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Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e. g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Further-more, main issues and some research trends about the applications of GNNs in power systems are discussed.
Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
Hydrogen is being considered as an important option to contribute to energy system decarbonization. However, currently its production from renewables is expensive compared with the methods that utilize fossil fuels. This paper proposes a comprehensive optimization-based techno-economic assessment of a hybrid renewable electricity-hydrogen virtual power plant (VPP) that boosts its business case by co-optimizing across multiple markets and contractual services to maximize its profits and eventually deliver hydrogen at a lower net cost. Additionally, multiple possible investment options are considered. Case studies of VPP placement in a renewable-rich, congested area of the Australian network and based on real market data and relevant sensitivities show that multi-market participation can significantly boost the business case for cleaner hydrogen. This highlights the importance of value stacking for driving down the cost of cleaner hydrogen. Due to the participation in multiple markets, all VPP configurations considered are found to be economically viable for a hydrogen price of 3 AUD /kg(2.25USD
Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges. The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems (CPPSs). This paper presents a comprehensive review of some of the latest attack detection and defense strategies. Firstly, the vulnerabilities brought by some new information and communication technologies (ICTs) are analyzed, and their impacts on the security of CPPSs are discussed. Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework, and their features and negative impacts are discussed. Secondly, two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed, and their benefits and drawbacks are discussed. Moreover, two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed. Finally, the trends and challenges in attack detection and defense strategies in CPPSs are provided.
This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency (RoCoF) protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.
The rapid development of electric vehicles (EVs) has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection. This paper focuses on the optimization of EV charging, which cannot be ignored in the rapid development of EVs. The increase in the penetration of EVs will generate new electrical loads during the charging process, which will bring new challenges to local power systems. Moreover, the uncoordinated charging of EVs may increase the peak-to-valley difference in the load, aggravate harmonic distortions, and affect auxiliary services. To stabilize the operations of power grids, many studies have been carried out to optimize EV charging. This paper reviews these studies from two aspects: EV charging forecasting and coordinated EV charging strategies. Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models. At the end of this paper, recommendations are given to address the challenges of EV charging and associated charging strategies.
By collecting and organizing historical data and typical model characteristics, hydrogen energy storage system (HESS)-based power-to-gas (P2G) and gas-to-power systems are developed using Simulink. The energy transfer mechanisms and numerical modeling methods of the proposed systems are studied in detail. The proposed integrated HESS model covers the following system components: alkaline electrolyzer (AE), high-pressure hydrogen storage tank with compressor (CM & H2 tank), and proton-exchange membrane fuel cell (PEMFC) stack. The unit models in the HESS are established based on typical U-I curves and equivalent circuit models, which are used to analyze the operating characteristics and charging/discharging behaviors of a typical AE, an ideal CM & H2 tank, and a PEMFC stack. The validities of these models are simulated and verified in the MicroGrid system, which is equipped with a wind power generation system, a photovoltaic power generation system, and an auxiliary battery energy storage system (BESS) unit. Simulation results in MATLAB/Simulink show that electrolyzer stack, fuel cell stack and system integration model can operate in different cases. By testing the simulation results of the HESS under different working conditions, the hydrogen production flow, stack voltage, state of charge (SOC) of the BESS, state of hydrogen pressure (SOHP) of the HESS, and HESS energy flow paths are analyzed. The simulation results are consistent with expectations, showing that the integrated HESS model can effectively absorb wind and photovoltaic power. As the wind and photovoltaic power generations increase, the HESS current increases, thereby increasing the amount of hydrogen production to absorb the surplus power. The results show that the HESS responds faster than the traditional BESS in the microgrid, providing a solid theoretical foundation for later wind-photovoltaic-HESS-BESS integration.
DC microgrids are gaining more attention with the increased penetration of various DC sources such as solar photovoltaic systems, fuel cells, batteries, etc., and DC loads. Due to the rapid integration of these components into the existing power system, the importance of DC microgrids has reached a salient point. Compared with conventional AC systems, DC systems are free from synchronization issues, reactive power control, frequency control, etc., and are more reliable and efficient. However, many challenges need to be addressed for utilizing DC power to its full potential. The absence of natural current zero is a significant issue in protecting DC systems. In addition, the stability of the DC microgrid, which relies on inertia, needs to be considered during system design. Moreover, power quality and communication issues are also significant challenges in DC microgrids. This paper presents a review of various value streams of DC microgrids including architectures, protection schemes, power quality, inertia, communication, and economic operation. In addition, comparisons between different microgrid configurations, the state-of-the-art projects of DC microgrid, and future trends are also set forth for further studies.
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Jingtao Zhao, Zhi Wu, Huan Long, Huapeng Sun, Xi Wu, Chingchuen Chan, Mohammad Shahidehpour
2024,12(5):1333-1344, DOI: 10.35833/MPCE.2023.000372
Abstract:
With the large-scale integration of distributed renewable generation (DRG) and increasing proportion of power electronic equipment, the traditional power distribution network (DN) is evolving into an active distribution network (ADN). The operation state of an ADN, which is equipped with DRGs, could rapidly change among multiple states, which include steady, alert, and fault states. It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs. In this paper, the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states. The multi-state identification indicators and identification methods are summarized in detail. The multi-state regulation capacity quantification methods are analyzed considering controllable resources, quantification indicators, and quantification methods. A detailed survey of optimal operation control strategies, including multiple state operations, is presented, and key problems and outlooks for the expansion of ADN are discussed.
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Boyu Zhao, Hao Liu, Tianshu Bi, Sudi Xu
2024,12(5):1345-1356, DOI: 10.35833/MPCE.2023.000824
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High-precision synchronized measurement data with short measurement latency are required for applications of phasor measurement units (PMUs). This paper proposes a synchrophasor measurement method based on cascaded infinite impulse response (IIR) and dual finite impulse response (FIR) filters, meeting the M-class and P-class requirements in the IEC/IEEE 60255-118-1 standard. A low-group-delay IIR filter is designed to remove out-of-band interference components. Two FIR filters with different center frequencies are designed to filter out the fundamental negative frequency component and obtain synchrophasor estimates. The ratio of the amplitudes of the synchrophasor is used to calculate the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the FIR filters and the frequency. To shorten the response time introduced by IIR filter, a step identification and processing method based on the rate of change of frequency (RoCoF) is proposed and analyzed. The synchrophasor is accurately compensated based on the frequency and the frequency response of the IIR and FIR filters, achieving high-precision synchrophasor and frequency estimates with short measurement latency. Simulation and experiment tests demonstrate that the proposed method is superior to existing methods and can provide synchronized measurement data for M-class PMU applications with short measurement latency.
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Jorge Uriel Sevilla-Romero, Alejandro Pizano-Martínez, Claudio Rubén Fuerte-Esquivel, Reymundo Ramírez-Betancour
2024,12(5):1357-1369, DOI: 10.35833/MPCE.2023.000461
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In practice, an equilibrium point of the power system is considered transiently secure if it can withstand a specified contingency by maintaining transient evolution of rotor angles and voltage magnitudes within set bounds. A novel sequential approach is proposed to obtain transiently stable equilibrium points through the preventive control of transient stability and transient voltage sag (TVS) problems caused by a severe disturbance. The proposed approach conducts a sequence of non-heuristic optimal active power re-dispatch of the generators to steer the system toward a transiently secure operating point by sequentially solving the transient-stability-constrained optimal power flow (TSC-OPF) problems. In the proposed approach, there are two sequential projection stages, with the first stage ensuring the rotor angle stability and the second stage removing TVS in voltage magnitudes. In both projection stages, the projection operation corresponds to the TSC-OPF, with its formulation directly derived by adding only two steady-state variable-based transient constraints to the conventional OPF problem. The effectiveness of this approach is numerically demonstrated in terms of its accuracy and computational performance by using the Western System Coordinated Council (WSCC) 3-machine 9-bus system and an equivalent model of the Mexican 46-machine 190-bus system.
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Peichuan Tian, Yexuan Jin, Ning Xie, Chengmin Wang, Chunyi Huang
2024,12(5):1370-1382, DOI: 10.35833/MPCE.2024.000185
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The power flow (PF) calculation for AC/DC hybrid systems based on voltage source converter (VSC) plays a crucial role in the operational analysis of the new energy system. The fast and flexible holomorphic embedding (FFHE) PF method, with its non-iterative format founded on complex analysis theory, exhibits superior numerical performance compared with traditional iterative methods. This paper aims to extend the FFHE method to the PF problem in the VSC-based AC/DC hybrid system. To form the AC/DC FFHE PF method, an AC/DC FFHE model with its solution scheme and a sequential AC/DC PF calculation framework are proposed. The AC/DC FFHE model is established with a more flexible form to incorporate multiple control strategies of VSC while preserving the constructive and deterministic properties of original FFHE to reliably obtain operable AC/DC solutions from various initializations. A solution scheme for the proposed model is provided with specific recursive solution processes and accelerated Padé approximant. To achieve the overall convergence of AC/DC PF, the AC/DC FFHE model is integrated into the sequential calculation framework with well-designed data exchange and control mode switching mechanisms. The proposed method demonstrates significant efficiency improvements, especially in handling scenarios involving control mode switching and multiple recalculations. In numerical tests, the superiority of the proposed method is confirmed through comparisons of accuracy and efficiency with existing methods, as well as the impact analyses of different initializations.
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2024,12(5):1383-1395, DOI: 10.35833/MPCE.2023.000639
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Wind-thermal-bundled system has emerged as the predominant type of power system, incorporating a significant proportion of renewable energy. The dynamic interaction mechanism of the system is complex, and the issue of oscillation stability is significant. In this paper, the damping characteristics of the direct current (DC) capacitance oscillation mode are analyzed using the path analysis method (PAM). This method combines the transfer-function block diagram with the damping torque analysis (DTA). Firstly, the linear models of the permanent magnet synchronous generator (PMSG), the synchronous generator (SG), and the alternating current (AC) grid are established based on the transfer functions. The closed-loop transfer-function block diagram of the wind-thermal-bundled systems is derived. Secondly, the block diagram reveals the damping path and the dynamic interaction mechanism of the system. According to the superposition principle, the transfer-function block diagram is reconstructed to achieve the damping separation. The damping coefficient of the DTA is used to quantify the effect of the interaction between the subsystems on the damping characteristics of the oscillation mode. Then, the eigenvalue analysis is used to analyze the system stability. Finally, the damping characteristic analysis is validated by time-domain simulations.
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Hongxia Wang, Bo Wang, Jiaxin Zhang, Chengxi Liu, Hengrui Ma
2024,12(5):1396-1407, DOI: 10.35833/MPCE.2023.000205
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Taking the advantage of Internet of Things (IoT) enabled measurements, this paper formulates the event detection problem as an information-plus-noise model, and detects events in power systems based on free probability theory (FPT). Using big data collected from phasor measurement units (PMUs), we construct the event detection matrix to reflect both spatial and temporal characteristics of power gird states. The event detection matrix is further described as an information matrix plus a noise matrix, and the essence of event detection is to extract event information from the event detection matrix. By associating the event detection problem with FPT, the empirical spectral distributions (ESDs) related moments of the sample covariance matrix of the information matrix is computed, to distinguish events from “noises”, including normal fluctuations, background noises, and measurement errors. Based on central limit theory (CLT), the alarm threshold is computed using measurements collected in normal states. Additionally, with the aid of sliding window, this paper builds an event detection architecture to reflect power grid state and detect events online. Case studies with simulated data from Anhui, China, and real PMU data from Guangdong, China, verify the effectiveness of the proposed method. Compared with other data-driven methods, the proposed method is more sensitive and has better adaptability to the normal fluctuations, background noises, and measurement errors in real PMU cases. In addition, it does not require large number of training samples as needed in the training-testing paradigm.
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Dongchen Hou, Yonghui Sun, Venkata Dinavahi, Yi Wang
2024,12(5):1408-1418, DOI: 10.35833/MPCE.2023.000352
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This paper develops an adaptive two-stage unscented Kalman filter (ATSUKF) to accurately track operation states of the synchronous generator (SG) under cyber attacks. To achieve high fidelity, considering the excitation system of SGs, a detailed 9 th-order SG model for dynamic state estimation is established. Then, for several common cyber attacks against measurements, a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel. Subsequently, to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions, a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix. Finally, a large number of simulation experiments are carried out on the IEEE 39-bus system, which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.
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Huating Xu, Bin Feng, Gang Huang, Mingyang Sun, Houbo Xiong, Chuangxin Guo
2024,12(5):1419-1430, DOI: 10.35833/MPCE.2023.000549
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The increasing integration of renewable energy sources (RESs) presents significant challenges for the safe and economical operation of power grids. Addressing the critical need to assess the effect of RES uncertainties on optimal scheduling schemes (OSSs), this paper introduces a convex hull based economic operating region (CH-EOR) for power grids. The CH-EOR is mathematically defined to delineate the impact of RES uncertainties on power grid operations. We propose a novel approach for generating the CH-EOR, enhanced by a big-M preprocessing method to improve the computational efficiency. Performed on four test systems, the proposed big-M preprocessing method demonstrates notable advancements: a reduction in average operating costs by over 10% compared with the box-constrained operating region (BC-OR) derived from robust optimization. Furthermore, the CH-EOR occupies less than 11.79% of the generators ’
adjustable region (GAR). Most significantly, after applying the proposed big-M preprocessing method, the computational efficiency is improved over 17 times compared with the traditional big-M method. - 1
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Abdallah A. Aboelnaga, Maher A. Azzouz
2024,12(5):1431-1444, DOI: 10.35833/MPCE.2023.000616
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Fault currents emanating from inverter-based resources (IBRs) are controlled to follow specific references to support the power grid during faults. However, these fault currents differ from the typical fault currents fed by synchronous generators, resulting in an improper operation of conventional phase selection methods (PSMs). In this paper, the relative angles between sequence voltages measured at the relay location are determined analytically in two stages ①
a short-circuit analysis is performed at the fault location to determine the relative angles between sequence voltages; and ②an analysis of the impact of transmission line on the phase difference between the sequence voltages of relay and fault is conducted for different IBR controllers. Consequently, new PSM zones based on relative angles between sequence voltages are devised to facilitate accurate PSM regardless of the fault currents, resistances, or locations of IBR. Comprehensive time-domain simulations confirm the accuracy of the proposed PSM with different fault locations, resistances, types, and currents. - 1
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Hailiang Xu, Chao Wang, Zhongxing Wang, Pingjuan Ge, Rende Zhao
2024,12(5):1445-1458, DOI: 10.35833/MPCE.2023.000482
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The brushless doubly-fed induction generator (BDFIG) presents significant potential for application in wind power systems, primarily due to the elimination of slip rings and brushes. The application of virtual synchronous control (VSynC) has been demonstrated to effectively augment the inertia of BDFIG systems. However, the dynamic characteristics and stability of BDFIG under weak grid conditions remain largely unexplored. The critical stabilizing factors for BDFIG-based wind turbines (WTs) are methodically investigated, and an enhanced VSynC method based on linear active disturbance rejection control (LADRC) is proposed. The stability analysis reveals that the proposed method can virtually enhance the stability of the grid-connected system under weak grid conditions. The accuracy of the theoretical analysis and the effectiveness of the proposed method are affirmed through extensive simulations and detailed experiments.
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Xiangjing Su, Chao Deng, Yanhao Shan, Farhad Shahnia, Yang Fu, Zhaoyang Dong
2024,12(5):1459-1471, DOI: 10.35833/MPCE.2023.000606
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Fault diagnosis (FD) for offshore wind turbines (WTs) are instrumental to their operation and maintenance (O&M). To improve the FD effect in the very early stage, a condition monitoring based sample set mining method from supervisory control and data acquisition (SCADA) time-series data is proposed. Then, based on the convolutional neural network (CNN) and attention mechanism, an interpretable convolutional temporal-spatial attention network (CTSAN) model is proposed. The proposed CTSAN model can extract deep temporal-spatial features from SCADA time-series data sequentially by ①
a convolution feature extraction module to extract features based on time intervals ； ②a spatial attention module to extract spatial features considering the weights of different features; and ③a temporal attention module to extract temporal features considering the weights of intervals. The proposed CTSAN model has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-understandable form of the temporal-spatial attention weights. The effectiveness and superiority of the proposed CTSAN model are verified by real offshore wind farms in China. - 1
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Xiaoyu Zhang, Yushuai Li, Tianyi Li, Yonghao Gui, Qiuye Sun, David Wenzhong Gao
2024,12(5):1472-1483, DOI: 10.35833/MPCE.2023.000351
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The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.
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Xu Yang, Haotian Liu, Wenchuan Wu, Qi Wang, Peng Yu, Jiawei Xing, Yuejiao Wang
2024,12(5):1484-1494, DOI: 10.35833/MPCE.2023.000893
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As numerous distributed energy resources (DERs) are integrated into the distribution networks, the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks (ADNs). Since accurate models are usually unavailable in ADNs, an increasing number of reinforcement learning (RL) based methods have been proposed for the optimal dispatch problem. However, these RL based methods are typically formulated without safety guarantees, which hinders their application in real world. In this paper, we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic (S3AC) for the optimal dispatch of DERs in ADNs, which not only minimizes the operational cost but also satisfies safety constraints during online execution. In the proposed S3AC, the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition (SCADA) system, effectively providing enhanced safety for executed actions. Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.
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Zhihua Yin, Yuping Zheng, Zhinong Wei, Guoqiang Sun, Sheng Chen, Haixiang Zang
2024,12(5):1495-1505, DOI: 10.35833/MPCE.2023.000225
Abstract:
When high-impedance faults (HIFs) occur in resonant grounded distribution networks, the current that flows is extremely weak, and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics. Consequently, locating a fault section with high sensitivity is difficult. Unlike existing technologies, this study presents a novel fault feature identification framework that addresses this issue. The framework includes three key steps ①
utilizing the variable mode decomposition (VMD) method to denoise the fault transient zero-sequence current (TZSC); ②employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding (t-SNE) to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space; and ③classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location. Numerical simulations and field testing confirm that the proposed method accurately detects the fault location, even under the influence of strong noise interference. - 1
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Yuchong Huo, Zaiyu Chen, Qun Li, Qiang Li, Minghui Yin
2024,12(5):1506-1519, DOI: 10.35833/MPCE.2023.000385
Abstract:
In this paper, we apply a model predictive control based scheme to the energy management of networked microgrid ，
which is reformulated based on column generation. Although column generation is effective in alleviating the computational intractability of large-scale optimization problems, it still suffers from slow convergence issues, which hinders the direct real-time online implementation. To this end, we propose a graph neural network based framework to accelerate the convergence of the column generation model. The acceleration is achieved by selecting promising columns according to certain stabilization method of the dual variables that can be customized according to the characteristics of the microgrid. Moreover, a rigorous energy management method based on the graph neural network accelerated column generation model is developed, which is able to guarantee the optimality and feasibility of the dispatch results. The computational efficiency of the method is also very high, which is promising for real-time implementation. We conduct case studies to demonstrate the effectiveness of the proposed energy management method. - 1
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2024,12(5):1520-1534, DOI: 10.35833/MPCE.2023.000652
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The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals, i.e., sharing disturbance mitigation among all controllable assets to even their burden. However, limited by neighboring communication, the time-consuming peer-to-peer coordination of the droop-free control slows down the nodal convergence to global consensus, reducing the power-sharing efficiency as the number of nodes increases. To this end, this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes, in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed. A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes, which also enables the autonomous switching between local and global power-sharing modes according to the system states. Systematic guidance for key control parameter designs is derived via the optimal control methods, by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to consensus. System stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct method. Moreover, simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode transitions. Moreover, compared with the classical global power-sharing scheme, the proposed scheme presents promising benefits in convergence speed and scalability.
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Ye Tang, Qiaozhu Zhai, Yuzhou Zhou
2024,12(5):1535-1547, DOI: 10.35833/MPCE.2023.000718
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Energy storage (ES), as a fast response technology, creates an opportunity for microgrid (MG) to participate in the reserve market such that MG with ES can act as an independent reserve provider. However, the potential value of MG with ES in the reserve market has not been well realized. From the viewpoint of reserve provider, a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process, which differs from the model that MG with ES acts as a reserve consumer in most existing studies. Based on the proposed model, MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling. Considering that the proposed model is just in concept and cannot be directly solved, a multi-stage robust optimization reserve provision method is proposed, which leverages the structure of model constraints. Next, the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility. Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.
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Yang Wang, Xiang Zhou, Junmiao Tang, Xianyong Xiao, Shu Zhang, Jiandong Si
2024,12(5):1548-1558, DOI: 10.35833/MPCE.2023.000447
Abstract:
The effects of nonlinear loads on voltage quality represent an emerging concern for islanded microgrids. Existing research works have mainly focused on harmonic power sharing among multiple inverters, which ignores the diversity of different inverters to mitigate harmonics from nonlinear loads. As a result, the voltage quality of microgrids cannot be effectively improved. To address this issue, this study proposes an adaptive harmonic virtual impedance (HVI) control for improving voltage quality of microgrids. Based on the premise that no inverter is overloaded, the main objective of the proposed control is to maximize harmonic power absorption by shaping the lowest output impedances of inverters. To achieve this, the proposed control is utilized to adjust the HVI of each inverter based on its operation conditions. In addition, the evaluation based on Monte Carlo harmonic power flow is designed to assess the performance of the proposed control in practice. Finally, comparative studies and control-in-the-loop experiments are conducted.
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Hanjiang Dong, Jizhong Zhu, Shenglin Li, Yuwang Miao, Chi Yung Chung, Ziyu Chen
2024,12(5):1559-1571, DOI: 10.35833/MPCE.2023.000841
Abstract:
Lately, the power demand of consumers is increasing in distribution networks, while renewable power generation keeps penetrating into the distribution networks. Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude. Hence, this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques. First, we formulate the short-term probabilistic residential load forecasting problem. Then, we propose a sequence-to-sequence (Seq2Seq) adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain (with massive consumption records of regular loads) to the target domain (with limited observations of new residential loads) and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem. For implementation, the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network, including the Seq2Seq recurrent neural networks (RNNs) composed of a long short-term memory (LSTM) encoder and an LSTM decoder, and quantile loss. Finally, this study conducts the case studies via multiple evaluation indices, comparative methods of classic machine learning and advanced deep learning, and various available data of the new residentical loads and other regular loads. The experimental results validate the effectiveness and stability of the proposed scheme.
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Hongtao Ren, Chung-Li Tseng, Fushuan Wen, Chongyu Wang, Guoyan Chen, Xiao Li
2024,12(5):1572-1583, DOI: 10.35833/MPCE.2023.000512
Abstract:
Joint operation optimization for electric vehicles (EVs) and on-site or adjacent photovoltaic generation (PVG) are pivotal to maintaining the security and economics of the operation of the power system concerned. Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station (EVCS). Firstly, an optimization model for real-time EV charging strategy is proposed to address these challenges, which accounts for environmental uncertainties of an EVCS, encompassing EV arrivals, charging demands, PVG outputs, and the electricity price. Then, a scenario-based two-stage optimization approach is formulated. The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory (B-LSTM) network. Finally, numerical results substantiate the efficacy of the proposed optimization approach, and demonstrate superior profitability compared with prevalent approaches.
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B. Vinod Kumar, Aneesa Farhan M A
2024,12(5):1584-1595, DOI: 10.35833/MPCE.2023.000674
Abstract:
The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.
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Weihang Yan, Vahan Gevorgian, Przemyslaw Koralewicz, S M Shafiul Alam, Emanuel Mendiola
2024,12(5):1596-1604, DOI: 10.35833/MPCE.2023.000730
Abstract:
Battery energy storage systems (BESSs) are an important asset for power systems with high integration levels of renewable energy, and they can be controlled to provide various critical services to the power grid. This paper presents the real-world experience of using a megawatt-scale BESS with grid-following (GFL) and grid-forming (GFM) controls and a run-of-river (ROR) hydropower plant to restore a regional power system. To demonstrate this, we carry out power-hardware-in-the-loop experiments integrating an actual GFL- or GFM-controlled BESS and a load bank. Both the simulation and experimental results presented in this paper show the different roles of GFL- or GFM-controlled BESS in power system black starts. The results provide further insight for system operators on how GFL- or GFM-controlled BESS can enhance grid stability and how an ROR hydropower plant can be converted into a black-start-capable unit with the support of a small-capacity BESS. The results show that an ROR hydropower plant combined with a BESS has the potential of becoming one of enabling elements to perform bottom-up black-start schemes as opposed to conventional bottom-down method, thus enhancing the system resiliency and robustness.
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Xi Lu, Xinzhe Fan, Haifeng Qiu, Wei Gan, Wei Gu, Shiwei Xia, Xiao Luo
2024,12(5):1605-1616, DOI: 10.35833/MPCE.2023.000613
Abstract:
In this paper, an operation model for distribution systems with energy storage (ES) is proposed and solved with the aid of machine learning. The model considers ES applications with uncertainty realizations. It also considers ES applications for economy and security purposes. Considering the special features of ES operations under day-ahead decision mechanisms of distribution systems, an ES operation scheme is designed for transferring uncertainties to later hours through ES to ensure the secure operation of distribution system. As a result, uncertainties from different time intervals are assembled and may counteract each other, thereby alleviating the uncertainties. As different ES applications rely on ES flexibility (in terms of charging and discharging) and interact with each other, by coordinating different ES applications, the proposed operation model achieves efficient exploit of ES flexibility. To shorten the computation time, a long short-term memory recurrent neural network is used to determine the binary variables corresponding to ES status. The proposed operation model then becomes a convex optimization problem and is solved precisely. Thus, the solving efficiency is greatly improved while ensuring the satisfactory use of ES flexibility in distribution system operation.
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Yi Yang, Peng Zhang, Can Wang, Zhuoli Zhao, Loi Lei Lai
2024,12(5):1617-1630, DOI: 10.35833/MPCE.2024.000090
Abstract:
The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system (IES). Therefore, this study proposes a state transition modeling method for an IES based on a cyber-physical system (CPS) to optimize the state transition of energy unit in the IES. This method uses the physical, integration, and optimization layers as a three-layer modeling framework. The physical layer is used to describe the physical models of energy units in the IES. In the integration layer, the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model, and the transition conditions between different states of the energy unit are given. The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state. Numerical simulations show that, compared with the traditional modeling method, the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle, which obtains an optimal state of the energy unit and further reduces the system operating costs.
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Qinglin Meng, Xiaolong Jin, Fengzhang Luo, Zhongguan Wang, Sheharyar Hussain
2024,12(5):1631-1642, DOI: 10.35833/MPCE.2023.000661
Abstract:
A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems (RIESs) with multiple stakeholders. A two-level Stackelberg game model is established, with the RIES operator as the leader and the users as the followers. It considers the interests of the RIES operator and demand response users in energy trading. The leader optimizes time-of-use (TOU) energy prices to minimize costs while users formulate response plans based on prices. A two-stage distributionally robust game model with comprehensive norm constraints, which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage, is constructed to manage wind power uncertainty. Karush-Kuhn-Tucker (KKT) conditions transform the two-level Stackelberg game model into a single-level robust optimization model, which is then solved using column and constraint generation (C&CG). Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders’ interests and mitigating wind power risks.
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Arif S. Malik, Majid A. Al Umairi
2024,12(5):1643-1651, DOI: 10.35833/MPCE.2023.000871
Abstract:
This paper presents a novel method for accurately estimating the cumulative capacity credit (CCC) of renewable energy (RE) projects. Leveraging data from the main interconnected system (MIS) of Oman for 2028, where a substantial increase in RE generation is anticipated, our novel method is introduced alongside the traditional effective load carrying capability (ELCC) method. To ensure its robustness, we compare CCC results with ELCC calculations using two distinct standards of reliability criteria: loss of load hours (LOLH) at 24 hour/year and 2.4 hour/year. Our method consistently gives accurate results, emphasizing its exceptional accuracy, efficiency, and simplicity. A notable feature of our method is its independence from loss of load probability (LOLP) calculations and the iterative procedures associated with analytic-based reliability methods. Instead, it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE plants. This innovation is of particular significance to prospective independent power producers (IPPs) in the RE sector, offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data, often restricted by privacy concerns.
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Guangsheng Pan, Zhongfan Gu, Yuanyuan Sun, Kaiqi Sun, Wei Gu
2024,12(5):1652-1665, DOI: 10.35833/MPCE.2024.000171
Abstract:
Decarbonization in the power sector is one of the critical factors in achieving carbon neutrality, and the top-level design needs to be carried out from the perspective of power planning. A multi-stage provincial power expansion planning (PPEP) model is proposed to simulate the power expansion planning at different stages of the power systems rich in renewable energy generation. This model covers 16 types of power supply, considering macro-policy demands and micro-operation constraints. The stand-alone capacity aggregation model for coal-based units within the PPEP model allows for accurate construction and retirement with different stand-alone capacities. Moreover, the soft dynamic time warping (soft-DTW) based K-medoids technique is adopted to generate typical scenarios for balancing the model accuracy and solution efficiency. Additionally, a multi-market trading equilibrium (MMTE) mechanism is proposed to address the differences in the levelized cost of energy between the coal-based and renewable-based units by participating in energy and ancillary service markets. Since the coal-based units take on the task of providing ancillary services from renewable-based units in the ancillary service market, the MMTE mechanism can effectively equalize the profits of both by having renewable-based units purchase ancillary services from coal-based units and pay for them, thus improving the motivation of coal-based units. A case study in Xinjiang province, China, verifies the effectiveness of the planning results of the PPEP model and the profit equilibrium realization of the MMTE mechanism.
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Shangning Tan, Junliang Liu, Xiong Du, Jingyuan Su, Lijuan Fan
2024,12(5):1666-1677, DOI: 10.35833/MPCE.2023.000648
Abstract:
The voltage source converter based multi-terminal high-voltage direct current (VSC-MTDC) system has attracted much attention because it can achieve the interconnection between AC grids. However, the initial phases and short-circuit ratios (SCRs) of the interconnected AC grids cause the steady-state phases (SSPs) of AC ports in the VSC-MTDC system to be different. This can lead to issues such as mismatches in multiple converter reference frame systems, potentially causing inaccuracies in stability analysis when this phenomenon is disregarded. To address the aforementioned issues, a multi-port network model of the VSC-MTDC system, which considers the SSPs of the AC grids and AC ports, is derived by multiplying the port models of different subsystems (SSs). The proposed multi-port network model can accurately describe the transmission characteristics between the input and output ports of the system. Additionally, this model facilitates accurate analysis of the system stability. Furthermore, it identifies the key factors affecting the system stability. Ultimately, the accuracy of the proposed multi-port network model and the analysis of key factors are verified by time-domain simulations.
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Chunyi Han, Lei Shang, Shi Su, Xuzhu Dong, Bo Wang, Hao Bai, Wei Li
2024,12(5):1678-1689, DOI: 10.35833/MPCE.2022.000738
Abstract:
This paper proposes a grid synchronization control strategy for the grid-connected voltage source converters (VSCs) based on the voltage dynamics of the DC-link capacitor in the VSC. The voltage dynamics of the DC-link capacitor are used to regulate the frequency and phase angle of the inner potential of the VSC, synchronizing the VSC with grid. Firstly, in the proposed strategy, the active power regulation and grid synchronization of the VSC are combined, which are separated in the traditional control strategy. This can avoid the instability of the VSC in a weak grid with a low short circuit ratio (SCR), aroused by the dynamic interaction between the separated control loops in traditional control strategies. Secondly, the energy stored in the DC-link capacitor is directly coupled with the grid via the inner potential of the VSC, and the inertia characteristic is naturally featured in the inner potential by the proposed strategy. With the increase of the capacitance, the natural inertial response of the VSC is helpful to improve the grid frequency dynamic. Finally, simulation results are presented to validate the correctness and effectiveness of the proposed strategy on the enhancement of the grid frequency and voltage dynamic support capability.
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2024,12(5):1690-1695, DOI: 10.35833/MPCE.2023.000394
Abstract:
Droop-based fast frequency response (FFR) control of wind turbines can improve the frequency performance of power systems with high penetration of wind power. Explicitly formulating the feasible region of the droop-based FFR controller parameters can allow system operators to conveniently assess the feasibility of FFR controller parameter settings to comply with system frequency security, and efficiently tune and optimize FFR controller parameters to meet frequency security requirements. However, the feasible region of FFR controller parameters is inherently nonlinear and implicit because the power point tracking controllers of wind turbine would counteract the effect of FFR controllers. To address this issue, this letter proposes a linear feasible region formulation method, where frequency regulation characteristics of wind turbines, the dead band, and reserve limits of generators are all considered. The effectiveness of the proposed method and its application is demonstrated on a 10-machine power system.
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Volume 12, Issue 5, 2024
>Review
>Original Paper
>Short Letter
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Shuwei Xu, Wenchuan Wu, Bin Wang, Yue Yang
2023,11(6):1734-1745, DOI: 10.35833/MPCE.2022.000526
Abstract:
This paper proposes a probabilistic energy and reserve co-dispatch (PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk (VaR) representation. Specifically, the VaR formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.
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Yonghui Sun, Yan Zhou, Sen Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, George Sideratos, Nikos Hatziargyriou, Pierluigi Siano
2023,11(5):1450-1461, DOI: 10.35833/MPCE.2022.000577
Abstract:
Regional photovoltaic (PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals (PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granule-based clustering (GC) and direct optimization programming (DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction (NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples’ utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.
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Dajun Du, Minggao Zhu, Xue Li, Minrui Fei, Siqi Bu, Lei Wu, Kang Li
2023,11(3):727-743, DOI: 10.35833/MPCE.2021.000604
Abstract:
Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges. The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems (CPPSs). This paper presents a comprehensive review of some of the latest attack detection and defense strategies. Firstly, the vulnerabilities brought by some new information and communication technologies (ICTs) are analyzed, and their impacts on the security of CPPSs are discussed. Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework, and their features and negative impacts are discussed. Secondly, two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed, and their benefits and drawbacks are discussed. Moreover, two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed. Finally, the trends and challenges in attack detection and defense strategies in CPPSs are provided.
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2023,11(3):705-713, DOI: 10.35833/MPCE.2023.000073
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Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
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Chengjin Ye, Libang Guo, Yi Ding, Ming Ding, Peng Wang, Lei Wang
2023,11(2):662-673, DOI: 10.35833/MPCE.2021.000491
Abstract:
With various components and complex topologies, the applications of high-voltage direct current (HVDC) links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates (ALRs) is established based on its reliability network. Then, dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system (RTS) with an HVDC link is adopted to verify the proposed approach.
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James Naughton, Shariq Riaz, Michael Cantoni, Xiao-Ping Zhang, Pierluigi Mancarella
2023,11(2):553-566, DOI: 10.35833/MPCE.2022.000324
Abstract:
Hydrogen is being considered as an important option to contribute to energy system decarbonization. However, currently its production from renewables is expensive compared with the methods that utilize fossil fuels. This paper proposes a comprehensive optimization-based techno-economic assessment of a hybrid renewable electricity-hydrogen virtual power plant (VPP) that boosts its business case by co-optimizing across multiple markets and contractual services to maximize its profits and eventually deliver hydrogen at a lower net cost. Additionally, multiple possible investment options are considered. Case studies of VPP placement in a renewable-rich, congested area of the Australian network and based on real market data and relevant sensitivities show that multi-market participation can significantly boost the business case for cleaner hydrogen. This highlights the importance of value stacking for driving down the cost of cleaner hydrogen. Due to the participation in multiple markets, all VPP configurations considered are found to be economically viable for a hydrogen price of 3 AUD$/kg (2.25 USD$/kg), which has been identified as a threshold value for Australia to export hydrogen at a competitive price. Additionally, if the high price volatility that has been seen in gas prices in 2022 (and by extension electricity prices) continues, the flexibility of hybrid VPPs will further improve their business cases.
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Yang Peng, Zhi Wu, Wei Gu, Suyang Zhou, Pengxiang Liu
2023,11(2):468-478, DOI: 10.35833/MPCE.2021.000615
Abstract:
Micro-phasor measurement units (μPMUs) with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations. Therefore, this paper investigates the problem of how to place a limited number of μPMUs to improve the state estimation accuracy. Combined with pseudo-measurements and supervisory control and data acquisition (SCADA) measurements, an optimal μPMU placement model is proposed based on a two-step state estimation method. The E-optimal experimental criterion is utilized to measure the state estimation accuracy. The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming (MISDP) problem, whose optimal solution can be obtained by using the improved Benders decomposition method. Simulations on several systems are carried out to evaluate the effective performance of the proposed model.
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Haftu Tasew Reda, Adnan Anwar, Abdun Mahmood, Naveen Chilamkurti
2023,11(2):455-467, DOI: 10.35833/MPCE.2020.000827
Abstract:
In a smart grid, state estimation (SE) is a very important component of energy management system. Its main functions include system SE and detection of cyber anomalies. Recently, it has been shown that conventional SE techniques are vulnerable to false data injection (FDI) attack, which is a sophisticated new class of attacks on data integrity in smart grid. The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model, which is different from the traditional weighted least square based SE model. This SE model has a number of unique advantages compared with traditional SE models. First, the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors. Second, the proposed SE model can learn the actual power system states. Finally, this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors. The proposed FDI attack detection technique is evaluated on a number of standard bus systems. The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-of-the-art techniques. Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.
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Fabricio Andrade Mourinho, Tatiana Mariano Lessa Assis
2023,11(2):412-420, DOI: 10.35833/MPCE.2022.000365
Abstract:
This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency（RoCoF） protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.
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Benedict J. Mortimer, Amandus Dominik Bach, Christopher Hecht, Dirk Uwe Sauer, Rik W. De Doncker
2022,10(6):1750-1760, DOI: 10.35833/MPCE.2021.000181
Abstract:
The current increase in the number of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage of charging options. In order to make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. This paper gives an overview of the differences in the utilization across the public charging infrastructure in Germany. To this end, a dataset on the utilization of 21164 public and semi-public charging stations in Germany is evaluated. The installation and operating costs of various charging stations are modeled and economically evaluated in combination with the utilization data. It is shown that in 2019-2020, the average utilization in Germany was rather low, albeit with striking regional differences. We consider future scenarios allowing the regional development forecasting of economic viability. It is demonstrated that a growth in electric mobility of 20%-30% per year leads to a large number of economically feasible charging parks in urban agglomeration areas.
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Ziyu Chen, Jizhong Zhu, Shenglin Li, Yun Liu, Tengyan Luo
2022,10(6):1576-1587, DOI: 10.35833/MPCE.2021.000546
Abstract:
Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples.
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Sichen Li, Weihao Hu, Di Cao, Tomislav Dragičević, Qi Huang, Zhe Chen, Frede Blaabjerg
2022,10(3):719-730, DOI: 10.35833/MPCE.2020.000460
Abstract:
A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner ’s commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods. - 1
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Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang
2022,10(2):345-360, DOI: 10.35833/MPCE.2021.000058
Abstract:
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e.g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
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Luka Strezoski, Harsha Padullaparti, Fei Ding, Murali Baggu
2022,10(2):277-285, DOI: 10.35833/MPCE.2021.000667
Abstract:
With the rapid integration of distributed energy resources (DERs), distribution utilities are faced with new and unprecedented issues. New challenges introduced by high penetration of DERs range from poor observability to overload and reverse power flow problems, under-/over-voltages, maloperation of legacy protection systems, and requirements for new planning procedures. Distribution utility personnel are not adequately trained, and legacy control centers are not properly equipped to cope with these issues. Fortunately, distribution energy resource management systems (DERMSs) are emerging software technologies aimed to provide distribution system operators (DSOs) with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources. However, as DERMS technology is still emerging, its definition is vague and can refer to very different levels of software hierarchies, spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems (called utility DERMS). Although they are all frequently simply called DERMS, these software technologies have different sets of tools and aim to provide different services to different stakeholders. This paper explores how these different software technologies can complement each other, and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.
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