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Erschienen in:
Overview of Demand Response Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

5. Integrated Demand Response in the Multi-Energy System

verfasst von : Pengwei Du, Ning Lu, Haiwang Zhong

Erschienen in: Demand Response in Smart Grids

Verlag: Springer International Publishing

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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.

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Literatur
1.
Yu, X. H., & Xue, Y. S. (2016). Smart grids: A cyber-physical systems perspective. Proceedings of the IEEE, 104(5), 1058–1070.MathSciNetCrossRef
2.
Ahlgren, L. W. (2012). The dual-fuel strategy: An energy transition plan. Proceedings of the IEEE, 100(11), 3001–3052.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
6.
Rifkin, J. (2011). The third industrial revolution: How lateral power is transforming energy, the economy and the world. New York: Palgrave Macmillan.
7.
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.CrossRef
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.CrossRef
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.CrossRef
11.
Nolan, S., & O’Malley, M. (2015). Challenges and barriers to demand response deployment and evaluation. Applied Energy, 152, 1–10.CrossRef
12.
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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).
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
24.
Geidl, M., & Andersson, G. (2007). Optimal coupling of energy infrastructures. 2007 IEEE Lausanne Power Tech (pp. 1398–1403).
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
30.
Mancarella, P. (2014). MES (multi-energy systems): An overview of concepts and evaluation models. Energy, 65, 1–17.CrossRef
31.
Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36, 4419–4426.CrossRef
32.
Hawkes, A. D., & Leach, M. A. (2007). Cost-effective operating strategy for residential micro-combined heat and power. Energy, 32(5), 711–723.CrossRef
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.
34.
Houwing, M., Negenborn, R. R., & De Schutter, B. (2011). Demand response with micro-CHP systems. Proceedings of the IEEE, 99(1), 200–213.CrossRef
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.
36.
Peacock, A. D., & Newborough, M. (2005). Impact of micro-CHP systems on domestic sector CO2 emissions. Applied Thermal Engineering, 25(17), 2653–2676.CrossRef
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.CrossRef
38.
Geidl, M., & Andersson, G. (2007). Optimal power flow of multiple energy carriers. IEEE Transactions on Power Systems, 22(1), 145–155.CrossRef
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.CrossRef
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.CrossRef
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.MathSciNetMATHCrossRef
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.CrossRef
43.
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.CrossRef
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.CrossRef
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.CrossRef
47.
Pasricha, S., & Shaw, S. R. (2006). A dynamic PEM fuel cell model. IEEE Transactions on Energy Conversion, 21(2), 484–490.CrossRef
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.
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
63.
64.
Huang, A. (2010). FREEDM system-a vision for the future grid. IEEE PES General Meeting IEEE, 18, 1–4.
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.
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.
67.
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.
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.CrossRef
70.
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.CrossRef
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).
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.CrossRef
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.
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
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).
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.CrossRef
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.
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.
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.CrossRef
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.CrossRef
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.CrossRef
Metadaten
Titel
Integrated Demand Response in the Multi-Energy System
verfasst von
Pengwei Du
Ning Lu
Haiwang Zhong
Copyright-Jahr
2019
Buch
Demand Response in Smart Grids

Print ISBN: 978-3-030-19768-1

Electronic ISBN: 978-3-030-19769-8

Copyright-Jahr: 2019

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