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Intelligent Price Optimization in Smart Distribution Networks: A Risk-Sensitive Cheetah Hunter Optimization Algorithm for Distribution Locational Marginal Price Assessment | ||
| AUT Journal of Electrical Engineering | ||
| دوره 57، شماره 1، 2025، صفحه 203-220 اصل مقاله (1.01 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2024.23576.5626 | ||
| نویسندگان | ||
| Seyed Amir Mohammad Lahaghi؛ Ehsan Azad-Farsani* | ||
| Electrical and Computer Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran | ||
| چکیده | ||
| Distribution locational marginal pricing (DLMP) is an efficient approach to optimize the pricing of distribution systems. This paper focuses on DLMP to minimize losses within the distribution network. This approach can be strategically manipulated to adjust the profits for distributed generation (DG) owners and the distribution company. Furthermore, the paper employs the information gap decision theory (IGDT) method scenarios to model the uncertainty surrounding electricity market prices. By incorporating the risk-averse (RA) scenario, network operators can discern RA solutions and optimal outcomes derived from the algorithm. On the other hand, the risk-tolerance (RT) scenario helps identify riskier solutions, enabling appropriate decision-making based on whether the solutions are RA or risky in nature. To further enhance the quality of outcomes, this paper combines IGDT scenarios with the cheetah hunter optimization (CHO) algorithm to ensure the obtained results are both optimal and accurate. The proposed method’s performance is evaluated through simulations conducted on a 69-bus IEEE power network using the MATLAB software environment. The results obtained from this approach demonstrate its superior accuracy when compared to previous methodologies. | ||
| کلیدواژه‌ها | ||
| Locational Marginal Pricing؛ Information Gap Decision Theory؛ Electricity Price؛ Cheetah Hunter Optimization | ||
| مراجع | ||
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[1] Yousefi, M. and H.R. Mashhadi, "A Price‐Based Load Flow Algorithm in Decentralized Distribution Markets," International Transactions on Electrical Energy Systems, 2023. 2023(1): p. 3923848.
[2] Frew, B., et al., "Analysis of multi-output hybrid energy systems interacting with the grid: Application of improved price-taker and price-maker approaches to nuclear-hydrogen systems," Applied Energy, 2023. 329: p. 120184.
[3] Bo, R. and F. Li, "Probabilistic LMP forecasting considering load uncertainty," IEEE Transactions on Power Systems, 2009. 24(3): pp. 1279-1289.
[4] Wang, X., et al., "DLMP of competitive markets in active distribution networks: Models, solutions, applications, and visions," Proceedings of the IEEE, 2022. 111(7): pp. 725-743.
[5] Yang, Z., et al., "LMP revisited: A linear model for the loss-embedded LMP," IEEE Transactions on Power Systems, 2017. 32(5): pp. 4080-4090.
[6] Bai, L., et al., "Distribution locational marginal pricing (DLMP) for congestion management and voltage support," IEEE Transactions on Power Systems, 2017. 33(4): pp. 4061-4073.
[7] Jordehi, A.R., "Two-stage stochastic programming for risk-aware scheduling of energy hubs participating in day-ahead and real-time electricity markets," Sustainable Cities and Society, 2022. 81: p. 103823.
[8] Zhou, K., Y. Chu, and H. Yin, "Peer-to-peer electricity trading model for urban virtual power plants considering prosumer preferences and power demand heterogeneity," Sustainable Cities and Society, 2024. 107: p. 105465.
[9] Fatemi, S., A. Ketabi, and S.A. Mansouri, "A four-stage stochastic framework for managing electricity market by participating smart buildings and electric vehicles: Towards smart cities with active end-users," Sustainable Cities and Society, 2023. 93: p. 104535.
[10] Palaniyappan, B. and T. Vinopraba, "Dynamic pricing for load shifting: Reducing electric vehicle charging impacts on the grid through machine learning-based demand response," Sustainable Cities and Society, 2024. 103: p. 105256.
[11] Vaishya, S.R., A.R. Abhyankar, and P. Kumar, "A novel loss sensitivity based linearized OPF and LMP calculations for active balanced distribution networks," IEEE Systems Journal, 2022. 17(1): pp. 1340-1351.
[12] Tan, Z., et al., "Extensions of the locational marginal price theory in evolving power systems: A review," IET Generation, Transmission & Distribution, 2022. 16(7): pp. 1277-1291.
[13] Farsani, E.A., et al., "A novel policy for LMP calculation in distribution networks based on loss and emission reduction allocation using nucleolus theory," IEEE Transactions on Power Systems, 2015. 31(1): pp. 143-152.
[14] Brooks, A.E. and B.C. Lesieutre, "A locational marginal price for frequency balancing operations in regulation markets," Applied Energy, 2022. 308: p. 118306.
[15] Saraiva, F.O. and V.L. Paucar, "Locational Marginal Price Decomposition Using a Fully Distributed Slack Bus Model," IEEE Access, 2022. 10: pp. 84913-84933.
[16] Tsai, C.W. and S. Franceschini, "Evaluation of probabilistic point estimate methods in uncertainty analysis for environmental engineering applications," Journal of environmental engineering, 2005. 131(3): pp. 387-395.
[17] Azad-Farsani, E., I.G. Sardou, and S. Abedini, "Distribution network reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA," Energy, 2021. 215: p. 119146.
[18] Celikyilmaz, A. and I.B. Turksen, "Modeling uncertainty with fuzzy logic," Studies in fuzziness and soft computing, 2009. 240(1): pp. 149-215.
[19] Cox, M.G. and B.R. Siebert, "The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty," Metrologia, 2006. 43(4): p. S178.
[20] Ben-Tal, A. and A. Nemirovski, "Robust optimization–methodology and applications," Mathematical programming, 2002. 92: pp. 453-480.
[21] Azad-Farsani, E., "Loss minimization in distribution systems based on LMP calculation using honey bee mating optimization and point estimate method," Energy, 2017. 140: pp. 1-9.
[22] Dehghani, H., B. Vahidi, and S.H. Hosseinian, "Wind farms participation in electricity markets considering uncertainties," Renewable Energy, 2017. 101: pp. 907-918.
[23] Huang, H., F. Li, and Y. Mishra, "Modeling dynamic demand response using monte carlo simulation and interval mathematics for boundary estimation," IEEE Transactions on Smart Grid, 2015. 6(6): pp. 2704-2713.
[24] Veeramsetty, V., "Distribution locational marginal price computation based on active power loss and emissions in active distribution system using nucleolus theory," Evolutionary Intelligence, 2024. 17(1): pp. 249-265.
[25] Kunapareddy, T.B. and S.K. Bali, "Optimal Power Flow of Power System with Unified Power Flow Controller Using Moth Flame Optimization with Locational Marginal Price," Contemporary Mathematics, 2024: pp. 1181-1196.
[26] Nazari, M.H., S.H. Hosseinian, and E. Azad-Farsani, "A multi-objective LMP pricing strategy in distribution networks based on MOGA algorith," Journal of Intelligent & Fuzzy Systems, 2019. 36(6): pp. 6143-6154.
[27] Sun, Y., et al., "Robust planning for distributed energy storage systems considering location marginal prices of distribution networks," Measurement and Control, 2024. 57(5): pp. 598-617.
[28] Ladwal, S. and A. Kumar, "Optimizing Distributed Generation Placement and Profit Maximization in Active Distribution Networks with Harmonics: A GEPSO Approach," Electric Power Components and Systems, 2024: pp. 1-21.
[29] Boonchuay, C., et al. "Robust optimization-based DC optimal power flow for managing wind generation uncertainty. in AIP Conference Proceedings," 2012. American Institute of Physics.
[30] Li, B., et al., "DER aggregator’s data-driven bidding strategy using the information gap decision theory in a non-cooperative electricity market," IEEE Transactions on Smart Grid, 2019. 10(6): pp. 6756-6767.
[31] Yazdaninejad, M., N. Amjady, and S. Dehghan, "VPP self-scheduling strategy using multi-horizon IGDT, enhanced normalized normal constraint, and bi-directional decision-making approach," IEEE Transactions on Smart Grid, 2019. 11(4): pp. 3632-3645.
[32] Renani, Y.K., M. Ehsan, and M. Shahidehpour, "Day-ahead self-scheduling of a transmission-constrained GenCo with variable generation units using the incomplete market information," IEEE Transactions on Sustainable Energy, 2017. 8(3): pp. 1260-1268.
[33] Najafi, A., M. Jasiński, and Z. Leonowicz, "A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem," Energy, 2022. 249: p. 123674.
[34] Soroudi, A. and M. Ehsan, "IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty," IEEE Transactions on Smart Grid, 2012. 4(2): pp. 886-895.
[35] Ghaedi, H., S.R. Kamel Tabbakh, and R. Ghaemi, "Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm," Majlesi Journal of Electrical Engineering, 2022. 16(4): pp. 103-115.
[36] Sharadqh, A.A., et al., "Hybrid chain: Blockchain enabled framework for bi-level intrusion detection and graph-based mitigation for security provisioning in edge assisted IoT environment," IEEE Access, 2023. 11: pp. 27433-27449.
[37] Jokar-Dehoie, M., et al., "Game Theory-Based Bidding Strategy in the Three-Level Optimal Operation of an Aggregated Microgrid in an Oligopoly Market," IEEE Access, 2022. 10: pp. 104719-104736.
[38] Seyyedeh-Barhagh, S., et al., "Risk-based Peer-to-peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles," Sustainable Cities and Society, 2023. 99: p. 104948.
[39] Reagen, B., et al. "Cheetah: Optimizing and accelerating homomorphic encryption for private inference. in 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)," 2021. IEEE.
[40] Tatikayala, V.K. and S. Dixit, "Multi-stage voltage control in high photovoltaic based distributed generation penetrated distribution system considering smart inverter reactive power capability," Ain Shams Engineering Journal, 2024. 15(1): p. 102265.
[41] Swarnalaxmi, V., N. Anbuselvan, and V. Thiruchelvam. "Power loss reduction of IEEE 69 bus system using evolutionary based bat algorithm compared with bat algorithm by compensating DSTATCOM size," in AIP Conference Proceedings. 2024. AIP Publishing.
[42] Emrani-Rahaghi, P., H. Hashemi-Dezaki, and A. Hasankhani, "Optimal stochastic operation of residential energy hubs based on plug-in hybrid electric vehicle uncertainties using two-point estimation method," Sustainable Cities and Society, 2021. 72: p. 103059.
[43] Bahramian Habil, H., E. Azad‐Farsani, and H. Askarian abyaneh, "A novel method for optimum fault current limiter placement using particle swarm optimization algorithm," International Transactions on Electrical Energy Systems, 2015. 25(10): pp. 2124-2132.
[44] Fayyazi, S. and E. Azad Farsani, "Sectionalizing Switches Placement in Distribution Networks Using BTLBO," Research and Technology in the Electrical Industry, 2022. 1(2): pp. 143-151. | ||
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آمار تعداد مشاهده مقاله: 345 تعداد دریافت فایل اصل مقاله: 365 |
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