Presenting a New Method to Evaluate the Impact of Power Aggregators on the Congestion Management of Network Lines Considering the Demand Response Program
محورهای موضوعی : Electrical EngineeringMohamad Hossein Erfani Majd 1 , Gholam-Reza Kamyab 2 , Saeed Balouchian 3
1 - Department of Electrical Engineering, Gon.C., Islamic Azad University, Gonabad, Iran
2 - Department of Electrical Engineering, Gon.C., Islamic Azad University, Gonabad, Iran
3 - Department of Electrical Engineering, Ma.C., Islamic Azad University, Mashhad, Iran
کلید واژه: Demand response, Electric vehicle, Demand side management, Line flow,
چکیده مقاله :
Background and Objectives: Power aggregators have different effects on network parameters, including line power. The effect of this equipment can cause congestion of network lines in peak load conditions.
Methods: In this paper, the effect of electric vehicle aggregators on power changes in grid lines is calculated using a method based on market analysis in low- and peak-load conditions. In addition, the demand response program is considered in this paper to reduce lane congestion in peak load conditions.The proposed method is implemented on a standard 24-bus network, and the results are analyzed.
Results: The results state that the effect of each aggregator has a different effect on the grid lines.
Conclusion: By using the proposed method, the network operator can prevent the formation of phenomena such as market power and line congestion.
[1] M. H. Nikkhah, M. Samadi, M. E. Hajiabadi, and H. Lotfi. "Providing a novel approach based on decomposition for congestion management considering EVCSs, wind farms, and DLC." International Journal of Energy Research 2023, no. 1 (2023): 1363377.
[2] R. Elazab, A. T. Abdelnaby, H. E. Keshta, and A. A. Ali. "Optimal techno-economic feasibility analysis of a grid-tied microgrid considering demand response strategy." Electric Power Systems Research 224 (2023): 109768.
[3] A. M. Jasim, B. H. Jasim, B. Neagu, and B. N. Alhasnawi. "Efficient optimization algorithm-based demand-side management program for smart grid residential load." Axioms 12, no. 1 (2022): 33.
[4] A. Samadi, H. Saidi, M. A. Latify, and M. Mahdavi. "Home energy management system based on task classification and the resident’s requirements." International Journal of Electrical Power & Energy Systems118 (2020): 105815.
[5] M. Hu, J. W. Xiao, S. C. Cui, and Y. W. Wang. "Distributed real-time demand response for energy management scheduling in smart grid." International Journal of Electrical Power & Energy Systems 99 (2018): 233-245.
[6] B. E. Sedhom, M. M. El-Saadawi, M. S. El Moursi, Mohamed A. Hassan, and A. A. Eladl. "IoT-based optimal demand side management and control scheme for smart microgrid." International Journal of Electrical Power & Energy Systems 127 (2021): 106674.
[7] L. Wang, S. Greenberg, J. Fiegel, A. Rubalcava, S. Earni, X. Pang, R. Yin, S. Woodworth, and J. Hernandez-Maldonado. "Monitoring-based HVAC commissioning of an existing office building for energy efficiency." Applied energy 102 (2013): 1382-1390.
[8] H. R. Rocha, I. H. Honorato, R. Fiorotti, W. C. Celeste, L. J. Silvestre, and J. AL Silva. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes." Applied Energy 282 (2021): 116145.
[9] S. Oprea, A. Bâra, G. A. Ifrim, and L. Coroianu. "Day-ahead electricity consumption optimization algorithms for smart homes." Computers & Industrial Engineering 135 (2019): 382-401.
[10] I. Mauser, J. Muller, F. Allerding, "Adaptive building energy management with multiple commodities and flexible evolutionary optimization”, Elsevier, Renewable Energy 87, 2016.
[11] F. Valencia, D. Sáez, J. Collado, F. Ávila, A. Marquez, and J. J. Espinosa. "Robust energy management system based on interval fuzzy models." IEEE Transactions on Control Systems Technology 24, no. 1 (2015): 140-157.
[12] D. A. Francesco, M. Boaro, D. Fuselli, S. Squartini, F. Piazza, and Q. Wei. "Optimal home energy management under dynamic electrical and thermal constraints." IEEE Transactions on Industrial Informatics 9, no. 3 (2012): 1518-1527.
[13] X. Chen, T. Wei, and S. Hu. "Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home." IEEE Transactions on Smart Grid 4, no. 2 (2013): 932-941.
[14] L. Qi, G. Cooper, N. Linge, H. Takruri, and R. Sowden. "DEHEMS: creating a digital environment for large-scale energy management at homes." IEEE Transactions on Consumer Electronics 59, no. 1 (2013): 62-69.
[15] H. Wujing, N. Zhang, C. Kang, M. Li, and M. Huo. "From demand response to integrated demand response: Review and prospect of research and application." Protection and Control of Modern Power Systems 4 (2019): 1-13.
[16] P. Pierre, and H. Madsen. "Benefits and challenges of electrical demand response: A critical review." Renewable and Sustainable Energy Reviews 39 (2014): 686-699.
[17] N. Sheila, and M. O’Malley. "Challenges and barriers to demand response deployment and evaluation." Applied Energy 152 (2015): 1-10.
[18] T. Müller, and D. Möst. "Demand response potential: available when needed?" Energy Policy 115 (2018): 181-198.
[19] R. Deng, Z. Yang, M. Chow, and J. Chen. "A survey on demand response in smart grids: Mathematical models and approaches." IEEE Transactions on Industrial Informatics 11, no. 3 (2015): 570-582.
[20] W. A. Gang, G. A. O. Yongli, F. E. N. G. Jiayin, S. O. N. G. Jinling, J. I. A. Dongyan, L. I. Guolin, L. I. Yuerong, and Aris Vartosh. "Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response." Energy Strategy Reviews 49 (2023): 101172.
[21] M. A. Mirzaei, H. Mehrjerdi, and A. M. Saatloo. "Robust Strategic Behavior of a Large Multi-Energy Consumer in Electricity Market Considering Integrated Demand Response." IEEE Systems Journal (2023).
[22] X. Da, S. Xiang, Z. Bai, J. Wei, and M. Gao. "Optimal multi-energy portfolio towards zero carbon data center buildings in the presence of proactive demand response programs." Applied Energy 350 (2023): 121806.
[23] M. Armioun, M. S. Nazar, M. Shafie-khah, and P. Siano. "Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions." Applied Energy 350 (2023): 121719.
[24] M. Pouladkhay, M. M. Moghaddam, and A. Sahab. "A two-stage stochastic unit commitment considering demand-side provider and wind power penetration from the ISO point of view." Electrical Engineering (2023): 1-20.
[25] A. Shewale, A. Mokhade, A. Lipare, and N. D. Bokde. "Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem." Arabian Journal for Science and Engineering (2023): 1-21.
[26] R. Elazab, A. T. Abdelnaby, H. E. Keshta, and A. A. Ali. "Optimal techno-economic feasibility analysis of a grid-tied microgrid considering demand response strategy." Electric Power Systems Research 224 (2023): 109768.
[27] J. Wang, Z. Wei, Y. Zhu, C. Zheng, B. Li, and X. Zhai. "Demand response via optimal pre-cooling combined with temperature reset strategy for air conditioning system: A case study of office building." Energy (2023): 128751.
[28] Y. Wang, Y. Xiang, H. Hu, K. W. Lao, J. Tong, and Y. Jiang. "Service-quality based pricing approach for charging electric vehicles in smart energy communities." Journal of Cleaner Production (2023): 138416.
[29] A. J. Conejo, J. M. Morales and L. Baringo, "Real-Time Demand Response Model," in IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 236-242, Dec. 2010, doi: 10.1109/TSG.2010.2078843.
[30] J. Kim, and A. Shcherbakova. "Common failures of demand response." Energy 36, no. 2 (2011): 873-880.
[31] P. Palensky and D. Dietrich, "Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads," in IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 381-388, Aug. 2011, doi: 10.1109/TII.2011.2158841.
[32] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi. "Modeling and prioritizing demand response programs in power markets." Electric power systems research 80, no. 4 (2010): 426-435.
[33] M. Fotuhi-Firuzabad and R. Billinton, "Impact of load management on composite system reliability evaluation short-term operating benefits," in IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 858-864, May 2000, doi: 10.1109/59.867185.
