollahi, A., & Rashidinejad, M. (2012). Flexible demand response programs modeling in competitive electricity markets. Applied Energy, 88, 3257–3269.CrossRef Parsa Moghaddam, M., Abdollahi, A., & Rashidinejad, M. (2012). Flexible demand response programs modeling in competitive electricity markets. Applied Energy, 88, 3257–3269.CrossRef
26.
go back to reference Aalami, H. A., Parsa Moghaddam, M., & Yousefi, G. R. (2015). Evaluation of nonlinear models for time-based rates demand response programs. Electrical Power and Energy Systems, 65, 282290.CrossRef Aalami, H. A., Parsa Moghaddam, M., & Yousefi, G. R. (2015). Evaluation of nonlinear models for time-based rates demand response programs. Electrical Power and Energy Systems, 65, 282290.CrossRef
27.
go back to reference Elaiwa, A. M., Xia, X., & Shehata, A. M. (2012). Solving dynamic economic emission dispatch problem with valve-point effects using hybrid DE-SQP. In Power engineering society conference and exposition in Africa (Power Africa), IEEE Johannesburg, 9–13 July 2012. Elaiwa, A. M., Xia, X., & Shehata, A. M. (2012). Solving dynamic economic emission dispatch problem with valve-point effects using hybrid DE-SQP. In Power engineering society conference and exposition in Africa (Power Africa), IEEE Johannesburg, 9–13 July 2012.
28.
go back to reference Alipour, M., Zare, K., & Mohammadi-Ivatloo, B. (2014). Short-term scheduling of combined heat and power generation units in the presence of demand response programs. Energy, 1–13. Alipour, M., Zare, K., & Mohammadi-Ivatloo, B. (2014). Short-term scheduling of combined heat and power generation units in the presence of demand response programs. Energy, 1–13.
29.
go back to reference Nwulu, N. I., & Xia, X. (2015). Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Conversion and Management, 89, 963–974.CrossRef Nwulu, N. I., & Xia, X. (2015). Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Conversion and Management, 89, 963–974.CrossRef
30.
go back to reference Falsafi, H., Zakariazadeh, A., & Jadid, S. (2013). "The role of demand response in single and multi-objective wind-thermal generation scheduling": A stochastic programing. Energy, 64, 853867. Falsafi, H., Zakariazadeh, A., & Jadid, S. (2013). "The role of demand response in single and multi-objective wind-thermal generation scheduling": A stochastic programing. Energy, 64, 853867.
31.
go back to reference Mandala, K. K., Mandalb, S., Bhattacharyac, B., & Chakraborty, N. (2015). Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique. Applied Soft Computing, 28, 188–195.CrossRef Mandala, K. K., Mandalb, S., Bhattacharyac, B., & Chakraborty, N. (2015). Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique. Applied Soft Computing, 28, 188–195.CrossRef
32.
go back to reference Behrangrad, M. (2015). A review of demand side management business models in the electricity market. Renewable and Sustainable Energy Reviews, 47, 270–283.CrossRef Behrangrad, M. (2015). A review of demand side management business models in the electricity market. Renewable and Sustainable Energy Reviews, 47, 270–283.CrossRef
33.
go back to reference Ashfaq, A., Yingyun, S., & Zia Khan, A. (2014). Optimization of economic dispatch problem integrated with stochastic demand side response. In IEEE international conference on Intelligent Energy and Power Systems (IEPS). Ashfaq, A., Yingyun, S., & Zia Khan, A. (2014). Optimization of economic dispatch problem integrated with stochastic demand side response. In IEEE international conference on Intelligent Energy and Power Systems (IEPS).
34.
go back to reference Nikzad, M., & Mozafari, B. (2014). Reliability assessment of incentive- and priced-based demand response programs in restructured power systems. Electrical Power and Energy Systems, 56, 83–96.CrossRef Nikzad, M., & Mozafari, B. (2014). Reliability assessment of incentive- and priced-based demand response programs in restructured power systems. Electrical Power and Energy Systems, 56, 83–96.CrossRef
35.
go back to reference Devlin, J., et al. (2017). A multi vector energy analysis for interconnected power and gas systems. Applied Energy, 192, 315–328.CrossRef Devlin, J., et al. (2017). A multi vector energy analysis for interconnected power and gas systems. Applied Energy, 192, 315–328.CrossRef
36.
go back to reference Benasla, L., Belmadani, A., & Rahli, M. (2016). Spiral optimization algorithm for solving combined economic and emission dispatch. International Journal of Electrical Power & Energy Systems, 62, 163–174.CrossRef Benasla, L., Belmadani, A., & Rahli, M. (2016). Spiral optimization algorithm for solving combined economic and emission dispatch. International Journal of Electrical Power & Energy Systems, 62, 163–174.CrossRef
37.
go back to reference Jiang, S., Ji, Z., & Shen, Y. (2017). A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch Problems with various practical constraints. International Journal of Electrical Power & Energy Systems, 55, 623–644. Jiang, S., Ji, Z., & Shen, Y. (2017). A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch Problems with various practical constraints. International Journal of Electrical Power & Energy Systems, 55, 623–644.
39.
go back to reference Ameli, H., Qadrdan, M., & Strbac, G. (2017). Value of gas network infrastructure flexibility in supporting cost effective operation of power systems. Journal of Applied Energy, 202, 571–580.CrossRef Ameli, H., Qadrdan, M., & Strbac, G. (2017). Value of gas network infrastructure flexibility in supporting cost effective operation of power systems. Journal of Applied Energy, 202, 571–580.CrossRef
40.
go back to reference Ameli, H. (2018). Value of flexibility options in transition to lower carbon natural gas and power systems. Thesis for doctor of philosophy, Imperial College London. Ameli, H. (2018). Value of flexibility options in transition to lower carbon natural gas and power systems. Thesis for doctor of philosophy, Imperial College London.
41.
go back to reference Davary, M. M. (2021, February). Optimal Modeling of demand response in the integrated network of electricity and gas for minimizing fuel costs and emission of greenhouse units. Master’s thesis, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. Davary, M. M. (2021, February). Optimal Modeling of demand response in the integrated network of electricity and gas for minimizing fuel costs and emission of greenhouse units. Master’s thesis, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
42.
go back to reference Cui, H., Xia, W., Yang, S., & Wang, X. (2020). Real-time emergency demand response strategy for optimal load dispatch of heat and power micro-grids. International Journal of Electrical Power & Energy Systems, 121, 106127.CrossRef Cui, H., Xia, W., Yang, S., & Wang, X. (2020). Real-time emergency demand response strategy for optimal load dispatch of heat and power micro-grids. International Journal of Electrical Power & Energy Systems, 121, 106127.CrossRef
43.
go back to reference Wang, F., Ge, X., Yang, P., Li, K., Mi, Z., Siano, P., & Duić, N. (2020, December). Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing. Energy, 213, 118765.CrossRef Wang, F., Ge, X., Yang, P., Li, K., Mi, Z., Siano, P., & Duić, N. (2020, December). Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing. Energy, 213, 118765.CrossRef
44.
go back to reference Zhong, W., Xie, K., Liu, Y., Yang, C., Xie, S., & Zhang, Y. (2021, January). Distributed demand response for multienergy residential communities with incomplete information. IEEE Transactions on Industrial Informatics, 17(1), 547–557.CrossRef Zhong, W., Xie, K., Liu, Y., Yang, C., Xie, S., & Zhang, Y. (2021, January). Distributed demand response for multienergy residential communities with incomplete information. IEEE Transactions on Industrial Informatics, 17(1), 547–557.CrossRef
45.
go back to reference Wang, F., Xiang, B., Li, K., Ge, X., Lu, H., Lai, J., & Dehghanian, P. (2020, March). Smart households’ aggregated capacity forecasting for load aggregators under incentive-based demand response programs. IEEE Transactions on Industrial Applications, 56(2), 1086–1097.CrossRef Wang, F., Xiang, B., Li, K., Ge, X., Lu, H., Lai, J., & Dehghanian, P. (2020, March). Smart households’ aggregated capacity forecasting for load aggregators under incentive-based demand response programs. IEEE Transactions on Industrial Applications, 56(2), 1086–1097.CrossRef
46.
go back to reference Real-time emergency demand response strategy for optimal load dispatch of heat and power micro-grids. (2020). IJEPES. Real-time emergency demand response strategy for optimal load dispatch of heat and power micro-grids. (2020). IJEPES.
47.
go back to reference The impact of customers’ participation level and various incentive values on implementing emergency demand response program in micro grid operation (2020). IJEPES. The impact of customers’ participation level and various incentive values on implementing emergency demand response program in micro grid operation (2020). IJEPES.
Metadata
Title
Impact of Demand Response Programs on the Operation of Power and Gas Systems
Authors
Mohammad Mehdi Davary
Mohammad Taghi Ameli
Hossein Ameli
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-87653-1_10