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Wind Farm Level Coordination for Optimal Inertial control With a Second-Order Cone Predictive Model

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021年第4期12卷 2353-2366页

作者： Zhang, Gang Zhang, Feng Ding, Lei Meng, Ke Dong, Zhao Yang Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Multiple wind farms (WFs) should be spatially and temporally coordinated during inertial control to avoid the under- or over-power supply. For this purpose, we propose a second-order cone predictive model to exploit the optimal inertial control scheme (ICS) of multiple WFs for under-frequency and over-frequency events. Specifically, we firstly build the nonlinear predictive model including the predictive models of synchronous generators and wind turbines (WTs), power flow model and the stable operating constraints. In this nonlinear model, multiple WFs can be properly coordinated to achieve the optimal ICS. Then, the approximation methods are proposed to recast the above nonlinear model as a second-order cone programming (SOCP), and a sequential SOCP algorithm (SSA) is presented for the optimal solution. The SOCP model is offline executed to generate the optimal power references of WFs, and power references are further fitted with 5-parameter piecewise curves to reduce the communication burden. Lastly, the fitted curves can be distributed to WF controllers to be online tracked after detecting the disturbance. Numerical tests are conducted based on the 5 WFs integrated IEEE 39-bus system to validate the effectiveness and superiority of the proposed method.

关键词： Predictive models Rotors Frequency control power system stability Synchronization Stability analysis Optimal inertial control second-order cone programming frequency regulation wind farm level

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A Forecast Error Correction Method Based on Seq2Seq and Auto Encoder for Short-term Wind power Forecast Enhancement

A Forecast Error Correction Method Based on Seq2Seq and Auto...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Haoyu Ma Ming Yang Zifen Han Bo Wang Jianfeng Che Menglin Li Key Laboratory of Power System Intelligent Dispatch Control Shandong University Jinan China State Grid Gansu Power Grid Corporation Lanzhou China National Key Laboratory of Renewable Energy Grid-Integration China Electric Power Research Institute Beijing China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Accurate wind power forecast (WPF) is highly significant for the safe and economic operation of power systems, and implementing error correction is a common and effective manner to further enhance the forecasting performance. Nonetheless, existing error correction methods fail to consider temporal relationship and may inadvertently increase the error under certain circumstances, thereby compromising the local precision of forecast. In order to address these issues, this paper proposes an error correction model based on Seq2Seq and auto encoder (AE) to improve forecasting accuracy. Firstly, the seq2seq with attention mechanism is introduced to infer the probable error sequence through learning the temporal relationship between the numerical weather prediction sequence and the actual error sequence. Secondly, the samples that the seq2seq providing a positive error inference are collected and the AE model is employed to extract the meteorological features at these moments. Finally, on the basis of the above two models, the error correction method is constructed consisting of preliminary error inference and posterior correction determination. Using operational data from Ningxia province, the proposed method is validated for its remarkable superiority based on the evaluation metrics.

关键词： Accuracy Wind energy Heuristic algorithms Wind power generation Predictive models Feature extraction Error correction Numerical models Wind forecasting Forecasting

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Fast Resilience Assessment of Distribution systems With a Non-Simulation-Based Method

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IEEE TRANSACTIONS ON power DELIVERY 2022年第2期37卷 1088-1099页

作者： Zhang, Gang Zhang, Feng Wang, Xiaoyu Zhang, Xin Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Carleton Univ Dept Elect Ottawa ON K1S 5B6 Canada Cranfield Univ Sch Water Energy & Environm Energy & Power Theme Cranfield MK43 0AL Beds England

Traditional simulation-based method for resilience assessment of distribution systems is very time consuming since the network topology is characterized with a large number of scenario-specific optimization models. In this paper, we propose a methodology for fast resilience assessment of distribution systems with a non-simulation-based method, which can significantly improve the assessment accuracy and computational efficiency. First, a probabilistic metric is proposed to assess the system resilience against extreme events, which quantifies the system performance starting from the pre-event stage to the post-event stage. Then, a mixed-integer linear programming (MILP) is proposed to model the energization paths (EPs) with binary decision variables. Subsequently, the resilience metric-related probability events are built using the EPs. Last, the probabilistic resilience metric is explicitly expressed based on the total probability formula, conditional probability formula and EP-topology simplification methods. In the proposed method, the topology evolution along with the system degradation, restoration (part healed) and recovery (all healed) is characterized with a non-simulation-based method, rather than the multiple scenarios in traditional methods. The numerical tests validate the effectiveness of the proposed method and superiority over the simulation-based approach.

关键词： Resilience Measurement power system reliability Data models Network topology Topology Probabilistic logic Distribution system non-simulation-based method resilience assessment topology characterization

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Two-stage multi-objective optimal dispatch of hybrid power generation system for ramp stress mitigation

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2024年 163卷

作者： Zhang, Kunpeng Liu, Tianhao Liu, Yutian Ma, Huan Ma, Linlin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China State Grid Shandong Elect Power Res Inst Power Grid Technol Ctr Jinan 250003 Peoples R China State Grid Shandong Elect Power Co Shandong Elect Power Dispatching & Control Ctr Jinan 250001 Peoples R China

As the penetration of renewable energy continues to rise, the occurrence of ramp events in renewable generation poses significant challenges to power system security and efficient renewable energy utilization. To optimize the power allocation of hybrid energy storage systems (HESS) and enhance adjustable reserves to mitigate ramp events, a day-ahead and intraday two-stage multi-objective optimal dispatch strategy is proposed for hybrid power generation systems containing wind, photovoltaic, battery and hydrogen energy storage system (ESS). First, a novel optimization objective is presented to regulate the response priorities of different ESS by minimizing the energy loss, and balance the conservatism by a penalty factor. Then, a two-stage optimal dispatch model is proposed including two sub-models. The day-ahead multi-objective dispatch model considers generation plan, available storage capacity and energy loss, which identifies time slots when adjustable reserves is insufficient;the intraday dispatch model dynamically adjusts penalty factor for each time slot based on the day- ahead results to enhance adjustable reserves in advance. This combination of day-ahead and intraday dispatch models improves the farsightedness and computational efficiency. Finally, a non-isometric scaling method is presented to improve the distribution of Pareto optimal solutions for the non-dominated sorting genetic algorithm III (NSGA-III). Simulation results based on the actual data from Belgium and China demonstrate that the proposed method effectively mitigates the ramp stress and improves renewable energy utilization, with high computational efficiency and robustness to parameters.

关键词： Hybrid energy storage system Multi-objective optimization NSGA-III power generation dispatch power ramping mitigation

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Optimization method of skeleton network partitioning scheme considering resilience active improvement in power system restoration after typhoon passes through

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2023年第1期148卷

作者： Li, Shaoyan Lin, Zimian Zhang, Youhao Gu, Xueping Wang, Hongtao North China Elect Power Univ Dept Elect Engn Baoding Hebei Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China

After a major blackout caused by typhoon, secondary meteorological or geological disasters may also occur, so the secondary fault probability of transmission components in the restoration process is considerably higher than that in the normal operation state. A robust and reliable skeleton network restoration scheme is important to prevent the recurrence of collapse during restoration. Secondary component faults or contingencies are rarely considered in the existing research on transmission system restoration. Given that, this paper proposes a network partitioning scheme considering active resilience improvement of the skeleton network in parallel system restoration after a typhoon passes through. First, the necessity of considering secondary faults in the restoration after the blackout caused by typhoon is analyzed, and a new resilience index considering the impact of secondary faults is proposed. Next, the optimization objectives are proposed, including maximizing system resilience and minimizing restoration operation cost, and the basic partitioning constraints were modeled. Linearized operation constraints considering secondary faults were proposed to consider the resilience index in the partitioning scheme optimization. Then, a multi-objective mixed integer linear programming (MILP) network partitioning model considering the active resilience improvement of the skeleton network was established to mitigate the effect of secondary faults caused by typhoon. Furthermore, the Normalized Normal Constraint (NNC) method and the Variation Coefficient (VC) method were used to solve the multi-objective MILP model. To reduce the scale of the decision space, a swing-bus-based solving simplification strategy was proposed. In this paper, New England 10-unit 39-bus system and IEEE 118-bus system are taken as cases to demonstrate the proposed par-titioning scheme optimization method considering the system resilience improvement. The simulation results show that the partitioning

关键词： power system restoration Optimal partitioning scheme Resilience Typhoon Secondary fault

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Data-Driven Early Warning for Dynamic Insecurity Risk of Hybrid AC/DC power Grids

Data-Driven Early Warning for Dynamic Insecurity Risk of Hyb...

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International Conference on power system Technology (powerCON)

作者： Jiongcheng Yan Changgang Li Yutian Liu Yuanzhen Zhu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Electric Power Research Institute of State Grid Shandong Electric Power Company Jinan China

The dynamic interactions between AC and DC systems and the uncertainty of renewable generation increase the risk of cascading failures and bring challenges for the secure operation of hybrid AC/DC grids. Insecurity early warning identifies the security risk and guides the control decision-making, which is important for the secure power system operation. To achieve fast insecurity early warning, a data-driven early warning method for dynamic insecurity risk is proposed in this paper. First, Latin hypercube sampling and improved K-medoids algorithm are utilized to generate typical operating conditions (OCs) in the future. Then, stacking denoising autoencoders and ensemble extreme learning machines are deployed to construct security risk assessment models to fast assess the dynamic security of typical OCs. And model update is performed to ensure the assessment accuracy when cascading failures occur. Finally, the insecure OCs are ranked into different insecure levels based on the control cost needed to ensure the system security. Simulation results of the Northwest hybrid AC/DC grid in China demonstrate that the proposed method can achieve fast insecurity early warning and effectively track the security risk tendency during the process of cascading failures.

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A Distributionally Robust Optimal Allocation Method of Energy Storage for Resilience Enhancement of Distribution Network

A Distributionally Robust Optimal Allocation Method of Energ...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Kuan Cao Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Development Planning Department State Grid Shandong Electric Power Company Jinan China

To solve the problem of active power imbalance in distribution network with high penetrations of photovoltaic (PV) under extreme weather, the paper proposes a distributionally robust optimal allocation method of energy storage considering extreme scenarios. Firstly, considering that the effect of energy storage on enhancing the resilience of distribution network is strongly related to its location, resilience indexes based on node evaluation are established to select the installation sites of energy storage. Secondly, in view of the correlation and randomness of PV output and load, the typical scenarios with the empirical probability distribution are generated based on Frank Copula joint probability distribution function. Then, Kullback-Leibler divergence is utilized to characterize the probability distribution ambiguity set of the typical scenarios. Based on the ambiguity set, a two-stage distributionally robust optimization model for energy storage capacity is established to minimize the cost of energy storage investment and distribution network operation. For further improving the resilience, an operational resilience index as a chance constraint considering extreme scenarios is embedded into the model. Finally, the case study shows that the proposed method can effectively improve PV accommodation capacity of distribution network and reduce load loss in extreme scenarios.

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Spatio-Temporal Convolutional Network Based Identification of Voltage-Coupling Commutation Failures in Multi-Infeed HVDC systems

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Xiaohui Lin, Renmao Song, Kaige Han, Jiyao Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

Cascading commutation failures (CFs) pose severe risks in multi-infeed high voltage direct current (HVDC) systems. Different from the single or concurrent CF, not only the time-relevance of signals but also the spatio coupling and even control correlation of HVDCs will attribute to the cascading CFs. The conventional approaches to identify them tend to fall into a dilemma due to their complicated dynamics, wide-area coupling and vague threshold of judgement. In this paper, a deep-learning method based on the data-driven idea is proposed to recognize the cascading CFs. It analyzes the crucial factors leading to the cascading relationship of multiple HVDCs, while classifying them into time and space signals. To extract the inherent correlation between HVDCs as well as the time relevance in question, a spatio-temporal convolutional network (STCN) is formulated. The data generated in case of faults with diverse severity are applied to train STCN. Finally, the proposed framework and STCN method are validated by a customized IEEE 39 bus system and a practical power grid.

关键词： HVDC cascading commutation failure graph convolutional network temporal convolutional network spatio-temporal convolutional network deep learning data-driven method

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Online Preventive control Method for Static Voltage Stability of Large power Grids

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IEEE TRANSACTIONS ON power systemS 2020年第6期35卷 4689-4698页

作者： Yun, Zhihao Cui, Xinhui Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

The real-time preventive control is particularly critical because the integration of renewable energy aggravates the random fluctuation of large power grid operating state. An online preventive control optimization method for static voltage stability is proposed based on the Thevenin equivalent parameter identification. According to the definition of the stability margin, its quantitative relationship with the control variable is derived to meet the real-time and reliability requirements of online applications. The preventive control optimization model is constructed to minimize control cost and consider voltage stability margin and operation constraints under multiple critical contingencies. The generator terminal voltage and shunt capacitor in this paper are taken as continuous and discrete control variables respectively. The derived analytical relationship between stability margin and control variable is linearized to form linear security constraints, and the optimization model is transformed into a mixed-integer linear programming problem, which can be solved by CPLEX quickly and reliably. The accuracy and rapidity of the proposed method are verified on several test systems.

关键词： Large power grid preventive control optimization voltage stability

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Distributed Collaborative Optimization of a Multi-Region Integrated Energy system Based on Edge Computing Unit

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Mengxue Zhao, Haoran Tian, Hang Wu, Qiuwei Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Department of Electrical Engineering Technical University of Denmark Lyngby Denmark

The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

关键词： edge computing unit multiregional integrated energy system distributed collaborative optimization energy hub energy conservation

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