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power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Xuemei Fan Hongtao Wang Xiaoting Lv Zhen Song Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

With the advancement of new energy technologies, renewable energy generation is predicted to dominate the power system's black-start recovery process as a black-start power source. This study investigates the operating principle of BSWPS, factors influencing its output, effective output capacity, and its potential to engage in primary and secondary frequency regulation during power system black start. A BSWPS simulation model is developed, the control strategy of BSWPS as a system black start power source is investigated, and the possibility of BSWPS participating in frequency regulation during the black start phase is investigated. In order to allocate power reasonably, the paper suggests a strategy that combines a black start optimization model and a battery storage-based wind power system scheduling system model. The wind power prediction method is used to assess the wind power output, and Gurobi is used to solve the scheduling system model. The black start of the power system is effectively accomplished, according to test results in a system that has been retrofitted with a real grid, and the frequency stability of the system is greatly improved.

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Black Start Scheme of Wind-storage Combined system Based on Virtual Synchronous Generator control

Black Start Scheme of Wind-storage Combined System Based on ...

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Electrical, power and Computer Engineering (EPCE), Asia Conference on

作者： Hao Wang Hongtao Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

With a high proportion of wind power generations connected to the grid, wind power generation is expected to be a black-start source. This paper proposed a black start scheme based on virtual synchronous generator (VSG) control for wind-storage combined system. The energy storage inverter was transformed into a VSG with the secondary frequency regulation ability by improved frequency synchronous control (IFSC), and a conventional VSG controller was designed for the rotor side inverter of doubly fed induction generator. And a control mode switching device was designed for the energy storage in order to maintain the grid stability after black start. This paper built the model of wind-storage combined system on the Matlab/Simulink for black start simulation. The simulation results show that the maximum frequency deviation is 68.75% lower than the conventional control when the ancillary machines of wind turbines were restored. What's more, the maximum frequency deviation shall not exceed 0.12%, and the maximum voltage deviation shall not exceed 8% during the black start process and the controllers can realize no-error frequency adjustment. Finally, control mode switching device can smoothly switch IFSC to VSG. Thus, the effectiveness of the proposed black start scheme and controllers are verified.

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Multi-Objective Topology Reconfiguration Strategy for Promoting PV Consumption of Distribution systems

Multi-Objective Topology Reconfiguration Strategy for Promot...

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International Conference on power system Technology (powerCON)

作者： Zhengxing Ren Xiaodong Chu Hua Ye Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

The use of intermittent new energy sources is an important part of driving the low-carbon transition of the power system. PV (Photovoltaic) grid-connected increases the volatility of the distribution system. As the basis of power system operation, the topology can directly influence the PV generation and flow distribution. To promote PV consumption, the flexibility of the distribution system topology needs to be fully utilized. A topology optimization strategy is proposed in this paper to cope with the increasing penetration of PV in distribution systems. First, a multi-objective topology reconfiguration model considering PV consumption and power quality is established. After that, an improved multi-objective discrete particle swarm optimization algorithm is used to find the optimal solution set. Finally, gray relational analysis is applied to select the optimal reconfiguration plan. The case study results of the IEEE 33-bus system demonstrate that this strategy can effectively promote PV consumption.

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The Effect of Phase Locking Error on control of Grid-Connected Inverter in Distribution Network

The Effect of Phase Locking Error on Control of Grid-Connect...

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International Conference on power system Technology (powerCON)

作者： Wang Yuanqi Yun Zhihao Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

The phase-locked loop provides a phase reference for the coordinate transformation in grid-connected inverters to ensure synchronous operation and power output. In the distribution network, under the conditions of three-phase unbalance because of the diversity of loads, untransposed line and the complex operating conditions, the phase-locked loop can produce phase locking errors that affect the inverter power output and thus the regulation of the active distribution network. Therefore, by analyzing the inverter control, the influence of phase error of the phase-locked loop on the Park transformation and modulation signal is deduced, and the relationship between phase error, voltage, current, current reference value, and modulation signal is quantified. The simulation results prove the correctness of the analysis.

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Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning

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ENERGIES 2023年第5期16卷 2410页

作者： Almani, Aftab Ahmed Han, Xueshan Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jingshi Rd 17923 Jinan 250000 Peoples R China

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply-demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual-predicted demand and actual-predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.

关键词： real-time pricing demand response LSTM Australian energy market operator (AEMO)

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power system Equivalent Inertia Estimation Method Using system Identification 5

Power System Equivalent Inertia Estimation Method Using Syst...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Ma, Huan Yang, Dong Qin, Hao Cao, Yongji Cheng, Dingyi Zhang, Bing Hao, Xudong Zhou, Ning State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power Company Jinan China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Shandong Power Dispatch & Control Center State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9781665485081

As the proportion of renewable energy sources increase, the inertia of the power system continues to decrease, leading to a serious impact on frequency security and stability. There is an urgent need for an online estimation of the equivalent inertia of power systems. Most of the existing inertia estimation methods are based on the rate of change of frequency under large disturbance scenarios. According to the measurement data under normal operation, this paper estimates the equivalent inertia using system identification. The inertia response of the power system is analysed and an expression for the equivalent inertia is derived. An improved numerical algorithm based on subspace state space system identification and a least squares estimation method is used to calculate the equivalent inertia of the system. The validity of the proposed method is verified in a WSCC 9-bus power system case, showing that the proposed method has a high identification accuracy and can be adapted to different forms of load fluctuations. © 2022 IEEE.

关键词： Numerical methods

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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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STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind power Forecasting

arXiv

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arXiv 2024年

作者： Dong, Xiaochong Zhang, Xuemin Yang, Ming Mei, Shengwei State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China

Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significant modeling challenges. To address this, we propose a spatio-temporal dynamic hypergraph learning (STDHL) model. This model uses a hypergraph structure to represent spatial features among wind farms. Unlike traditional graph structures, which only capture pair-wise node features, hypergraphs create hyperedges connecting multiple nodes, enabling the representation and transmission of higher-order spatial features. The STDHL model incorporates a novel dynamic hypergraph convolutional layer to model dynamic spatial correlations and a grouped temporal convolutional layer for channel-independent temporal modeling. The model uses spatio-temporal encoders to extract features from multi-source covariates, which are mapped to quantile results through a forecast decoder. Experimental results using the GEFCom dataset show that the STDHL model outperforms existing state-of-the-art methods. Furthermore, an in-depth analysis highlights the critical role of spatio-temporal covariates in improving ultra-short-term forecasting accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Wind forecasting

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power system Load Restoration Based on Data-Driven Constraints Linearization Method

Power System Load Restoration Based on Data-Driven Constrain...

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International Conference on power and Energy Technology (ICPET)

作者： Xiaoting Lv Hongtao Wang Xuemei Fan Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

In recent years, power outages have occurred frequently around the world, and rapid load recovery after power outages can reduce economic losses caused by power outages. In the restoration model, the power flow calculation is a nonlinear equality constraint. The idea based on mathematical optimization is to linearize the power flow equation based on certain assumptions or perform convex relaxation. Such processing may affect the solution accuracy or speed. Therefore, a data-driven constraints linearization method is proposed, using a large amount of simulation data to linearly fit the power flow through the partial least square method. Then, based on the linear constraints, the load restoration problem is established as a mixed integer linear programming (MILP) problem to reduce the difficulty of solving, improve calculation accuracy and provide dispatchers with optimal recovery decisions. Finally, the validity of the method proposed in this paper is verified in IEEE39 node system.

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Online Cascading Failure Searching Based on Gradient Boosting Decision Tree

Online Cascading Failure Searching Based on Gradient Boostin...

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power Tech Conference

作者： Tianhao Liu Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

With the penetration of renewable energy sources (RES) increasing rapidly, cascading failures become more complex due to the uncertainty and vulnerability of RES. This paper presents an online cascading failure searching method based on gradient boosting decision tree (GBDT) for the hybrid AC/DC system with high penetration of wind power. First, the cascading failure risk index is established, which considers the impact, probability, and loss of failures. Then, a cascading failure searching based on Monte Carlo tree search and dynamic simulation is performed to acquire the high-risk cascading failures, which are utilized as the training samples. Finally, GBDT is deployed to construct the relationship between operating conditions and failure propagation directions. Online cascading failure searching is realized by combining GBDT failure estimation and dynamic failure simulation. Simulation results of the modified New England 39-bus test system demonstrate that the proposed method can fast and accurately online screen the high-risk cascading failures considering the uncertainty of wind power.

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