Skip to main content
SpringerLink
Log in

Integrated Demand Response in the Multi-Energy System

  • Chapter
  • First Online:
Demand Response in Smart Grids

  • 973 Accesses

Abstract

Demand response (DR) is a critical and effective measure to stimulate the demand side resources to interact with renewable generation in the power system. However, the conventional scope of DR cannot fully exploit the interaction capabilities of demand side resources, which limits the energy users in the electric power system. With the revolution of the traditional economic and social pattern based on centralized fossil energy consumption, “Energy Internet” is impelling the development of the third industrial revolution, which aims at promoting the incorporation of sustainable energy and internet technology, and facilitating the integration of multi-energy systems (MESs). By integrating electricity, thermal energy, natural gas, and other forms of energy, the smart energy hub (SEH) makes it possible for energy users to flexibly switch the source of consumed energy. With the complementarity of MESs, even the inelastic loads can actively participate in DR programs, which fully exploits the interaction capability of DR resources while maintaining the consumers’ comfort. This novel vision of the DR programs is termed as “Integrated Demand Response (IDR).” In this context, the state of the art of IDR in the MESs is reviewed for the first time. First, the basic concept of IDR and the value analysis are introduced. The research on IDR in the MES is then summarized. The overviews of the engineering projects around the world are introduced. Finally, the key issues and potential research topics on IDR in the MES are proposed. Hopefully, this chapter will provide reference for future research and engineering projects on IDR programs in the MES.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yu, X. H., & Xue, Y. S. (2016). Smart grids: A cyber-physical systems perspective. Proceedings of the IEEE, 104(5), 1058–1070.

    Article  MathSciNet  Google Scholar 

  2. Ahlgren, L. W. (2012). The dual-fuel strategy: An energy transition plan. Proceedings of the IEEE, 100(11), 3001–3052.

    Article  Google Scholar 

  3. Glinkowski, M., Hou, J., & Rackliffe, G. (2011). Advances in wind energy technologies in the context of smart grid. Proceedings of the IEEE, 99(6), 1083–1097.

    Article  Google Scholar 

  4. Kong, F. X., Dong, C. S., Liu, X., et al. (2014). Quantity versus quality: Optimal harvesting wind power for the smart grid. Proceedings of the IEEE, 102(11), 1762–1776.

    Article  Google Scholar 

  5. Huang, Q. A., Crow, L. M., Heydt, T. G., et al. (2011). The future renewable electric energy delivery and management (FREEDM) system: The energy internet. Proceedings of the IEEE, 99(1), 133–148.

    Article  Google Scholar 

  6. Rifkin, J. (2011). The third industrial revolution: How lateral power is transforming energy, the economy and the world. New York: Palgrave Macmillan.

    Google Scholar 

  7. The State Council. Guidelines of the state council on actively pushing “Internet+” action. Retrieved June 9, 2016, from http://www.gov.cn/zhengce/content/2015-07/04/content_10002.htm.

  8. Krause, T., Andersson, G., Frohlich, K., et al. (2011). Multiple-energy carriers: Modeling of production, delivery and consumption. Proceedings of the IEEE, 99(1), 15–27.

    Article  Google Scholar 

  9. Biegel, B., Andersen, P., Stoustrup, J., et al. (2016). Sustainable reserve power from demand response and fluctuating production-two Danish demonstrations. Proceedings of the IEEE, 104(4), 780–788.

    Article  Google Scholar 

  10. Zhong, H., Xie, L., & Xia, Q. (2013). Coupon incentive-based demand response: Theory and case study. IEEE Transactions on Power Apparatus and Systems, 28(2), 1266–1276.

    Article  Google Scholar 

  11. Nolan, S., & O’Malley, M. (2015). Challenges and barriers to demand response deployment and evaluation. Applied Energy, 152, 1–10.

    Article  Google Scholar 

  12. US Department of Energy. Benefits of demand response in electricity markets and recommendations for achieving them: A report to the united states congress pursuant to section 1252 of the energy policy act of 2005. Retrieved June 9, 2016, from http://www.oe.energy.gov/DocumentsandMedia/congress_1252d.pdf.

  13. Siano, P., & Sarno, D. (2016). Assessing the benefits of residential demand response in a real time distribution energy market. Applied Energy, 161, 533–551.

    Article  Google Scholar 

  14. Finn, P., & Fitzpatrick, C. (2014). Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing. Applied Energy, 113, 11–21.

    Article  Google Scholar 

  15. Chehreghani, M., Hashmi, A., Hasse, H., et al. (2012). Optimal operation of residential EH in smart grids. IEEE Transactions on Smart Grid, 3(4), 1755–1766.

    Article  Google Scholar 

  16. Yang, H., Xiong, T., Qiu, J., et al. (2016). Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response. Applied Energy, 167, 353–365.

    Article  Google Scholar 

  17. Zhang, Q., & Li, J. (2012). Demand response in electricity markets: A review. 2012 9th international conference on the European Energy market (pp. 1–8).

    Google Scholar 

  18. Fadlullah, M., Quan, D., Kato, N., et al. (2014). GTES: An optimized game-theoretic demand-side management scheme for smart grid. IEEE Systems Journal, 8(2), 588–597.

    Article  Google Scholar 

  19. Hong, Y. W., Shahidehpour, M., & Khodayar, M. (2013). Hourly demand response in day-ahead scheduling considering generating unit ramping cost. IEEE Transactions on Power Apparatus and Systems, 28(3), 2446–2454.

    Article  Google Scholar 

  20. Shao, S., Pipattanasomporn, M., & Rahman, S. (2013). Development of physical-based demand response-enabled residential load models. IEEE Transactions on Power Apparatus and Systems, 28(2), 607–614.

    Article  Google Scholar 

  21. Alabdulwahab, A., Abusorrah, A., Zhang, X. P., et al. (2015). Coordination of interdependent natural gas and electricity infrastructures for firming the variability of wind energy in stochastic day-ahead scheduling. IEEE Transactions on Sustainable Energy, 6(2), 606–615.

    Article  Google Scholar 

  22. Zhang, X. P., Shahidehpour, M., Alabdulwahab, A., et al. (2016). Hourly electricity demand response in the stochastic day-ahead scheduling of coordinated electricity and natural gas networks. IEEE Transactions on Power Apparatus and Systems, 31(1), 592–601.

    Article  Google Scholar 

  23. Zhang, X. P., Che, L., Shahidehpour, M., et al. (2016). Electricity-natural gas operation planning with hourly demand response for deployment of flexible ramp. IEEE Transactions on Sustainable Energy, 7(3), 996–1004.

    Article  Google Scholar 

  24. Geidl, M., & Andersson, G. (2007). Optimal coupling of energy infrastructures. 2007 IEEE Lausanne Power Tech (pp. 1398–1403).

    Google Scholar 

  25. Behboodi, S., Chassin, D., Crawford, C., et al. (2016). Renewable resources portfolio optimization in the presence of demand response. Applied Energy, 162, 139–148.

    Article  Google Scholar 

  26. Mazidi, M., Monsef, H., & Siano, P. (2016). Robust day-ahead scheduling of smart distribution networks considering demand response programs. Applied Energy, 178, 929–942.

    Article  Google Scholar 

  27. Clegg, S., & Mancarella, P. (2015). Integrated modeling and assessment of the operational impact of power-to-gas (P2G) on electrical and gas transmission networks. IEEE Transactions on Sustainable Energy, 6(4), 1234–1244.

    Article  Google Scholar 

  28. Clegg, S., & Mancarella, P. (2016). Storing renewables in the gas network: Modelling of power-to-gas seasonal storage flexibility in low-carbon power systems. IET Generation, Transmission & Distribution, 10(3), 566–575.

    Article  Google Scholar 

  29. Wang, J., Zhong, H., Xia, Q., et al. (2016). Optimal joint-dispatch of energy and reserve for CCHP-based microgrids. IET Generation, Transmission & Distribution. 2016.

    Google Scholar 

  30. Mancarella, P. (2014). MES (multi-energy systems): An overview of concepts and evaluation models. Energy, 65, 1–17.

    Article  Google Scholar 

  31. Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36, 4419–4426.

    Article  Google Scholar 

  32. Hawkes, A. D., & Leach, M. A. (2007). Cost-effective operating strategy for residential micro-combined heat and power. Energy, 32(5), 711–723.

    Article  Google Scholar 

  33. Cesena, E. A. M., & Mancarella, P. (2016). Distribution network support from multi-energy demand side response in smart districts. Innovative Smart Grid Technologies-Asia, 99, 753–758.

    Google Scholar 

  34. Houwing, M., Negenborn, R. R., & De Schutter, B. (2011). Demand response with micro-CHP systems. Proceedings of the IEEE, 99(1), 200–213.

    Article  Google Scholar 

  35. Good, N., Zhang, L., Navarro-Espinosa, A., & Mancarella, P. (2013). Physical modeling of electro-thermal domestic heating systems with quantification of economic and environmental costs. Eurocon, 1164–1171.

    Google Scholar 

  36. Peacock, A. D., & Newborough, M. (2005). Impact of micro-CHP systems on domestic sector CO2 emissions. Applied Thermal Engineering, 25(17), 2653–2676.

    Article  Google Scholar 

  37. Geidl, M., Koeppel, G., Favre-Perrod, P., et al. (2007). Energy hubs for the future. IEEE Power and Energy Magazine, 5(1), 24–30.

    Article  Google Scholar 

  38. Geidl, M., & Andersson, G. (2007). Optimal power flow of multiple energy carriers. IEEE Transactions on Power Systems, 22(1), 145–155.

    Article  Google Scholar 

  39. Parisio, A., Del Vecchio, C., & Vaccaro, A. (2012). A robust optimization approach to energy hub management. International Journal of Electrical Power & Energy Systems, 42(1), 98–104.

    Article  Google Scholar 

  40. Evins, R., Orehounig, K., Dorer, V., et al. (2014). New formulations of the ‘energy hub’ model to address operational constraints. Energy, 73, 387–398.

    Article  Google Scholar 

  41. Rong, A., Hakonen, H., & Lahdelma, R. (2006). An efficient linear model and optimisation algorithm for multi-site combined heat and power production. European Journal of Operational Research, 168(2), 612–632.

    Article  MathSciNet  MATH  Google Scholar 

  42. Bracco, S., Dentici, G., & Siri, S. (2013). Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area. Energy, 55, 1014–1024.

    Article  Google Scholar 

  43. Energy Government. Choosing and installing geothermal heat pumps. Retrieved March 13, 2017, from https://energy.gov/energysaver/choosing-and-installing-geothermal-heat-pumps

  44. Waheed, M. A., Oni, A. O., Adejuyigbe, S. B., et al. (2014). Performance enhancement of vapor recompression heat pump. Applied Energy, 114, 69–79.

    Article  Google Scholar 

  45. Rad, F. M., Fung, A. S., & Leong, W. H. (2013). Feasibility of combined solar thermal and ground source heat pump systems in cold climate, Canada. Energy and Buildings, 61, 224–232.

    Article  Google Scholar 

  46. Corrêa, J. M., Farret, F. A., Canha, L. N., et al. (2004). An electrochemical-based fuel-cell model suitable for electrical engineering automation approach. IEEE Transactions on Industrial Electronics, 51(5), 1103–1112.

    Article  Google Scholar 

  47. Pasricha, S., & Shaw, S. R. (2006). A dynamic PEM fuel cell model. IEEE Transactions on Energy Conversion, 21(2), 484–490.

    Article  Google Scholar 

  48. Huang, J. H., Zhou, H. S., Wu, Q. H., et al. (2016). Assessment of an integrated energy system embedded with power-to-gas plant. Innovative Smart Grid Technologies-Asia, 77, 196–201.

    Google Scholar 

  49. Sheikhi, A., Rayati, M., Bahrami, S., et al. (2015). Integrated demand side management game in smart energy hubs. IEEE Transactions on Smart Grid, 6(2), 675–683.

    Article  Google Scholar 

  50. Aghaei, J., & Alizadeh, M. I. (2013). Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems). Energy, 55, 1044–1054.

    Article  Google Scholar 

  51. Mancarella, P., & Chicco, G. (2013). Real-time demand response from energy shifting in distributed multi-generation. IEEE Transactions on Smart Grid, 4(4), 1928–1938.

    Article  Google Scholar 

  52. 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, 71, 289–301.

    Article  Google Scholar 

  53. Good, N., Karangelos, E., Navarro-Espinosa, A., et al. (2015). Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users’ discomfort. IEEE Transactions on Smart Grid, 6(5), 2333–2342.

    Article  Google Scholar 

  54. Sheikhi, A., Bahrami, S., & Ranjbar, A. M. (2015). An autonomous demand response program for electricity and natural gas networks in smart energy hubs. Energy, 89, 490–499.

    Article  Google Scholar 

  55. Zhang, X., Shahidehpour, M., Alabdulwahab, A., 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.

    Article  Google Scholar 

  56. Pazouki, S., Haghifam, M. R., & Olamaei, J. (2013). Economical scheduling of multi carrier energy systems integrating renewable, energy storage and demand response under energy hub approach. Smart Grid Conference, 67, 80–84.

    Google Scholar 

  57. Pazouki, S., Haghifam, M. R., & Moser, A. (2014). Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. International Journal of Electrical Power & Energy Systems, 61, 335–345.

    Article  Google Scholar 

  58. Orehounig, K., Evins, R., & Dorer, V. (2015). Integration of decentralized energy systems in neighbourhoods using the energy hub approach. Applied Energy, 154, 277–289.

    Article  Google Scholar 

  59. Nguyen, D. T., & Le, L. B. (2014). Optimal bidding strategy for microgrids considering renewable energy and building thermal dynamics. IEEE Transactions on Smart Grid, 5(4), 1608–1620.

    Article  Google Scholar 

  60. Vrettos, E., Oldewurtel, F., & Andersson, G. (2016). Robust energy-constrained frequency reserves from aggregations of commercial buildings. IEEE Transactions on Power Systems, 31(6), 4272–4285.

    Article  Google Scholar 

  61. Bahrami, S., & Sheikhi, A. (2016). From demand response in smart grid toward integrated demand response in smart energy hub. IEEE Transactions on Smart Grid, 7(2), 650–658.

    Google Scholar 

  62. Ma, L., Zhang, J., Tushar, W., & Yuen, C. (2016). Energy management for joint operation of CHP and PV prosumers inside a grid-connected microgrid: A game theoretic approach. IEEE Transactions on Industrial Informatics, 12(5), 1930–1942.

    Article  Google Scholar 

  63. DOE of United States.Grid 2030: A national vision for electricity’s second 100 years. Retrieved June 9, 2016, from https://www.ferc.gov/eventcalendar/Files/20050608125055-grid-2030.pdf.

  64. Huang, A. (2010). FREEDM system-a vision for the future grid. IEEE PES General Meeting IEEE, 18, 1–4.

    Google Scholar 

  65. Xu, Y., Zhang, J., Wang, W., et al. (2011). Energy router: Architectures and functionalities toward Energy internet. Smart grid communications. 2011 IEEE international conference on IEEE, 16, 31–36.

    Google Scholar 

  66. Zhang, J., Wang, W., & Bhattacharya, S. (2012). Architecture of solid state transformer-based energy router and models of energy traffic. IEEE PES Innovative Smart Grid Technologies IEEE, 2012, 1–8.

    Google Scholar 

  67. MIT Technology Review. A startup’ s smart batteries reduce buildings’ electric bills. Retrieved June 9, 2016, from https://www.technologyreview.com/s/506776/a-startups-smart-batteries-reduce-buildings-electric-bills/.

  68. Vermesan, O., Blystad, L. C., Zafalon, R., et al. (2011). Internet of energy–Connecting energy anywhere anytime (Advanced microsystems for automotive applications) (pp. 33–48). Berlin: Springer.

    Google Scholar 

  69. Qadrdan, M., Chaudry, M., Wu, J., et al. (2010). Impact of a large penetration of wind generation on the GB gas network. Energy Policy, 38(10), 5684–5695.

    Article  Google Scholar 

  70. CATAPULT. Energy Systems Catapult. Retrieved June 9, 2016, from http://catapult.org.uk/energy-systems-catapult/.

  71. Zhang, X., Karady, G. G., & Ariaratnam, S. T. (2014). Optimal allocation of CHP-based distributed generation on urban energy distribution networks. IEEE Transactions on Sustainable Energy, 5(1), 246–253.

    Article  Google Scholar 

  72. Chen, X., Xia, Q., Kang, C., et al. (2012). A rural heat load direct control model for wind power integration in China. 2012 IEEE Power and Energy Society General Meeting (pp. 1–6).

    Google Scholar 

  73. Ellis, M. W., Von Spakovsky, M. R., & Nelson, D. J. (2001). Fuel cell systems: Efficient, flexible energy conversion for the 21st century. Proceedings of the IEEE, 89(12), 1808–1818.

    Article  Google Scholar 

  74. Lemofouet, S., & Rufer, A. (2005). Hybrid energy storage systems based on compressed air and supercapacitors with maximum efficiency point tracking. Power electronics and applications, 2005 European conference on. IEEE 2005: 10.

    Google Scholar 

  75. Mukherjee, U., Walker, S., Maroufmashat, A., et al. (2016). Power-to-gas to meet transportation demand while providing ancillary services to the electrical grid. Smart Energy Grid Engineering, IEEE, 89, 221–225.

    Article  Google Scholar 

  76. Liu, C., Shahidehpour, M., Fu, Y., et al. (2009). Security-constrained unit commitment with natural gas transmission constraints. IEEE Transactions on Power Systems, 24(3), 1523–1536.

    Article  Google Scholar 

  77. Tushar, W., Chai, B., Yuen, C., et al. (2016). Energy storage sharing in smart grid: A modified auction-based approach. IEEE Transactions on Smart Grid, 7(3), 1462–1475.

    Article  Google Scholar 

  78. Abbasi, A. R., & Seifi, A. R. (2014). Simultaneous integrated stochastic electrical and thermal energy expansion planning. IET Generation, Transmission & Distribution, 8(6), 1017–1027.

    Article  Google Scholar 

  79. Yao, W., Zhao, J., Wen, F., et al. (2014). A multi-objective collaborative planning strategy for integrated power distribution and electric vehicle charging systems. IEEE Transactions on Power Systems, 29(4), 1811–1821.

    Article  Google Scholar 

  80. Salimi, M., Ghasemi, H., Adelpour, M., et al. (2015). Optimal planning of energy hubs in interconnected energy systems: A case study for natural gas and electricity. IET Generation, Transmission & Distribution, 9(8), 695–707.

    Article  Google Scholar 

  81. Kirschen, D. S., Strbac, G., Cumperayot, P., et al. (2000). Factoring the elasticity of demand in electricity prices. IEEE Transactions on Power Systems, 15(2), 612–617.

    Article  Google Scholar 

  82. Solanki, B. V., Raghurajan, A., Bhattacharya, K., et al. (2015). Including smart loads for optimal demand response in integrated Energy Management Systems for Isolated Microgrids. IEEE Transactions on Smart Grid, 99, 1–10.

    Google Scholar 

  83. Wang, Q., Zhang, C., Ding, Y., et al. (2015). Review of real-time electricity markets for integrating distributed energy resources and demand response. Applied Energy, 138, 695–706.

    Article  Google Scholar 

Future Reading

  1. Neyestani, N., Damavandi, M. Y., Shafie-khah, M., et al. (2015). Uncertainty characterization of carrier-based demand response in smart multi-energy systems. 2015 IEEE 5th international conference on power engineering, Energy and electrical drives IEEE (pp. 366–371).

    Google Scholar 

  2. Arteconi, A., Patteeuw, D., Bruninx, K., et al. (2016). Active demand response with electric heating systems: Impact of market penetration. Applied Energy, 177, 636–648.

    Article  Google Scholar 

  3. Pazouki, S., Haghifam, M. R., & Olamaei, J. (2013). Economical scheduling of multi carrier energy systems integrating renewable, Energy storage and demand response under Energy hub approach. Smart Grid Conference IEEE, 12, 80–84.

    Google Scholar 

  4. Gitizadeh, M., Farhadi, S., & Safarloo, S. (2014). Multi-objective energy management of CHP-based microgrid considering demand response programs. Smart Grid Conference IEEE, 15, 1–7.

    Google Scholar 

  5. Zhang, X., Shahidehpour, M., Alabdulwahab, A., et al. (2015). Optimal expansion planning of energy hub with multiple energy infrastructures. IEEE Transactions on Smart Grid, 6(5), 2302–2311.

    Article  Google Scholar 

  6. Arteconi, A., Hewitt, N. J., & Polonara, F. (2012). State of the art of thermal storage for demand-side management. Applied Energy, 93, 371–389.

    Article  Google Scholar 

  7. Lopes, J. A. P., Soares, F. J., & Almeida, P. M. R. (2011). Integration of electric vehicles in the electric power system. Proceedings of the IEEE, 99(1), 168–183.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electric Reliability Council of Texas, Taylor, TX, USA

    Pengwei Du

  2. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

    Ning Lu

  3. Tsinghua University, Beijing, China

    Haiwang Zhong

Authors
  1. Pengwei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ning Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiwang Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Du, P., Lu, N., Zhong, H. (2019). Integrated Demand Response in the Multi-Energy System. In: Demand Response in Smart Grids. Springer, Cham. https://doi.org/10.1007/978-3-030-19769-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-19769-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19768-1

  • Online ISBN: 978-3-030-19769-8

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation