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IEEE Transactions on Power Systems
Journal Prestige (SJR): 2.742  Citation Impact (citeScore): 7 Number of Followers: 48  Hybrid journal (It can contain Open Access articles)ISSN (Print) 0885-8950 Published by IEEE  [228 journals]
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- IEEE Power & Energy Society Information
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Pages: C2 - C2
Abstract: null
PubDate: TUE, 20 AUG 2024 09:16:57 -04
Issue No: Vol. 39, No. 5 (2024)
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Pages: C4 - C4
Abstract: null
PubDate: TUE, 20 AUG 2024 09:16:56 -04
Issue No: Vol. 39, No. 5 (2024)
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- IEEE Transactions on Power Systems Information for Authors
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Pages: C3 - C3
Abstract: null
PubDate: TUE, 20 AUG 2024 09:16:55 -04
Issue No: Vol. 39, No. 5 (2024)
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- Feasible Coefficient Region Analysis and Dual-Loop Adaptive Feedback
Control for Transient Stability of VSG Under Severe Grid Voltage Sag-
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Authors: Kun Sun;Wei Yao;Qihang Zong;Jinyu Wen;Lin Jiang;
Pages: 6127 - 6143
Abstract: Aiming at the transient instability and overcurrent issues of the virtual synchronous generator (VSG) under severe grid voltage sag, the accurate and simultaneous control for the phase angle and current of VSG is hard to be achieved without using the fault information. And the requirement of the grid code for the reactive current should be also considered. To address the issues, this paper proposes a non-fault information based dual-loop adaptive feedback control to take transient angle stability, current limitation and the demand of the reactive current of VSG into account. First the large-signal model of VSG with a dual-loop control is built. To design the feedback coefficients, the feasible coefficient region under different fault degrees and cases is analyzed subsequently. It provides reference for the curve fitting, which is further applied in the self-adaptive regulation of the feedback coefficients. Thereby, a dual-loop adaptive feedback control is realized based on an additional reactive power feedback loop. With the proposed control scheme, all of the three control objectives can be achieved without the fault information, since the feedback coefficients are within the feasible coefficient region by the self-adaptive regulation. Finally, the effectiveness and robustness of the proposed control scheme for both VSG and a paralleled system of VSG and grid-following (GFL) converter are validated by the simulation results and the experimental results.
PubDate: THU, 01 FEB 2024 09:16:53 -04
Issue No: Vol. 39, No. 5 (2024)
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- High-Performance Computing-Based Open-Source Power Transmission and
Distribution Grid Co-Simulation-
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Authors: Lei Zheng;Yuxin Cui;Shuangshuang Jin;Yousu Chen;
Pages: 6144 - 6153
Abstract: As the electric power grid becomes more and more complex due to the penetration of various distributed energy resources (DERs), co-simulation of power transmission and distribution (T&D) grid in one unified environment emerges as an effective approach to accurately account for the changes and impact of DERs on the power system. Fast and scalable T&D co-simulation leveraging high-performance computing (HPC) techniques is key to the successful deployment of large-scale grid monitoring and assessment to support online operation. In this article, two open-source power grid simulators — GridPACK (for transmission grid simulation) and GridLAB-D (for distribution grid simulation) are coupled together on Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) — an open-source scalable co-simulation engine, to enable fast parallel dynamic simulation in transmission grid with enriched system dynamics captured from its interconnected distribution grids that are simulated in a distributed way. Experiments on a synthetic co-simulation study comprising one 3000-bus transmission grid model with detailed generator models and various interconnected IEEE-123-node distribution systems. The results show the faster-than-real-time performance of parallel dynamic simulation for the transmission system and high scalability with multiple distribution grids when running the co-simulation on both commodity-level multi-core computers and high-end supercomputing clusters.
PubDate: THU, 25 JAN 2024 09:16:44 -04
Issue No: Vol. 39, No. 5 (2024)
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- A Semi-Markov Stochastic Model for Operational Reliability Assessment of
Hybrid AC and LCC-VSC-Based DC System With Remote Wind Farms-
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Authors: Yimin Bo;Minglei Bao;Bowen Yang;Yi Ding;Ying Huang;
Pages: 6154 - 6167
Abstract: By combining the advantages of line commutated converter (LCC) and voltage source converter (VSC), the hybrid AC and LCC-VSC-based DC system (HALVDS) has broad application prospects to deliver remote wind power to the load center. Faced with the increasing uncertainties, the reliability issues of HALVDS with remote wind farms can be significantly serious, especially in the operational phase. In previous studies, the traditional reliability models are usually developed based on Markov stochastic process (MSP) where the duration times of different states are supposed to follow exponential distributions. However, in intricate operational environments, several components may not obey the above assumptions, e.g., the state duration times of wind turbines and electronic components following arbitrary distributions. Hence, the traditional method cannot be suitable for evaluating the operational reliability of HALVDS with complicated structures of various components whose state duration times follow different distributions. To address this, a semi-Markov stochastic process (SMSP) model is innovatively proposed in this paper for evaluating the operational reliability of HALVDS. By solving the integral equations of the SMSP, the operational reliability of components following arbitrary distributions can be determined. On this basis, the Lz-transform technique is applied to develop the generalized reliability models of different components, whose time-varying characteristics can be described in a unified way. The optimal AC/DC power flow (OADPF) operator is then defined to aggregate the reliability models of components to determine the operational reliability of HALVDS with complex structures. Furthermore, time-varying reliability indices of nodes and systems are defined to evaluate the spatial-temporal reliability of HALVDS. Case studies validate the effectiveness of the proposed technique.
PubDate: TUE, 23 JAN 2024 09:18:31 -04
Issue No: Vol. 39, No. 5 (2024)
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- Database Generation for Data-Driven Power System Security Assessment Under
Uncertainty-
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Authors: Tian Xia;Qingchun Hou;Ning Zhang;Qihuan Dong;Weiran Li;Chongqing Kang;
Pages: 6168 - 6182
Abstract: High renewable penetration brings diversified operation states and complex dynamic behaviors to power systems and challenges the dynamic security assessment calculation. Data-driven methods have become increasingly important to address this challenge. However, the performance of data-driven DSA is heavily driven by the quality of the database generated for training the model, i.e., how well the database represents the operation states which need to be evaluated by the data-driven DSA. This paper proposes a database generation method that can generate samples following the probability distribution of operation states which need to be evaluated by the data-driven DSA in high renewable penetrated power system. In the method, the probability distribution of operation states which need to be evaluated by data-driven DSA in high renewable penetrated power system is modeled as probabilistic feasible region, which is a probability distribution of unnormalized PDF on convex polytope. An efficient sampling method is designed to generate operation state samples from the probability distribution of unnormalized PDF on polytope. The effectiveness of the proposed method and the improvement compared to OPF-based method, Gapsplit method, and optimization-based exploration method are demonstrated by case study on the transient angular stability problem of a 170-bus dynamic test system.
PubDate: THU, 11 JAN 2024 09:16:30 -04
Issue No: Vol. 39, No. 5 (2024)
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- Optimal Reconfiguration for Active Distribution Networks Incorporating a
Phase Demand Balancing Model-
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Authors: Long Fu;Wei Wang;Zhao Yang Dong;Yaran Li;
Pages: 6183 - 6195
Abstract: Optimally reconfiguring an active distribution network (ADN) during power outages has been regarded as a reasonable approach to facilitate system secure operation and reliability. Nevertheless, most existing studies for the reconfiguration virtually focus on taking actions from the generation- and network-side, in which the potential achievement from the demand-side is underestimated. Moreover, the phase-unbalance and voltage violation in ADNs should be restricted to avoid extreme conditions of distributed generators (DGs) that jeopardize system reliability. To bridge the gap, a new approach to reconfigure ADNs under multiple faults is proposed in this paper, incorporating a phase demand balancing (PDB) model to improve dispatch performance. The model regulates asymmetrical loads to mitigate the phase-unbalance issue from the demand-side, co-optimized with step voltage regulators (SVRs) and DG dispatching to enhance reliability and flexibility in reconfiguring ADNs. The derived optimization is a challenging mixed-integer non-convex programming (MINCP), which is reformulated as an efficiently solvable mixed-integer second-order cone programming (MISOCP) via exact equivalence and accurate approximation techniques. Case studies based on modified IEEE benchmark systems validate the effectiveness and advantages of the proposed method.
PubDate: WED, 17 JAN 2024 09:16:44 -04
Issue No: Vol. 39, No. 5 (2024)
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- Flexibility Characterization of Sustainable Power Systems in Demand Space:
A Data-Driven Inverse Optimization Approach-
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Authors: Mohamed Awadalla;François Bouffard;
Pages: 6196 - 6209
Abstract: The deepening of the penetration of renewable energy is challenging how power system operators cope with their variability and uncertainty. The inherent flexibility of dispathchable assets present in power systems, which is often ill-characterized, is essential in addressing this challenge. Several proposals for explicit flexibility characterization focus on defining a feasible region that secures operations either in generation or uncertainty spaces. The main drawback of these approaches is the difficulty associated with relying solely on visualizing this feasibility region when there are multiple uncertain parameters. Moreover, these approaches focus on system operational constraints and often neglect the impact of inherent couplings (e.g., spatial correlation) of renewable generation and demand. To address these challenges, we propose a novel data-driven inverse optimization framework for flexibility characterization of power systems in the demand space along with its geometric intuition. The approach captures the spatial correlation of multi-site renewable generation and load using polyhedral uncertainty sets. Moreover, the framework projects the uncertainty on the feasibility region of power systems in the demand space, which are also called loadability sets. The proposed inverse optimization scheme, recast as a linear optimization problem, is used to infer system flexibility adequacy from loadability sets as a scalar quantity.
PubDate: FRI, 09 FEB 2024 09:16:48 -04
Issue No: Vol. 39, No. 5 (2024)
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- Decentralized Adaptive Nonlinear Controller for Voltage Regulation of
Output-Constrained DC Microgrids With ZIP Loads-
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Authors: Somayeh Bahrami;Shima Sadat Mousavi;Marjan Shafiee-Rad;
Pages: 6210 - 6221
Abstract: In this article, a decentralized adaptive nonlinear controller is designed for islanded DC microgrids (DCmGs) containing several distributed generation units (DGUs). DGUs are connected by distribution lines represented by a general resistive-inductive-capacitive dynamic model. It is assumed each DGU feeds an unknown nonlinear ZIP (constant impedance ‘Z’, constant current ‘I’, and constant power ‘P’) load at its point of common coupling (PCC). The proposed controller ensures voltage adjustment in the large signal sense in the presence of unknown ZIP loads and DC input voltages, unknown topological changes, and plug-and-play (PnP) operations of DGUs. The current of P-load, as a function of the inverse of the DGU's output voltage, to remain bounded requires that the output voltage never cross zero. To fulfill this requirement, a Barrier Lyapunov Function (BLF) is employed, which gets to infinity when the output voltage becomes zero. Then, the local controller of each DGU is designed based on the adaptive backstepping procedure such that for the closed-loop system, the adopted BLF remains bounded. The boundedness of this BLF ensures that the output voltage never becomes zero, and thus the mentioned singularity is prevented. Each local controller is synthesized independently of the distribution lines and by employing only the values of the local measurements and parameters of the DGU. The efficiency of the proposed nonlinear controller is evaluated by simulating a DCmG system under different scenarios in MATLAB/Simscape Electrical environment.
PubDate: TUE, 23 JAN 2024 09:18:31 -04
Issue No: Vol. 39, No. 5 (2024)
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- News and Load: A Quantitative Exploration of Natural Language Processing
Applications for Forecasting Day-Ahead Electricity System Demand-
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Authors: Yun Bai;Simon Camal;Andrea Michiorri;
Pages: 6222 - 6234
Abstract: The relationship between electricity demand and weather is well established in power systems, along with the importance of behavioral and social aspects such as holidays and significant events. This study explores the link between electricity demand and more nuanced information about social events. This is done using mature Natural Language Processing (NLP) and demand forecasting techniques. The results indicate that day-ahead forecasts are improved by textual features such as word frequencies, public sentiments, topic distributions, and word embeddings. The social events contained in these features include global pandemics, politics, international conflicts, transportation, etc. Causality effects and correlations are discussed to propose explanations for the mechanisms behind the links highlighted. This study is believed to bring a new perspective to traditional electricity demand analysis. It confirms the feasibility of improving forecasts from unstructured text, with potential consequences for sociology and economics.
PubDate: THU, 01 FEB 2024 09:16:53 -04
Issue No: Vol. 39, No. 5 (2024)
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- PPenergyNET: Privacy-Preserving Multi-Energy Load Forecasting in Energy
Internet Considering Energy Coupling-
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Authors: Yigong Zhang;Qiushi Cui;Lixian Shi;Jianyu Pan;Jian Li;
Pages: 6235 - 6248
Abstract: Accurate multi-energy load forecasting (MELF) is critical to the management and operation of energy internet (EI). Current MELF methods gather the data from various energy loads for model training in a centralized manner. However, different energy utilities in EI intend to keep the raw data locally due to privacy reasons, which leads to the data silos of EI. To this end, we propose a PPenergyNET to perform privacy-preserving MELF in a distributed manner and break data silos in EI. Specifically, a ring-structure vertical federated learning is devised to protect vertical partition data privacy, and fix the mismatch that leads to incalculable training loss in the cloud to reconstruct the loss back-propagation for model updating. Then, a split feature extraction method is designed to prevent the characteristics of specific load from being submerged by the data of multi-energy loads to improve forecast resolution. Thirdly, a modified homoscedastic uncertainty based multi-task learning method is proposed to consider the multi-energy coupling with a convergence proof. Numerical results show that PPenergyNET achieves superior trade-offs between privacy protection and forecasting accuracy. More importantly, PPenergyNET contributes a new idea to improve interoperability among different energy systems.
PubDate: WED, 31 JAN 2024 09:28:30 -04
Issue No: Vol. 39, No. 5 (2024)
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- Fractional Moments Based Adaptive Scaled Unscented Transformation for
Probabilistic Power Flow of AC-DC Hybrid Grids-
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Authors: Sui Peng;Jing Zuo;Wanwan Xu;Junjie Tang;Antonello Monti;Kaigui Xie;Ferdinanda Ponci;Wenyuan Li;
Pages: 6249 - 6262
Abstract: Probabilistic power flow (PPF) is the fundamental to reveal the influence of stochastic sources on the AC-DC hybrid grids. In the operational PPF analysis, the accurate probability density functions (PDFs) of PPF responses must be obtained in a very short period, which is quite challenging for existing methods. In this paper, a fractional moments based adaptive scaled unscented transformation (ASUT) is proposed to overcome the operational PPF challenge. The ASUT creates a new path to fully catch the probability information, which adaptively selects the sample point sets from each random input variable through the fractional moment assessment. A handful of fractional moments can contain the information identical to the one generated from a great many integer moments (e.g., central or raw moments). This feature is beneficial to present and propagate the abundant probability information using only a few sample points, which enables the reconstruction the PDFs of PPF results accurately by the maximum entropy, leading to the great enhancement in the execution accuracy and efficiency of PPF calculations. Test results on the IEEE 118 bus system integrated with DC systems and a provincial AC-DC hybrid grid in South China validate the effectiveness and advantages of proposed method herein.
PubDate: MON, 12 FEB 2024 09:23:49 -04
Issue No: Vol. 39, No. 5 (2024)
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- Attention Mechanism Multi-Size Depthwise Convolutional Long Short-Term
Memory Neural Network for Forecasting Real-Time Electricity Prices-
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Authors: Huifeng Xu;Feihu Hu;Xinhao Liang;Mohammad Abu Gunmi;
Pages: 6277 - 6289
Abstract: Real-time electricity price forecasting affects both the interests of power companies and the stability of power systems. Although deep learning models have achieved rich results in forecasting, due to the variable temporal characteristics and numerous influencing factors of real-time electricity prices, it is difficult for general deep learning models to extract electricity price features with obvious regularity, which affects forecasting accuracy. To solve this problem, this paper proposes an attention mechanism multi-size depthwise convolutional long short-term memory neural network (AM-MDC-LSTM) for predicting real-time electricity prices. The model improves prediction capability in the following aspects. 1) Using an attention mechanism to adaptively assign weights to electricity price time series and electricity price exogenous variables (production, consumption, electricity prices in neighboring regions) to improve electricity price feature extraction efficiency. 2) Using convolution kernels of different sizes to convolve individual electricity price exogenous variables one by one to extract local burst and global periodic electricity price features with obvious regularity. This is then combined with long short-term memory networks to extract temporal features reflected in electricity prices. Experimental results conducted in the Nordic and PJM electricity markets demonstrate that the proposed model outperforms other models discussed in the paper, exhibiting higher prediction accuracy.
PubDate: MON, 15 JAN 2024 09:22:54 -04
Issue No: Vol. 39, No. 5 (2024)
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- Model-Free Distributed Voltage Control for Distribution Networks Based on
State Space Mapping and Super-Linear Feedback-
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Authors: Zhongguan Wang;Jiachen Liu;Xiang Zhu;Xialin Li;Li Guo;Linquan Bai;Chengshan Wang;
Pages: 6290 - 6304
Abstract: Large-scale integration of renewable generation has changed the way that distribution networks are operated, posing significant challenges to voltage control. To address this challenge, system operators need to exploit the potential of inverter-based renewable generation for reactive power regulation. However, the distribution network parameters are usually inaccurate or incomplete, making conventional centralized or model-dependent distributed control methods difficult for fast voltage tracking and optimal reactive power distribution. This paper proposes a model-free distributed Newton method for voltage control based on data-driven lift-dimension linear power flow which does not reply on accurate and complete network parameters. By using matrix splitting to calculate Hessian matrix, the proposed method possesses a super-linear convergence and exhibits superiority in fast response over existing linear methods. Furthermore, a Koopman-based state space mapping method is proposed to obtain global sensitivity and tune the iteration direction in an off-line manner, which can realize model-free voltage control. The convergence and optimality of the proposed method are validated by case studies. Especially, the parameter independence feature of the model-free scheme provides decided superiority in scenarios of incomplete model. Even under communication failures, the proposed method still can maintain voltage stability at a suboptimal point.
PubDate: THU, 18 JAN 2024 09:16:37 -04
Issue No: Vol. 39, No. 5 (2024)
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- Multi-Period Power System Risk Minimization Under Wildfire Disruptions
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Authors: Hanbin Yang;Noah Rhodes;Haoxiang Yang;Line Roald;Lewis Ntaimo;
Pages: 6305 - 6318
Abstract: Natural wildfire becomes increasingly frequent as climate change evolves, posing a growing threat to power systems, while grid failures simultaneously fuel the most destructive wildfires. Preemptive de-energization of grid equipment is effective in mitigating grid-induced wildfires but may cause significant power outages during natural wildfires. This paper proposes a novel two-stage stochastic program for planning preemptive de-energization and solves it via an enhanced Lagrangian cut decomposition algorithm. We model wildfire events as stochastic disruptions with random magnitude and timing. The stochastic program maximizes the electricity delivered while proactively de-energizing components over multiple time periods to reduce wildfire risks. We use a cellular automaton process to sample grid failure and wildfire scenarios driven by realistic risk and environmental factors. We test our method on an augmented version of the RTS-GLMC test case in Southern California and compare it with four benchmark cases, including deterministic, wait-and-see, and robust optimization formulations as well as a comparison with prior wildfire risk optimization. Our method reduces wildfire damage costs and load-shedding losses, and our nominal plan is robust against uncertainty perturbation.
PubDate: THU, 25 JAN 2024 09:16:44 -04
Issue No: Vol. 39, No. 5 (2024)
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- Modeling Microgrids for Analytical Distribution System Reliability
Evaluation-
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Authors: Mahsa Omri;Mohammad Jooshaki;Ali Abbaspour;Mahmud Fotuhi-Firuzabad;
Pages: 6319 - 6331
Abstract: Focusing on the ability of microgrids (MGs) to provide a backup power supply to the distribution system in the event of main grid outages or disruptions, this article aims to propose a reliability model for self-controlled distributed energy resource-rich MGs from the perspective of the distribution system to which they are connected. To that end, the reliability model of MGs is derived using the Monte Carlo simulation approach and a mixed-integer linear programming-based model for optimal scheduling and resource management of MGs. An analytical framework for assessing the reliability of distribution systems in the presence of MGs is then developed by leveraging the proposed reliability model for MGs. Modeling and analyzing MGs using the proposed methodology result in the simplification and acceleration of computations associated with the reliability assessment of a distribution system including multiple MGs. The effectiveness and applicability of the proposed method are demonstrated through its implementation on a modified IEEE test system.
PubDate: TUE, 16 JAN 2024 09:16:20 -04
Issue No: Vol. 39, No. 5 (2024)
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- PI-Controlled Variable Time-Step Power System Simulation Using an Adaptive
Order Differential Transformation Method-
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Authors: Kaiyang Huang;Yang Liu;Kai Sun;Feng Qiu;
Pages: 6332 - 6344
Abstract: Dynamic simulation plays a crucial role in power system transient stability analysis, but traditional numerical integration-based methods are time-consuming due to the small time step sizes. Other semi-analytical solution methods, such as the Differential Transformation method, often struggle to select proper orders and steps, leading to slow performance and numerical instability. To address these challenges, this paper proposes a novel adaptive dynamic simulation approach for power system transient stability analysis. The approach adds feedback control and optimization to select the step and order, utilizing the Differential Transformation method and a proportional-integral control strategy to control truncation errors. Order selection is formulated as an optimization problem resulting in a variable-step-optimal-order method that achieves significantly larger time step sizes without violating numerical stability. It is applied to three systems: the IEEE 9-bus, 3-generator system, IEEE 39-bus, 10-generator system, and a Polish 2383-bus, 327-generator system, promising computational efficiency and numerical robustness for large-scale power systems is demonstrated in comprehensive case studies.
PubDate: FRI, 02 FEB 2024 09:16:46 -04
Issue No: Vol. 39, No. 5 (2024)
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- Optimum Partition of Power Networks Using Singular Value Decomposition and
Affinity Propagation-
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Authors: Maymouna Ez Eddin;Mohamed Massaoudi;Haitham Abu-Rub;Mohammad Shadmand;Mohamed Abdallah;
Pages: 6359 - 6371
Abstract: Due to coupling and correlation between nodes and buses in the power system, Power Grid Partitioning (PGP) is a promising approach to analyze large power systems and provide timely actions during disturbances. From this perspective, this paper proposes an efficient framework for fast and optimal PGP, based on singular value decomposition analysis of the graph's Laplacian. An Affinity Propagation clustering algorithm-based PGP is tailored for automatically forming highly interconnected clusters based on pairwise similarities without requiring a predefined number of partitions. The core objective is to quantify the clustering performance based on internal clustering validity indices, such as the Silhouette Index, Calinski-Harabasz Index, and Davies-Bouldin Index. The adopted methodology aims to enhance partitioning efficiency substantially while preserving a high level of partitioning quality. The proposed framework is verified on IEEE 14, 39, 118, and 2000-bus systems and compared to nine other well-known and widely used clustering techniques, including K-Means and Gaussian Mixture models. The simulation results demonstrate the scalability of the proposed approach and its high-quality partitioning output with a Silhouette index of 0.6162, 0.6597, 0.6664, and 0.6555 for the IEEE 14, 39, 118, and 2000-bus systems, respectively.
PubDate: THU, 01 FEB 2024 09:16:53 -04
Issue No: Vol. 39, No. 5 (2024)
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- Optimal Sizing of Isolated Renewable Power Systems With Ammonia Synthesis:
Model and Solution Approach-
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Authors: Zhipeng Yu;Jin Lin;Feng Liu;Jiarong Li;Yuxuan Zhao;Yonghua Song;
Pages: 6372 - 6385
Abstract: Isolated renewable power to ammonia (IRePtA) has been recognized as a promising way to decarbonize the chemical industry. Optimal sizing of the renewable power system is significant to improve the techno-economic of IRePtA since the investment of power sources exceeds 80% of the total investment. However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multi-year uncertainty of renewable generation lead to difficulties in planning. To address the issues above, an IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is directly formulated as the objective, rendering a mixed integer linear fractional programming (MILFP) problem. Information gap decision theory (IGDT) is utilized to handle the multi-year uncertainty of renewable generation. Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound (B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model, giving robust and opportunistic planning results. Then, Markov Chain Monte Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the long-run performance. Finally, a real-life system in Inner Mongolia, China, is studied. The results indicate that the proposed methods could reduce the computational burden by orders of magnitude for solving a large-scale MILFP problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to obtain an optimal capacity allocation with the lowest expected LCOA (3645 RMB/t) in long-run simulations.
PubDate: TUE, 30 JAN 2024 09:17:41 -04
Issue No: Vol. 39, No. 5 (2024)
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- A Peer-to-Peer Joint Kilowatt and Negawatt Trading Framework Incorporating
Battery Cycling Degradation-
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Authors: Chenxi Zhang;Jing Qiu;Yi Yang;
Pages: 6386 - 6398
Abstract: This paper presents a novel unified peer-to-peer (P2P) framework for both kilowatt (kW) and negawatt (nW) trading. The aim is to coordinate these two transaction types and maximize customer benefits, by reducing customers’ electricity bills and helping customers trade energy with their neighbors while also adhering to the physical limits of the electricity networks. An auction-based joint kW and nW P2P trading approach is proposed to enable prosumers to conduct the optimal strategy in switching their market roles between kW and nW trading and scheduling their demand. As such, in the virtual layer, the household energy management system (HEMS) models are formulated to determine the optimal market bidding strategy during each trading period. For the agent equipped with a battery energy storage system (BESS), the battery cycling degradation issue is also considered through the embedded battery cycle life model. A double-sided auction method with an average pricing market (APM) mechanism is performed in P2P kW and nW trading for market clearing. In the physical layer, voltage issues and line congestion management are considered through the distribution network models. The proposed formulation is tested on the modified IEEE 33-bus distribution system and simulation results demonstrate that the proposed framework outperforms the single trading mode.
PubDate: WED, 31 JAN 2024 09:28:30 -04
Issue No: Vol. 39, No. 5 (2024)
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- Data Augmentation for Data-Driven Methods in Power System Operation: A
Novel Framework Using Improved GAN and Transfer Learning-
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Authors: Jian Lan;Yanzhen Zhou;Qinglai Guo;Hongbin Sun;
Pages: 6399 - 6411
Abstract: Power system dispatching and operation rely on massive operation data, especially for data-driven methods. However, power systems often face challenges with insufficient or imbalanced data, significantly affecting the accuracy of power system analysis. To address these challenges, this research introduces a novel data augmentation framework specifically designed to efficiently generate high-quality target data to enhance data-driven methods in power system operation. First, this framework decomposes the task of generating data considering multiple objectives into feature generation and combination, enabling efficient data generation with broad applicability. Then, an improved generative adversarial network integrated with transfer learning is proposed, which significantly improves the data generation performance even with limited data. Furthermore, a method based on the least absolute shrinkage and selection operator (LASSO) is presented to select and combine the critical features from massive variables considering different objectives, improving the method's applicability especially for complex power systems. Through data generation, large amounts of target data can supplement the original dataset, thereby improving the performance of data-driven methods. The case studies on the IEEE 39-bus system and the realistic 300-bus system demonstrated the efficacy of the proposed method, showcasing its capability to significantly enhance power system operation.
PubDate: FRI, 09 FEB 2024 09:16:48 -04
Issue No: Vol. 39, No. 5 (2024)
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- Probabilistic Frequency Stability Analysis Considering Dynamics of Wind
Power Generation With Different Control Strategies-
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Authors: Zhaoyuan Wang;Siqi Bu;
Pages: 6412 - 6425
Abstract: Most existing studies on probabilistic frequency stability analysis ignore the dynamics of wind power generations (WPGs) and thus result in inaccurate analysis results especially when the fast frequency response of WPGs is expected. This paper proposes a method of probabilistic frequency stability analysis that considers the dynamics of WPGs with different control strategies. Firstly, a multi-interval sensitivity (MIS) method is proposed to simulate the frequency response, thereby significantly saving the simulation time. Then, a multi-element low-rank approximation (MELRA) uncertainty propagation analysis method suitable for large-scale uncertainty analysis is proposed. And the introduction of multi-element effectively improves the accuracy. In addition, by applying the Gaussian mixture model (GMM), the limitations of moment-based uncertainty propagation analysis methods are discussed, demonstrating the comparative superiority of the proposed method. Also, the necessity of considering the dynamics of WPGs in frequency stability analysis is revealed by analyzing the differences of frequency response with and without dynamics of WPGs using different control strategies. The performance of the proposed method is verified on the IEEE 68-bus system and the provincial large-scale power system.
PubDate: MON, 05 FEB 2024 09:16:53 -04
Issue No: Vol. 39, No. 5 (2024)
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- Non-Euclidean Grid-Partitioning to Mitigate Cascading Risk in Multi-Infeed
HVDC System-
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Authors: Xiaohui Wang;Kaige Song;Quanrui Hao;Feng Gao;
Pages: 6426 - 6440
Abstract: The presence of multiple high voltage direct current (HVDC) systems in close proximity creates voltage-related cascading risks that are not adequately addressed by conventional grid-partitioning. This paper proposes an improved partitioning scheme to mitigate these new risks in addition to conventional objective of preventing parallel power flow from transferring adversely. Unlike classical partitioning approach, which relies solely on either optimization or clustering, our proposed bi-level architecture includes an additional HVDC clustering before optimization. However, this paper innovatively reveals that the distribution of correlation data to be used in clustering is non-Euclidean due to unusual equivalent reactance different from the normal operating condition, resulting from HVDC station's reactive power control. This non-Euclidean distribution makes heuristic clustering algorithms infeasible. To address this issue, an alternative solution is proposed to embed the correlation data into a dimension-reduced eigenspace spanned by selected eigenvectors, allowing clustering to be performed. The optimization implementing other objectives inherits the results of HVDC clustering as constraint, and the graphic betweenness weighted by power flows is presented to promote efficiency. Our proposed scheme is validated using cases studied in modified IEEE 118-bus benchmark system and practical regional grid, demonstrating its effectiveness in mitigating cascading risks in multi-infeed HVDC systems.
PubDate: MON, 12 FEB 2024 09:23:47 -04
Issue No: Vol. 39, No. 5 (2024)
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- Voltage Security Region of a Three-Phase Unbalanced Distribution Power
System With Dynamics-
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Authors: Ignacio Losada Carreño;Anna Scaglione;Daniel Arnold;Tong Wu;
Pages: 6441 - 6455
Abstract: Distributed energy resources (DER) and control assets on the grid provide mechanisms to ensure voltage support and power quality, and can be used as a means to maintain voltages close to nominal values. In this work, we study the security region of a system in the presence of devices with dynamics. We focus our efforts on the dynamics of devices that apply discrete changes, such as voltage regulators or capacitor banks, and DERs that change the power injections with Volt/Var/Watt functionality. The slow dynamics of these devices, coupled through the power flow equations, are modeled as a switching dynamical system where the switching condition depends on the operating point of the system. We identify the feasible set of DER power injections that prevent the switching of devices and oscillations in the voltage profile, and formulate an optimization problem that finds a robust operating point for voltage control. This operating point is the center of the largest Euclidean ball inscribed in the security region, and the radius of the ball is the security margin of the system. We showcase our models using the IEEE test cases. Our work has applications in voltage control and distribution planning.
PubDate: TUE, 16 JAN 2024 09:16:20 -04
Issue No: Vol. 39, No. 5 (2024)
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- Dynamic State Estimation for Inverter-Based Resources: A Control-Physics
Dual Estimation Framework-
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Authors: Heqing Huang;Yuzhang Lin;Xiaonan Lu;Yue Zhao;Avinash Kumar;
Pages: 6456 - 6468
Abstract: As Inverter-Based Resources (IBRs) gradually replace conventional synchronous generators (SGs), Dynamic State Estimation (DSE) techniques must be extended for the monitoring of IBRs. The key difference between IBRs and SGs is that the dynamics of IBRs comprise a heavy mix of physical processes and digital controller computations. This paper develops a generic framework of Control-Physics Dynamic State Estimation (CPDSE) for IBRs. First, a control-physics state-space representation of IBRs is presented. Noting the symmetry of the control and physical state spaces, it is proposed to use two dual estimators to track the states of the physical inverter subsystem and the digital controller subsystem, respectively. The CPDSE framework has the capability of suppressing errors in both measurement signals and control signals flowing between the two subsystems and the potential to distinguish between cyber and physical events. The advantages and versatility of the proposed CPDSE framework are validated on a variety of IBR systems (solar, wind, and storage), control strategies (grid-following and grid-forming), and both transmission and distribution systems.
PubDate: TUE, 06 FEB 2024 09:19:44 -04
Issue No: Vol. 39, No. 5 (2024)
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- Inertia Estimation of a Power System Area Based on Iterative Equation
Error System Identification-
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Authors: Davide Gotti;Federico Bizzarri;Angelo Brambilla;Davide del Giudice;Samuele Grillo;Daniele Linaro;Pablo Ledesma;Hortensia Amaris;
Pages: 6469 - 6481
Abstract: This paper proposes an area inertia estimator based on an iterative equation error (EE) system identification (SI) approach. The inertia value can be accurately extracted for areas of different compositions with different penetrations of converter-interfaced generators. Firstly, the internal frequency variation of the generator units is computed by means of a frequency divider-based estimator. Subsequently, these internal frequency variations are used to carry out a generator clustering, which provides groups of coherent generators to the proposed inertia estimator. Finally, the iterative EE SI approach provides a joint inertia estimation for each of these coherent groups. Numerical results on a properly modified version of both the IEEE 39-bus and the IEEE 118-bus test systems highlight the accuracy of the proposed method using both ambient measurements and ringdown signal measurements (power imbalance events). Furthermore, the proposed method presents a low computational burden that allows fast estimation updating times.
PubDate: THU, 11 JAN 2024 09:16:29 -04
Issue No: Vol. 39, No. 5 (2024)
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- The Use of Machine Learning for Prediction of Post-Fault Rotor Angle
Trajectories-
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Authors: Xinlin Ye;Ana Radovanović;Jovica V. Milanović;
Pages: 6496 - 6507
Abstract: This paper proposes a machine learning-based method for predicting generator rotor angle responses (trajectories) following large disturbance in power system. A Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) is used to predict responses at any time instant after the fault inception by designing the input and output of the network with predefined sliding time windows. The numbers of neurons in the LSTM and Fully-Connected (FC) layers are optimised with the Particle Swarm Optimisation (PSO) algorithm, which was proved to be effective in similar tasks in past research. A wide range of realistic constraints associated with the use of the Phasor Measurement Unit (PMU) data has been considered, to demonstrate the feasibility of the proposed method when applied in real systems. Results obtained using modified IEEE 68 bus test system show that the proposed method can predict the future rotor angle responses accurately, and that is highly robust towards the imperfections of the realistic PMU data.
PubDate: MON, 19 FEB 2024 09:19:14 -04
Issue No: Vol. 39, No. 5 (2024)
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- Distribution System Operation Amidst Wildfire-Prone Climate Conditions
Under Decision-Dependent Line Availability Uncertainty-
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Authors: Alexandre Moreira;Felipe Piancó;Bruno Fanzeres;Alexandre Street;Ruiwei Jiang;Chaoyue Zhao;Miguel Heleno;
Pages: 6522 - 6538
Abstract: Wildfires can severely damage electricity grids leading to long periods of power interruption. Climate change will exacerbate this threat by increasing the frequency of dry weather conditions. Under these climate conditions, human-related actions that initiate wildfires should be avoided, including those induced by power systems operation. In this paper, we propose a novel optimization model that is capable of determining appropriate network topology changes (via switching actions) to alleviate the levels of power flows through vulnerable parts of the grid so as to decrease the probability of wildfire ignition. Within this framework, the proposed model captures the relationship between failure probabilities and line-flow decisions by explicitly considering the former as a function of the latter. The resulting formulation is a two-stage model with endogenous decision-dependent probabilities, where the first stage determines the optimal switching actions and the second stage evaluates the worst-case expected operation cost. We propose an exact iterative method to deal with this intricate problem and the methodology is illustrated with a 54-bus and a 138-bus distribution system.
PubDate: FRI, 12 JAN 2024 09:18:18 -04
Issue No: Vol. 39, No. 5 (2024)
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- Current-Based Dynamic Power Network: Modeling, Control, and Applications
-
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Authors: Yong Wan;
Pages: 6539 - 6551
Abstract: This paper proposes a novel unified nonlinear dynamic power network model in an arbitrary dimensional real vector space to study the quantitative interactions among the generalized current injections, the frequency oscillations, and the voltage variations. Further, an innovative dynamic current decoupling control (DCDC) approach is developed. The DCDC cancels the adverse influences of the nonlinear couplings between the active and the reactive control loops. Also, it increases the system damping of the changes of both frequency and voltage simultaneously. The closed-loop system with the proposed DCDC scheme is proved theoretically to be globally exponentially stable in the Lyapunov sense. Then, we apply the presented DCDC approach to the control syntheses of the voltage-sourced converter (VSC) based static synchronous compensator (STATCOM) and the doubly fed induction generator (DFIG). The superiority of the DCDC method is further evaluated comparatively by implementing the designed controllers of STATCOM and DFIG on WSCC 9-bus system, IEEE New England 39-bus system, and IEEE 14-bus system.
PubDate: WED, 21 FEB 2024 09:17:13 -04
Issue No: Vol. 39, No. 5 (2024)
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- Two-Stage Decentralized Optimal Voltage Control in Wind Farms With Hybrid
ESSs-
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Authors: Hanzhi Peng;Sheng Huang;Juan Wei;Chao Wei;Qiuwei Wu;Feifan Shen;Wei Zhang;Pengda Wang;
Pages: 6552 - 6565
Abstract: Due to the flexibility and economy, energy storage systems (ESSs) have been widely deployed in wind energy power plants for power regulation. The coordination of generators and ESSs is critical for the wind farm (WF) operation. In this article, a two-stage voltage control is proposed in WFs equipped with distributed ESSs to achieve a near-global optimal performance without any centralized computations and communication links. To achieve it, the WF control is divided into two regions based on the demand-supply relationship of reactive power to consider the different operating conditions and multiple optimization objectives. The distributed battery-supercapacitor hybrid ESSs are deployed in each wind turbine within the WF to regulate the power output while ensuring voltage stability and enhancing the Var support capability. An optimization problem is formulated considering the node voltage deviation, the state of charge, and the cost of the storage units, which can be simply calculated using only local measurements through a decoupling method based on network topology characteristics. Further, an asynchronous response framework is designed for the battery and the supercapacitor to achieve a better economic benefit. Case studies in MATLAB/Simulink verify the effectiveness and reliability of the proposed method.
PubDate: WED, 31 JAN 2024 09:28:30 -04
Issue No: Vol. 39, No. 5 (2024)
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- FRMNet: A Feasibility Restoration Mapping Deep Neural Network for AC
Optimal Power Flow-
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Authors: Jiayu Han;Wei Wang;Chao Yang;Mengyang Niu;Cheng Yang;Lei Yan;Zuyi Li;
Pages: 6566 - 6577
Abstract: Increasing renewable energy resources introduces uncertainties into utility grids, calling for more frequent use of alternative current optimal power flow (AC-OPF) than before. Conventional AC-OPF solvers are too slow for real-time applications; to speed up the computation, deep neural network (DNN) based solvers are introduced. However, their results typically cannot enforce the security constraints of the utility grid. To overcome this difficulty, FRMNet is proposed in this paper, which combines DNN with feasibility restoration mapping (FRM). The DNN receives the load as input and outputs a partial solution. The following FRM maps the infeasible solution to the feasibility region to satisfy the AC-OPF constraints. The proposed FRMNet has four advantages compared to conventional and DNN-based solvers. First, the feasibility of overall solutions is guaranteed via FRM. Second, FRMNet is a self-supervised training method so that it avoids the laborious preparation of the optimal solutions for a training dataset using conventional solvers. Third, FRM is differentiable so that AC-OPF information encoded in gradients is transferred to DNN, which leads to a DNN with high feasibility rate. Fourth, the overall computation time of FRMNet is fast due to its DNN's high feasibility rate. Comprehensive experiments are conducted on IEEE standard cases. Results show that FRMNet's outcomes satisfy all constraints at a significantly faster average speed than conventional solvers. When FRM participates in training, the performance of DNN is stably improved and achieves better results than other DNN-based models. Even DNN alone achieves a high feasibility rate, a negligible degree of constraint violations and a small optimality gap, compared to the state-of-the-art DNN based solvers.
PubDate: MON, 22 JAN 2024 09:22:14 -04
Issue No: Vol. 39, No. 5 (2024)
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- Effects of Droop Based Fast Frequency Response on Rotor Angle Stability
During System Wide Active Power Deficits-
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Authors: Zaichun Zhang;Robin Preece;
Pages: 6578 - 6591
Abstract: The effects of fast frequency response (FFR) on rotor angle stability have predominately been established by examining the oscillatory behavior of synchronous generators (SGs). What remains largely unexamined, however, is the effect that FFR has on the angle separation and power transfer between SGs. This paper systematically examines the evolution of the angle separation and power transfer between SGs during FFR provision in the context of frequency containment events. Droop based FFR schemes, which are popular and effective in practical systems, are analyzed. This research investigates how the location of the initiating system wide active power deficit, the location of resources providing FFR, and the delays associated with FFR provision all impact rotor angle stability. The key results are obtained using a modified IEEE 39-bus system and further verified using a reduced-order dynamic Great Britain system model. The results show that the angle separation and power transfer between SGs decrease when power deficits occur in areas with extensive generation sources which, conversely, implies that angle stability deteriorates if power deficits occur near load centers. A key finding is that providing the FFR at locations closest to the source of the initial power deficit does not always enhance angle stability, and sometimes has a significant adverse effect. The effect that FFR delays have on rotor angle stability is explained, highlighting the necessity to carefully consider and design FFR provision timing, particularly in areas with diminishing levels of inertia.
PubDate: FRI, 09 FEB 2024 09:16:48 -04
Issue No: Vol. 39, No. 5 (2024)
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- Wholesale Market Participation of DERAs: DSO-DERA-ISO Coordination
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Authors: Cong Chen;Subhonmesh Bose;Timothy D. Mount;Lang Tong;
Pages: 6605 - 6614
Abstract: Distributed energy resource aggregators (DERAs) must share the distribution network together with the distribution utility in order to participate in the wholesale electricity markets that are operated by independent system operators (ISOs). We propose a forward auction that a distribution system operator (DSO) can utilize to allocate distribution network access limits to DERAs. As long as the DERAs operate within their acquired limits, these limits define operating envelopes that guarantee distribution network security, thus defining a mechanism that requires no real-time intervention from the DSOs for DERAs to participate in the wholesale markets. Our auctions take the form of robust and risk-sensitive markets with bids/offers from DERAs and utility's operational costs. Properties of the proposed auction, e.g., resulting surpluses of DSO and the DERAs, and the auction prices, along with empirical performance studies, are presented.
PubDate: FRI, 12 JAN 2024 09:18:18 -04
Issue No: Vol. 39, No. 5 (2024)
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- Convex-Hull Pricing of Ancillary Services for Power System Frequency
Regulation With Renewables and Carbon-Capture-Utilization-and-Storage
Systems-
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Authors: Zelong Lu;Jianxue Wang;Mohammad Shahidehpour;Linquan Bai;Zuyi Li;
Pages: 6615 - 6635
Abstract: In pursuit of achieving carbon neutrality goals, modern power systems are increasingly characterized by low-carbon and low-inertia properties, leading to significant concerns regarding the security of system frequency. These ancillary services for providing frequency regulation (FR) can contribute to the system inertia, FR reserve capacity, and the response rate of FR reserves. However, it could be challenging to motivate low-carbon resources, like carbon-capture-utilization-and-storage (CCUS) systems and grid-forming inverter-based renewable energy systems (RESs), to participate in FR ancillary service markets. A critical focus of this paper lies in assessing the marginal value of diverse FR ancillary services for improving the performance of frequency-secured systems in under-frequency and over-frequency cases. Given the tight relations among energy and FR ancillary services, the frequency-secured performance criteria are introduced, including maximum rate of change of frequency (RoCoF), maximum frequency deviation, and quasi-steady-state (Q-S-S) frequency which are devised in a joint energy, carbon, and FR ancillary service market. To solve this nonconvex and nonlinear market problem, while minimizing the uplift payment, a tractable shrunken convex hull pricing method is presented. Multiple case studies confirm the proposed method's effectiveness in enhancing the system frequency stability, reducing total costs, and curtailing carbon emissions.
PubDate: THU, 22 FEB 2024 09:18:31 -04
Issue No: Vol. 39, No. 5 (2024)
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- Chance Constrained Economic Dispatch Considering the Capability of Network
Flexibility Against Renewable Uncertainties-
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Authors: Yue Song;Tao Liu;David J. Hill;
Pages: 6636 - 6648
Abstract: This paper incorporates a continuous-type network flexibility into chance constrained economic dispatch (CCED). In the proposed model, both power generations and line susceptances are continuous variables to minimize the expected generation cost and guarantee a low probability of constraint violation in terms of generations and line flows under renewable uncertainties. From the analytical form of CCED, we figure out the mechanism of network flexibility against uncertainties—while renewable uncertainties shrink the usable line capacities and aggravate transmission congestion, network flexibility mitigates congestion by re-routing the base-case line flows and reducing the line capacity shrinkage caused by uncertainties. Further, we propose an alternate iteration solver for this problem. By duality theory, we set up a master problem in the form of second-order cone programming to optimize generation dispatch scheme and a subproblem in the form of linear programming to optimize line susceptances. A satisfactory solution can be obtained efficiently by alternately solving these two problems. The proposed method applies to both Gaussian uncertainty and non-Gaussian uncertainty by means of Gaussian mixture model. The case studies on the IEEE 14-bus system and IEEE 118-bus system suggest that network flexibility can significantly improve operational economy while ensuring security under uncertainties.
PubDate: WED, 21 FEB 2024 09:17:13 -04
Issue No: Vol. 39, No. 5 (2024)
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- Measurement-Driven Damping Control Based on the Deep Transfer
Reinforcement Learning to Suppress Sub-synchronous Oscillations in a
Large-Scale Renewable Power System-
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Authors: Yufan He;Wenjuan Du;Qiang Fu;Haifeng Wang;
Pages: 6649 - 6661
Abstract: Maintaining power system stability necessitates optimizing power system dynamics and suppressing oscillations. To achieve this objective, significant progress has been made in proposing optimization strategies based on model-based analysis theory. However, accurately obtaining parametric models and operation conditions in a large-scale renewable power system to establish precise analysis models remains challenging. In this article, we introduce a novel strategy termed Disentangled Factor Transfer Reinforcement Learning (DFTRL) for designing supplementary damping controllers (SDCs) in static synchronous compensators (STATCOM) online to enhance the stability of practical power system. The proposed DFTRL approach allows the reinforcement learning (RL) agent, trained on a simplified power system, to be directly applied to an unseen practical power system. Through case studies, the proposed optimization strategy demonstrates the RL agent's capability to generalize effectively to the target practical power system and successfully suppress oscillations. Moreover, the agent exhibits robustness to variations in power system operating scenarios and noise present in observations.
PubDate: TUE, 16 JAN 2024 09:16:20 -04
Issue No: Vol. 39, No. 5 (2024)
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- A Model Predictive Approach for Enhancing Transient Stability of
Grid-Forming Converters-
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Authors: Ali Arjomandi-Nezhad;Yifei Guo;Bikash C. Pal;Damiano Varagnolo;
Pages: 6675 - 6688
Abstract: A model predictive control (MPC) method for enhancing post-fault transient stability of grid-forming (GFM) inverter-based resources (IBRs) is developed in this paper. This proposed controller is activated as soon as the converter enters into the post-fault current-saturation mode. It aims at mitigating the instability arising from insufficient deceleration due to current saturation and thus improving the transient stability of a GFM-IBR. The MPC approach optimises the post-fault trajectory of GFM IBRs by introducing appropriate corrective phase angle jumps and active power references where the post-fault dynamics of GFM IBRs are addressed. These two signals provide controllability over GFM IBR's post-fault trajectory. This paper addresses the mitigation of oscillations between current-saturation mode and normal mode by forced saturation if conditions for remaining in the normal mode do not hold. The performance of the proposal is tested via dynamic simulations under various grid conditions and compared with other existing strategies. The results demonstrate significant improvement in transient stability.
PubDate: THU, 22 FEB 2024 09:18:31 -04
Issue No: Vol. 39, No. 5 (2024)
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- Modeling and Assessment of Cyber Attacks Targeting Converter-Driven
Stability of Power Grids With PMSG-Based Wind Farms-
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Authors: Hang Du;Jun Yan;Mohsen Ghafouri;Rawad Zgheib;Mourad Debbabi;
Pages: 6716 - 6728
Abstract: In a grid with PMSG-based wind farms, a lack of system strength can cause severe converter-driven instability issues, such as subsynchronous oscillation (SSO). Cyber adversaries can target the wind farm by maliciously changing the system strength observed from the power grid, triggering SSO, and further causing power outages and equipment damage. To understand the threat, this paper proposes a new model of cyber attacks targeting the system strength. Considering the common mitigations required by the operator's regulations, this paper demonstrates that the proposed attack model can trigger rapid SSO propagation by (i) simultaneously compromising the system strength provision for multiple wind farms or (ii) directly disrupting local system strength mitigation. Based on the developed attacks, this paper also presents an anomalous command verification (ACV) module incorporating a novel converter-driven stability assessment. The ACV module is designed to estimate the system strength buffer capacity in response to malicious tripping commands and indicate how close the system is to converter-driven instability. The impacts of attack-triggered SSOs are demonstrated by electromagnetic transient (EMT) simulations on the New England 39-bus system. The considered case studies validate the effectiveness of the proposed converter-driven stability assessment to detect malicious commands targeting the SSO in a timely manner.
PubDate: TUE, 13 FEB 2024 09:19:45 -04
Issue No: Vol. 39, No. 5 (2024)
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- A Three-Phase State Estimation Based on Alternating Optimization for Power
Systems Including Conductor Temperatures-
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Authors: Chawasak Rakpenthai;Sermsak Uatrongjit;
Pages: 6729 - 6739
Abstract: This article presents a temperature-dependent three-phase power system state estimation method in which the bus voltage phasor and line conductor temperature are considered as state variables. The heat-balance conditions of the overhead lines and underground cables are integrated into the state estimation problem. To improve the robustness of the estimation results, the objective function of the constrained optimization problem is based on the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator. A solution strategy based on an alternating optimization approach has been proposed. The method begins by estimating the voltage phasors of all buses while keeping the line conductor temperatures fixed to their present values. The optimization then uses the estimated voltage phasors and environmental information around all transmission lines to estimate the conductor temperature variables. These two optimization stages are alternatively repeated until the convergence conditions are satisfied. The effectiveness of the proposed state estimation has been demonstrated on IEEE 30-bus and 118-bus systems modified as three-phase systems containing both overhead lines and buried underground cables. The results show that the proposed method can provide good estimated state accuracy and reduce the computational burden.
PubDate: MON, 15 JAN 2024 09:22:54 -04
Issue No: Vol. 39, No. 5 (2024)
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- Communication-Less Reactive Power Control of Grid-Forming Wind Turbines
Connected to Cascaded LCC-DR HVDC System-
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Authors: Peiyu Meng;Wang Xiang;Jinyu Wen;
Pages: 6740 - 6752
Abstract: To achieve long-distance transmission of onshore wind power, different high-voltage direct-current (HVDC) technologies have been proposed, including the line-commutated converter (LCC), the modular multilevel converter (MMC), and the cascaded LCC-MMC converter. However, there is still room for improvement in existing topologies. This paper proposes a cascaded LCC-DR HVDC transmission system and the corresponding communication-less reactive power control (CLRPC) for the grid-forming wind turbines, ensuring operational reliability with good economic efficiency. Firstly, the topology and control strategies of the cascaded LCC-DR HVDC transmission system are introduced. Then, the operation principle and threshold design of CLRPC are analyzed. Finally, the feasibility of the proposed structure and control strategies under various operation scenarios are verified in PSCAD/EMTDC.
PubDate: FRI, 02 FEB 2024 09:16:46 -04
Issue No: Vol. 39, No. 5 (2024)
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- Improving Linear OPF Model via Incorporating Bias Factor of Optimality
Condition-
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Authors: Zhexin Fan;Lan Lou;Jian Zhang;Dali Zhou;Ying Shi;
Pages: 6753 - 6763
Abstract: The linearization of the optimal power flow (OPF) model is widely-used to meet the computational demands of power system dispatch. To improve the accuracy of the OPF solution, existing studies are devoted to reducing the linearization error of nonlinear power flow constraints. However, the linearization accuracy of model fractions does not necessarily represent the linearization error of the OPF result. In this paper, an improved linear OPF formulation is derived from the optimality condition of the OPF solution. Based on the Karush–Kuhn–Tucker (KKT) condition, we transform the OPF optimization into solving a set of nonlinear equations, which consists of non-gradient and gradient terms. The traditional approach to linearize OPF constraints is regarded as deriving the first-order and zero-order Taylor expansions of non-gradient and gradient terms, respectively. The missing first-order component of gradient terms causes considerable linearization error. We formulate it as a bias factor in the OPF objective to improve the accuracy and maintain linearity of the OPF model. By considering the bias factor, the performance of linear OPF optimization is notably improved, which is illustrated in theory and verified in numerous IEEE and Polish test systems.
PubDate: TUE, 20 FEB 2024 09:16:44 -04
Issue No: Vol. 39, No. 5 (2024)
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- Adaptive-Droop-Coefficient VSG Control for Cost-Efficient Grid Frequency
Support-
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Authors: Jia Liu;Xinyu Liu;Jinjun Liu;Xiangjun Li;Jinsong Wang;
Pages: 6768 - 6771
Abstract: Existing adaptive virtual synchronous generator (VSG) control methods are aiming at improving transient performance or enhance frequency support effect, at the cost of more transient energy demand (TED), which leads to a larger energy storage system. Therefore, an adaptive-droop-coefficient (ADC) VSG control is proposed to reduce the TED without sacrificing grid frequency support effect. Simulation and experimental results show that the TED of the proposed control is significantly reduced compared to existing fixed and adaptive parameter VSG methods.
PubDate: FRI, 05 JUL 2024 09:16:20 -04
Issue No: Vol. 39, No. 5 (2024)
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- Piecewise Mixed Decision Rules Based Multi-Stage Distributionally Robust
Unit Commitment for Integrated Electricity-Heat Systems-
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Authors: Weiwei Li;Tong Qian;Xuehua Xie;Wenhu Tang;
Pages: 6772 - 6775
Abstract: This letter presents a multi-stage distributionally robust unit commitment (DRUC) optimization method for integrated electricity-heat systems (IEHSs) considering wind power uncertainty. To respect the non-anticipativity of uncertain optimization, a multi-stage framework for DRUC is developed. Then, a novel decision rule for continuous and binary variables to fill the limitations of typical continuous recourse is proposed, namely, piecewise mixed decision rules, which leverages the flexibility and scalability of nonlinear and binary decision rules. On this basis, the multi-stage DRUC problem is reformulated into a tractable form using dual theory. The proposed method can significantly reduce the total cost of IEHSs under wind uncertainty.
PubDate: FRI, 05 JUL 2024 09:16:20 -04
Issue No: Vol. 39, No. 5 (2024)
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- Optimization of Local Voltage Control With Coordinating Droop Functions
Under High PV Penetration-
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Authors: Cuo Zhang;Ruipeng Xu;Linfeng Yang;
Pages: 6776 - 6779
Abstract: PV power generation has significantly penetrated distribution networks, and inverter based local voltage control has been applied in practice. However, volt-var and volt-watt droop control functions are not optimized or coordinated. This letter proposes an effective optimization model for inverter based local voltage control, systematically optimizing both volt-var and volt-watt droop functions under uncertainties and adopts a new solution method. The simulation results show high efficiency of the optimized droop control functions in minimizing power losses and PV power curtailment and addressing the overvoltage issue.
PubDate: TUE, 18 JUN 2024 09:17:00 -04
Issue No: Vol. 39, No. 5 (2024)
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- Enhanced Power System State Estimation With Overhead Line Sensors
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Authors: Gang Cheng;Yuzhang Lin;
Pages: 6780 - 6783
Abstract: This letter addresses the incorporation of transmission line sensor measurements into power system state estimation (SE). These measurements are obtained from various locations along a line and are different from conventional RTU measurements at line terminals (i.e., substations). We propose a measurement function that can relate the current measurements at arbitrary positions along a line to the existing state variables at substation buses using line parameters and sensor locations. Hence, these measurements can be easily incorporated into SE without altering its state vector or system model. Simulation results on the IEEE 14-bus system and the NPCC 140-bus system demonstrate that the incorporation of line sensor measurements can significantly enhance SE's noise filtering and bad data processing performances.
PubDate: TUE, 28 MAY 2024 09:15:50 -04
Issue No: Vol. 39, No. 5 (2024)
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- A Virtual Graph Constrained Learning Method for Power Flow Calculation
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Authors: Jianping Yang;Yue Xiang;
Pages: 6784 - 6787
Abstract: To enhance the practical consistency and interpretability of deep learning approaches in power flow (PF) calculation, this letter proposes a virtual graph constrained message passing neural network (VGC-MPNN) for PF analysis, which defines a virtual graph from the mathematical expression of variables to enhance the binding force of power flow equations. Different from the existing methods that simply adopt the form of penalty function to learn the physical constraints, the proposed method empowers the mathematical expression into the feedforward process of the neural network to ensure a consistent solution, which performs internal solution logic instead of fitting the labeled output of the Newton-Raphson solver. Numerical analysis shows that the proposed VGC-MPNN could guarantee the physical consistency of original PFEs and improve the sensitivity of physical non-convergence, while the topological adaptability is also proved by considering network variations.
PubDate: WED, 17 JUL 2024 09:16:38 -04
Issue No: Vol. 39, No. 5 (2024)
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- Chance Constrained MDP Formulation and Bayesian Advantage Policy
Optimization for Stochastic Dynamic Optimal Power Flow-
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Authors: Yizhi Wu;Yujian Ye;Jianxiong Hu;Peilin Zhao;Liu Liu;Goran Strbac;Chongqing Kang;
Pages: 6788 - 6791
Abstract: Although deep reinforcement learning based on Markov Decision Process (MDP) constitutes a well-suited method for real-time control under uncertainties, its application to stochastic dynamic optimal power flow (SDOPF) problem is still challenging in the presence of increasing proliferation of various distributed energy resources, driven by its limitations on constraints satisfaction under uncertainties. While pioneering research explored Constrained MDP and Risk-Aware MDP formulations of SDOPF pursuing cumulative constraint violation minimization, they both struggle with satisfaction of state-wise safety constraints. This letter proposes a Chance Constrained MDP formulation of SDOPF and a Bayesian advantage policy optimization solution method. Bayesian neural networks are used to construct the probability distributions of state- and trajectory-wise constraint violations, and a novel advantage function is incorporated to improve both the policy's quality and safety. Case studies validated the cost-efficiency and comprehensive safety performance of the proposed method against the state-of-the-art.
PubDate: THU, 18 JUL 2024 09:17:01 -04
Issue No: Vol. 39, No. 5 (2024)
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- Introducing the IEEE PES Resource Center
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Pages: 6792 - 6792
Abstract: null
PubDate: TUE, 20 AUG 2024 09:16:56 -04
Issue No: Vol. 39, No. 5 (2024)
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