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学者姓名：张雪寒
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Abstract ：
The interval power flow (IPF) method is widely employed to address the uncertainties of renewable energy sources (RESs) in power systems. However, limited research exists on the application of mathematical optimization-based approaches to compute IPF results. Furthermore, a comprehensive framework for analyzing the derived IPF results and formulating appropriate countermeasures is still lacking. Therefore, this paper proposes a novel linear programming-based framework of IPF analysis for distribution systems, designed to enhance IPF calculation efficiency and keep system state variables within recommended limits utilizing controllable equipment. First, a linearized IPF model is proposed to improve calculation efficiency. The over-limit of system state variables is analysed based on the IPF results. Then, A countermeasure strategy utilizing controllable equipment is proposed to maintain system security under potential extreme scenarios. The output intervals of the controllable equipment are determined as scheduling references ensuring secure operation under the uncertainties. The numerical results demonstrate that the linearized formulation computes the IPF results 6.57 times faster than the non-linear method, with insignificant calculation errors (below 0.06 % for magnitudes and 0.02 degrees for angles). The countermeasure method can successfully keep state variables within predefined ranges and provide system operators with effective scheduling reference intervals of controllable equipment under uncertainties.
Keyword ：
Countermeasure method Countermeasure method Interval power flow analysis Interval power flow analysis Linear programming Linear programming Renewable energy sources Renewable energy sources Uncertainty Uncertainty
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| GB/T 7714 | Zhang, Xuehan , Deng, Bairong , Pan, Zhenning et al. A linear programming-based framework of interval power flow analysis for distribution systems [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2025 , 167 . |
| MLA | Zhang, Xuehan et al. "A linear programming-based framework of interval power flow analysis for distribution systems" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 167 (2025) . |
| APA | Zhang, Xuehan , Deng, Bairong , Pan, Zhenning , Yu, Tao . A linear programming-based framework of interval power flow analysis for distribution systems . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2025 , 167 . |
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Under disaster conditions, an intentional islanding operation in distribution systems is required to enhance resilience. This paper proposes a strategic islanding operation utilizing grid-forming (GFM) inverter-based distributed generation (DG) within a grid containing both GFM and grid-following (GFL) inverters. Unlike previous studies, this work uniquely addresses the temporary overvoltage (TOV) constraints that arise during multi-fault and consecutive fault conditions in islanded systems, which is critical for stable grid operation in disaster scenarios. The islanding operation problem is formulated as a knapsack problem and solved by a genetic algorithm, enabling optimal load supply and enhanced DG utilization under diverse disaster scenarios. Numerical simulations validate the effectiveness of the proposed method by exploring the impact of varying the percentage of ungrounded loads, the capacity of GFM inverter-based DGs, and the location of GFM inverters. The results emphasize that the reduction of grounded sources, which has not been evaluated in existing studies, negatively affects TOV and, therefore, impacts the reliability of islanding operations.
Keyword ：
Distributed generation Distributed generation Grounding Grounding Intentional islanding Intentional islanding Resilience Resilience Temporary overvoltage Temporary overvoltage
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| GB/T 7714 | Yoon, Myungseok , Zhang, Xuehan , Choi, Sungyun . Strategic intentional islanding method considering temporary overvoltage in disaster situations [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2025 , 165 . |
| MLA | Yoon, Myungseok et al. "Strategic intentional islanding method considering temporary overvoltage in disaster situations" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 165 (2025) . |
| APA | Yoon, Myungseok , Zhang, Xuehan , Choi, Sungyun . Strategic intentional islanding method considering temporary overvoltage in disaster situations . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2025 , 165 . |
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Reinforcement learning, as an efficient method for solving uncertainty decision making in power systems, is widely used in multi-stage stochastic power dispatch and dynamic optimization. However, the low generalization and practicality of traditional reinforcement learning algorithms limit their online application. The dispatch strategy learned offline can only adapt to specific scenarios, and its policy performance degrades significantly if the sample drastically change or the topology variation. To fill these gaps, a novel contextual meta graph reinforcement learning (Meta-GRL) method a more general contextual Markov decision process (CMDP) modeling are proposed. The proposed Meta-GRL adopts CMDP scheme and graph representation, extracts and encodes the differentiated scene context, and can be extended to various scene changes. The upper meta-learner embedded in context in Meta-GRL is proposed to realize scene recognition. While the lower base-earner is guided to learn generalized context-specified policy. The test results in IEEE39 and open environment show that the Meta-GRL achieves more than 90% optimization and entire period applicability under the premise of saving computing resources.
Keyword ：
Contextual MDP Contextual MDP Generalized context-specified policy Generalized context-specified policy Graph representation Graph representation Meta reinforcement learning Meta reinforcement learning Stochastic power dispatch Stochastic power dispatch
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| GB/T 7714 | Huang, Zhanhong , Yu, Tao , Pan, Zhenning et al. Stochastic dynamic power dispatch with high generalization and few-shot adaption via contextual meta graph reinforcement learning [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 162 . |
| MLA | Huang, Zhanhong et al. "Stochastic dynamic power dispatch with high generalization and few-shot adaption via contextual meta graph reinforcement learning" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 162 (2024) . |
| APA | Huang, Zhanhong , Yu, Tao , Pan, Zhenning , Deng, Bairong , Zhang, Xuehan , Wu, Yufeng et al. Stochastic dynamic power dispatch with high generalization and few-shot adaption via contextual meta graph reinforcement learning . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 162 . |
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Recent trends in climate change have led to more frequent extreme weather events, such as typhoons and wildfires. These events pose significant risks to power grids, especially as we increasingly rely on renewable energy sources in the future. To address this, there 's a growing need for strategies ensuring that grids can quickly recover from outages caused by high impact low possibility (HILP) events. Currently, grids use energy storage to maximize economic benefits, but during disasters, this might not suffice to meet high demand, worsening the impact of outages. This paper suggests using energy storage systems like battery energy storage systems (BESS) and hydrogen storage systems (HSS) to proactively store energy, improving grid resilience by maintaining a minimum charge level. Through simulations on an IEEE 123 -bus system with variable energy sources such as photovoltaics (PVs), wind turbines (WTs), and storage, the simulation results show that the proposed variabletype minimum state of charge (SOC) incurs an operating cost loss of 2.5 % to 2.8 % compared to the fixed-type minimum SOC, but provides a power supply capacity improvement of 11.43 % to 18.53 %.
Keyword ：
Battery energy storage system (BESS) Battery energy storage system (BESS) Disaster Disaster Hydrogen storage system (HSS) Hydrogen storage system (HSS) Resilience Resilience State of charge (SOC) management State of charge (SOC) management
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| GB/T 7714 | Son, Yongju , Woo, Hyeon , Noh, Juan et al. Optimization of energy storage scheduling considering variable-type minimum SOC for enhanced disaster preparedness [J]. | JOURNAL OF ENERGY STORAGE , 2024 , 93 . |
| MLA | Son, Yongju et al. "Optimization of energy storage scheduling considering variable-type minimum SOC for enhanced disaster preparedness" . | JOURNAL OF ENERGY STORAGE 93 (2024) . |
| APA | Son, Yongju , Woo, Hyeon , Noh, Juan , Dehghanian, Payman , Zhang, Xuehan , Choi, Sungyun . Optimization of energy storage scheduling considering variable-type minimum SOC for enhanced disaster preparedness . | JOURNAL OF ENERGY STORAGE , 2024 , 93 . |
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The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy.
Keyword ：
continuation power flow continuation power flow distributed generations distributed generations load margin load margin load parameterization load parameterization P-V curve P-V curve renewable integration renewable integration stability assessment stability assessment voltage stability voltage stability
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| GB/T 7714 | Kim, Haelee , Woo, Hyeon , Yoon, Yeunggurl et al. An Enhanced Continuation Power Flow Method Using Hybrid Parameterization [J]. | SUSTAINABILITY , 2024 , 16 (17) . |
| MLA | Kim, Haelee et al. "An Enhanced Continuation Power Flow Method Using Hybrid Parameterization" . | SUSTAINABILITY 16 . 17 (2024) . |
| APA | Kim, Haelee , Woo, Hyeon , Yoon, Yeunggurl , Kim, Hyun-Tae , Kim, Yong Jung , Kang, Moonho et al. An Enhanced Continuation Power Flow Method Using Hybrid Parameterization . | SUSTAINABILITY , 2024 , 16 (17) . |
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Unbalanced voltages are one of the voltage quality issues affecting customer devices in distribution systems. Conventional optimization methods are time-consuming to mitigate unbalanced voltage in real time because these approaches must solve each scenario after observation. Deep reinforcement learning (DRL) is effectively trained offline for real-time operations that overcome the time-consumption problem in practical implementation. This paper proposes a safe deep reinforcement learning (SDRL) based distribution system operation method to mitigate unbalanced voltage for real-time operation and satisfy operational constraints. The proposed SDRL method incorporates a learning module (LM) and a constraint module (CM), controlling the energy storage system (ESS) to improve voltage balancing. The proposed SDRL method is compared with the hybrid optimization (HO) and typical DRL models regarding time consumption and voltage unbalance mitigation. For this purpose, the models operate in modified IEEE-13 node and IEEE-123 node test feeders.
Keyword ：
Deep reinforcement learning Deep reinforcement learning hybrid optimization hybrid optimization Mathematical models Mathematical models Optimization Optimization quadratic programming quadratic programming Reactive power Reactive power Real-time systems Real-time systems safe deep reinforcement learning safe deep reinforcement learning Systems operation Systems operation Uncertainty Uncertainty Voltage control Voltage control voltage unbalance factor voltage unbalance factor
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| GB/T 7714 | Yoon, Yeunggurl , Yoon, Myungseok , Zhang, Xuehan et al. Safe Deep Reinforcement Learning-Based Real-Time Operation Strategy in Unbalanced Distribution System [J]. | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS , 2024 , 60 (6) : 8273-8283 . |
| MLA | Yoon, Yeunggurl et al. "Safe Deep Reinforcement Learning-Based Real-Time Operation Strategy in Unbalanced Distribution System" . | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 60 . 6 (2024) : 8273-8283 . |
| APA | Yoon, Yeunggurl , Yoon, Myungseok , Zhang, Xuehan , Choi, Sungyun . Safe Deep Reinforcement Learning-Based Real-Time Operation Strategy in Unbalanced Distribution System . | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS , 2024 , 60 (6) , 8273-8283 . |
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Abstract ：
Interval power flow (IPF) analysis is a promising approach to handling the uncertainty of renewable energy sources and loads in power systems. However, most existing studies are based on standard interval arithmetic or affine arithmetic. Little work can be found that employs traditional optimization methods to obtain the interval results, especially based on convexified power flow formulation. Therefore, this paper proposes an IPF method based on a hybrid second-order cone and linear programming for radial distribution systems. First, the optimization process of IPF based on the standard power flow model is introduced. Given the non-convexity of the standard power flow model, an IPF framework based on second-order cone programming (SOCP) for radial distribution systems is proposed. Moreover, considering that the SOCP formulation can only achieve half of the whole IPF process, a revised linear DistFlow formulation is adopted, and the IPF method based on a hybrid second-order cone and linear programming is devised. Finally, the proposed IPF method is compared with the standard power flow equation-based IPF method and the Monte Carlo method. The simulation results on modified IEEE 33-node and 69-node distribution systems demonstrate the effectiveness and prospects of the proposed IPF method. (c) 2023 Elsevier Ltd. All rights reserved.
Keyword ：
Interval power flow Interval power flow Radial distribution system Radial distribution system Renewable energy sources Renewable energy sources Revised linearized distFlow formulation Revised linearized distFlow formulation Second-order cone programming Second-order cone programming
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| GB/T 7714 | Zhang, Xuehan , Woo, Hyeon , Choi, Sungyun . An interval power flow method for radial distribution systems based on hybrid second-order cone and linear programming [J]. | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2023 , 36 . |
| MLA | Zhang, Xuehan et al. "An interval power flow method for radial distribution systems based on hybrid second-order cone and linear programming" . | SUSTAINABLE ENERGY GRIDS & NETWORKS 36 (2023) . |
| APA | Zhang, Xuehan , Woo, Hyeon , Choi, Sungyun . An interval power flow method for radial distribution systems based on hybrid second-order cone and linear programming . | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2023 , 36 . |
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