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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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A framework of day-ahead wind supply power forecasting by risk scenario perception

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IEEE Transactions on Sustainable Energy 2025年

作者： Yang, Mao Huang, Yutong Wang, Zhao Wang, Bo Su, Xin Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Contr Ol of Renewable Energy & Storage Systems Beijing100912 China

Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method. © 2010-2012 IEEE.

关键词： Wind forecasting

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Advanced Grid-Forming Undersea Pumped Storage to Enable 100% Renewable Offshore Oilfield power systems

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IEEE Transactions on Sustainable Energy 2025年

作者： Su, Kaiyuan Wang, Xi Xie, Xiaorong Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China

To advance carbon reduction of the offshore oilfield power system (OOPS), the grid-forming undersea pumped storage system (GFM-UPSS) emerges as a promising solution. This paper introduces a novel framework for a 100% renewable OOPS utilizing the GFM-UPSS. Firstly, the control strategy of the GFM-UPSS is presented. It consists of the grid-side converter (GSC), machine-side converter (MSC), and reversible pump-turbine (RPT) to achieve frequency and voltage regulation. A steady-state model is then developed detailing the water head, power, and volume of the spherical shell. In addition, the paper explores the converter parameter impacts on the GFM-UPSS transient model and derives the closed-form solutions. With the steady-state model, an optimal sizing method is presented and economic advantages in the marine environment are studied for the GFM-UPSS. Finally, EMT simulations are conducted to assess the frequency & voltage stabilities and verify the effectiveness of the GFM-UPSS in enabling a 100% renewable OOPS. The optimal sizing results show that construction costs, mainly for OWP, are dominated and are influenced by sphere radius, placement depth, and start-stop cycles, while a 2.5 capacity ratio between OWP and GFM-UPSS consistently emerges as optimal. Moreover, analysis of transient stability shows that it improves with higher frequency & voltage modulation coefficient and lower virtual impedance. The impact of RPT and MSC, mainly on frequency regulation, is determined by the DC droop coefficient and turbine inertia. © 2010-2012 IEEE.

关键词： Offshore oil fields

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Extraction and application of intrinsic predictable component in day-ahead power prediction for wind farm cluster

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Energy 2025年 328卷

作者： Yang, Mao Jiang, Renxian Yu, Xinnan Wang, Bo Su, Xin Ma, Chenglian The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing100912 China

With the continuous updating and iteration of artificial intelligence algorithms, prediction models emerge one after another, but the research and utilization of the predictability of wind power is still less. Therefore, this paper proposed a day-ahead power prediction method for Wind Farm Cluster (WFC) based on intrinsic predictable component extraction. Firstly, based on the difference of power distribution between wind farm and WFC, a method of Wind power Curve (WPC) modeling for WFC is proposed, which provides the basis for establishing the Final Set of Wind power Curve (FSWPC). Secondly, the Intrinsic Predictable Component (IPC) of wind power is extracted based on the FSWPC, and the Interference Component (IC) of wind power is separated to eliminate the influence of IC on IPC in the process of prediction. Thirdly, the historical similarity matching method with large threshold is used to predict the IC to make up for the numerical deficiency of the IPC. Finally, the proposed method was applied to a WFC in China to verify its effectiveness. Compared with the traditional prediction strategy, the NRMSE, NMAE and MAPE are reduced by 1.59 %, 1.15 % and 6.42 %, respectively. © 2025 Elsevier Ltd

关键词： Iterative methods

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A Short-Term power Prediction Method for Wind Farm Cluster Based on Time Dynamic Graph Network and Improved Transformer Model

A Short-Term Power Prediction Method for Wind Farm Cluster B...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Chao Han Zihang Chen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Jiangmen Power Supply Bureau of Guangdong Power Grid Co. Ltd Jiangmen China

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

In recent years, wind power has gradually replaced thermal power as the main source of new energy generation. Wind power prediction in advance can help formulate scheduling strategies and improve the stability of the power system. With the advent of the Industry 4.0 and the widespread application of artificial intelligence technology, artificial algorithms have been extensively used in the field of wind power prediction. To further explore the spatiotemporal correlation of every wind farm in the wind farm cluster, a temporal dynamic graph network is used to divide the wind farm farm into several sub-clusters. Then, the KAN-BiLSTM-Transformer algorithm is used to predict wind power of each wind sub-cluster. Finally, the wind power of each sub-cluster is added to attain the final predicted power of the wind farm cluster. After verification, the RMSE and MAE of the proposed method is 0.06849 and 0.05805 respectively, with an accuracy of 93.15%, which is superior to the compared algorithms. Proposed method can provide more accurate prediction results.

关键词： Accuracy Heuristic algorithms Clustering algorithms Wind power generation Wind farms Predictive models Prediction algorithms Transformers Spatiotemporal phenomena Wind forecasting

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Prediction of Distributed Photovoltaic Accommodation Capability Based on Ridge Regression 9th

Prediction of Distributed Photovoltaic Accommodation Capabil...

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9th Purple Mountain Form on Smart Grid Protection and control, PMF 2024

作者： Ji, Xingquan Zhang, Bowen Zhang, Yumin Sun, Pengkai Zhang, Xiaofeng Liu, Wei dong Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China Information and Communication Company State Grid Shandong Electric Power Company Jinan250013 China Qingdao Yihe Electric Group Co. Ltd. Qingdao266510 China

ISBN: (纸本)9789819616701

A prediction method of distributed photovoltaic accommodation capability is proposed based on ridge regression. First, the factors influencing the distributed photovoltaic accommodation capacity are analyzed, and the concept of the contribution degree of distributed photovoltaic accommodation is introduced. The gray relation analysis method is used to evaluate the correlation between different factors and distributed photovoltaic accommodation, and a model of factors affecting distributed photovoltaic accommodation is constructed. Then, a prediction model of distributed photovoltaic accommodation capability is established based on ridge regression. The factors with high photovoltaic accommodation contribution degrees are incorporated in the prediction model. The mapping relationship between the driving factors of distributed photovoltaic accommodation and the prediction model is derived, then the development strategies for future photovoltaic accommodation capacity are formulated. Finally, simulation is performed on the SPSSPRO platform, and the results show that the proposed method can predict the distributed photovoltaic accommodation capability accurately and provide strategic suggestions for improving future photovoltaic accommodation capacity. © state Grid Electric power 2025.

关键词： Photovoltaics

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A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation

arXiv

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arXiv 2025年

作者： Dong, Xiaochong Liu, Yilin Zhang, Xuemin Mei, Shengwei State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China

Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A novel decoupled mapping path is employed for joint probability distribution modeling, formulating regression tasks for both marginal distributions and the correlation structure using proper scoring rules to ensure the rationality of the modeling process. The scenario generation process is divided into two stages. Firstly, the dynamic correlation network models temporal correlations based on a dynamic covariance matrix, capturing the time-varying features of renewable energy while enhancing the interpretability of the black-box model. Secondly, the implicit quantile network models the marginal quantile function in a nonparametric, continuous manner, enabling scenario generation through marginal inverse sampling. Experimental results demonstrate that the proposed dynamic correlation quantile network outperforms state-of-the-art methods in quantifying uncertainty and capturing dynamic correlation for short-term renewable energy scenario generation. The code is open-source and available at: https://***/DREAMDXC/Dynamic-Correlation-Quantile-Network Copyright © 2025, The Authors. All rights reserved.

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Evaluation of Regulating Capacity of power system Nodes and Transmission Lines

Evaluation of Regulating Capacity of Power System Nodes and ...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Xiuyu Yang Zekun Fan Almasbek Shakenbek Baoyu Di Chongbi Li Yi Wang Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education Northeasst Electric Power University Jilin China State Grid Xinjiang Electric Power Co. Ltd. State Grid Xinjiang Electric Power Co. Ltd. Electric Power Research Institute Urumqi China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

The large-scale access of renewable energy to the power grid leads to a sharp increase in the demand for power system regulation capacity. In order to solve this problem, this paper explores the mechanism of supply and demand balance of power system regulation capacity, analyzes the transmission characteristics of regulation capacity, and proposes the adequacy index of node regulation capacity and the adequacy index of network transmission regulation capacity. The Garver-6 node system is used to verify the effectiveness and practicability of the proposed method. The example shows that the proposed evaluation method can accurately identify the weak links of regulation ability and clarify the causes of insufficient regulation ability.

关键词： Renewable energy sources Supply and demand power transmission lines Evaluation models Regulation Smart grids Planning Artificial intelligence Load flow Capacity planning

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The staged optimal scheduling method for a multi-source industrial park with wind, solar, thermal, and energy storage 3

The staged optimal scheduling method for a multi-source indu...

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3rd International Conference on Smart Energy and Electrical Engineering, SEEE 2024

作者： Hao, Ling Xu, Hui Tan, Zhenlong Zheng, Zhibao Xu, Fei Yang, Ruixuan Xu, Zhenyu Dai, Xuhui State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Co. Ltd. Beijing101199 China School of Economics and Management Tsinghua University Beijing100084 China State Key Laboratory of Power Transmission Equipment Technology Chongqing University Chongqing400044 China

The increase in the proportion of renewable energy generation capacity has placed significant pressure on thermal power operations. Constructing energy storage facilities within the metering output of thermal power plants, allowing them to participate in peak load regulation with generating units jointly, is one effective way to alleviate the operational pressure on thermal power. To achieve comprehensive scheduling of thermal power plants with energy storage, this paper first establishes an overall output model for the coupling operation of thermal power and energy storage. Secondly, a staged optimal scheduling method for a multi-source industrial park with wind, solar, thermal, and energy storage is proposed to solve the optimization scheduling problem for the industrial park involving thermal power plants with energy storage participation. Finally, the simulation results based on a specific regional power grid in Inner Mongolia validate the feasibility of the proposed method. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Thermoelectric power plants

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FDCA-DSTGCN: A Wind Farm Cluster power Day-ahead Prediction Model Based on Frequency Domain Information Gain and Dynamic Trend Sensing

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IEEE Transactions on Sustainable Energy 2025年

作者： Yang, Mao Niu, Jiajun Wang, Bo Huang, Dawei Su, Xin Ma, Chenglian Ministry of Education Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Beijing100912 China

Accurate wind farm cluster power prediction (WFCPP) is of vital significance for new power systems with large-scale wind power integration. The current WFCPP modeling method ignores the important role of wind direction and frequency domain information, resulting in insufficient use of spatial information and difficult to improve the prediction accuracy. In order to solve the above problems, a day-ahead power prediction model for wind farm cluster power is proposed based on information gain in frequency domain and dynamic trend sensing. Firstly, a cluster division method based on graph theory and progressive fusion of multiple information is proposed, and a virtual information node is set up. Secondly, a method to identify prevailing wind direction under time window is proposed for subsets, and a dynamic weighted directed graph structure is designed based on prevailing wind direction and wind speed. Thirdly, a dynamic spatio-temporal graph convolutional network model with frequency domain gain channel attention mechanism (FDCA-DSTGCN) is proposed to integrate the above information and complete the prediction. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia of China. Compared with the existing and traditional prediction methods, the normalized root mean square error of the proposed method is reduced by 1.13% -2.15%, which verifies the effectiveness of the proposed method. © 2010-2012 IEEE.

关键词： Mean square error

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