151
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A Multilevel Model of Energy Market Considering Coupon Incentive Based Demand Response with Wind Power Uncertainty

&
Pages 2030-2046 | Received 28 Mar 2023, Accepted 17 Sep 2023, Published online: 11 Oct 2023

References

  • I. Abdelmotteleb, E. Fumagalli and M. Gibescu, “Assessing customer engagement in electricity distribution-level flexibility product provision: the Norwegian case,” Sustain. Energy Grids Netw., vol. 29, pp. 100564, 2022.
  • P. Faria and Z. Vale, “Distributed energy resource scheduling with focus on demand response complex contracts,” J. Mod. Power Syst. Clean Energy, vol. 9, no. 5, pp. 1172–1182, September 2021. DOI: 10.35833/MPCE.2020.000317.
  • G. Morales Espana, R. Martinez Gordon and J. Sijm, “Classifying and modeling demand response in power systems,” Energy, vol. 242, pp. 122544, 2022. DOI: 10.1016/j.energy.2021.122544.
  • P. Cappers, C. Goldman and D. Kathan, “Demand response in U.S. electricity markets: empirical evidence,” Energy, vol. 35, no. 4, pp. 1526–1535, 2010. DOI: 10.1016/j.energy.2009.06.029.
  • M. Yu, et al., “An incentive-based demand response (DR) model considering composited DR resources,” IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1488–1498, 2019. DOI: 10.1109/TIE.2018.2826454.
  • Z. Haiwang, X. Le and X. Qing, “Coupon incentive-based demand response: theory and case study,” IEEE Trans. Power Syst., vol. 28, pp. 1266–1276, 2013.
  • M. Hussain, Y. Gao, S. Semba, et al., “Examination of optimum benefits of customer and LSE by incentive and dynamic price-based demand response,” Energy Sour. B Econ. Plan. Policy, vol. 15, no. 6, pp. 383–401, 2020. DOI: 10.1080/15567249.2020.1808913.
  • H. Takano, A. Kudo, H. Taoka and A. Ohara, “A basic study on incentive pricing for demand response programs based on social welfare maximization,” J. Int. Council Electr. Eng., vol. 8, no. 1, pp. 136–144, 2018. DOI: 10.1080/22348972.2018.1477092.
  • Y. Chai, Y. Xiang, J. Liu, C. Gu, W. Zhang and W. Xu, “Incentive-based demand response model for maximizing benefits of electricity retailers,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 6, pp. 1644–1650, 2019. DOI: 10.1007/s40565-019-0504-y.
  • E. Shahryari, H. Shayeghi, B. Mohammadi-Ivatloo and M. Moradzadeh, “An improved incentive-based demand response program in day-ahead and intra-day electricity markets,” Energy, vol. 155, pp. 205–214, 2018. DOI: 10.1016/j.energy.2018.04.170.
  • M. A. Fotouhi Ghazvini, et al., “Congestion management in active distribution networks through demand response implementation,” Sustain. Energy Grids Netw., vol. 17, pp. 100185, 2019. DOI: 10.1016/j.segan.2018.100185.
  • Y. He and J. Zhang, “Real-time electricity pricing mechanism in China based on system dynamics,” Energy Convers. Manag., vol. 94, pp. 394–405, 2015. DOI: 10.1016/j.enconman.2015.02.007.
  • Q. Shi, C.-F. Chen, A. Mammoli and F. Li, “Estimating the profile of incentive-based demand response (IBDR) by integrating technical models and social-behavioral factors,” IEEE Trans. Smart Grid, vol. 11, no. 1, pp. 171–183, 2020. DOI: 10.1109/TSG.2019.2919601.
  • M. Pipattanasomporn, M. Kuzlu and S. Rahman, “An algorithm for intelligent home energy management and demand response analysis,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 2166–2173, 2012. DOI: 10.1109/TSG.2012.2201182.
  • R. de Sá Ferreira, et al., “Time-of-use tariff design under uncertainty in price-elasticities of electricity demand: a stochastic optimization approach,” IEEE Trans. Smart Grid, vol. 4, pp. 2285–2295, 2013.
  • X. Li, H. Yang, M. Yang and G. Yang, “Flexible time-of-use tariff with dynamic demand using artificial bee colony with transferred memory scheme,” Swarm Evol. Comput., vol. 46, pp. 235–251, 2019. DOI: 10.1016/j.swevo.2019.02.006.
  • S. H. Hong, M. Yu and X. Huang, “A real-time demand response algorithm for heterogeneous devices in buildings and homes,” Energy, vol. 80, pp. 123–132, 2015. DOI: 10.1016/j.energy.2014.11.053.
  • F. Li and R. Bo, “DCOPF-based LMP simulation: algorithm, comparison with ACOPF, sensitivity,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1475–1485, 2007. DOI: 10.1109/TPWRS.2007.907924.
  • N. G. Paterakis, O. Erdinç and J. P. S. Catalão, “An overview of demand response: key-elements and international experience,” Renew. Sustain. Energy Rev., vol. 69, pp. 871–891, Mar. 2017. DOI: 10.1016/j.rser.2016.11.167.
  • A. Giordano, C. Mastroianni, D. Menniti, A. Pinnarelli, L. Scarcello and N. Sorrentino, “A two-stage approach for efficient power sharing within energy districts,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 3, pp. 1679–1689, Mar. 2021. DOI: 10.1109/TSMC.2019.2902077.
  • N. Shang, Y. Ding and W. Cui, “Review of market power assessment and mitigation in reshaping of power systems,” J. Mod. Power Syst. Clean Energy, vol. 10, no. 5, pp. 1067–1084, September 2022. DOI: 10.35833/MPCE.2021.000029.
  • S. Zheng, Y. Sun, B. Qi and B. Li, “Incentive-based integrated demand response considering S&C effect in demand side with incomplete information,” IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4465–4482, Nov. 2022. DOI: 10.1109/TSG.2022.3149959.
  • M. Yu and S. H. Hong, “Supply–demand balancing for power management in smart grid: a Stackelberg game approach,” Appl. Energy, vol. 164, pp. 702–710, Feb. 2016. DOI: 10.1016/j.apenergy.2015.12.039.
  • W. Liu, Q. Wu, F. Wen, et al., “Day-Ahead congestion management in distribution systems through household demand response and distribution congestion prices,” IEEE Trans. Smart Grid, vol. 5, no. 6, pp. 2739–2747, 2014. DOI: 10.1109/TSG.2014.2336093.
  • L. Baringo and A. J. Conejo, “Strategic offering for a wind power producer,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 4645–4654, 2013. DOI: 10.1109/TPWRS.2013.2273276.
  • X. Fang, H. Cui, H. Yuan, et al., “Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties,” Appl. Energy, vol. 252, pp. 113420, 2019. DOI: 10.1016/j.apenergy.2019.113420.
  • X. Fang, B.-M. Hodge, H. Jiang and Y. Zhang, “Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow,” Appl. Energy, vol. 239, pp. 938–947, 2019. DOI: 10.1016/j.apenergy.2019.01.259.
  • H. Chao, “Demand response in wholesale electricity markets: the choice of customer baseline,” J. Regul. Econ., vol. 39, no. 1, pp. 68–88, 2011. DOI: 10.1007/s11149-010-9135-y.
  • “Demand response programs.” Available: http://www.sce.com/b-rs/demand-response-programs/.
  • Y. Chen, Z. Zhang, H. Chen and H. Zheng, “Robust UC model based on multi-band uncertainty set considering the temporal correlation of wind/load prediction errors,” IET Gener. Transm. Distrib., vol. 14, no. 2, pp. 180–190, 2020. DOI: 10.1049/iet-gtd.2019.1439.
  • Y. Chen, et al., “Robust N–k CCUC model considering the fault outage probability of units and transmission lines,” IET Gener. Transm. Distrib., vol. 13, no. 17, pp. 3782–3791, Sep. 2019. DOI: 10.1049/iet-gtd.2019.0780.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.