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:: Volume 11, Issue 4 (11-2022) ::
ieijqp 2022, 11(4): 28-38 Back to browse issues page
Smart Distribution Networks Potential for Demand Response Contribution to Enhance Energy Service Provider Performance
Esmaeil Mahboubi Moghaddam * 1, Ahmad Nikoobakht2 , Mohsen Zare3
1- Assistant Professor, Department of Electrical Engineering, Quchan University of Technology, Quchan, Iran
2- Assistant Professor, Department of Electrical Engineering, Higher Education Center of Eghlid, Eghlid, Iran
3- Assistant Professor, Department of Electrical Engineering, Jahrom University, Jahrom, Iran
Abstract:   (1357 Views)
The concept of demand response (DR) continues to evolve, and its various capabilities are being investigated to enhance the efficiency of nowadays electric power industries. To this end, the barriers that limit DR capabilities should be resolved. This paper provides a new efficient decision model for energy service providers in smart distribution networks to make the maximum use of DR potential as the most cost-effective solution. The correct and proper application of the DR problem provides special capabilities for these entities and can lead to more profit. On the other hand, participating in the upstream market and demand allocation in the downstream network are two main tasks of energy providers. These two tasks affect each other, and simultaneous attention to them is needed for more efficiency. Generally, conservative participation in the upstream market is the main problem of these entities due to the uncertainty of load forecasting, especially considering that the DR problem will aggravate this uncertainty. In these conditions, the interactions between the load curve and price changes should also be considered. To better understand, suppose that an energy provider wants to reduce its energy purchase cost by applying DR. This entity initially forecasts its load consumption and participates in the electricity market. After market clearing, the values of locational marginal price (LMP) are determined for the next 24 hours. Now, applying DR and moving the load consumption to the less expensive hours will reduce the final purchase cost. However, moving the load consumption leads to changes in the LMP values in the substation bus of the distribution network. It is due to the dependencies between the load consumption and the prices. Disregarding these dependencies will limit DR capabilities. Therefore, a new two-step sequential framework is proposed in this paper to enhance the performance of the energy providers in the smart distribution network. The main problem is the optimization of the power purchase cost for the downstream network using DR. The subsidiary problem includes electricity market modeling. The load curve is determined in the main problem, and the amounts of the energy price under different conditions are determined in the subsidiary problem recursively. This framework guides the energy provider to analyze how market clearing affects DR and vice versa. To model load flexibility, a residential distribution network with different types of responsive appliances is utilized, and the model is studied using two case studies. The results demonstrate that applying the proposed framework leads to more reliable and optimal results and has significant benefits for the strategic performance of energy service providers.
Keywords: Demand response, Energy service provider, Day-ahead market, Smart distribution network, Transmission network.
Full-Text [PDF 905 kb]   (409 Downloads)    
Type of Study: Research |
Received: 2021/12/26 | Accepted: 2023/01/28 | Published: 2023/02/22
References
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26. [1] Lui T. J., Stirling W., and Marcy H. O., "Get smart", Power and Energy Magazine, vol. 8, no. 3, pp. 66 - 78, 2010. [DOI:10.1109/MPE.2010.936353]
27. [2] Khodaei A., Shahidehpour M., and Bahramirad S., "SCUC with hourly demand response considering intertemporal load characteristics", Smart Grid, IEEE Transactions on, vol. 2, no. 3, pp. 564-571, 2011. [DOI:10.1109/TSG.2011.2157181]
28. [3] Yang Y., Peng J.C., Ye Z., "A Market Clearing Mechanism Considering Primary Frequency Response Rate", Power Systems, IEEE Transactions on, vol. 36, no. 6, pp. 5952-5955, 2021. [DOI:10.1109/TPWRS.2021.3109807]
29. [4] QDR Q., "Benefits of demand response in electricity markets and recommendations for achieving them", 2006.
30. [5] Wang X., Palazoglu A., and El-Farra N. H., "Operational optimization and demand response of hybrid renewable energy systems", Applied Energy, vol. 143, pp. 324-335, 2015. [DOI:10.1016/j.apenergy.2015.01.004]
31. [6] Wan Y., Qin J., Yu X., Yang T., Kang Y., "Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach", IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 1, pp. 123-134, 2022. [DOI:10.1109/JAS.2021.1004287]
32. [7] Reka S. S., Venugopal P., Alhelou H. H., Siano P., Golshan M. E. H., "Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach", IEEE Access, vol. 9, pp. 56551 - 56562, 2021. [DOI:10.1109/ACCESS.2021.3071993]
33. [8] Zhang D., Zhu H., Zhang H., Goh H. H., Liu H., Wu T., "Multi objective Optimization for Smart Integrated Energy System Considering Demand Responses and Dynamic Prices", Smart Grid, IEEE Transactions on, 2021. [DOI:10.1109/TSG.2021.3128547]
34. [9] Jiang L., and Low S., "Multi-period optimal energy procurement and demand response in smart grid with uncertain supply", Technical Report, Caltech, 2011. Available: http://www.its.caltech.edu/libinj/DR.pdf. [DOI:10.1109/CDC.2011.6161320]
35. [10] Zugno M., Morales J. M., Pinson P., and Madsen H., "A bilevel model for electricity retailers' participation in a demand response market environment", Energy Economics, vol. 36, pp. 182-197, 2013. [DOI:10.1016/j.eneco.2012.12.010]
36. [11] Conejo A. J., Morales J. M., and Baringo L., "Real-time demand response model", Smart Grid, IEEE Transactions on, vol. 1, no. 3, pp. 236-242, 2010. [DOI:10.1109/TSG.2010.2078843]
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38. [13] Vardakas J. S., Zorba N., and Verikoukis C. V., "A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms", Communications Surveys & Tutorials, IEEE, vol. 17, no. 1, pp. 152-178, 2015. [DOI:10.1109/COMST.2014.2341586]
39. [14] Aghaei J., and Alizadeh M.-I., "Demand response in smart electricity grids equipped with renewable energy sources: A review", Renewable and Sustainable Energy Reviews, vol. 18, pp. 64-72, 2013. [DOI:10.1016/j.rser.2012.09.019]
40. [15] Abdollahi A., Moghaddam M. P., Rashidinejad M., and Sheikh-El-Eslami M. K., "Investigation of economic and environmental-driven demand response measures incorporating UC", Smart Grid, IEEE Transactions on, vol. 3, no. 1, pp. 12-25, 2012. [DOI:10.1109/TSG.2011.2172996]
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42. [17] Ruiz C., and Conejo A. J., "Pool strategy of a producer with endogenous formation of locational marginal prices", Power Systems, IEEE Transactions on, vol. 24, no. 4, pp. 1855-1866, 2009. [DOI:10.1109/TPWRS.2009.2030378]
43. [18] Garcés L. P., Conejo A. J., García-Bertrand R., and Romero R., "A bilevel approach to transmission expansion planning within a market environment", Power Systems, IEEE Transactions on, vol. 24, no. 3, pp. 1513-1522, 2009. [DOI:10.1109/TPWRS.2009.2021230]
44. [19] Baringo L., and Conejo A. J., "Risk-constrained multi-stage wind power investment", Power Systems, IEEE Transactions on, vol. 28, no. 1, pp. 401-411, 2013. [DOI:10.1109/TPWRS.2012.2205411]
45. [20] Wu H., Shahidehpour M., Alabdulwahab A., and Abusorrah A., "Demand Response Exchange in the Stochastic Day-Ahead Scheduling With Variable Renewable Generation", Sustainable Energy, IEEE Transactions on, vol. 6, no. 2, pp. 516-525, 2015. [DOI:10.1109/TSTE.2015.2390639]
46. [21] Safdarian A., Lehtonen M., Fotuhi-Firuzabad M., and Billinton R., "Customer interruption cost in smart grids", IEEE Transactions on Power Systems, vol. 2, no. 29, pp. 994-995, 2014. [DOI:10.1109/TPWRS.2013.2288019]
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48. [23] Generalized Algebraic Modeling Systems (GAMS). [Online]. Available: http://www.gams.com.
49. [24] Safdarian A., Fotuhi-Firuzabad M., and Lehtonen M., "Benefits of demand response on operation of distribution networks: A case study", Systems Journal, IEEE, no. 99, pp. 1 - 9, 2014.
50. [25] Wong P., Albrecht P., Allan R., Billinton R., Chen Q., Fong C., Haddad S., Li W., Mukerji R., and Patton D., "The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee", Power Systems, IEEE Transactions on, vol. 14, no. 3, pp. 1010-1020, 1999. [DOI:10.1109/59.780914]

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Mahboubi Moghaddam E, Nikoobakht A, Zare M. Smart Distribution Networks Potential for Demand Response Contribution to Enhance Energy Service Provider Performance. ieijqp 2022; 11 (4) :28-38
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Volume 11, Issue 4 (11-2022) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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