Integrating Reinforcement Learning and Optimal Power Dispatch to Enhance Power Grid Resilience

Li, Qingming; Zhang, Xi; Guo, Jianbo; Shan, Xiwen; Wang, Zuowei; Li, Zhen; Tse, Chi K.

doi:10.1109/tcsii.2021.3131316

Cited by 21 publications

(7 citation statements)

References 19 publications

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“…In Appendix B, we analyze the coupling droop characteristics of the VOC inverter, and here, we assume that the coupling droop relation of active power and frequency is (20) where m P−ω is the droop coefficient. ω i is the angular frequency for node i. P i is the active power of node i. ω * is the rated angular frequency.…”

Section: Determination Of Virtual Inductormentioning

confidence: 99%

“…A multistage restoration approach that utilizes heuristic optimization algorithms for system reconfiguration and DG islanding has been proposed [ 17 ], emphasizing the potential of RESs in emergency response scenarios. This includes approaches like transmission line switching [ 18 ], load redispatching [ 19 ], and resilience-based unit commitment [ 20 ], which collectively underscore the growing importance of RESs in enhancing the resilience of power systems against extreme weather impacts.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Level Operation Method for Improving the Resilience of Power Systems under Extreme Weather through Preventive Control and a Virtual Oscillator

Li,

Zhang,

Han

et al. 2024

Sensors

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This paper proposes a multi-level operation method designed to enhance the resilience of power systems under extreme weather conditions by utilizing preventive control and virtual oscillator (VO) technology. Firstly, a novel model for predicting time intervals between successive failures of the power system during extreme weather is introduced. Based on this, this paper proposes a preventive control method considering the system ramping and transmission constraints prior to failures so as to ensure the normal electricity demand within the system. Further, a VO-based adaptive frequency control strategy is designed to accelerate the regulation speed and eliminate the frequency deviation. Finally, the control performance is comprehensively compared under different experimental conditions. The results verify that the method accurately predicted the time of the line fault occurrence, with a maximum error not exceeding 3 min compared to the actual occurrence; also, the virtual oscillator control (VOC) strategy outperformed traditional droop control in frequency stabilization, achieving stability within 2 s compared to the droop control’s continued fluctuations beyond 20 s. These results highlight VOC’s superior effectiveness in frequency stability and control in power systems.

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Section: Determination Of Virtual Inductormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Level Operation Method for Improving the Resilience of Power Systems under Extreme Weather through Preventive Control and a Virtual Oscillator

Li,

Zhang,

Han

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In [80], the authors addressed the vulnerability of power grids to extreme events and the critical need for efficient restoration strategies to bolster grid resilience. They proposed an integrated recovery strategy that aimed to maximize the total electricity supplied to loads during the recovery process, taking into account various time scales of restoration methods.…”

Section: Recovery and Restorationmentioning

confidence: 99%

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam

2023

Electricity

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In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

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“…In nonlinear and discontinuous situations, genetic algorithms can find the optimal solution within the globally permissible solution space, thereby avoiding falling into local optima. This method can effectively overcome the "dimensional chaos" and "optimization" problems existing in traditional methods [3].…”

Section: Introductionmentioning

confidence: 99%

Power grid planning and optimal dispatch based on intelligent optimization and genetic algorithm

Wang

2024

J. Phys.: Conf. Ser.

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Power grid planning is a prerequisite for optimal design, operation, and planning of the power grid. Based on the characteristics of the power system, this article explored a genetic algorithm-based power network optimization method. In genetic algorithms, simulated annealing algorithms were used to optimize candidate individuals, resulting in the optimal ratio of the number of selected individuals to the number of all mutated individuals. At the same time, a simulated annealing algorithm was added to the optimization process to improve the shortcomings of GA (Genetic Algorithm). When the genetic algorithm selected individuals from the parent population, a simulated annealing algorithm was used to optimize candidate individuals, making the ratio of the number of selected individuals to the number of all mutated individuals optimal. In order to overcome the shortcomings of GA, the tabu search algorithm was introduced into the optimization process, making it have better optimization ability. Through optimization experiments on 15 nodes, it was found that this method had good convergence speed. The final experiment also proved that the power planning based on the simulated annealing genetic algorithm was superior to other models (the load balance, power supply capacity matching, and coordination index scores of the substation based on the algorithm model in this article were 0.5111, 2.2693, and 120.215, respectively, which were higher than other schemes).

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Integrating Reinforcement Learning and Optimal Power Dispatch to Enhance Power Grid Resilience

Cited by 21 publications

References 19 publications

A Multi-Level Operation Method for Improving the Resilience of Power Systems under Extreme Weather through Preventive Control and a Virtual Oscillator

A Multi-Level Operation Method for Improving the Resilience of Power Systems under Extreme Weather through Preventive Control and a Virtual Oscillator

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Power grid planning and optimal dispatch based on intelligent optimization and genetic algorithm

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