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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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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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Zhe Zhang, Siyang Liao, Yuanzhang Sun, Jian Xu, Deping Ke, Bo Wang, Rui Chen, Yibo Jiang
2024,12(4):1019-1030, DOI: 10.35833/MPCE.2023.000754
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Renewable energy sources (RESs) are rapidly developing and their substitution for traditional power generation poses significant challenges to the frequency regulation in power systems. The load damping factor D characterizes the active power of load that changes with power system frequency, which is an important factor influencing the frequency response. However, the value of D is small, resulting in the limitation in frequency regulation of the power system. This paper proposes a parallel-type load damping factor controller to enhance load damping factor by utilizing static var generators (SVGs) in substations. Additionally, it discusses the configuration method for the relevant parameters of the controller, evaluates its frequency regulation capability, investigates the impact of large-scale application of the controller on static and dynamic loads, and conducts a comprehensive evaluation of the impact of the damping factor control process on the voltage stability of the main grid. The large-scale application of the proposed controller can significantly improve the frequency regulation capability, and almost have no influence on the working status of the load. It can also significantly improve the dynamic performance of system frequency. The proposed controller can provide technical support for the frequency regulation of new power systems with high proportion of RESs.
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Bo Sun, Xi Wu, Xi Chen, Zixiao Zou, Qiang Li, Bixing Ren
2024,12(4):1031-1041, DOI: 10.35833/MPCE.2023.000179
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In recent years, with increasing amounts of renewable energy sources connecting to power networks, sub-/super-synchronous oscillations (SSOs) have occurred more frequently. Due to the time-variant nature of SSO magnitudes and frequencies, as well as the mutual interferences among SSO modes with close frequencies, the accurate parameter estimation of SSO has become a particularly challenging topic. To solve this issue, this paper proposes an improved spectrum analysis method by improving the window function and a spectrum correction method to achieve higher precision. First, by aiming at the sidelobe characteristics of the window function as evaluation criteria, a combined cosine function is optimized using a genetic algorithm (GA). Furthermore, the obtained window function is self-convolved to extend its excellent characteristics, which have better performance in reducing mutual interference from other SSO modes. Subsequently, a new form of interpolated all-phase fast Fourier transform (IpApFFT) using the optimized window function is proposed to estimate the parameters of SSO. This method allows for phase-unbiased estimation while maintaining algorithmic simplicity and expedience. The performance of the proposed method is demonstrated under various conditions, compared with other estimation methods. Simulation results validate the effectiveness and superiority of the proposed method.
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Rehan Sadiq, Zhen Wang, Chi Yung Chung, Deqiang Gan, Cunzhi Tong
2024,12(4):1042-1051, DOI: 10.35833/MPCE.2023.000347
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In recent years, with the growth of wind energy resources, the capability of wind farms to damp low-frequency oscillations (LFOs) has provided a notable advantage for the stability enhancement of the modern power grid. Meanwhile, owing to variations in the power system operating point (OP), the damping characteristics of LFOs may be affected adversely. In this respect, this paper presents a coordinated robust proportional-integral-derivative (PID) based damping control approach for permanent magnet synchronous generators (PMSGs) to effectively stabilize LFOs, while considering power system operational uncertainties in the form of a polytopic model constructed by linearizing the power system under a given set of OPs. The proposed approach works by modulating the DC-link voltage control loop of the grid-side converter (GSC) via a supplementary PID controller, which is synthesized by transforming the design problem into H-infinity static output feedback (SOF) control methodology. The solution of H-infinity SOF control problem involves satisfying linear matrix inequality (LMI) constraints based on the parameter-dependent Lyapunov function to ensure asymptotic stability such that the minimal H-infinity performance objective is simultaneously accomplished for the entire polytope. The coordinated damping controllers for the multiple wind farms are then designed sequentially by using the proposed approach. Eigenvalue analysis confirms the improved damping characteristics of the closed-loop system for several representative OPs. Afterward, the simulation results, including the performance comparison with existing approaches, validate the higher robustness of the proposed approach for a wide range of operating scenarios.
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Zheng Huang, Kewen Wang, Yi Wang, Jing Han, Jun Liang
2024,12(4):1052-1062, DOI: 10.35833/MPCE.2023.000135
Abstract:
In the existing small-signal stability constrained optimal power flow (SSSC-OPF) algorithms, only the rightmost eigenvalue or eigenvalues that do not satisfy a given threshold, e.g., damping ratio threshold and real-part threshold of eigenvalue, are considered in the small-signal stability constraints. The effect of steady-state, i.e., operating point, changes on eigenvalues is not fully taken into account. In this paper, the small-signal stability constraint that can fully reflect the eigenvalue change and system dynamic performance requirement is formed by analyzing the eigenvalue distribution on the complex plane. The small-signal stability constraint is embedded into the standard optimal power flow model for generation rescheduling. The simultaneous solution formula of the SSSC-OPF is established and solved by the quasi-Newton approach, while penalty factors corresponding to the eigenvalue constraints are determined by the stabilization degree of constrained eigenvalues. To improve the computation speed, a hybrid algorithm for eigenvalue computation in the optimization process is proposed, which includes variable selection for eigenvalue estimation and strategy selection for eigenvalue computation. The effectiveness of the proposed algorithm is tested and validated on the New England 10-machine 39-bus system and a modified practical 68-machine 2395-bus system.
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Zhuorong Wang, Qingxin Shi, Ke Fan, Haiteng Han, Wenxia Liu, Fangxing Li
2024,12(4):1063-1073, DOI: 10.35833/MPCE.2023.000591
Abstract:
Continuous power supply of urban power networks (UPNs) is quite essential for the public security of a city because the UPN acts as the basis for other infrastructure networks. In recent years, UPN is threatened by extreme weather events. An accurate modeling of load loss risk under extreme weather is quite essential for the preventive action of UPN. Considering the forecast intensity of a typhoon disaster, this paper proposes analytical modeling of disaster-induced load loss for preventive allocation of mobile power sources (MPSs) in UPNs. First, based on the topological structure and fragility model of overhead lines and substations, we establish an analytical load loss model of multi-voltage-level UPN to quantify the spatial distribution of disaster-induced load loss at the substation level. Second, according to the projected load loss distribution, a preventive allocation method of MPS is proposed, which makes the best use of MPS and dispatches the limited power supply to most vulnerable areas in the UPN. Finally, the proposed method is validated by the case study of a practical UPN in China.
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2024,12(4):1074-1086, DOI: 10.35833/MPCE.2023.000618
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Battery energy storage stations (BESSs) pose several challenges for both phasor-based differential protection and the newly-proposed time-domain differential protection. These challenges include low sensitivity and even rejection. Besides, the negative impact of various nonideal conditions, including current transformer (CT) saturation, errors, and outliers, on the security of differential protection remains an important problem. Motivated by the aforementioned issues, this study accounts for the trajectory distribution discrepancy on Cartesian plane under various conditions and proposes a time-domain differential protection method. In this paper, the trajectory formed by operating and restraining current samples is developed. Subsequently, after considering different operating states, the fault severity levels, and nonideal conditions, the variances in trajectory distribution between internal and external faults are extensively analyzed. On this basis, the Cartesian plane is divided into operating, uncertainty, and restraining zones. Further, the operating and restraining trajectory indices are meticulously designed and a protection criterion based on these indices is formed to accurately separate internal faults from other events, unaffected by CT saturation, errors, and outliers. The exceptional performance of the proposed protection method is extensively validated through PSCAD simulations and a hardware-in-the-loop testing platform. Regarding the dependability, sensitivity, and security, the proposed protection method outperforms three state-of-the-art differential protection methods.
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2024,12(4):1087-1095, DOI: 10.35833/MPCE.2023.000321
Abstract:
Since the scale and uncertainty of the power system have been rapidly increasing, the computation efficiency of constructing the security region boundary (SRB) has become a prominent problem. Based on the topological features of historical operation data, a sample generation method for SRB identification is proposed to generate evenly distributed samples, which cover dominant security modes. The boundary sample pair (BSP) composed of a secure sample and an unsecure sample is defined to describe the feature of SRB. The resolution, sampling, and span indices are designed to evaluate the coverage degree of existing BSPs on the SRB and generate samples closer to the SRB. Based on the feature of flat distribution of BSPs over the SRB, the principal component analysis (PCA) is adopted to calculate the tangent vectors and normal vectors of SRB. Then, the sample distribution can be expanded along the tangent vector and corrected along the normal vector to cover different security modes. Finally, a sample set is randomly generated based on the IEEE standard example and another new sample set is generated by the proposed method. The results indicate that the new sample set is closer to the SRB and covers different security modes with a small calculation time cost.
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Zhengze Wei, Kaigui Xie, Bo Hu, Yu Wang, Changzheng Shao, Pierluigi Siano, Jun Zhong
2024,12(4):1096-1112, DOI: 10.35833/MPCE.2023.000173
Abstract:
Improving the restoration efficiency of a distribution system is essential to enhance the ability of power systems to deal with extreme events. The distribution system restoration (DSR) depends on the interaction among the electric network (EN), cyber network (CN), and traffic network (TN). However, the coordination of these three networks and co-dispatching of multiple recovery resources have been mostly neglected. This paper proposes a novel DSR framework, which is formulated as a mixed-integer linear programming (MILP) problem. The failures in cyber lines result in cyber blind areas, which restrict the normal operation of remote-controlled switches. To accelerate the recovery process, multiple recovery resources are utilized including electric maintenance crews (EMCs), cyber maintenance crews (CMCs), and emergency communication vehicles (ECVs). Specifically, CMCs and ECVs restore the cyber function of switches in cooperation, and EMCs repair damaged electric lines. The travel time of these three dispatchable resources is determined by TN. The effectiveness and superiority of the proposed framework are verified on the modified IEEE 33-node and 123-node test systems.
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Warnakulasuriya Sonal Prashenajith Fernando, Mostafa Barzegar-Kalashani, Md Apel Mahmud, Shama Naz Islam, Nasser Hosseinzadeh
2024,12(4):1113-1125, DOI: 10.35833/MPCE.2023.000065
Abstract:
An nonlinear model predictive controller (NMPC) is proposed in this paper for compensations of single line-to-ground (SLG) faults in resonant grounded power distribution networks (RGPDNs), which reduces the likelihood of power line bushfire due to electric faults. Residual current compensation (RCC) inverters with arc suppression coils (ASCs) in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG faults. The proposed NMPC is incorporated with the estimation of ASC inductance, where the estimation is carried out based on voltage and current measurements from the neutral point of the distribution network. The compensation scheme is developed in the discrete time using the equivalent circuit of RGPDNs. The proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults, which is verified through both simulations and control hardware-in-the-loop (CHIL) validations. Comparative results are also presented against an integral sliding mode controller (ISMC) by demonstrating the capability of power line bushfire mitigation.
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Behrouz Azimian, Shiva Moshtagh, Anamitra Pal, Shanshan Ma
2024,12(4):1126-1134, DOI: 10.35833/MPCE.2023.000432
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Recently, we demonstrated the success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems. In this paper, we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements. It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations. As such, we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming (MILP) problems. The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted. The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system, both of which are incompletely observed by micro-phasor measurement units.
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Charalampos G. Arsoniadis, Vassilis C. Nikolaidis
2024,12(4):1135-1146, DOI: 10.35833/MPCE.2023.000379
Abstract:
This paper proposes a novel fault location method for overhead feeders, which is based on the direct load flow approach. The method is developed in the phase domain to effectively deal with unbalanced network conditions, while it can also handle distributed generation (DG) units of any type without requiring equivalent models. By utilizing the line series parameters and synchronized or unsynchronized voltage and current phasor measurements taken from the sources, the method reliably identifies the most probable faulty sections. With the aid of an index, the exact faulty section among the multiple candidates is determined. Extensive simulation studies for the IEEE 123-bus test feeder demonstrate that the proposed method accurately estimates the fault position under numerous short-circuit conditions with varying pre-fault system loading conditions, fault resistances, and measurement errors. The proposed method is promising for practical applications due to the limited number of required measurement devices as well as the short computation time.
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Yaqi Sun, Wenchuan Wu, Yi Lin, Hai Huang, Hao Chen
2024,12(4):1147-1158, DOI: 10.35833/MPCE.2023.000760
Abstract:
The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained by specified reliability requirements. The reliability-constrained distribution network planning (RcDNP) problem can be cast as an instance of mixed-integer linear programming (MILP) which involves ultra-heavy computation burden especially for large-scale DNs. In this paper, we propose a parallel computing based solution method for the RcDNP problem. The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination. Then, a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems. The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem. Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition. Under mild conditions, the optimality and convergence of the proposed method are proven. Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-world problems.
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Silu Zhang, Nian Liu, Jianpei Han
2024,12(4):1159-1169, DOI: 10.35833/MPCE.2023.000024
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With the large-scale connection of 5G base stations (BSs) to the distribution networks (DNs), 5G BSs are utilized as flexible loads to participate in the peak load regulation, where the BSs can be divided into base station groups (BSGs) to realize inter-district energy transfer. A Stackelberg game-based optimization framework is proposed, where the distribution network operator (DNO) works as a leader with dynamic pricing for multi-BSGs; while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension. Subsequently, the presence and uniqueness of the Stackelberg equilibrium (SE) are provided. Moreover, differential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling. Finally, through simulation of a practical system, the results show that the DNO operation profit is increased via cutting down the peak load and the operation costs for multi-BSGs are reduced, which reaches a win-win effect.
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Xilin Li, Jingyi Zhang, Zhen Tian, Xiaoming Zha, Wei Wang, Meng Huang, Chong Shao
2024,12(4):1170-1182, DOI: 10.35833/MPCE.2023.000291
Abstract:
With the rapid increase in the installed capacity of renewable energy in modern power systems, the stable operation of power systems with considerable power electronic equipment requires further investigation. In converter-based islanded microgrid (CIM) systems equipped with grid-following (GFL) and grid-forming (GFM) voltage-source converters (VSCs), it is challenging to maintain stability due to the mutual coupling effects between different VSCs and the loss of voltage and frequency support from the power system. In previous studies, quantitative transient stability analysis was primarily used to assess the active power loop of GFM-VSCs. However, frequency and voltage dynamics are found to be strongly coupled, which strongly affects the estimation result of stability boundary. In addition, the varying damping terms have not been fully captured. To bridge these gaps, this paper investigates the transient stability of CIM considering reactive power loop dynamics and varying damping. First, an accuracy-enhanced nonlinear model of the CIM is derived based on the effects of reactive power loop and post-disturbance frequency jump phenomena. Considering these effects will eliminates the risk of misjudgment. The reactive power loop dynamics make the model coefficients be no longer constant and thus vary with the power angle. To evaluate quantitatively the effects of reactive power loop and varying damping on the transient stability of CIM, an iterative criterion based on the equal area criterion theory is proposed. In addition, the effects of parameters on the stable boundary of power system are analyzed, and the dynamic interaction mechanisms are revealed. Simulation and experiment results verify the merits of the proposed method.
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Wei Dong, Fan Zhang, Meng Li, Xiaolun Fang, Qiang Yang
2024,12(4):1183-1193, DOI: 10.35833/MPCE.2023.000386
Abstract:
The intermittency of renewable energy generation, variability of load demand, and stochasticity of market price bring about direct challenges to optimal energy management of microgrids. To cope with these different forms of operation uncertainties, an imitation learning based real-time decision-making solution for microgrid economic dispatch is proposed. In this solution, the optimal dispatch trajectories obtained by solving the optimal problem using historical deterministic operation patterns are demonstrated as the expert samples for imitation learning. To improve the generalization performance of imitation learning and the expressive ability of uncertain variables, a hybrid model combining the unsupervised and supervised learning is utilized. The denoising autoencoder based unsupervised learning model is adopted to enhance the feature extraction of operation patterns. Furthermore, the long short-term memory network based supervised learning model is used to efficiently characterize the mapping between the input space composed of the extracted operation patterns and system state variables and the output space composed of the optimal dispatch trajectories. The numerical simulation results demonstrate that under various operation uncertainties, the operation cost achieved by the proposed solution is close to the minimum theoretical value. Compared with the traditional model predictive control method and basic clone imitation learning method, the operation cost of the proposed solution is reduced by 6.3% and 2.8%, respectively, over a test period of three months.
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Farahnaz Ahmadi, Yazdan Batmani, Hassan Bevrani
2024,12(4):1194-1202, DOI: 10.35833/MPCE.2023.000277
Abstract:
In an autonomous droop-based microgrid, the system voltage and frequency (VaF) are subject to deviations as load changes. Despite the existence of various control methods aimed at correcting system frequency deviations at the secondary control level without any communication network, the challenges associated with these methods and their abilities to simultaneously restore microgrid VaF have not been fully investigated. In this paper, a multi-input multi-output (MIMO) model reference adaptive controller (MRAC) is proposed to achieve VaF restoration while accurate power sharing among distributed generators (DGs) is maintained. The proposed MRAC, without any communication network, is designed based on two methods: droop-based and inertia-based methods. For the microgrid, the suggested design procedure is started by defining a model reference in which the control objectives, such as the desired settling time, the maximum tolerable overshoot, and steady-state error, are considered. Then, a feedback-feedforward controller is established, of which the gains are adaptively tuned by some rules derived from the Lyapunov stability theory. Through some simulations in MATLAB/SimPowerSystem Toolbox, the proposed MRAC demonstrates satisfactory performance.
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Jipeng Gu, Xiaodong Yang, Youbing Zhang, Luyao Xie, Licheng Wang, Wenwei Zhou, Xiaohui Ge
2024,12(4):1203-1216, DOI: 10.35833/MPCE.2023.000119
Abstract:
The unbalanced state of charge (SOC) of distributed energy storage systems (DESSs) in autonomous DC microgrid causes energy storage units (ESUs) to terminate operation due to overcharge or overdischarge, which severely affects the power quality. In this paper, a fuzzy droop control for SOC balance and stability analysis of DC microgrid with DESSs is proposed to achieve SOC balance in ESUs while maintaining a stable DC bus voltage. First, the charge and discharge modes of ESUs are determined based on the power supply requirements of the DC microgrid. One-dimensional fuzzy logic is then applied to establish the relationship between SOC and the droop coefficient Rd in the aforementioned two modes. In addition, when integrated with voltage-current double closed-loop control, SOC balance in different ESUs is realized. To improve the balance speed and precision, an exponential acceleration factor is added to the input variable of the fuzzy controller. Finally, based on the average model of converter, the system-level stability of microgrid is analyzed. MATLAB/Simulink simulation results verify the effectiveness and rationality of the proposed method.
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2024,12(4):1217-1226, DOI: 10.35833/MPCE.2023.000565
Abstract:
Networked microgrids (NMGs) are critical in the accommodation of distributed renewable energy. However, the existing centralized state estimation (SE) cannot meet the demands of NMGs in distributed energy management. The current estimator is also not robust against bad data. This study introduces the concepts of relative error to construct an improved robust SE (IRSE) optimization model with mixed-integer nonlinear programming (MINLP) that overcomes the disadvantage of inaccurate results derived from different measurements when the same tolerance range is considered in the robust SE (RSE). To improve the computation efficiency of the IRSE optimization model, the number of binary variables is reduced based on the projection statistics and normalized residual methods, which effectively avoid the problem of slow convergence or divergence of the algorithm caused by too many integer variables. Finally, an embedded consensus alternating direction of multiplier method (ADMM) distribution algorithm based on outer approximation (OA) is proposed to solve the IRSE optimization model. This algorithm can accurately detect bad data and obtain SE results that communicate only the boundary coupling information with neighbors. Numerical tests show that the proposed algorithm effectively detects bad data, obtains more accurate SE results, and ensures the protection of private information in all microgrids.
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Abdullah Azhar Al-Obaidi, Mohammed Zaki El-Sharafy, Hany E. Z. Farag, Saifullah Shafiq, Ali Al-Awami
2024,12(4):1227-1238, DOI: 10.35833/MPCE.2023.000234
Abstract:
Adopting high penetration levels of electric vehicles (EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to ①
the lack of utility-grade communication systems in many cases such as secondary (low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and ②existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators (DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability. - 1
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Jizhong Zhu, Yuwang Miao, Hanjiang Dong, Shenglin Li, Ziyu Chen, Di Zhang
2024,12(4):1239-1249, DOI: 10.35833/MPCE.2023.000646
Abstract:
In recent years, the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network. However, due to the scarcity of historical data for these new consumers, it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods. This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering, deep learning, and transfer learning technologies to address this issue. To begin, this paper leverages the domain adversarial transfer network. It employs limited data as target domain data and more abundant data as source domain data, thus enabling the utilization of source domain insights for the forecasting task of the target domain. Moreover, a K-shape clustering method is proposed, which effectively identifies source domain data that align optimally with the target domain, and enhances the forecasting accuracy. Subsequently, a composite architecture is devised, amalgamating attention mechanism, long short-term memory network, and seq2seq network. This composite structure is integrated into the domain adversarial transfer network, bolstering the performance of feature extractor and refining the forecasting capabilities. An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically. In the case study, the relative mean square error of the proposed method is within 30 MW, and the mean absolute percentage error is within 2%. A significant improvement in accuracy, compared with other comparative experimental results, underscores the reliability of the proposed method. The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecasting methods.
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Yuqing Bao, Zhonghui Zuo, Xuehua Wu
2024,12(4):1250-1259, DOI: 10.35833/MPCE.2023.000183
Abstract:
Electrical water heaters (EWHs) are important candidates to provide demand-response services. The traditional optimization method for EWHs focuses on the optimization of the electricity consumption, without considering the shifting potential of the water-use activities. This paper proposes an optimization method for EWHs considering the shifting potentials of both the electricity consumption and water-use activities. Considering that the water-use activities could be monolithically shifted, the shifting model of the water-use activities was developed. In addition to the thermodynamic model of the EWH, the optimal scheduling model of the EWH was developed and solved using mixed-integer linear programming. Case studies were performed on a single EWH and aggregate EWHs, demonstrating that the proposed method can shift the water-use activities and therefore increase the load-shifting potential of the EWHs.
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Yingjun Wu, Chengjun Liu, Zhiwei Lin, Zhaorui Chen, Runrun Chen, Yuyang Chen
2024,12(4):1260-1271, DOI: 10.35833/MPCE.2023.000157
Abstract:
Demand response transactions between electric consumers, load aggregators, and the distribution network manager based on the “combination of price and incentive” are feasible and efficient. However, the incentive payment of demand response is quantified based on private information, which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information. This paper proposes a method based on Vickrey-Clark-Groves (VCG) theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive declaration in the demand response transactions. Firstly, a demand response transaction framework with the price-and-incentive combined mode is established to illustrate the deceptive behavior in the demand response transaction. Then, the idea for eradicating deceptive declarations based on VCG theory is given, and a detailed VCG-based mathematical model is constructed following the demand response transaction framework. Further, the proofs of incentive compatibility, individual rationality, cost minimization, and budget balance of the proposed VCG-based method are given. Finally, a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.
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Leijiao Ge, Tianshuo Du, Zhengyang Xu, Luyang Hou, Jun Yan, Yuanliang Li
2024,12(4):1272-1284, DOI: 10.35833/MPCE.2023.000909
Abstract:
The accurate identification of smart meter (SM) fault types is crucial for enhancing the efficiency of operation and maintenance (O&M) and the reliability of power collection systems. However, the intelligent classification of SM fault types faces significant challenges owing to the complexity of features and the imbalance between fault categories. To address these issues, this study presents a fault diagnosis method for SM incorporating three distinct modules. The first module employs a combination of standardization, data imputation, and feature extraction to enhance the data quality, thereby facilitating improved training and learning by the classifiers. To enhance the classification performance, the data imputation method considers feature correlation measurement and sequential imputation, and the feature extractor utilizes the discriminative enhanced sparse autoencoder. To tackle the interclass imbalance of data with discrete and continuous features, the second module introduces an assisted classifier generative adversarial network, which includes a discrete feature generation module. Finally, a novel Stacking ensemble classifier for SM fault diagnosis is developed. In contrast to previous studies, we construct a two-layer heuristic optimization framework to address the synchronous dynamic optimization problem of the combinations and hyperparameters of the Stacking ensemble classifier, enabling better handling of complex classification tasks using SM data. The proposed fault diagnosis method for SM via two-layer stacking ensemble optimization and data augmentation is trained and validated using SM fault data collected from 2010 to 2018 in Zhejiang Province, China. Experimental results demonstrate the effectiveness of the proposed method in improving the accuracy of SM fault diagnosis, particularly for minority classes.
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Zetian Zheng, Shaowei Huang, Jun Yan, Qiangsheng Bu, Chen Shen, Mingzhong Zheng, Ye Liu
2024,12(4):1285-1294, DOI: 10.35833/MPCE.2023.000101
Abstract:
The oscillation phenomena associated with the control of voltage source converters (VSCs) are concerning, making it crucial to locate the sources of such oscillations and suppress the oscillations. Therefore, this paper presents a location scheme based on the energy structure and nonlinearity detection. The energy structure, which conforms to the principle of the energy-based method and dissipativity theory, is developed to describe the transient energy flow for VSCs, based on which a defined characteristic quantity is implemented to narrow the scope for locating the sources of oscillations. Moreover, based on the self-sustained oscillation characteristics of VSCs, an index for nonlinearity detection is applied to locate the VSCs that produce the oscillation energy. The combination of the energy structure and nonlinearity detection distinguishes the contributions of different VSCs to the oscillation. The results of a case study implemented by the PSCAD/EMTDC simulation validate the proposed scheme.
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2024,12(4):1295-1308, DOI: 10.35833/MPCE.2023.000519
Abstract:
Previous studies have demonstrated that disharmony among voltage-source-controlled units (VSCUs) may occur on an alternating current (AC) transmission or distribution line under steady-state operating conditions (SSOCs) or quasi-static operating conditions (QSSOCs). As the studies on frequency disharmony have been expanded to multiple disharmonized VSCUs in the local power grid, its adverse effects on AC lines and equivalent load (EL) at the bus without active voltage control ability (non-active bus) need to be investigated further. Considering the locality of disharmony and common topological connections among VSCUs, this paper adopts a Y-type three-terminal local power grid (LPG) as the research object. The disharmony among the three VSCUs is discussed. Firstly, for the load at non-active bus, the formulas for single-phase instantaneous voltage, load current, load power, as well as average power under disharmony operating conditions (DOCs) are derived. The characteristic indicators of the above electrical quantities are defined, which can measure the amplification and reduction degrees of the above electrical quantities before and after disharmony. Secondly, for the line directly connected to VSCUs, the formulas for single-phase instantaneous line current and power and the average power under DOCs are derived. The characteristic indicators of power flow are defined, which can be used to quantify the peak amplification impact of oscillation before and after disharmony. Finally, the case study on the Y-type three-terminal LPG under the single-disharmony and the multi-disharmony switching scenarios indicates that the long-period power oscillation caused by disharmony may occur in the load flow at the non-active bus and the line flow. The oscillation causes a serious decrease in load capability and a significant amplification of the peak of line power oscillation.
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Qin Jiang, Ruiting Xu, Baohong Li, Xiang Chen, Yue Yin, Tianqi Liu, Frede Blaabjerg
2024,12(4):1309-1319, DOI: 10.35833/MPCE.2023.000340
Abstract:
In line commutated converter based high-voltage direct current (LCC-HVDC) transmission systems, the transformer saturation can induce harmonic instability, which poses a serious threat to the safe operation of the power system. However, the nonlinear characteristics of the power grids introduced by the transformer saturation considerably limit the application of the conventional analysis methods. To address the issue, this paper derives a linear model for the transformer saturation caused by the DC current due to the converter modulation. Afterwards, the nonlinear characteristics of power grids with the transformer saturation is described by a complex valued impedance matrix. Based on the derived impedance matrix, the system harmonic stability is analyzed and the mechanism of the transformer saturation induced harmonic instability is revealed. Finally, the sensitivity analysis is conducted to find the key factors that influence the system core saturation instability. The proposed impedance model is verified by the electromagnetic transient simulation, and the simulation results corroborate the effectiveness of the proposed impedance model.
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Bin Liu, Julio H. Braslavsky, Nariman Mahdavi
2024,12(4):1320-1326, DOI: 10.35833/MPCE.2023.000653
Abstract:
Dynamic operating envelopes (DOEs), as a key enabler to facilitate distributed energy resource (DER) integration, have attracted increasing attention in the past years. However, uncertainties, which may come from load forecasting errors or inaccurate network parameters, have been rarely discussed in DOE calculation, leading to compromised quality of the hosting capacity allocation strategy. This letter studies how to calculate DOEs that are immune to such uncertainties based on a linearised unbalanced three-phase optimal power flow (UTOPF) model. With uncertain parameters constrained by norm balls, formulations for calculating robust DOEs (RDOEs) are presented along with discussions on their tractability. Two cases, including a 2-bus illustrative network and a representative Australian network, are tested to demonstrate the effectiveness and efficiency of the proposed approach.
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Yang Wang, Fei Xia, Ying Wang, Xianyong Xiao
2024,12(4):1327-1332, DOI: 10.35833/MPCE.2023.000093
Abstract:
This study presents a harmonic transfer function (HTF) based single-input single-output (SISO) impedance modeling method. The method converts an HTF from phase domain to sequence domain and then transforms it into an SISO impedance while preserving the frequency coupling information of different sequences and different harmonics. Applications of this method to a line-commutated converter based high-voltage direct current (LCC-HVDC) system are presented. The results demonstrate the accuracy of the derived SISO impedance, and a truncation-order selection is suggested. The case study shows that the proposed method facilitates simpler impedance measurements and associated stability analysis.
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Volume 12, Issue 4, 2024
>Review
>Original Paper
>Short Letter
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Shuwei Xu, Wenchuan Wu, Bin Wang, Yue Yang
2023,11(6):1734-1745, DOI: 10.35833/MPCE.2022.000526
Abstract:
This paper proposes a probabilistic energy and reserve co-dispatch (PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk (VaR) representation. Specifically, the VaR formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.
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Yonghui Sun, Yan Zhou, Sen Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, George Sideratos, Nikos Hatziargyriou, Pierluigi Siano
2023,11(5):1450-1461, DOI: 10.35833/MPCE.2022.000577
Abstract:
Regional photovoltaic (PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals (PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granule-based clustering (GC) and direct optimization programming (DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction (NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples’ utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.
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Dajun Du, Minggao Zhu, Xue Li, Minrui Fei, Siqi Bu, Lei Wu, Kang Li
2023,11(3):727-743, DOI: 10.35833/MPCE.2021.000604
Abstract:
Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges. The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems (CPPSs). This paper presents a comprehensive review of some of the latest attack detection and defense strategies. Firstly, the vulnerabilities brought by some new information and communication technologies (ICTs) are analyzed, and their impacts on the security of CPPSs are discussed. Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework, and their features and negative impacts are discussed. Secondly, two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed, and their benefits and drawbacks are discussed. Moreover, two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed. Finally, the trends and challenges in attack detection and defense strategies in CPPSs are provided.
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2023,11(3):705-713, DOI: 10.35833/MPCE.2023.000073
Abstract:
Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
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Chengjin Ye, Libang Guo, Yi Ding, Ming Ding, Peng Wang, Lei Wang
2023,11(2):662-673, DOI: 10.35833/MPCE.2021.000491
Abstract:
With various components and complex topologies, the applications of high-voltage direct current (HVDC) links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates (ALRs) is established based on its reliability network. Then, dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system (RTS) with an HVDC link is adopted to verify the proposed approach.
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James Naughton, Shariq Riaz, Michael Cantoni, Xiao-Ping Zhang, Pierluigi Mancarella
2023,11(2):553-566, DOI: 10.35833/MPCE.2022.000324
Abstract:
Hydrogen is being considered as an important option to contribute to energy system decarbonization. However, currently its production from renewables is expensive compared with the methods that utilize fossil fuels. This paper proposes a comprehensive optimization-based techno-economic assessment of a hybrid renewable electricity-hydrogen virtual power plant (VPP) that boosts its business case by co-optimizing across multiple markets and contractual services to maximize its profits and eventually deliver hydrogen at a lower net cost. Additionally, multiple possible investment options are considered. Case studies of VPP placement in a renewable-rich, congested area of the Australian network and based on real market data and relevant sensitivities show that multi-market participation can significantly boost the business case for cleaner hydrogen. This highlights the importance of value stacking for driving down the cost of cleaner hydrogen. Due to the participation in multiple markets, all VPP configurations considered are found to be economically viable for a hydrogen price of 3 AUD$/kg (2.25 USD$/kg), which has been identified as a threshold value for Australia to export hydrogen at a competitive price. Additionally, if the high price volatility that has been seen in gas prices in 2022 (and by extension electricity prices) continues, the flexibility of hybrid VPPs will further improve their business cases.
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Yang Peng, Zhi Wu, Wei Gu, Suyang Zhou, Pengxiang Liu
2023,11(2):468-478, DOI: 10.35833/MPCE.2021.000615
Abstract:
Micro-phasor measurement units (μPMUs) with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations. Therefore, this paper investigates the problem of how to place a limited number of μPMUs to improve the state estimation accuracy. Combined with pseudo-measurements and supervisory control and data acquisition (SCADA) measurements, an optimal μPMU placement model is proposed based on a two-step state estimation method. The E-optimal experimental criterion is utilized to measure the state estimation accuracy. The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming (MISDP) problem, whose optimal solution can be obtained by using the improved Benders decomposition method. Simulations on several systems are carried out to evaluate the effective performance of the proposed model.
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Haftu Tasew Reda, Adnan Anwar, Abdun Mahmood, Naveen Chilamkurti
2023,11(2):455-467, DOI: 10.35833/MPCE.2020.000827
Abstract:
In a smart grid, state estimation (SE) is a very important component of energy management system. Its main functions include system SE and detection of cyber anomalies. Recently, it has been shown that conventional SE techniques are vulnerable to false data injection (FDI) attack, which is a sophisticated new class of attacks on data integrity in smart grid. The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model, which is different from the traditional weighted least square based SE model. This SE model has a number of unique advantages compared with traditional SE models. First, the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors. Second, the proposed SE model can learn the actual power system states. Finally, this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors. The proposed FDI attack detection technique is evaluated on a number of standard bus systems. The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-of-the-art techniques. Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.
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Fabricio Andrade Mourinho, Tatiana Mariano Lessa Assis
2023,11(2):412-420, DOI: 10.35833/MPCE.2022.000365
Abstract:
This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency（RoCoF） protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.
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Benedict J. Mortimer, Amandus Dominik Bach, Christopher Hecht, Dirk Uwe Sauer, Rik W. De Doncker
2022,10(6):1750-1760, DOI: 10.35833/MPCE.2021.000181
Abstract:
The current increase in the number of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage of charging options. In order to make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. This paper gives an overview of the differences in the utilization across the public charging infrastructure in Germany. To this end, a dataset on the utilization of 21164 public and semi-public charging stations in Germany is evaluated. The installation and operating costs of various charging stations are modeled and economically evaluated in combination with the utilization data. It is shown that in 2019-2020, the average utilization in Germany was rather low, albeit with striking regional differences. We consider future scenarios allowing the regional development forecasting of economic viability. It is demonstrated that a growth in electric mobility of 20%-30% per year leads to a large number of economically feasible charging parks in urban agglomeration areas.
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Ziyu Chen, Jizhong Zhu, Shenglin Li, Yun Liu, Tengyan Luo
2022,10(6):1576-1587, DOI: 10.35833/MPCE.2021.000546
Abstract:
Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples.
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Sichen Li, Weihao Hu, Di Cao, Tomislav Dragičević, Qi Huang, Zhe Chen, Frede Blaabjerg
2022,10(3):719-730, DOI: 10.35833/MPCE.2020.000460
Abstract:
A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner ’s commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods. - 1
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Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang
2022,10(2):345-360, DOI: 10.35833/MPCE.2021.000058
Abstract:
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e.g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
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Luka Strezoski, Harsha Padullaparti, Fei Ding, Murali Baggu
2022,10(2):277-285, DOI: 10.35833/MPCE.2021.000667
Abstract:
With the rapid integration of distributed energy resources (DERs), distribution utilities are faced with new and unprecedented issues. New challenges introduced by high penetration of DERs range from poor observability to overload and reverse power flow problems, under-/over-voltages, maloperation of legacy protection systems, and requirements for new planning procedures. Distribution utility personnel are not adequately trained, and legacy control centers are not properly equipped to cope with these issues. Fortunately, distribution energy resource management systems (DERMSs) are emerging software technologies aimed to provide distribution system operators (DSOs) with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources. However, as DERMS technology is still emerging, its definition is vague and can refer to very different levels of software hierarchies, spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems (called utility DERMS). Although they are all frequently simply called DERMS, these software technologies have different sets of tools and aim to provide different services to different stakeholders. This paper explores how these different software technologies can complement each other, and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.
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- 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
