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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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Xiaoyu Zhang, Yushuai Li, Tianyi Li, Yonghao Gui, Qiuye Sun, David Wenzhong Gao
2024,12(5):1472-1483, DOI: 10.35833/MPCE.2023.000351
Abstract:
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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Qifan Chen, Siqi Bu, Chi Yung Chung
2024,12(4):1003-1018, DOI: 10.35833/MPCE.2023.000526
Abstract:
To tackle emerging power system small-signal stability problems such as wideband oscillations induced by the large-scale integration of renewable energy and power electronics, it is crucial to review and compare existing small-signal stability analysis methods. On this basis, guidance can be provided on determining suitable analysis methods to solve relevant small-signal stability problems in power electronics-dominated power systems (PEDPSs). Various mature methods have been developed to analyze the small-signal stability of PEDPSs, including eigenvalue-based methods, Routh stability criterion, Nyquist/Bode plot based methods, passivity-based methods, positive-net-damping method, lumped impedance-based methods, bifurcation-based methods, etc. In this paper, the application conditions, advantages, and limitations of these criteria in identifying oscillation frequencies and stability margins are reviewed and compared to reveal and explain connections and discrepancies among them. Especially, efforts are devoted to mathematically proving the equivalence between these small-signal stability criteria. Finally, the performance of these criteria is demonstrated and compared in a 4-machine 2-area power system with a wind farm and an IEEE 39-bus power system with 3 wind farms.
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Abdelfatah Ali, Hossam H. H. Mousa, Mostafa F. Shaaban, Maher A. Azzouz, Ahmed S. A. Awad
2024,12(3):675-694, DOI: 10.35833/MPCE.2023.000107
Abstract:
Electric vehicles (EVs) are becoming more popular worldwide due to environmental concerns, fuel security, and price volatility. The performance of EVs relies on the energy stored in their batteries, which can be charged using either AC (slow) or DC (fast) chargers. Additionally, EVs can also be used as mobile power storage devices using vehicle-to-grid (V2G) technology. Power electronic converters (PECs) have a constructive role in EV applications, both in charging EVs and in V2G. Hence, this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications. It examines PECs from the point of view of their classifications, configurations, control approaches, and future research prospects and their impacts on power quality. These can be classified into various topologies: DC-DC converters, AC-DC converters, DC-AC converters, and AC-AC converters. To address the limitations of traditional DC-DC converters such as switching losses, size, and high-electromagnetic interference (EMI), resonant converters and multiport converters are being used in high-voltage EV applications. Additionally, power-train converters have been modified for high-efficiency and reliability in EV applications. This paper offers an overview of charging topologies, PECs, challenges with solutions, and future trends in the field of the EV charging station applications.
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Pavitra Sharma, Krishna Kumar Saini, Hitesh Datt Mathur, Puneet Mishra
2024,12(2):381-392, DOI: 10.35833/MPCE.2023.000761
Abstract:
The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These prosumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.
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Jianlin Li, Zhijin Fang, Qian Wang, Mengyuan Zhang, Yaxin Li, Weijun Zhang
2024,12(2):359-370, DOI: 10.35833/MPCE.2023.000345
Abstract:
As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.
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Hongchao Gao, Tai Jin, Guanxiong Wang, Qixin Chen, Chongqing Kang, Jingkai Zhu
2024,12(2):346-358, DOI: 10.35833/MPCE.2023.000762
Abstract:
The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, wherein energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear reconstruction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.
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Mubarak J. Al-Mubarak, Antonio J. Conejo
2024,12(2):323-333, DOI: 10.35833/MPCE.2023.000306
Abstract:
We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i.e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.
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Seyed Ali Arefifar, Md Shahin Alam, Abdullah Hamadi
2023,11(6):1719-1733, DOI: 10.35833/MPCE.2022.000032
Abstract:
The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short- and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.
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Sichen Li, Di Cao, Weihao Hu, Qi Huang, Zhe Chen, Frede Blaabjerg
2023,11(5):1606-1617, DOI: 10.35833/MPCE.2022.000473
Abstract:
The multi-directional flow of energy in a multi-microgrid (MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.
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Kolampurath Jithin, Puthan Purayil Haridev, Nanappan Mayadevi, Raveendran Pillai Harikumar, Valiyakulam Prabhakaran Mini
2023,11(5):1375-1395, DOI: 10.35833/MPCE.2022.000053
Abstract:
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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Rasool Kahani, Mohsin Jamil, M. Tariq Iqbal
2023,11(4):1165-1175, DOI: 10.35833/MPCE.2022.000245
Abstract:
This paper aims to improve the performance of the conventional perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. As the oscillation around the maximum power point (MPP) is the main disadvantage of this technique, we introduce a modified P&O algorithm to conquer this handicap. The new algorithm recognizes approaching the peak of the photovoltaic (PV) array power curve and prevents the oscillation around the MPP. The key to achieve this goal is testing the change of output power in each cycle and comparing it with the change in array terminal power of the previous cycle. If a decrease in array terminal power is observed after an increase in the previous cycle or in the opposite direction, an increase in array terminal power is observed after a decrease in the previous cycle; it means we are at the peak of the power curve, so the duty cycle of the boost converter should remain the same as the previous cycle. Besides, an optimized duty cycle is introduced, which is adjusted based on the operating point of PV array. Furthermore, a DC-DC boost converter powered by a PV array simulator is used to test the proposed concept. When the irradiance changes, the proposed algorithm produces an average
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Wenlong Liao, Shouxiang Wang, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Zhe Yang, Kuangpu Liu
2023,11(4):1100-1114, DOI: 10.35833/MPCE.2022.000632
Abstract:
Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems. However, the volatility and intermittence of wind power pose uncertainties to traditional point prediction, resulting in an increased risk of power system operation. To represent the uncertainty of wind power, this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network (GNN) and an improved Bootstrap technique. Specifically, adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective. Then, the graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) are proposed to capture spatiotemporal features between nodes in the graph. To obtain high-quality prediction intervals (PIs), an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively. Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph, and the prediction results outperform popular baselines on two real-world datasets, which implies a high potential for practical applications in power systems.
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Jianlin Li, Guanghui Li, Suliang Ma, Zhonghao Liang, Yaxin Li, Wei Zeng
2023,11(3):885-895, DOI: 10.35833/MPCE.2021.000705
Abstract:
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 & H 2 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 & H 2 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.
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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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Zhaoyuan Wu, Jianxiao Wang, Haiwang Zhong, Feng Gao, Tianjiao Pu, Chin-Woo Tan, Xiupeng Chen, Gengyin Li, Huiru Zhao, Ming Zhou, Qing Xia
2023,11(3):714-726, DOI: 10.35833/MPCE.2022.000521
Abstract:
With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of “access over ownership”, the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.
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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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Parameter Estimation for Hot-spot Thermal Model of Power Transformers Using Unscented Kalman Filters
Miguel Ángel González-Cagigal, José Antonio Rosendo-Macías, Antonio Gómez-Expósito
2023,11(2):634-642, DOI: 10.35833/MPCE.2022.000439
Abstract:
This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.
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Hanyu Yang, Canbing Li, Ruanming Huang, Feng Wang, Lili Hao, Qiuwei Wu, Long Zhou
2023,11(2):567-578, DOI: 10.35833/MPCE.2021.000632
Abstract:
Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3%, 18.1%, and 18.9% at 30%, 40%, and 50% PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.
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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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Shengyuan Liu, Yicheng Jiang, Zhenzhi Lin, Fushuan Wen, Yi Ding, Li Yang
2023,11(2):523-533, DOI: 10.35833/MPCE.2021.000196
Abstract:
In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted K-nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.
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Alejandro Latorre, Wilmar Martinez, Camilo A. Cortes
2023,11(2):511-522, DOI: 10.35833/MPCE.2021.000359
Abstract:
Among hybrid energy storage systems (HESSs), battery-ultracapacitor systems in active topology use DC/DC power converters for their operations. HESSs are part of the solutions designed to improve the operation of power systems in different applications. In the residential microgrid applications, a multilevel control system is required to manage the available energy and interactions among the microgrid components. For this purpose, a rule-based power management system is designed, whose operation is validated in the simulation, and the performances of different controllers are compared to select the best strategy for the DC/DC converters. The average current control with internal model control and real-time frequency decoupling is proposed as the most suitable controller according to the contemplated performance parameters, allowing voltage regulation values close to 1%. The results are validated using real-time hardware-in-the-loop (HIL). These systems can be easily adjusted for other applications such as electric vehicles.
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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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Martin Pfeifer, Felicitas Mueller, Steven de Jongh, Frederik Gielnik, Thomas Leibfried, Sören Hohmann
2023,11(2):446-454, DOI: 10.35833/MPCE.2021.000761
Abstract:
In this paper, we present a time-domain dynamic state estimation for unbalanced three-phase power systems. The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network, i.e., the dynamics of the electrical lines. This enables our approach to release the assumption of the network being in quasi-steady state. Initially, based on the line dynamics, we derive a graph-based dynamic system model. To handle the large number of interacting variables, we propose a port-Hamiltonian modeling approach. Based on the port-Hamiltonian model, we then follow an observer-based approach to develop a dynamic estimator. The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents. The design and implementation of the estimator are illustrated through the IEEE 33-bus system. Numerical simulations verify the estimator to produce asymptotic exact estimates, which are able to detect harmonic distortion and sub-second transients as arising from converter-based resources.
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2023,11(2):421-433, DOI: 10.35833/MPCE.2021.000318
Abstract:
Considering a variety of sampled value (SV) attacks on busbar differential protection (BDP) which poses challenges to conventional learning algorithms, an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper. The healthy SV data of BDP are defined as self-data composed of spheres of the same size, whereas the SV attack data, i.e., the nonself data, are preserved in the nonself space covered by spherical detectors of different sizes. To avoid the confusion between busbar faults and SV attacks, a self-shape optimization algorithm is introduced, and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives. Based on the difficulty of boundary coverage in traditional negative selection algorithms, a self-data-driven detector generation algorithm is proposed to enhance the detector coverage. A testbed of differential protection for a 110 kV double busbar system is then established. Typical SV attacks of BDP such as amplitude and current phase tampering, fault replays, and the disconnection of the secondary circuits of current transformers are considered, and the delays of differential relay operation caused by detection algorithms are investigated.
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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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Tannan Xiao, Ying Chen, Jianquan Wang, Shaowei Huang, Weilin Tong, Tirui He
2023,11(2):401-411, DOI: 10.35833/MPCE.2022.000099
Abstract:
With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design for AI-oriented power system dynamic simulators is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AI-assisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out in four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation for AI-oriented power system dynamic simulators.
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Zixuan Jia, Jianing Li, Xiao-Ping Zhang, Ray Zhang
2023,11(2):389-400, DOI: 10.35833/MPCE.2021.000777
Abstract:
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.
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Ali Azizi, Behrooz Vahidi, Amin Foroughi Nematollahi
2023,11(1):212-222, DOI: 10.35833/MPCE.2022.000425
Abstract:
The purpose of active distribution networks (ADNs) is to provide effective control approaches for enhancing the operation of distribution networks (DNs) and greater accommodation of distributed generation (DG) sources. With the integration of DG sources into DNs, several operational problems have drawn attention such as overvoltage and power flow alteration issues. These problems can be dealt with by utilizing distribution network reconfiguration (DNR) and soft open points (SOPs). An SOP is a power electronic device capable of accurately controlling active and reactive power flows. Another significant aspect often overlooked is the coordination of protection devices needed to keep the network safe from damage. When implementing DNR and SOPs in real DNs, protection constraints must be considered. This paper presents an ADN reconfiguration approach that includes DG sources, SOPs, and protection devices. This approach selects the ideal configuration, DG output, and SOP placement and control by employing particle swarm optimization (PSO) to minimize power loss while ensuring the correct operation of protection devices under normal and fault conditions. The proposed approach explicitly formulates constraints on network operation, protection coordination, DG size, and SOP size. Finally, the proposed approach is evaluated using the standard IEEE 33-bus and IEEE 69-bus networks to demonstrate the validity.
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Qirun Sun, Zhi Wu, Wei Gu, Pengxiang Liu, Jingxuan Wang, Yuping Lu, Shu Zheng, Jingtao Zhao
2023,11(1):80-93, DOI: 10.35833/MPCE.2022.000337
Abstract:
The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system (PDS) and hydrogen energy system (HES). Considering that power to hydrogen (PtH) has great potential to facilitate the usage of renewable energy sources (RESs), the coordination of PDS and HES is important for planning purposes under high RES penetration. To this end, this paper proposes a multi-stage co-planning model for the PDS and HES. For the PDS, investment decisions on network assets and RES are optimized to supply the growing electric load and PtHs. For the HES, capacities of PtHs and hydrogen storages (HSs) are optimally determined to satisfy hydrogen load considering the hydrogen production, tube trailer transportation, and storage constraints. The overall planning problem is formulated as a multi-stage stochastic optimization model, in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over periods. Case studies validate that the proposed co-planning model can reduce the total planning cost, promote RES consumption, and obtain more flexible decisions under long-term load growth uncertainties.
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2023,11(1):52-65, DOI: 10.35833/MPCE.2022.000424
Abstract:
The increasing flexibility of active distribution systems (ADSs) coupled with the high penetration of renewable distributed generators (RDGs) leads to the increase of the complexity. It is of practical significance to achieve the largest amount of RDG penetration in ADSs and maintain the optimal operation. This study establishes an alternating current (AC)/direct current (DC) hybrid ADS model that considers the dynamic thermal rating, soft open point, and distribution network reconfiguration (DNR). Moreover, it transforms the optimal dispatching into a second-order cone programming problem. Considering the different control time scales of dispatchable resources, the following two-stage dispatching framework is proposed. ① The day-ahead dispatch uses hourly input data with the goal of minimizing the grid loss and RDG dropout. It obtains the optimal 24-hour schedule to determine the dispatching plans for DNR and the energy storage system. ② The intraday dispatch uses 15 min of input data for 1-hour rolling-plan dispatch but only executes the first 15 min of dispatching. To eliminate error between the actual operation and dispatching plan, the first 15 min is divided into three 5-min step-by-step executions. The goal of each step is to trace the tie-line power of the intraday rolling-plan dispatch to the greatest extent at the minimum cost. The measured data are used as feedback input for the rolling-plan dispatch after each step is executed. A case study shows that the comprehensive cooperative ADS model can release the line capacity, reduce losses, and improve the penetration rate of RDGs. Further, the two-stage dispatching framework can handle source-load fluctuations and enhance system stability.
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- 2024 MPCE Editorial Board Meeting Held in Seattle, WA, USA
- [Extended to July 31] Special Section on Dynamic Performance and Flexibility Enhancement of RES-dominated Power Systems with Grid-forming Converters
- Report Abstracts for Invited Speakers on MPCE 10th Anniversary Forum
- MPCE 10th Anniversary Forum on Resilience and Flexibility of Modern Power Systems will be held on Dec. 15, 2023
- 2023 MPCE Editorial Board Meeting Held in Orlando, FL, USA
- JCR Q1! MPCE 2022 Impact factor is 6.3
- [Extended to September 30] Special Section on Battery Energy Storage Systems for Net-zero Power Systems and Markets
- MPCE 10th Anniversary Forum will be held on Feb. 27, 2023
- 10 Papers Awarded as "MPCE Best Papers 2021"
- 2022 MPCE Editorial Board Meeting Held Online
