Data-driven adjustable robust Day-ahead economic dispatch strategy considering uncertainties of wind power generation and electric vehicles

Wu, Junjie; Liu, Yang; Chen, Xianbang; Wang, Changhao; Li, Wenfeng

doi:10.1016/j.ijepes.2021.107898

Cited by 20 publications

(7 citation statements)

References 31 publications

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“…Every probability function is linked to a different possibility weight, which will vary. For instance, in [88], the authors use an imprecise Dirichlet model-based nonparametric ambiguity set to represent the uncertainty set of a wind power generator's power production. Every time, the function varies, and the ambiguity set is constructed with all the possible probability functions.…”

Section: Possibility Theorymentioning

confidence: 99%

“…Thus, in this case, machine learning techniques could help the development of digital twins for microgrids [183]. [5] 2022 [13] x 2022 [88] x x 2022 [89] x 2022 [171] x 2022 [44] x x 2019 [12] x x 2020 [15] x 2020 [25] x 2020 [30] x 2020 [50] x 2020 [64] x 2020 [92] x 2020 [94] x 2020 [97] x x 2020 [147] x x x 2020 [155] x x 2020 [164] x 2020 [165] x 2020 [14] x x x 2019 [31] x 2019 [45] x 2019 [93] x x 2019 [122] x x 2019 [140] x 2019 [145] x 2019 [162] x 2019 [170] x 2019 [180] x 2019 [53] x 2018 [58] x 2018 [81] x x 2018 [82] x x 2018 [121] x 2018 [123] x 2018 [131] x 2018 [141] x x 2018 [146] x 2018 [184] x 2018 [166] x 2018 [181] x 2018 [43] x x 2017 [47] x 2017 [84] x 2017 …”

Section: Challenges and Future Trendsmentioning

confidence: 99%

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A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

et al. 2022

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This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed.

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Section: Possibility Theorymentioning

confidence: 99%

Section: Challenges and Future Trendsmentioning

confidence: 99%

A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

et al. 2022

View full text Add to dashboard Cite

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“…1 For power companies, accurate and reliable load predictions are very important, as higher predictions lead to increases of the power generation costs, while lower predictions lead to the need to purchase expensive peak electricity. 2 However, with the increasing penetration of renewable energy 3 and the continuous application of flexible load, 4,5 more and more highly uncertain factors are injected into the power system, which increases the difficulty of STLF.…”

Section: Introductionmentioning

confidence: 99%

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

Feng,

Li,

Zhou

et al. 2023

Energy Science & Engineering

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With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches.

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“…As mentioned above, the uncertainty of wind-power generation in the microgrid leads to difficulty in dispatching decisions. Mathematical approaches such as stochastic optimization (SO) and robust optimization (RO) are widely employed to address dispatching decisions involving uncertainties [11]. Li et al [10] presented a scenario-based optimal operational model based on SO, mainly to handle the uncertainties involved in energy demand and renewable generation.…”

Section: Introductionmentioning

confidence: 99%

A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power

Liu

2023

Sensors

Self Cite

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In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.

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Data-driven adjustable robust Day-ahead economic dispatch strategy considering uncertainties of wind power generation and electric vehicles

Cited by 20 publications

References 31 publications

A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power

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