Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Enhance Low-Carbon Power System Operation via Carbon-Aware Demand Response (2404.05713v3)

Published 8 Apr 2024 in eess.SY and cs.SY

Abstract: As the electrification process advances, enormous power flexibility is becoming available on the demand side, which can be harnessed to facilitate power system decarbonization. Hence, this paper studies the carbon-aware demand response (C-DR) paradigm, where individual users aim to minimize their carbon footprints through the optimal scheduling of flexible load devices. The specific operational dynamics and constraints of deferrable loads and thermostatically controlled loads are considered, and the carbon emission flow method is employed to determine users' carbon footprints using nodal carbon intensities. Then, an optimal power dispatch model that integrates the C-DR mechanism is proposed for low-carbon power system operation, based on the carbon-aware optimal power flow (C-OPF) method. Two solution algorithms, including a centralized Karush-Kuhn-Tucker (KKT) reformulation algorithm and an iterative solution algorithm, are developed to solve the bi-level power dispatch optimization model. Numerical simulations on the IEEE New England 39-bus system demonstrate the effectiveness of the proposed methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. World Emission Clock, “Carbon emissions by sectors,” 2024, https://worldemissions.io/.
  2. H. Lee, K. Calvin et al., “IPCC: Climate change 2023 synthesis report.”   IPCC, 2023, https://www.ipcc.ch/report/ar6/syr/.
  3. M. F. Akorede, H. Hizam, and E. Pouresmaeil, “Distributed energy resources and benefits to the environment,” Renewable and sustainable energy reviews, vol. 14, no. 2, pp. 724–734, 2010.
  4. D. Pudjianto, C. Ramsay, and G. Strbac, “Virtual power plant and system integration of distributed energy resources,” IET Renewable power generation, vol. 1, no. 1, pp. 10–16, 2007.
  5. Google, “Moving toward 24×\times×7 carbon-free energy at google data centers: Progress and insights,” 2018.
  6. Microsoft, “Expanding carbon-free electricity globally: Microsoft electricity policy brief,” 2022, https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE57d2R.
  7. RE100, “Climate group RE100 committed to 100% renewable electricity,” 2024, https://www.there100.org/.
  8. Kevala, “Total carbon accounting: A framework to deliver locational carbon intensity data. [Online]: https://kevala.com/wp-content/uploads/2021/11/Total-Carbon-Accounting.pdf,” 2021.
  9. World Resources Institute, “GHG protocol scope 2 guidance: An amendment to the GHG protocol corporate standard,” 2015.
  10. M. Brander, M. Gillenwater, and F. Ascui, “Creative accounting: A critical perspective on the market-based method for reporting purchased electricity (scope 2) emissions,” Energy Policy, vol. 112, pp. 29–33, 2018.
  11. X. Chen, H. Chao, W. Shi, and N. Li, “Towards carbon-free electricity: A flow-based framework for power grid carbon accounting and decarbonization,” arXiv preprint arXiv:2308.03268, 2023.
  12. C. Kang, T. Zhou, Q. Chen, J. Wang, Y. Sun, Q. Xia, and H. Yan, “Carbon emission flow from generation to demand: A network-based model,” IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2386–2394, 2015.
  13. B. Li, Y. Song, and Z. Hu, “Carbon flow tracing method for assessment of demand side carbon emissions obligation,” IEEE Transactions on Sustainable Energy, vol. 4, no. 4, pp. 1100–1107, 2013.
  14. C. Kang, T. Zhou, Q. Chen, Q. Xu, Q. Xia, and Z. Ji, “Carbon emission flow in networks,” Scientific reports, vol. 2, no. 1, p. 479, 2012.
  15. X. Chen, A. Sun, W. Shi, and N. Li, “Carbon-aware optimal power flow,” arXiv preprint arXiv:2308.03240, 2023.
  16. X. Chen, E. Dall’Anese, C. Zhao, and N. Li, “Aggregate power flexibility in unbalanced distribution systems,” IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 258–269, 2019.
  17. A. R. Jordehi, “Optimisation of demand response in electric power systems, a review,” Renewable and sustainable energy reviews, vol. 103, pp. 308–319, 2019.
  18. Z. Chen, L. Wu, and Y. Fu, “Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization,” IEEE transactions on smart grid, vol. 3, no. 4, pp. 1822–1831, 2012.
  19. M. Muratori and G. Rizzoni, “Residential demand response: Dynamic energy management and time-varying electricity pricing,” IEEE Transactions on Power systems, vol. 31, no. 2, pp. 1108–1117, 2015.
  20. Q. Hu, F. Li, X. Fang, and L. Bai, “A framework of residential demand aggregation with financial incentives,” IEEE Transactions on Smart Grid, vol. 9, no. 1, pp. 497–505, 2016.
  21. X. Chen, Y. Li, J. Shimada, and N. Li, “Online learning and distributed control for residential demand response,” IEEE Transactions on Smart Grid, vol. 12, no. 6, pp. 4843–4853, 2021.
  22. X. Chen, Y. Nie, and N. Li, “Online residential demand response via contextual multi-armed bandits,” IEEE Control Systems Letters, vol. 5, no. 2, pp. 433–438, 2020.
  23. C. Shao, Y. Ding, and J. Wang, “A low-carbon economic dispatch model incorporated with consumption-side emission penalty scheme,” Applied Energy, vol. 238, pp. 1084–1092, 2019.
  24. J. Zhang, L. Sang, Y. Xu, and H. Sun, “Networked multiagent-based safe reinforcement learning for low-carbon demand management in distribution networks,” IEEE Transactions on Sustainable Energy, 2024.
  25. C. Yang, B. He, H. Liao, J. Ruan, and J. Zhao, “Price-based low-carbon demand response considering the conduction of carbon emission costs in smart grids,” Frontiers in Energy Research, vol. 10, p. 959786, 2022.
  26. C. Xu, W. Liao, D. Liu, Y. Wu, and Q. Fan, “Event-driven low-carbon economic dispatch for multi-regional power grid considering power consumption behavior,” 2022.
  27. World Resources Institute, “The greenhouse gas protocol: A corporate accounting and reporting standard,” 2004.
  28. N. Li, L. Chen, and S. H. Low, “Optimal demand response based on utility maximization in power networks,” in 2011 IEEE power and energy society general meeting.   IEEE, 2011, pp. 1–8.
  29. A. Sinha, P. Malo, and K. Deb, “A review on bilevel optimization: From classical to evolutionary approaches and applications,” IEEE Transactions on Evolutionary Computation, vol. 22, no. 2, pp. 276–295, 2017.
  30. E.-G. Talbi, “A taxonomy of metaheuristics for bi-level optimization,” in Metaheuristics for bi-level optimization.   Springer, 2013, pp. 1–39.
  31. C. Shi, J. Lu, and G. Zhang, “An extended kuhn–tucker approach for linear bilevel programming,” Applied Mathematics and Computation, vol. 162, no. 1, pp. 51–63, 2005.
  32. G. Savard and J. Gauvin, “The steepest descent direction for the nonlinear bilevel programming problem,” Operations Research Letters, vol. 15, no. 5, pp. 265–272, 1994.
  33. Y. Lv, T. Hu, G. Wang, and Z. Wan, “A penalty function method based on kuhn–tucker condition for solving linear bilevel programming,” Applied mathematics and computation, vol. 188, no. 1, pp. 808–813, 2007.
  34. X. Li, P. Tian, and X. Min, “A hierarchical particle swarm optimization for solving bilevel programming problems,” in International Conference on Artificial Intelligence and Soft Computing.   Springer, 2006, pp. 1169–1178.
  35. A. Sinha, P. Malo, and K. Deb, “An improved bilevel evolutionary algorithm based on quadratic approximations,” in 2014 IEEE congress on evolutionary computation (CEC).   IEEE, 2014, pp. 1870–1877.
  36. L. T. Biegler and V. M. Zavala, “Large-scale nonlinear programming using ipopt: An integrating framework for enterprise-wide dynamic optimization,” Computers & Chemical Engineering, vol. 33, no. 3, pp. 575–582, 2009.
  37. X. Chen, “Configuration and parameters of modified 39-bus New England test system,” 2023, [Online]: https://xchen.engr.tamu.edu/wp-content/uploads/sites/294/2024/01/39system.pdf.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now