A scenario-based two-stage stochastic optimization approach for multi-energy microgrids

Li, Ke; Yang, Fan; Wang, Lupan; Yan, Yi; Wang, Haiyang; Zhang, Chenghui

doi:10.1016/j.apenergy.2022.119388

Cited by 46 publications

(7 citation statements)

References 23 publications

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“…K‐means algorithm is a unsupervised learning method in machine learning, which can mine the similarity of data objects without any experience to achieve the purpose of data grouping 27 …”

Section: Algorithm Introduction and Protection Methods Flowmentioning

confidence: 99%

“…K-means algorithm is a unsupervised learning method in machine learning, which can mine the similarity of data objects without any experience to achieve the purpose of data grouping. 27 This algorithm requires pre specifying the number of clusters K and K initial clustering centers, calculating the distance between the given data object and the initial clustering center, and dividing each data object into the closest cluster. After all data objects are allocated, the average value of data objects for each type of cluster is recalculated as the new clustering center.…”

Section: Principle Of Improved K-means Algorithmmentioning

confidence: 99%

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A new method for single‐phase high resistance grounding fault protection in low resistance grounded distribution networks

Wang,

Wang

2023

Engineering Reports

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The neutral point of the distribution network is grounded with low resistance, which can quickly remove single‐phase grounding fault and effectively suppress overvoltage. However, the existing zero‐sequence overcurrent protection has a high setting value. In the case of large fault resistance and weak fault current, the existing protection methods may refuse to operate. Based on the waveform characteristics of the zero‐sequence current of each outgoing line in the distribution network, this article proposes a new method for single‐phase high resistance grounding fault protection in low resistance grounded distribution network, which combines cosine distance and improved K‐means algorithm. First, the cosine distance between zero‐sequence current of every two outgoing lines is obtained, and the cosine distance matrix used to describe the fault characteristics is constructed. Then the improved K‐means algorithm is used to cluster the row vectors of the cosine distance matrix to obtain the convergent cluster center and cluster label, so that the fault line can be determined. This protection method does not need to set the threshold value manually, and has strong ability to handle large fault resistance. It can effectively deal with noise interference and intermittent arc grounding fault, and still has good adaptability in scenarios such as change of fault initial phase angle, change of fault location, access of distributed generations and occurrence of two outgoing lines in phase to grounding fault. Finally, a model is built, and the effectiveness of the proposed method is proved by the simulation of grounding fault.

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“…K‐means algorithm is a unsupervised learning method in machine learning, which can mine the similarity of data objects without any experience to achieve the purpose of data grouping 27 …”

Section: Algorithm Introduction and Protection Methods Flowmentioning

confidence: 99%

Section: Principle Of Improved K-means Algorithmmentioning

confidence: 99%

A new method for single‐phase high resistance grounding fault protection in low resistance grounded distribution networks

Wang,

Wang

2023

Engineering Reports

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“…Apart from MGs involving only power sector, integrated MEMGs have recently attracted much interest in terms of energy management owing to their various advantages, such as increased system reliability and efficiency, reduced fuel consumption, energy cost, and carbon emissions [4]. For instance, in [14], a two-stage stochastic optimization approach based on scenario analysis is proposed for the energy management of MEMGs, considering the stochastic processes of wind power generation and demand profiles. In [15], an adjustable and robust formulation is developed for the optimal energy management of MEMGs, capturing uncertainties associated with energy demand and RESs.…”

Section: Literature Review On Model-based Approachesmentioning

confidence: 99%

“…To highlight the contributions of this paper, existing literature associated with the energy management problems of both MG and MEMG has been systematically organized in Table 1. On one hand, compared to the model-based optimization methods [9][10][11][12][14][15][16][17][18][19], this paper employs a model-free safe RL method that can learn the energy management control policy of an MEMG, while ensuring the secure network operation. On the other hand, compared to the existing RL methods [7,22,[24][25][26][27][28][29][30][31]33,34], a significant research gap has been identified, which drives the motivation behind this paper: no previous work has developed a safe and automatic control method for the realtime energy management of MEMGs that can satisfy all the physical constraints pertaining to the MEMG model.…”

Section: Paper Contributionsmentioning

confidence: 99%

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

et al. 2023

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“…Currently, two predominant research methodologies are employed to address the uncertainty inherent in integrating clean energy into the power system. These are stochastic optimization (SO) [11][12][13][14] and robust optimization (RO) [15][16][17][18]. Each method provides strategic frameworks for managing the unpredictable nature of clean energy outputs.…”

Section: Introductionmentioning

confidence: 99%

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

Zhang,

Zhu

2024

Processes

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With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness.

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A scenario-based two-stage stochastic optimization approach for multi-energy microgrids

Cited by 46 publications

References 23 publications

A new method for single‐phase high resistance grounding fault protection in low resistance grounded distribution networks

A new method for single‐phase high resistance grounding fault protection in low resistance grounded distribution networks

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

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