Journal of Modern Power Systems and Clean Energy
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ISSN 2196-5625 CN 32-1884/TK

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  • Small-signal Stability Criteria in Power Electronics-dominated Power Systems: A Comparative Review
    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.
  • What May Future Electricity Markets Look Like?
    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.
  • Digital Twin Empowered PV Power Prediction
    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.
  • A Review on Cybersecurity Analysis, Attack Detection, and Attack Defense Methods in Cyber-physical Power Systems
    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.
  • Comprehensive Optimization-based Techno-economic Assessment of Hybrid Renewable Electricity-hydrogen Virtual Power Plants
    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
  • Impact of Cascade Disconnection of Distributed Energy Resources on Bulk Power System Stability: Modeling and Mitigation Requirements
    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.
  • Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging
    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.
  • Modeling and Simulation of Hydrogen Energy Storage System for Power-to-gas and Gas-to-power Systems
    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.
  • Data-driven Approach for State Prediction and Detection of False Data Injection Attacks in Smart Grid
    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.
  • A Review on Challenges in DC Microgrid Planning and Implementation
    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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    Volume 13, Issue 2, 2025

    >Original Paper
  • Dynamic Nonlinear Droop-based Fast Frequency Regulation for Power Systems with Flexible Resources Using Meta-reinforcement Learning Approach

    Yuxin Ma, Zechun Hu, Yonghua Song

    2025,13(2):379-390, DOI: 10.35833/MPCE.2024.000062

    Abstract: (5) HTML (0) PDF (2.93 MKB) (6)

    Abstract:

    The increasing penetration of renewable energy resources and reduced system inertia pose risks to frequency security of power systems, necessitating the development of fast frequency regulation (FFR) methods using flexible resources. However, developing effective FFR policies is challenging because different power system operating conditions require distinct regulation logics. Traditional fixed-coefficient linear droop-based control methods are suboptimal for managing the diverse conditions encountered. This paper proposes a dynamic nonlinear P-f droop-based FFR method using a newly established meta-reinforcement learning (meta-RL) approach to enhance control adaptability while ensuring grid stability. First, we model the optimal FFR problem under various operating conditions as a set of Markov decision processes and accordingly formulate the frequency stability-constrained meta-RL problem. To address this, we then construct a novel hierarchical neural network (HNN) structure that incorporates a theoretical frequency stability guarantee, thereby converting the constrained meta-RL problem into a more tractable form. Finally, we propose a two-stage algorithm that leverages the inherent characteristics of the problem, achieving enhanced optimality in solving the HNN-based meta-RL problem. Simulations validate that the proposed FFR method shows superior adaptability across different operating conditions, and achieves better trade-offs between regulation performance and cost than benchmarks.

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  • Energy Equity-constrained Tie-line Scheduling Model in Interconnected Systems

    Sufan Jiang, Fangxing （Fran） Li, Xiaofei Wang, Chenchen Li

    2025,13(2):391-402, DOI: 10.35833/MPCE.2024.000529

    Abstract: (2) HTML (0) PDF (2.51 MKB) (3)

    Abstract:

    Energy equity refers to the condition in which access to the cleaner energy required by individuals is equally available to all. To relieve the energy expenditures-the key component in the concept of energy equity–of low-income communities, governments worldwide have imposed caps on soaring energy prices. However, the inherent mechanisms within the operational schedule remain undiscussed. This paper innovatively provides guidelines for operators to embed energy burden policies into the bulk power system model, by answering two critical questions. ①What is the impact on system price pattern when embedding the locational price constraints? ② How to reformulate the tie-line schedule to meet the equity thresholds? Consequently, a novel bi-level energy equity-constrained tie-line scheduling model is proposed. The conventional economic dispatch is solved at the upper level, and then a preliminary operational schedule is given to the lower level, where we propose an energy equity slackness component variable to evaluate the gap between preliminary and desired equity-satisfied operational schedules. The implicit constraints on the price are converted into explicit feasibility cuts with dual theory. Case studies on test systems demonstrate the reduced energy expenditure for underserved communities, and the optimal tie-line schedule is also validated.

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  • Online Tracking of Local Damping in Power Systems with High Proportion of Renewable Energy Sources Under Ambient Data

    Zhenglong Sun, Zewei Li, Hao Yang, Lixin Wang, Bo Wang, Chao Pan, Cheng Liu, Guowei Cai

    2025,13(2):403-414, DOI: 10.35833/MPCE.2024.000169

    Abstract: (2) HTML (0) PDF (3.90 MKB) (4)

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    As the proportion of renewable energy sources continues to increase, the local damping contributions of sources in power system decrease, posing a challenge to the power system stability. Therefore, online tracking of the damping contributions of each source is crucial for the prevention of low-frequency oscillations. This paper proposes an online tracking method of local damping under ambient data. The proposed method is based on dissipation energy spectrum analysis (DESA) and the energy dissipation factor (EDF). First, the feasibility of using frequency-domain analysis for the dissipation energy of generator is analyzed. The frequency spectral function of dissipation energy of generator is then derived by integrating with Parseval’s theorem, and the EDF is defined. Second, the generator energy dissipation factor (GEDF) for the dominant oscillation mode frequency is established. The modal information of the dominant oscillation in the power system is obtained through DESA. The relationship between the frequency spectral function and eigenvalues is also established. Finally, an online tracking method of local damping is proposed based on DESA and GEDF. The effectiveness of the proposed method is validated through simulations on a four-machine 11-bus power system and an actual power system in Northwest China.

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  • Multi-stage Robust Unit Commitment with Discrete Load Shedding Based on Partially Affine Policy and Two-stage Reformulation

    Zhongjie Guo, Jiayu Bai, Wei Wei, Haifeng Qiu, Weihao Hu

    2025,13(2):415-425, DOI: 10.35833/MPCE.2024.000202

    Abstract: (2) HTML (0) PDF (2.08 MKB) (2)

    Abstract:

    This paper studies the problem of multi-stage robust unit commitment with discrete load shedding. In the day-ahead phase, the on-off status of thermal units is scheduled. During each period of real-time dispatch, the output of thermal units and the action of load shedding are determined, and the discrete choice of load shedding corresponds to the practice of tripping substation outlets. The entire decision-making process is formulated as a multi-stage adaptive robust optimization problem with mixed-integer recourse, whose solution takes three steps. First, we propose and apply partially affine policy, which is optimized ahead of the day and restricts intertemporal dispatch variables as affine functions of previous uncertainty realizations, leaving remaining continuous and binary dispatch variables to be optimized in real time. Second, we demonstrate that the resulting model with partially affine policy can be reformulated as a two-stage robust optimization problem with mixed-integer recourse. Third, we modify the standard nested column-and-constraint generation algorithm to accelerate the inner loops by warm start. The modified algorithm solves the two-stage problem more efficiently. Case studies on the IEEE 118-bus system verify that the proposed partially affine policy outperforms conventional affine policy in terms of optimality and robustness; the modified nested column-and-constraint generation algorithm significantly reduces the total computation time; and the proposed method balances well optimality and efficiency compared with state-of-the-art methods.

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  • A Continuous Operating Envelope for Managing Intra-interval Fluctuations: Modeling and Solution

    Menghan Zhang, Zhifang Yang, Juan Yu, Wenyuan Li

    2025,13(2):426-438, DOI: 10.35833/MPCE.2024.000636

    Abstract: (1) HTML (0) PDF (3.66 MKB) (3)

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    Maintaining a continuous power balance is crucial for ensuring operational feasibility in power systems. However, due to forecasting difficulties and computational limitations, economic dispatch often relies on discrete interval horizons, which fail to guarantee feasibility within each interval. This paper introduces the concept of a continuous operating envelope for managing intra-interval fluctuations, delineating the range within which fluctuations remain manageable. We propose a parametric programming model to construct the envelope, represented as a polytope that accounts for both timescale and fluctuation dimensions. To address the computational challenges inherent in the parametric programming model, we develop a fast solution method to provide an approximated polytope. The approximated polytope, initially derived from lower-dimensional projections, represents a subset of the exact polytope that ensures operational feasibility. Additionally, we apply a polytope expansion strategy in the original dimensions to refine the approximated polytope, bringing the approximation closer to the exact polytope. Case studies on an illustrative 5-bus and a utility-scale 661-bus system demonstrate that the method effectively and stably provides a continuous operating envelope, particularly for high-dimensional problems.

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  • Torsional Oscillation Damping Analysis and Suppression Strategy for PMSG-based Wind Generation System

    Yizhuo Ma, Graduate, Jin Xu, Guojie Li, Keyou Wang

    2025,13(2):439-451, DOI: 10.35833/MPCE.2024.000219

    Abstract: (1) HTML (0) PDF (6.84 MKB) (5)

    Abstract:

    External disturbances can induce torsional oscillation with weak damping in the shaft system of permanent magnet synchronous generators (PMSGs) based wind generation system, thereby inducing low-frequency oscillations. However, the influence of electromagnetic torque on the shaft system damping and corresponding parameter laws have been scarcely explored. We define the electrical damping coefficient as a quantitative measure for the influence of electromagnetic torque on the shaft system damping. The torsional oscillation damping characteristics of the shaft system under vector control are analyzed, and the transfer function for electromagnetic torque and speed is derived. Additionally, we elucidate the mechanism by which the electromagnetic torque influences the shaft system damping. Simultaneously, laws describing the influence of wind speed, system parameters, and control parameters on the torsional oscillation damping are analyzed. Accordingly, the optimal damping angle of the shaft system a torsional oscillation suppression strategy is proposed to compensate for with uncertainty in the parameters affecting damping. The studied system is modeled using MATLAB/Simulink, and the simulation results validate the effectiveness of the theoretical analysis and proposed torsional oscillation suppression strategy.

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  • DR-MMC Hub Based Hybrid AC/DC Collection and HVDC Transmission System for Large-scale Offshore Wind Farms

    Wang Xiang, Mingrui Yang, Jinyu Wen

    2025,13(2):452-461, DOI: 10.35833/MPCE.2024.000229

    Abstract: (2) HTML (0) PDF (3.23 MKB) (4)

    Abstract:

    Conventional offshore wind farm (OWF) integration systems typically employ AC cables to gather power to a modular multilevel converter (MMC) platform, subsequently delivering it to onshore grids through high-voltage direct current (HVDC) transmission. However, scaling up the capacity of OWFs introduces significant challenges due to the high costs associated with AC collection cables and offshore MMC platforms. This paper proposes a diode rectifier (DR)-MMC hub based hybrid AC/DC collection and HVDC transmission system for large-scale offshore wind farms. The wind farms in proximity to the offshore converter platform utilize AC collection, while distant wind farms connect to the platform using DC collection. The combined AC/DC power is then transmitted to the offshore DR-MMC hub platform. The topology and operation principle of the DR-MMC hub as well as the integration system are presented. Based on the operational characteristics, the capacity design method for DR-MMC hub is proposed. And the control and startup strategies of the integration system are designed. Furthermore, an economic comparison with the conventional MMC-HVDC based offshore wind power integration system is conducted. Finally, the technical feasibility of the proposed integration scheme is verified through PSCAD/EMTDC simulation with the integration scale of 2 GW.

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  • A Dynamic Equivalent Method for PMSG Based Wind Farms Under Asymmetrical Faults

    Dongsheng Li, Zetian Zheng, Chen Shen

    2025,13(2):462-474, DOI: 10.35833/MPCE.2023.001024

    Abstract: (1) HTML (0) PDF (9.05 MKB) (5)

    Abstract:

    In this paper, a dynamic equivalent method applicable to the direct-drive permanent magnet synchronous generator (PMSG) based wind farms under asymmetrical faults is proposed. Firstly, PMSGs are clustered based on their different active power characteristics under asymmetrical faults. Further, single-machine equivalent models (SMEMs) are constructed for different clusters of PMSGs. In particular, an SMEM with multi-segmented slope recovery is introduced for PMSGs with ramp recovery characteristics. Further, a collector network equivalent method for wind farms applicable to both symmetrical and asymmetrical faults is presented. Moreover, an iterative simulation method is used to gain the required clustering indicators before the fault actually occurs. Eventually, the effectiveness of the proposed dynamic equivalent method is verified on a modified IEEE 39-bus system.

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  • Learning-aided Collaborative Optimization of Power, Hydrogen, and Transportation Networks

    Sheng Chen, Hao Cheng, Si Lv, Zhinong Wei, Peiyue Li, Jiahui Jin

    2025,13(2):475-487, DOI: 10.35833/MPCE.2024.000563

    Abstract: (1) HTML (0) PDF (2.98 MKB) (4)

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    The gradual replacement of gasoline vehicles with electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) in recent years has provided a growing incentive for the collaborative optimization of power distribution network (PDN), urban transportation network (UTN), and hydrogen distribution network (HDN). However, an appropriate collaborative optimization framework that addresses the prevalent privacy concerns has yet to be developed, and a sufficient pool of system operators that can competently operate all three networks has yet to be obtained. This study proposes a differentiated taxation-subsidy mechanism for UTNs, utilizing congestion tolls and subsidies to guide the independent traffic flow of EVs and HFCVs. An integrated optimization model for this power-hydrogen-transportation network is established by treating these vehicles and the electrolysis equipment as coupling bridges. We then develop a learning-aided decoupling approach to determine the values of the coupling variables acting among the three networks to ensure the economic feasibility of collaborative optimization. This approach effectively decouples the network, allowing it to operate and be optimized independently. The results for a numerical simulation of a coupled system composed of a IEEE 33-node power network, 13-node Nguyen-Dupuis transportation network, and 20-node HDN demonstrate that the proposed learning-aided approach provides nearly equivalent dispatching results as those derived from direct solution of the physical models of the coupled system, while significantly improving the computational efficiency.

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  • Operational Coordination Optimization of Electricity and Natural Gas Networks Based on Sequential Symmetrical Second-order Cone Programming

    Liang Min, Chengwei Lou, Jin Yang, James Yu, Zhibin Yu

    2025,13(2):488-499, DOI: 10.35833/MPCE.2023.000750

    Abstract: (1) HTML (0) PDF (2.55 MKB) (2)

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    The variable and unpredictable nature of renewable energy generation (REG) presents challenges to its large-scale integration and the efficient and economic operation of the electricity network, particularly at the distribution level. In this paper, an operational coordination optimization method is proposed for the electricity and natural gas networks, aiming to overcome the identified negative impacts. The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants. A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption. This method allows for the full consideration of unbalanced operational scenarios. Meanwhile, the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines. The sequential symmetrical second-order cone programming (SS-SOCP) method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process. The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network. The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost, the curtailment of installed REG, the voltage imbalance of three-phase power system, and the overall carbon emissions.

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  • Node Power Injection Modification Model Based on Direct Derivation for Lossy Power Flow in Hybrid AC-DC Distribution Networks

    Hao Wang, Changzheng Shao, Yu Wang, Bo Hu, Kaigui Xie, Pierluigi Siano

    2025,13(2):500-513, DOI: 10.35833/MPCE.2024.000059

    Abstract: (1) HTML (0) PDF (2.30 MKB) (3)

    Abstract:

    Lossy power flow naturally extends lossless linear power flow to lossy distribution networks, further improving the accuracy of approximate computation and analysis. However, these enhanced versions are only applicable at the alternating current (AC) transmission level, and the accuracy is limited in distribution networks, especially in hybrid AC-direct current (DC) distribution networks. In this paper, we revisit the lossy power flow model and extend it to hybrid AC-DC distribution networks with multi-terminal voltage source converters. The proposed lossy power flow model can be reformulated as an iteration problem with node power injection as the fixed point. For this purpose, a node power injection modification model based on direct derivation is proposed by exploiting the negligibility of the phase angle differences, and iteratively solving lossy power flows for both AC and DC sub-networks. For coupling devices, to guarantee that the power flow is matched on both AC and DC sides, we formulate a rigorous fixed-point problem to solve the lossy power flow of voltage source converters. Finally, the high accuracy and computational efficiency of the proposed model are verified on multiple test cases.

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  • A Flexibility Scheduling Method for Distribution Network Based on Robust Graph DRL Against State Adversarial Attacks

    Ziyang Yin, Shouxiang Wang, Qianyu Zhao

    2025,13(2):514-526, DOI: 10.35833/MPCE.2024.000409

    Abstract: (1) HTML (0) PDF (2.50 MKB) (4)

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    In the context of large-scale photovoltaic integration, flexibility scheduling is essential to ensure the secure and efficient operation of distribution networks (DNs). Recently, deep reinforcement learning (DRL) has been widely applied to scheduling problems. However, most methods neglect the vulnerability of DRL to state adversarial attacks such as load redistribution attacks, significantly undermining its security and reliability. To this end, a flexibility scheduling method is proposed based on robust graph DRL (RoGDRL). A flexibility gain improvement model considering temperature-dependent resistance is first proposed, which considers weather factors as additional variables to enhance the precision of flexibility analysis. Based on this, a state-adversarial two-player zero-sum Markov game (SA-TZMG) model is proposed, which converts the robust DRL scheduling problem into a Nash equilibrium problem. The proposed SA-TZMG model considers the physical constraints of state attacks that guarantee the maximal flexibility gain for the defender when confronted with the most sophisticated and stealthy attacker. A two-stage RoGDRL algorithm is proposed, which introduces the graph sample and aggregate (GraphSAGE) driven soft actor-critic to capture the complex feature about the neighbors of nodes and their properties via inductive learning, thereby solving the Nash equilibrium policies more efficiently. Simulations based on the modified IEEE 123-bus system demonstrates the efficacy of the proposed method.

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  • Communication-aware Restoration of Smart Distribution Grids Based on Optimal Allocation of Resilience Resources

    Youba Nait-Belaid, Yiping Fang, Zhiguo Zeng, Patrick Coudray, Anne Barros

    2025,13(2):527-539, DOI: 10.35833/MPCE.2024.000015

    Abstract: (2) HTML (0) PDF (2.33 MKB) (3)

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    Although power grids have become safer with increased situational awareness, major extreme events still pose reliability and resilience challenges, primarily at the distribution level, due to increased vulnerabilities and limited recovery resources. Information and communication technologies (ICTs) have introduced new vulnerabilities that have been widely investigated in previous studies. These vulnerabilities include remote device failures, communication channel disturbances, and cyberattacks. However, only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat. This paper proposes a communication-aware restoration approach of smart distribution grids, which leverages power-telecom interdependencies to determine the optimal restoration strategies. The states of grid-energized telecom points are tracked to provide the best restoration actions, which are enabled through the resilience resources of repair, manual switching, remote reconfiguration, and distributed generators. As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies, different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations. System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming. Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources. The existing disparity between overhead and underground power line configurations is also quantified.

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  • Optimal Power Dispatch of Active Distribution Network and P2P Energy Trading Based on Soft Actor-critic Algorithm Incorporating Distributed Trading Control

    Yongjun Zhang, Jun Zhang, Guangbin Wu, Jiehui Zheng, Dongming Liu, Yuzheng An

    2025,13(2):540-551, DOI: 10.35833/MPCE.2024.000471

    Abstract: (1) HTML (0) PDF (4.36 MKB) (4)

    Abstract:

    Peer-to-peer (P2P) energy trading in active distribution networks (ADNs) plays a pivotal role in promoting the efficient consumption of renewable energy sources. However, it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests. Hence, this paper proposes a soft actor-critic algorithm incorporating distributed trading control (SAC-DTC) to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers. First, the soft actor-critic (SAC) algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost, and the primary environmental information of the ADN at this point is published to prosumers. Then, a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues. Subsequently, the results of trading are encrypted based on the differential privacy technique and returned to the ADN. Finally, the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning. Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost, boosts the P2P market revenue, maximizes the social welfare, and exhibits high computational accuracy, demonstrating its practical application to the operation of power systems and power markets.

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  • Probabilistic Small-signal Stability Assessment and Cooperative Control for Interconnected Microgrids via Back-to-back Converters

    Yuhong Wang, Xinyao Wang, Jianquan Liao, Miaohong Su, Yongyue Liu

    2025,13(2):552-563, DOI: 10.35833/MPCE.2024.000449

    Abstract: (1) HTML (0) PDF (6.35 MKB) (5)

    Abstract:

    The flexible interconnection of microgrids (MGs) adopting back-to-back converters (BTBCs) has emerged as a new development trend in the field of MGs. This approach enables larger-scale integration and higher utilization of distributed renewable energy sources (RESs). However,their stability characteristics are very different from single MG due to the control characteristics of flexible interconnection. Meanwhile, the uncertainty and stochastic dependence structures of RESs and loads create challenges for stability analysis and cooperative control. In this paper, a probabilistic small-signal stability assessment and cooperative control framework is proposed for interconnected MGs via BTBCs. First, a cooperative control architecture for MGs is constructed. Then, a small-signal model of interconnected MGs via BTBCs containing primary control and secondary control is developed. This model facilitates the analysis of the impacts of BTBCs and various control strategies on the system stability. Subsequently, Copula functions and polynomial chaos expansion (PCE) are combined to achieve the probabilistic small-signal stability assessment. On this basis, the parameters of the cooperative control are optimized, enhancing the robustness of interconnected MGs via BTBCs. Finally, a case of interconnected MGs via BTBCs are built in MATLAB/Simulink to verify the accuracy and effectiveness of the proposed framework.

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  • Decentralized Frequency Restoration and Power Oscillation Damping Control for Islanded Microgrids with Multiple VSGs

    Xueping Li, Sheng Huang, Zili Wang, Zhijie Lian, Yinpeng Qu, Yandong Chen, Derong Luo

    2025,13(2):564-573, DOI: 10.35833/MPCE.2024.000255

    Abstract: (1) HTML (0) PDF (8.24 MKB) (6)

    Abstract:

    Traditional virtual synchronous generator (VSG) suffers from frequency steady-state deviation in islanded microgrids, which negatively affects the frequency-sensitive loads. Moreover, similar to the synchronous generator, VSG introduces active power oscillation, especially under the condition of multiple parallel VSGs, which may cause overload or damage to the VSG because of its low overcurrent capability as a power electronic inverter. To address these issues, a decentralized frequency restoration and power oscillation damping control method is proposed in this paper, in which the global variable characteristic of the microgrid frequency is considered to restore it to the rated value while ensuring precise active power sharing. Moreover, the proposed control method can dampen the power oscillation during load disturbance without affecting the steady-state characteristics. In addition, the fully decentralized manner obviates the requirement for communication networks, thereby considerably reducing the communication burden and improving system reliability. Finally, simulations and experiments are conducted to validate the effectiveness of the proposed control method.

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  • Self-organizing Energy Management Modeling for Multi-microgrids in Contingencies

    Jiachen Chen, Zhong Chen, Qiang Xing

    2025,13(2):574-584, DOI: 10.35833/MPCE.2024.000007

    Abstract: (1) HTML (0) PDF (3.83 MKB) (4)

    Abstract:

    Contingencies, such as behavior shifts of microgrid operators (MGOs) and abrupt weather fluctuations, significantly impact the economic operations of multi-microgrids (MMGs). To address these contingencies and enhance the economic and autonomous performance of MGOs, a self-organizing energy management modeling approach is proposed. A second-order stochastic dynamical equation (SDE) is developed to accurately characterize the self-organizing evolution of the operating cost of MGO incurred by contingencies. Firstly, an operating model of MMG relying on two random graph-driven information matrices is constructed and the order parameters are introduced to extract the probabilistic properties of variations in operating cost. Subsequently, these order parameters, which assist individuals in effectively capturing system correlations and updating state information, are incorporated as inputs into second-order SDE. The second-order SDE is then solved by using the finite difference method (FDM) within a loop-structured solution framework. Case studies conducted within a practical area in China validate that the proposed self-organizing energy management model (SEMM) demonstrates spontaneous improvements in economic performance compared with conventional models.

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  • Effectively Dispatchable Solar Power with Hierarchical Reconciliation and Firm Forecasting

    Dazhi Yang, Guoming Yang, Marc J. Perez, Richard Perez

    2025,13(2):585-596, DOI: 10.35833/MPCE.2024.000451

    Abstract: (1) HTML (0) PDF (4.44 MKB) (4)

    Abstract:

    The variable nature of solar power has hitherto been regarded as a major barrier preventing large-scale high-penetration solar energy into the power grid. Based on decades of research, particularly those advances made over the recent few years, it is now believed that dispatchable solar power is no longer a conception but will soon become techno-economically feasible. The policy-driven information exchange among the weather centers, grid operators, and photovoltaic plant owners is the key to realizing dispatchable solar power. In this paper, a five-step forecasting framework for enabling dispatchable solar power is introduced. Among the five steps, the first three, namely numerical weather prediction (NWP), forecast post-processing, and irradiance-to-power conversion, have long been familiar to most. The last two steps, namely hierarchical reconciliation and firm forecasting, are quite recent conceptions, which have yet to raise widespread awareness. The proposed framework is demonstrated through a case study on achieving effectively dispatchable solar power generation at plant and substation levels.

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  • A Mix-integer Programming Based Deep Reinforcement Learning Framework for Optimal Dispatch of Energy Storage System in Distribution Networks

    Shengren Hou, Edgar Mauricio Salazar, Peter Palensky, Qixin Chen, Pedro P. Vergara

    2025,13(2):597-608, DOI: 10.35833/MPCE.2024.000391

    Abstract: (1) HTML (0) PDF (2.09 MKB) (4)

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    The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.

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  • 3D Data Scattergram Image Classification Based Protection for Transmission Line Connecting BESS Using Depth-wise Separable Convolution Based CNN

    Yingyu Liang, Yi Ren, Xiaoyang Yang, Wenting Zha

    2025,13(2):609-621, DOI: 10.35833/MPCE.2023.001008

    Abstract: (1) HTML (0) PDF (3.78 MKB) (3)

    Abstract:

    The distinctive fault characteristics of battery energy storage stations (BESSs) significantly affect the reliability of conventional protection methods for transmission lines. In this paper, the three-dimensional (3D) data scattergrams are constructed using current data from both sides of the transmission line and their sum. Following a comprehensive analysis of the varying characteristics of 3D data scattergrams under different conditions, a 3D data scattergram image classification based protection method is developed. The depth-wise separable convolution is used to ensure a lightweight convolutional neural network (CNN) structure without compromising performance. In addition, a Bayesian hyperparameter optimization algorithm is used to achieve a hyperparametric search to simplify the training process. Compared with artificial neural networks and CNNs, the depth-wise separable convolution based CNN (DPCNN) achieves a higher recognition accuracy. The 3D data scattergram image classification based protection method using DPCNN can accurately separate internal faults from other disturbances and identify fault phases under different operating states and fault conditions. The proposed protection method also shows first-class tolerability against current transformer (CT) saturation and CT measurement errors.

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  • Consideration on Present and Future of Battery Energy Storage System to Unlock Battery Value

    Ji-Soo Kim, Jin-Sol Song, Chul-Hwan Kim, Jean Mahseredjian, Seung-Ho Kim

    2025,13(2):622-636, DOI: 10.35833/MPCE.2023.000723

    Abstract: (1) HTML (0) PDF (1.50 MKB) (2)

    Abstract:

    To address environmental concerns, there has been a rapid global surge in integrating renewable energy sources into power grids. However, this transition poses challenges to grid stability. A prominent solution to this challenge is the adoption of battery energy storage systems (BESSs). Many countries are actively increasing BESS deployment and developing new BESS technologies. Nevertheless, a crucial initial step is conducting a comprehensive analysis of BESS capabilities and subsequently formulating policies. We analyze the current roles of BESS and review existing BESS policies worldwide, which focuses on key markets in Asia, Europe, and the U.S.. Using collected survey data, we propose a comprehensive three-phase framework for policy formulation, providing insights into future policy development directions.

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  • Reinforcement Learning- and Option-jointed Modeling for Cross-market and Cross-time Trading of Generators in Electricity and Carbon Markets

    Kai Jiang, Kunyu Wang, Lin Yang, Nian Liu

    2025,13(2):637-649, DOI: 10.35833/MPCE.2024.000013

    Abstract: (1) HTML (0) PDF (2.49 MKB) (4)

    Abstract:

    With the development of the carbon markets (CMs) and electricity markets (EMs), discrepancies in prices between the two markets and between two time periods offer profit opportunities for generation companies (GenCos). Motivated by the carbon option and Black-Scholes (B-S) model, GenCos are given the right but not the obligation to trade carbon emission allowances (CEAs) and use instruments to hedge against price risks. To model the strategic behaviors of GenCos that capitalize on these cross-market and cross-time opportunities, a multi-market trading strategy that incorporates option-jointed daily trading and reinforcement learning-jointed weekly continuous trading are modeled. The daily trading is built with a bi-level structure, where a profit-oriented bidding model that jointly considers both the optimal CEA holding shares and the best bidding curves is developed at the upper level. At the lower level, in addition to market clearing models of the day-ahead EM and auction-based CM, a B-S model that considers carbon trading asynchronism and option pricing is constructed. Then, by expanding the daily trading, the weekly continuous trading is modeled and solved using reinforcement learning. Binary expansion and strike-to-spot price ratio are utilized to address the nonlinearity. Finally, case studies on an IEEE 30-bus system are conducted to validate the effectiveness of the proposed trading strategy. Results show that the proposed trading strategy can increase GenCo profits by influencing market prices and leveraging carbon options.

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  • A Clearing Mechanism with Reduced Computational Complexity for Spot Flexibility Markets

    Sameer Sabir, Sousso Kelouwani, Nilson Henao, David Toquica, Michaël Fournier, Kodjo Agbossou, Juan C. Oviedo

    2025,13(2):650-662, DOI: 10.35833/MPCE.2024.000264

    Abstract: (2) HTML (0) PDF (3.10 MKB) (5)

    Abstract:

    The spot flexibility markets are before the real-time energy exchange, allowing demand-side management to reduce energy consumption during peak periods. In these markets, demand aggregators must quickly choose the customers ’reduction bids that fulfill grid requirements. This clearing procedure is challenging due to the computational complexity of selecting the optimal bids. Therefore, developing a clearing mechanism that avoids searching the entire flexibility bid space while respecting grid constraints is essential for the smooth operation of the spot flexibility market. This paper presents a clearing mechanism with reduced computational complexity of the winner determination problem in spot flexibility market for demand aggregators carrying out reductions in energy consumption. The proposed approach transforms customers’flexibility bids into a reward-based function. Afterward, the gradient-based optimization solves the bid selection problem. This approach helps demand aggregators achieve satisfactory energy reductions within an appropriate delay for spot flexibility markets. A comparative study presents the effectiveness of the proposed approach against commonly used approaches: hybrid particle swarm optimization genetic algorithm and combinatorial search.

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  • Balancing Benefits of Distribution System Operator in Peer-to-peer Energy Trading Among Microgrids Based on Optimal Dynamic Network Usage Fees

    Songmei Wu, Hui Guo, Fei Wang, Yuxin Zhu

    2025,13(2):663-674, DOI: 10.35833/MPCE.2024.000521

    Abstract: (1) HTML (0) PDF (3.12 MKB) (0)

    Abstract:

    Peer-to-peer (P2P) energy trading provides a promising solution for integrating distributed microgrids (MGs). However, most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator (DSO). Therefore, a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO. The interaction between DSO and MG is formulated as a Stackelberg game, in which the existence and uniqueness of optimal dynamic network usage fees are proven. Additionally, the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions. Furthermore, the underlying relationship among optimal dynamic network usage fees, electrical distance, and power flow is revealed, and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs. Finally, simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08% reduction in operation costs for MG while increasing DSO revenue by 15.36%.

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  • Multi-temporal Optimization of Virtual Power Plant in Energy-frequency Regulation Market Under Uncertainties

    Wenping Qin, Xiaozhou Li, Xing Jing, Zhilong Zhu, Ruipeng Lu, Xiaoqing Han

    2025,13(2):675-687, DOI: 10.35833/MPCE.2024.000118

    Abstract: (1) HTML (0) PDF (3.30 MKB) (0)

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    The virtual power plant (VPP) facilitates the coordinated optimization of diverse forms of electrical energy through the aggregation and control of distributed energy resources (DERs), offering as a potential resource for frequency regulation to enhance the power system flexibility. To fully exploit the flexibility of DER and enhance the revenue of VPP, this paper proposes a multi-temporal optimization strategy of VPP in the energy-frequency regulation (EFR) market under the uncertainties of wind power (WP), photovoltaic (PV), and market price. Firstly, all schedulable electric vehicles (EVs) are aggregated into an electric vehicle cluster (EVC), and the schedulable domain evaluation model of EVC is established. A day-ahead energy bidding model based on Stackelberg game is also established for VPP and EVC. Secondly, on this basis, the multi-temporal optimization model of VPP in the EFR market is proposed. To manage risks stemming from the uncertainties of WP, PV, and market price, the concept of conditional value at risk (CVaR) is integrated into the strategy, effectively balancing the bidding benefits and associated risks. Finally, the results based on operational data from a provincial electricity market demonstrate that the proposed strategy enhances comprehensive revenue by providing frequency regulation services and encouraging EV response scheduling.

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  • Parallel Converter-based Hybrid HVDC System for Integration and Delivery of Large-scale Renewable Energy

    Hanlin Guo, Zheren Zhang, Zheng Xu

    2025,13(2):688-697, DOI: 10.35833/MPCE.2023.001033

    Abstract: (1) HTML (0) PDF (5.69 MKB) (1)

    Abstract:

    In this study, a novel parallel converter-based hybrid high-voltage direct current (HVDC) system is proposed for the integration and delivery of large-scale renewable energy. The rectifier uses the line commutated converter (LCC) and low-capacity modular multilevel converter (MMC) in parallel, while the inverter uses MMC. This configuration combines the economic advantages of LCC with the flexibility of MMC. Firstly, the steady-state control strategies are elaborated. The low-capacity MMC operates in the grid-forming mode to offer AC voltage support. It also provides active filtering for the LCC and maintains the reactive power balance of the sending-end system. The LCC efficiently transmits all active power at the rectifier side, fully exploiting its bulk-power transmission capability. Secondly, the fault ride-through strategies of both the AC faults at two terminals and the DC fault are proposed, in which the MMCs at both terminals can remain unblocked under various faults. Thus, the proposed system can mitigate the impact of the faults and ensure continuous voltage support for the sending-end system. Finally, simulations in PSCAD/EMTDC verify the effectiveness and performance of the proposed system.

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  • Oscillation Suppression Considering Characteristics of Interaction Energy in Grid-connected DFIG-based Wind Farms via VSC-HVDC Transmission System

    Jing Ma, Ningsai Su, Yawen Deng, Weifeng Xia, Honglu Xu, Yaqi Shen

    2025,13(2):698-709, DOI: 10.35833/MPCE.2024.000240

    Abstract: (1) HTML (0) PDF (6.72 MKB) (0)

    Abstract:

    For doubly-fed induction generator (DFIG)-based wind farms connected to flexible DC transmission system, the oscillation suppression after fault clearance proves very difficult. Addressing this problem, this paper constructs the dynamic energy model of the interconnected system, reveals the mechanism of oscillation instability after fault clearance, and designs an oscillation suppression strategy. First, by considering the dynamic characteristics of the control links in grid-connected DFIG-based wind farms via voltage source converter based high-voltage direct current (VSC-HVDC) transmission system, the interconnected system is divided into several subsystems, and the energy model of each subsystem is constructed. Furthermore, the magnitudes and directions of different interaction energy items are quantitatively analyzed, so that the key control links that transmit and magnify the system energy can be identified. On this basis, the corresponding supplementary control links are designed to suppress the system oscillation. Finally, the accuracy and effectiveness of the proposed oscillation suppression strategy are verified by hardware-in-loop tests. The results prove that the d-axis subsystem of DFIG grid-side converter (GSC) current inner loop, phase-locked loop (PLL), and q-axis subsystem of VSC-HVDC voltage outer loop are the key links that induce the oscillation to occur, and the proposed strategy shows promising results in oscillation suppression.

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  • Dynamic Modelling, Control, and Stability Analysis of DC Modular Multilevel Converter Connected to HVDC Cables

    Salman Badkubi, Aliakbar Jamshidi Far, Sumeet S. Aphale

    2025,13(2):710-719, DOI: 10.35833/MPCE.2023.001004

    Abstract: (1) HTML (0) PDF (4.80 MKB) (1)

    Abstract:

    Innovative dynamic models for the DC modular multilevel converter (DC-MMC) in rotating dq frame are presented in this paper, which are specifically designed to enhance converter design and stability analysis. Open-loop and closed-loop models are developed using three dq frames, providing a detailed examination of the impact of 2 nd and 3 rd harmonic components on the model accuracy. A novel contribution of this paper is the integration of a 2 nd harmonic current suppression controller (SHCSC) within the closed-loop model, offering new insights into its effects on system stability. The DC-MMC model is further extended by coupling it with high-voltage direct current (HVDC) cables on each side, forming an interconnected system model that accurately represents a more authentic scenario for future DC grids. The proposed model is rigorously validated against PSCAD benchmark model, confirming their precision and reliability. The interconnected system model is then utilized to analyze the influence of cable length on system stability, demonstrating practical applications. The closed-loop model is subsequently employed for stability assessment of the interconnected system, showcasing its applicability in real-world scenarios. Additionally, a damping controller is designed using participation factor and residue approaches, offering a refined approach to oscillation damping and stability optimization. The effectiveness of the controller is evaluated through eigenvalue analysis, supported by simulation results, underscoring its potential for enhancing system stability.

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  • Subsequent Commutation Failure Suppression Considering Negative-sequence Voltage Caused by Symmetrical Fault at AC Side of Inverter

    Shenghu Li, Yikai Li

    2025,13(2):720-731, DOI: 10.35833/MPCE.2024.000352

    Abstract: (1) HTML (0) PDF (3.85 MKB) (0)

    Abstract:

    The negative-sequence voltage is often caused by the asymmetrical fault in the AC system, as well as the harmonics after the symmetrical fault at the AC side of inverter in line commutated converter based high-voltage DC (LCC-HVDC). The negative-sequence voltage affects the phase-locked loop (PLL) and the inverter control, thus the inverter is vulnerable to the subsequent commutation failure (SCF). In this paper, the analytical expression of the negative-sequence voltage resulting from the symmetrical fault with the commutation voltage is derived using the switching function and Fourier decomposition. The analytical expressions of the outputs of the PLL and inverter control with respect to time are derived to quantify the contribution of the negative-sequence voltage to the SCF. To deal with the AC component of the input signals in the PLL and the inverter control due to the negative-sequence voltage, the existing proportional-integral controls of the PLL, constant current control, and constant extinction angle control are replaced by the linear active disturbance rejection control against the SCF. Simulation results verify the contributing factors to the SCF. The proposed control reduces the risk of SCF and improves the recovery speed of the system under different fault conditions.

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  • Self-adaptive Action and Parameter Optimization of DC Series-parallel Power Flow Controller for Fault Current Limiting in Bipolar DC Distribution Systems

    Yangtao Liu, Jianquan Liao, Chunsheng Guo, Zipeng Tan, Qianggang Wang, Yuhong Wang, Niancheng Zhou

    2025,13(2):732-746, DOI: 10.35833/MPCE.2024.000212

    Abstract: (1) HTML (0) PDF (7.58 MKB) (0)

    Abstract:

    DC series-parallel power flow controller (SP-PFC) is a highly efficient device to solve the problem of uncontrolled line current in the bipolar DC distribution system. However, its potential in fault current limiting is not fully explored. In this paper, a self-adaptive action strategy (SAAS) and a parameter optimization method of SP-PFC in bipolar DC distribution systems are proposed. Firstly, the common- and different-mode (CDM) equivalent circuits of the bipolar DC distribution system with SP-PFC in different fault stages are established, which avoids the line coupling inductance. Based on this, the influence of different parameters and line coupling inductance on the fault current limiting capability are investigated. It is found that the SP-PFC has the best fault current limiting capability when the capacitance and inductance of filter are inversely proportional. To realize the adaptability of fault current limiting capability under different fault severities, the SAAS of SP-PFC is proposed. The validity of the CDM equivalent circuits and parameter optimization method, and the effectiveness of the SAAS are verified by simulations and experiments.

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      Display Method::
      • Digital Twin Empowered PV Power Prediction

        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: (85) HTML (119) PDF (2.66 MKB) (1395)

        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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      • Small-signal Stability Criteria in Power Electronics-dominated Power Systems: A Comparative Review

        Qifan Chen, Siqi Bu, Chi Yung Chung

        2024,12(4):1003-1018, DOI: 10.35833/MPCE.2023.000526

        Abstract: (194) HTML (72) PDF (3.80 MKB) (2015)

        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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      • A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles

        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: (198) HTML (120) PDF (7.11 MKB) (1475)

        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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      • Improved Energy Management Strategy for Prosumer Buildings with Renewable Energy Sources and Battery Energy Storage Systems

        Pavitra Sharma, Krishna Kumar Saini, Hitesh Datt Mathur, Puneet Mishra

        2024,12(2):381-392, DOI: 10.35833/MPCE.2023.000761

        Abstract: (1069) HTML (100) PDF (4.42 MKB) (1454)

        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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      • Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-scale Renewable Energy

        Jianlin Li, Zhijin Fang, Qian Wang, Mengyuan Zhang, Yaxin Li, Weijun Zhang

        2024,12(2):359-370, DOI: 10.35833/MPCE.2023.000345

        Abstract: (762) HTML (116) PDF (2.13 MKB) (1452)

        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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      • Low-carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

        Hongchao Gao, Tai Jin, Guanxiong Wang, Qixin Chen, Chongqing Kang, Jingkai Zhu

        2024,12(2):346-358, DOI: 10.35833/MPCE.2023.000762

        Abstract: (786) HTML (69) PDF (3.23 MKB) (1605)

        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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      • Storing Freshwater Versus Storing Electricity in Power Systems with High Freshwater Electric Demand

        Mubarak J. Al-Mubarak, Antonio J. Conejo

        2024,12(2):323-333, DOI: 10.35833/MPCE.2023.000306

        Abstract: (791) HTML (143) PDF (2.79 MKB) (1507)

        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.

        • 1
      • Exploiting Flexibility of Integrated Demand Response to Alleviate Power Flow Violation During Line Tripping Contingency

        Tong Cheng, Zhenfei Tan, Haiwang Zhong

        2023,11(6):1971-1981, DOI: 10.35833/MPCE.2021.000535

        Abstract: (225) HTML (22) PDF (2.00 MKB) (673)

        Abstract:

        Multi-energy integrations provide great opportunities for economic and efficient resource utilization. In the meantime, power system operation requires enough flexible resources to deal with contingencies such as transmission line tripping. Besides economic benefits, this paper focuses on the security benefits that can be provided by multi-energy integrations. This paper first proposes an operation scheme to coordinate multiple energy production and local system consumption considering transmission networks. The integrated flexibility model, constructed by the feasible region of integrated demand response (IDR), is then formulated to aggregate and describe local flexibility. Combined with system security constraints, a multi-energy system operation model is formulated to schedule multiple energy production, transmission, and consumption. The effects of local system flexibility on alleviating power flow violations during N-1 line tripping contingencies are then analyzed through a multi-energy system case. The results show that local system flexibility can not only reduce the system operation costs, but also reduce the probability of power flow congestion or violations by approximately 68.8% during N-1 line tripping contingencies.

        • 1
      • A Review on Self-healing in Modern Power Distribution Systems

        Seyed Ali Arefifar, Md Shahin Alam, Abdullah Hamadi

        2023,11(6):1719-1733, DOI: 10.35833/MPCE.2022.000032

        Abstract: (554) HTML (65) PDF (1.33 MKB) (910)

        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.

        • 1
      • Multi-energy Management of Interconnected Multi-microgrid System Using Multi-agent Deep Reinforcement Learning

        Sichen Li, Di Cao, Weihao Hu, Qi Huang, Zhe Chen, Frede Blaabjerg

        2023,11(5):1606-1617, DOI: 10.35833/MPCE.2022.000473

        Abstract: (387) HTML (55) PDF (2.51 MKB) (941)

        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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      • A Review on Challenges in DC Microgrid Planning and Implementation

        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: (757) HTML (89) PDF (5.27 MKB) (1232)

        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.

        • 1
      • Stability Comparison Between Grid-forming and Grid-following Based Wind Farms Integrated MMC-HVDC

        Rongcai Pan, Dong Liu, Shan Liu, Jie Yang, Longze Kou, Guangfu Tang

        2023,11(4):1341-1355, DOI: 10.35833/MPCE.2022.000158

        Abstract: (402) HTML (9) PDF (3.53 MKB) (703)

        Abstract:

        Grid-forming (GFM) control based high-voltage DC (HVDC) systems and renewable energy sources (RESs) provide support for enhancing the stability of power systems. However, the interaction and coordination of frequency support between the GFM-based modular multilevel converter based HVDC (MMC-HVDC) and grid-following (GFL) based RESs or GFM-based RESs have not been fully investigated, which are examined in this study. First, the detailed AC- and DC-side impedances of GFM-based MMC-HVDC are analyzed. The impedance characteristics of GFL- and GFM-based wind turbines are next analyzed. Then, the influences of GFL- and GFM-based wind farms (WFs) on the DC- and AC-side stabilities of WF-integrated MMC-HVDC systems are compared and evaluated. The results show that the GFM-based wind turbine performs better than the GFL-based wind turbine. Accordingly, to support a receiving-end AC system, the corresponding frequency supporting strategies are proposed based on the GFM control for WF-integrated MMC-HVDC systems. The GFM-based WF outperforms the GFL-based WF in terms of stability and response time. Simulations in PSCAD/EMTDC demonstrate the DC- and AC-side stability issues and seamless grid support from the RESs, i.e., WFs, to the receiving-end AC system.

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      • An Improved Perturb and Observed Maximum Power Point Tracking Algorithm for Photovoltaic Power Systems

        Rasool Kahani, Mohsin Jamil, M. Tariq Iqbal

        2023,11(4):1165-1175, DOI: 10.35833/MPCE.2022.000245

        Abstract: (397) HTML (46) PDF (6.33 MKB) (1081)

        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 ηMPPT of nearly 3.1% greater than that of the conventional P&O algorithm and the incremental conductance (InC) algorithm. In addition, under strong partial shading conditions and drift avoidance tests, the proposed algorithm produces an average ηMPPT of nearly 9% and 8% greater than that of the conventional algorithms, respectively.

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      • Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

        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: (399) HTML (12) PDF (3.35 MKB) (1034)

        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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      • Modeling and Simulation of Hydrogen Energy Storage System for Power-to-gas and Gas-to-power Systems

        Jianlin Li, Guanghui Li, Suliang Ma, Zhonghao Liang, Yaxin Li, Wei Zeng

        2023,11(3):885-895, DOI: 10.35833/MPCE.2021.000705

        Abstract: (531) HTML (36) PDF (3.74 MKB) (1114)

        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.

        • 1
      • A Review on Cybersecurity Analysis, Attack Detection, and Attack Defense Methods in Cyber-physical Power Systems

        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: (561) HTML (99) PDF (2.72 MKB) (1947)

        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.

        • 1
      • Sharing Economy in Local Energy Markets

        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: (687) HTML (59) PDF (1.80 MKB) (1069)

        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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      • What May Future Electricity Markets Look Like?

        Pierre Pinson

        2023,11(3):705-713, DOI: 10.35833/MPCE.2023.000073

        Abstract: (519) HTML (84) PDF (703.70 KKB) (1767)

        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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      • Reliability Assessment of Interconnected Power Systems with HVDC Links Considering Frequency Regulation Process

        Chengjin Ye, Libang Guo, Yi Ding, Ming Ding, Peng Wang, Lei Wang

        2023,11(2):662-673, DOI: 10.35833/MPCE.2021.000491

        Abstract: (545) HTML (81) PDF (2.60 MKB) (1621)

        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.

        • 1
      • 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: (453) HTML (18) PDF (2.64 MKB) (1051)

        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.

        • 1
      • Bi-level Energy Trading Model Incorporating Large-scale Biogas Plant and Demand Response Aggregator

        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: (482) HTML (44) PDF (2.88 MKB) (1018)

        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.

        • 1
      • Comprehensive Optimization-based Techno-economic Assessment of Hybrid Renewable Electricity-hydrogen Virtual Power Plants

        James Naughton, Shariq Riaz, Michael Cantoni, Xiao-Ping Zhang, Pierluigi Mancarella

        2023,11(2):553-566, DOI: 10.35833/MPCE.2022.000324

        Abstract: (478) HTML (48) PDF (1.92 MKB) (1943)

        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.

        • 1
      • Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

        Shengyuan Liu, Yicheng Jiang, Zhenzhi Lin, Fushuan Wen, Yi Ding, Li Yang

        2023,11(2):523-533, DOI: 10.35833/MPCE.2021.000196

        Abstract: (505) HTML (10) PDF (2.71 MKB) (1077)

        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.

        • 1
      • Average Current Control with Internal Model Control and Real-time Frequency Decoupling for Hybrid Energy Storage Systems in Microgrids

        Alejandro Latorre, Wilmar Martinez, Camilo A. Cortes

        2023,11(2):511-522, DOI: 10.35833/MPCE.2021.000359

        Abstract: (474) HTML (25) PDF (3.71 MKB) (1114)

        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.

        • 1
      • Data-driven Approach for State Prediction and Detection of False Data Injection Attacks in Smart Grid

        Haftu Tasew Reda, Adnan Anwar, Abdun Mahmood, Naveen Chilamkurti

        2023,11(2):455-467, DOI: 10.35833/MPCE.2020.000827

        Abstract: (506) HTML (62) PDF (4.49 MKB) (1885)

        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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      • Time-domain Dynamic State Estimation for Unbalanced Three-phase Power Systems

        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: (697) HTML (26) PDF (1.94 MKB) (1019)

        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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      • Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

        Jun Mo, Hui Yang

        2023,11(2):421-433, DOI: 10.35833/MPCE.2021.000318

        Abstract: (544) HTML (23) PDF (4.29 MKB) (1207)

        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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      • Impact of Cascade Disconnection of Distributed Energy Resources on Bulk Power System Stability: Modeling and Mitigation Requirements

        Fabricio Andrade Mourinho, Tatiana Mariano Lessa Assis

        2023,11(2):412-420, DOI: 10.35833/MPCE.2022.000365

        Abstract: (509) HTML (35) PDF (3.14 MKB) (2336)

        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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      • Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

        Tannan Xiao, Ying Chen, Jianquan Wang, Shaowei Huang, Weilin Tong, Tirui He

        2023,11(2):401-411, DOI: 10.35833/MPCE.2022.000099

        Abstract: (598) HTML (28) PDF (2.51 MKB) (1175)

        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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      • Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging

        Zixuan Jia, Jianing Li, Xiao-Ping Zhang, Ray Zhang

        2023,11(2):389-400, DOI: 10.35833/MPCE.2021.000777

        Abstract: (1395) HTML (21) PDF (1.75 MKB) (1121)

        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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      • A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

        Yuzhou Zhou, Qiaozhu Zhai, Lei Wu, Moammad Shahidehpour

        2023,11(1):254-266, DOI: 10.35833/MPCE.2021.000382

        Abstract: (333) HTML (17) PDF (3.28 MKB) (747)

        Abstract:

        This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment (TCUC). Lagrangian relaxation (LR) and mixed-integer linear programming (MILP) are popular approaches to solving TCUC. However, with many binary unit commitment variables, LR suffers from slow convergence and MILP presents heavy computation burden. The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems. A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible (ASF) approaches, which is then leveraged to efficiently solve new TCUC instances online. On/off statuses of considerable units can be fixed in the online calculation according to the database, which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances. A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables, which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes. Numerical tests illustrate the efficiency of the proposed approach.

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