An optimization model for power grid fortification to maximize attack immunity

2018 ◽  
Vol 99 ◽  
pp. 594-602 ◽  
Author(s):  
Alberto Costa ◽  
Dionysios Georgiadis ◽  
Tsan Sheng Ng ◽  
Melvyn Sim
Keyword(s):  
Optimization Model ◽  
Power Grid

Application of Reactive Power Optimization in Power Grid in AVC

2014 ◽  
Vol 971-973 ◽  
pp. 979-982
Author(s):  
Yan Hong Li ◽  
Zhi Rong Zhang
Keyword(s):  
Optimization Model ◽  
Reactive Power ◽  
Power Optimization ◽  
Power Grid ◽  
Optimization Method ◽  
Reactive Power Optimization ◽  
Synchronous Generators ◽  
Hierarchical Partition ◽  
Operating Limits ◽  
External Conditions

Automatic voltage control(AVC) is the highest form of current power grid voltage and reactive power control,during the implementation of AVC, the whole network reactive power optimization isthe core and foundation. Thispaper researches and discuses the application of reactive power optimization inpower grid AVC. In the traditional reactive power optimization, the reactivepower limits of synchronous generators are fixed. In this paper, thesynchronous generator PQ operating limits change with external conditions,thus establishes reactive power optimization model in accordance with therequirements of AVC. Thispaper presents reactive power optimization method based on the principle ofpartition. The method decomposes the system to several partitions. Eachpartition separately optimized, thus reduces the system scale.And the convergence of the algorithm, the calculation speed and the discretevariable processing etc. improve. At the same time, this method reflects theclassification, hierarchical, partition, characteristics of coordinated controlof AVC.

scholarly journals Optimization Model of Emission Reduction Benefits Based on Integrated Development of Electric Vehicle and Power Grid

2021 ◽  
Author(s):  
Zhao Jia ◽  
Fang Haifeng ◽  
Zhu Yueyan ◽  
Li Yueke ◽  
Chen Yisong ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Carbon Emissions ◽  
Automobile Industry ◽  
Optimization Model ◽  
Emission Reduction ◽  
Large Scale ◽  
Power Grid ◽  
Storage Unit ◽  
Integrated Development ◽  
Power Load

Under the goal of carbon peak and carbon neutrality, the carbon emission reduction of the automobile industry has attracted more and more attention in recent years. Electric vehicle has the dual attributes of power load and energy storage unit. With the increase of the number of electric vehicles, reducing carbon emissions through the collaborative interaction between electric vehicle and power network will become an important way to control carbon emissions in the automotive field. In this study, an optimization model of emission reduction benefits based on integrated development of electric vehicle and power grid is proposed, which explores the best technical way of synergy between power grid and electric vehicle, achieves the best carbon reduction effect and provides a model basis for large-scale demonstration application. Numerical simulations based on the real case in Beijing are conducted to validate the effectiveness of the proposed method.

scholarly journals Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanhua Yang ◽  
Ligang Yao
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimization Model ◽  
Power Grid ◽  
Optimization Method ◽  
Optimal Decision ◽  
Maintenance Cost ◽  
Equipment Maintenance ◽  
Safety Constraints ◽  
Maintenance Plan

The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.

A risk optimization model for enhanced power grid resilience against physical attacks

2017 ◽  
Vol 143 ◽  
pp. 329-338 ◽  
Author(s):  
Nasim Nezamoddini ◽  
Seyedamirabbas Mousavian ◽  
Melike Erol-Kantarci
Keyword(s):  
Optimization Model ◽  
Power Grid ◽  
Risk Optimization

Study on the Line Overload Harmonization and Optimization Control

2015 ◽  
Vol 740 ◽  
pp. 456-459
Author(s):  
Yang Guang Wang ◽  
Xiao Fang Guan ◽  
Lei Chen ◽  
Jing Zhong Wang ◽  
Wei Min Hu ◽  
...  
Keyword(s):  
Optimization Model ◽  
Power Grid ◽  
Reverse Phase ◽  
Emergency Control ◽  
Accident Probability ◽  
Optimization Control ◽  
Matching Adjustment ◽  
Line Overload ◽  
Influence Degree

The line overload is discussed and the coordination and optimization model of preventive and emergency control is proposed in this paper. It combines with the sensitivity’s concept and reverse phase equivalent matching adjustment principle, takes into account the severity of the accident probability and consequences, and introduces the important factor of the corresponding accident; adjust automatically according to the influence degree of the accident to the power grid. Getting reasonable coordination optimization strategies to ensure the safety and economy of the system is effective and verified by this paper.

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