برنامه ريزی مقاوم حمله تزريق داده غلط روی بازارهای انرژی الکتريکی در شبکه های هوشمند
محورهای موضوعی : مهندسی برق و کامپیوترحامد بدرسیمایی 1 , رحمت‌الله هوشمند 2 , صغري نوبختيان 3
1 - دانشكده فنی مهندسی،دانشگاه اصفهان
2 - دانشگاه اصفهان،دانشگاه اصفهان
3 - دانشکده علوم ریاضی،دانشگاه اصفهان
کلید واژه: بازار انرژی الکتریکی, حمله سایبری, حمله تزریق داده غلط, شبکه هوشمند, عدم قطعیت,
چکیده مقاله :
حمله تزریق داده غلط (FDIA) یک تهدید سایبری مخرب برای عملکرد اقتصادی بازارهای انرژی الکتریکی در شبکه‌های هوشمند است. یک مهاجم سایبری می‌تواند با پیاده‌سازی یک FDIA و با نفوذ در معاملات مجازیبازارهای انرژی الکتریکی، از طریق دستکاری قیمت برق به سود مالی گزافی دست پیدا کند. در این مقاله، روش جدیدی در مسأله برنامه‌ریزی یک FDIA به صورت کاملاً مخفی و با هدف دستیابی به بیشترین سود مالی از دیدگاه یک مهاجم سایبری مشارکت‌کننده در معاملات مجازی در دو بازار روز پیش (DA) و زمان حقیقی (RT) ارائه شده است. یک فرضیه رایج که در مطالعات موجود روی FDIAs در مقابل بازارهای برق صورت گرفته، این است که مهاجم، اطلاعات کاملی از شبکه هوشمند در اختیار دارد. اما واقعیت این است که مهاجم، منابع محدودی دارد و به سختی می‌تواند به همه اطلاعات شبکه دسترسی پیدا کند. این مقاله روش مقاومی را در طراحی استراتژی حمله با شرایط اطلاعات شبکه ناقص پیشنهاد می‌کند. به طور خاص فرض گردیده که مهاجم نسبت به ماتریس‌های مدل‌کننده شبکه دارای عدم قطعیت است. اعتبار روش پیشنهادی بر اساس سیستم معیار 14- باس IEEE و با استفاده از ابزار Matpower سنجیده شده است. نتایج عددی، موفقیت نسبی حمله پیشنهادی را در حالت‌های از درجه مختلف اطلاعات ناقص تأیید می‌کنند.
False data injection attack (FDIA) is a destructive cyber threat to the economic performance of electricity markets in smart grids. A cyber attacker can make a huge financial profit by implementing an FDIA through penetrating the virtual transactions of the electricity markets and manipulating electricity prices. In this paper, a new approach to planning an absolutely stealthily FDIA is presented with the aim of achieving maximum financial profit from the perspective of a cyber attacker participating in virtual transactions from two markets of day-ahead (DA) and real-time (RT). A common hypothesis in studies of FDIAs against electricity markets is that the attacker has complete information about the smart grid. But the fact is that the attacker has limited resources and can hardly access all the network information. This paper proposes a robust approach in designing an attack strategy under incomplete network information conditions. In particular, it is assumed that the attacker has uncertainties about the network modeling matrices. The validity of the proposed method is evaluated based on the IEEE 14-bus standard system using the Matpower tool. Numerical results confirm the relative success of the proposed attack in cases of varying degrees of incomplete information.
[1] Y. Liu, P. Ning, and M. K. Reiter, "False data injection attacks against state estimation in electric power grids," ACM Trans. on Information and System Security, vol. 14, no. 1, Article ID: 13, 33 pp., Jun. 2011.
[2] R. Deng, G. Xiao, R. Lu, H. Liang, and A. V. Vasilakos, "False data injection on state estimation in power systems-attacks, impacts, and defense: a survey," IEEE Trans. on Industrial Informatics, vol. 13, no. 2, pp. 411-423, Apr. 2016.
[3] A. Xu, et al., "Research on false data injection attack in smart grid," in IOP Conf. Series: Earth and Environmental Science, Proc. 8th Annual Int. Conf. on Geo-Spatial Knowledge and Intelligence, vol. 693, Article ID: 012010, Xi'an, Shaanxi, China, 18-19 Dec. 2020.
[4] Q. Zhang et al., "Profit-oriented false data injection on energy market: reviews, analyses and insights," IEEE Trans. on Industrial Informatics, vol. 17, no. 9, pp. 5876-5886, Sept. 2020.
[5] L. Xie, Y. Mo, and B. Sinopoli, "Integrity data attacks in power market operations," IEEE Trans. on Smart Grid, vol. 2, no. 4, pp. 659-666, Dec. 2011.
[6] B. Jin, C. Dou, and D. Wu, "False data injection attacks and detection on electricity markets with partial information in a micro‐grid‐based smart grid system," International Trans. on Electrical Energy Systems, vol. 30, no. 12, Article ID: e12661, Dec. 2020.
[7] L. Jia, R. J. Thomas, and L. Tong, "Malicious data attack on real-time electricity market," in Proc. IEEE In. Conf. on Acoustics, Speech and Signal Processing, ICASSP'11, pp. 5952-5955, Prague, Czech Republic, 22-27 May 2011.
[8] Y. Yuan, Z. Li, and K. Ren, "Modeling load redistribution attacks in power systems," IEEE Trans. on Smart Grid, vol. 2, no. 2, pp. 382-390, Jun. 2011.
[9] B. Huang, Y. Li, F. Zhan, Q. Sun, and H. Zhang, "A distributed robust economic dispatch strategy for integrated energy system considering cyber-attacks," IEEE Trans. on Industrial Informatics, vol. 18, no. 2, pp. 880-890, Feb. 2021.
[10] R. Tan, V. Badrinath Krishna, D. K. Yau, and Z. Kalbarczyk, "Impact of integrity attacks on real-time pricing in smart grids," in Proc. of the ACM SIGSAC Conf. on Computer & Communications Security, pp. 439-450, Berlin, Germany, 4-8 Nov. 2013.
[11] M. Tian, Z. Dong, and X. Wang, "Analysis of false data injection attacks in power systems: a dynamic Bayesian game-theoretic approach," ISA Trans., vol. 115, pp. 108-123, Sept. 2021.
[12] M. Esmalifalak, G. Shi, Z. Han, and L. Song, "Bad data injection attack and defense in electricity market using game theory study," IEEE Trans. on Smart Grid, vol. 4, no. 1, pp. 160-169, Mar. 2013.
[13] C. Jin, Z. Bao, M. Yu, J. Zheng, and C. Sha, "Optimization of joint cyber topology attack and FDIA in electricity market considering uncertainties," in Proc. IEEE Power & Energy Society General Meeting, PESGM'21, 5 pp., Washington, DC, USA, 26-29 Jul. 2021.
[14] D. H. Choi and L. Xie, "Economic impact assessment of topology data attacks with virtual bids," IEEE Trans. on Smart Grid, vol. 9, no. 2, pp. 512-520, Mar. 2018.
[15] H. Xu, Y. Lin, X. Zhang, and F. Wang, "Power system parameter attack for financial profits in electricity markets," IEEE Trans. on Smart Grid, vol. 11, no. 4, pp. 3438-3446, Jul. 2020.
[16] K. Lai, M. Illindala, and K. Subramaniam, "A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment," Applied Energy, vol. 235, pp. 204-218, Feb. 2019.
[17] P. K. Jena, S. Ghosh, and E. Koley, "A binary-optimization-based coordinated cyber-physical attack for disrupting electricity market operation," IEEE Systems J., vol. 15, no. 2, pp. 2619-2629, Jun. 2020.
[18] P. K. Jena, S. Ghosh, E. Koley, D. K. Mohanta, and I. Kamwa, "Design of AC state estimation based cyber-physical attack for disrupting electricity market operation under limited sensor information," Electric Power Systems Research, vol. 205, Article ID: 107732, Apr. 2022.
[19] M. Esmalifalak, et al., "A stealthy attack against electricity market using independent component analysis," IEEE Systems J., vol. 12, no. 1, pp. 297-307, Mar. 2018.
[20] S. Tan, W. Z. Song, M. Stewart, J. Yang, and L. Tong, "Online data integrity attacks against real-time electrical market in smart grid," IEEE Trans. on Smart Grid, vol. 9, no. 1, pp. 313-322, Jan. 2018.
[21] A. Tajer, "False data injection attacks in electricity markets by limited adversaries: stochastic robustness," IEEE Trans. on Smart Grid, vol. 10, no. 1, pp. 128-138, Jan. 2019.
[22] H. Badrsimaei, R. A. Hooshmand, and S. Nobakhtian, "Monte-Carlo-based data injection attack on electricity markets with network parametric and topology uncertainties," International J. of Electrical Power Energy Systems, vol. 138, Article ID: 107915, Jun. 2022.
[23] H. Badrsimaei, R. A. Hooshmand, and S. Nobakhtian, "Stealthy and profitable data injection attack on real time electricity market with network model uncertainties," Electric Power Systems Research, vol. 205, Article ID: 107742, Apr. 2022.
[24] H. R. Lewis, Computers and Intractability. A Guide to the Theory of NP-Completeness, Ed: JSTOR, 1983.
[25] Y. Nesterov and A. Nemirovskii, Interior-Point Polynomial Algorithms in Convex Programming, Philadelphia, PA: Society for Industrial and Applied Mathematics, 1994.
[26] A. L. Ott, "Experience with PJM market operation, system design, and implementation," IEEE Trans. on Power Systems, vol. 18, no. 2, pp. 528-534, May 2003.
[27] F. Li and R. Bo, "DCOPF-based LMP simulation: algorithm, comparison with ACOPF, and sensitivity," IEEE Trans. on Power Systems, vol. 22, no. 4, pp. 1475-1485, Nov. 2007.
[28] T. Zheng and E. Litvinov, "Ex post pricing in the co-optimized energy and reserve market," IEEE Trans. on Power Systems, vol. 21, no. 4, pp. 1528-1538, Nov. 2006.
[29] F. Li, Y. Wei, and S. Adhikari, "Improving an unjustified common practice in ex post LMP calculation," IEEE Trans. on Power Systems, vol. 25, no. 2, pp. 1195-1197, May 2010.
[30] L. Jia, J. Kim, R. J. Thomas, and L. Tong, "Impact of data quality on real-time locational marginal price," IEEE Trans. on Power Systems, vol. 29, no. 2, pp. 627-636, Mar. 2014.
[31] A. Abur and A. G. Exposito, Power System State Estimation: Theory and Implementation, New York, NY, USA: Marcel Dekker, 2004.
[32] W. W. Hogan, "Virtual bidding and electricity market design," The Electricity J., vol. 29, no. 5, pp. 33-47, Jun. 2016.
