Robust Optimization Research of Cyber–Physical Power System Considering Wind Power Uncertainty and Coupled Relationship

To mitigate the impact of wind power uncertainty and power–communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupl...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 26; číslo 9; s. 795
Hlavní autoři: Dong, Jiuling, Song, Zilong, Zheng, Yuanshuo, Luo, Jingtang, Zhang, Min, Yang, Xiaolong, Ma, Hongbing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 17.09.2024
MDPI
Témata:
Alternative energy sources
Analysis
Case studies
Communication
Communication networks
Communications networks
Communications traffic
Constraints
coupled relationship
Coupling
Cyber-physical systems
cyber–physical power system
Electric power production
Electric properties
Electrical loads
Electricity distribution
Error reduction
Forecasts and trends
Graph theory
Green technology
Integer programming
Load shedding
Mathematical programming
Mixed integer
Optimization
Optimization models
Power flow
power system resilience
Random variables
Research methodology
Robust control
robust optimization
Simulation methods
Topology
Traffic flow
Traffic models
Uncertainty
Wind power
Wind power generation
wind power uncertainty
China
power system resilience
wind power uncertainty
coupled relationship
cyber–physical power system
robust optimization
ISSN:1099-4300, 1099-4300
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Shrnutí:To mitigate the impact of wind power uncertainty and power–communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology–function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e26090795