A Power Dispatch Optimization Method to Enhance the Resilience of Renewable Energy Penetrated Power Networks

Huang, Yuehui; Li, Pai; Zhang, Xi; Mu, Bingchun; Mao, Xuefei; Li, Zhen

doi:10.3389/fphy.2021.743670

Cited by 7 publications

(5 citation statements)

References 18 publications

(19 reference statements)

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“…Renewable energy generation has uncertainty and volatility, and the high penetration of multiple renewable energies exacerbates the uncertainty, strong coupling, and non-linearity of the power system. Although traditional modelbased optimization methods (Ji et al, 2018;Huang et al, 2021;López-Garza et al, 2022) with certainty have strong interpretability, they are difficult to handle the dispatch problems of renewable energy generation with uncertainty (Han et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Li,

Huang,

Liang

et al. 2023

Front. Energy Res.

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Reinforcement learning (RL) is recently studied for realizing fast and adaptive power system dispatch under the increasing penetration of renewable energy. RL has the limitation of relying on samples for agent training, and the application in power systems often faces the difficulty of insufficient scenario samples. So, scenario generation is of great importance for the application of RL. However, most of the existing scenario generation methods cannot handle time-series correlation, especially the correlation over long time scales, when generating the scenario. To address this issue, this paper proposes an RL-based dispatch method which can generate power system operational scenarios with time-series correlation for the agent’s training. First, a time-generative adversarial network (GAN)-based scenario generation model is constructed, which generates system operational scenarios with long- and short-time scale time-series correlations. Next, the “N-1” security is ensured by simulating “N-1” branch contingencies in the agent’s training. Finally, the model is trained in parallel in an actual power system environment, and its effectiveness is verified by comparisons against benchmark methods.

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Section: Introductionmentioning

confidence: 99%

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Li,

Huang,

Liang

et al. 2023

Front. Energy Res.

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“…Zhao et al (2022) provided a review of the use of energy storage methods for black start services. Huang et al (2021b) studied the role of energy storage as an available resource in enhancing the resilience of power systems with high penetrations of renewable energies under emergencies. Energy storage system plays an important role in improving the resilience of the power system with high penetration of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Underground energy storage system supported resilience enhancement for power system in high penetration of renewable energy

Qin

Shi

Fang³

et al. 2023

Front. Energy Res.

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High penetration of renewable energy is becoming an important development trend in new generation power system. However, frequent extreme weather events and fragile renewable energy sources pose a huge challenge to the power system resilience. As an important support technology of renewables, energy storage system is of great significance in improving the resilience of the power system. In this paper, a resilience enhancement method for power systems with high penetration of renewable energy based on underground energy storage systems (UESS) is proposed. Firstly, a resilience assessment model is established and the influence of extreme weather is quantified as the failure rate of power system components. Secondly, a bi-level optimization model for UESS operation and planning under extreme weather is built, and the life cycle of UESS is considered. Finally, taking the modified IEEE RTS-79 as an example, the optimal scheme for UESS configuration and operation is given, the investment and effectiveness of UESS is also analyzed. The results show that UESS can significantly enhance the power system resilience under extreme weather events.

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“…From the perspective of an isolated complex network, research on the impact of natural disasters on electric power systems has explored how to increase the resilience of a grid [7]. Reliability analysis (to obtain a picture of a system's likely behavior) and vulnerability analysis (to estimate the magnitude of the negative consequences that arise, given that a strain is imposed on the system) are two main approaches [8].…”

Section: Introductionmentioning

confidence: 99%

An Interacting Negative Feedback Mechanism in a Coupled Extreme Weather–Humans–Infrastructure System: A Case Study of the 2021 Winter Storm in Texas

Yang

et al. 2022

Front. Phys.

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Extreme weather has long been a threat to human life and critical infrastructures. Previous studies have focused on the reliability and vulnerability of single or interdependent infrastructures under extreme weather threats. However, knowledge of the interactions between coupled real-world complex systems, especially the cascading failure process induced by external shocks, is essential, but the interactions receive less attention. Here, we took the historical winter storm of Texas that occurred in February 2021 as a case study and collected multisource data to explore the interaction between humans and the power system affected by extreme weather. A connectivity-based network was proposed to analyze the connectivity robustness and simulate the cascade of overload failures under random and malicious attacks. Results showed that this network presents higher robustness under random attacks in terms of network connectivity. However, a highly heterogeneous distribution of load was shown in this network, making it particularly vulnerable to attacks and easier to trigger cascading failures. An interacting negative feedback mechanism was discovered in this coupled extreme weather–humans–infrastructure system. Extreme weather events directly caused physical failure in infrastructures, while their impact on individuals stimulated the power demand for heat. An increase in demand further intensified the load on the power network, which induced functional failure in infrastructure systems and finally aggravated the adverse impact on people as end-users in return. This feedback loop inspired us to reconsider the relationship among natural disasters, critical infrastructure, and humans. Furthermore, even under the background of climate change, the impact of extremely cold weather on electric infrastructures is still worthy of attention since the fluctuation of yearly minimum temperature outstood in eastern Texas, where the majority of the population and electric transmission facilities are located. Thus, it is noteworthy to integrate the interaction between systems in the vulnerability assessment of infrastructure systems or the impact prediction of intense external shocks in future research.

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A Power Dispatch Optimization Method to Enhance the Resilience of Renewable Energy Penetrated Power Networks

Cited by 7 publications

References 18 publications

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Underground energy storage system supported resilience enhancement for power system in high penetration of renewable energy

An Interacting Negative Feedback Mechanism in a Coupled Extreme Weather–Humans–Infrastructure System: A Case Study of the 2021 Winter Storm in Texas

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