Journal of Modern Power Systems and Clean Energy

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
  • A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles
    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.
  • 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: (92) HTML (24) PDF (2.93 MKB) (502)

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    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: (67) HTML (14) PDF (2.51 MKB) (459)

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    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: (58) HTML (35) PDF (3.90 MKB) (484)

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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: (39) HTML (31) PDF (2.08 MKB) (440)

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    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: (45) HTML (28) PDF (3.66 MKB) (415)

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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: (32) HTML (28) PDF (6.84 MKB) (455)

    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: (36) HTML (19) PDF (3.23 MKB) (446)

    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: (28) HTML (10) PDF (9.05 MKB) (357)

    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: (48) HTML (28) PDF (2.98 MKB) (231)

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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: (46) HTML (51) PDF (2.55 MKB) (217)

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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: (60) HTML (36) PDF (2.30 MKB) (214)

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    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: (47) HTML (58) PDF (2.50 MKB) (209)

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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: (55) HTML (33) PDF (2.33 MKB) (202)

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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: (44) HTML (35) PDF (4.36 MKB) (226)

    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: (51) HTML (32) PDF (6.35 MKB) (215)

    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: (41) HTML (19) PDF (8.24 MKB) (188)

    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: (37) HTML (25) PDF (3.83 MKB) (201)

    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: (37) HTML (32) PDF (4.44 MKB) (232)

    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: (39) HTML (31) PDF (2.09 MKB) (212)

    Abstract:

    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: (55) HTML (20) PDF (3.78 MKB) (194)

    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: (49) HTML (38) PDF (1.50 MKB) (236)

    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: (31) HTML (39) PDF (2.49 MKB) (195)

    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: (55) HTML (27) PDF (3.10 MKB) (182)

    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: (44) HTML (30) PDF (3.12 MKB) (184)

    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: (24) HTML (38) PDF (3.30 MKB) (167)

    Abstract:

    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: (45) HTML (18) PDF (5.69 MKB) (184)

    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: (53) HTML (41) PDF (6.72 MKB) (190)

    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: (44) HTML (41) PDF (4.80 MKB) (205)

    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: (46) HTML (19) PDF (3.85 MKB) (174)

    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: (52) HTML (29) PDF (7.58 MKB) (182)

    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::
      • 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: (49) HTML (38) PDF (1.50 MKB) (236)

        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.

        • 1
      • 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: (39) HTML (31) PDF (2.09 MKB) (212)

        Abstract:

        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.

        • 1
      • Matching Synchronous Machine Control for Improving Active Support of Grid-forming PV Systems with Enhanced DC Voltage Dynamics

        Zizhen Guo, Wenchuan Wu

        2025,13(1):179-189, DOI: 10.35833/MPCE.2023.000624

        Abstract: (39) HTML (44) PDF (3.67 MKB) (583)

        Abstract:

        With photovoltaic (PV) sources becoming more prevalent in the energy generation mix, transitioning grid-connected PV systems from grid-following (GFL) mode to grid-forming (GFM) mode becomes essential for offering self-synchronization and active support services. Although numerous GFM methods have been proposed, the potential of DC voltage control malfunction during the provision of the primary and inertia support in a GFM PV system remains insufficiently researched. To fill the gap, some main GFM methods have been integrated into PV systems featuring detailed DC source dynamics. We conduct a comparative analysis of their performance in active support and DC voltage regulation. AC GFM methods such as virtual synchronous machine (VSM) face a significant risk of DC voltage failure in situations like alterations in solar radiation, leading to PV system tripping and jeopardizing local system operation. In the case of DC GFM methods such as matching control (MC), the active support falls short due to the absence of an accurate and dispatchable droop response. To address the issue, a matching synchronous machine (MSM) control method is developed to provide dispatchable active support and enhance the DC voltage dynamics by integrating the MC and VSM control loops. The active support capability of the PV systems with the proposed method is quantified analytically and verified by numerical simulations and field tests.

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      • A Systematic Small-signal Analysis Procedure for Improving Synchronization Stability of Grid-forming Virtual Synchronous Generators

        Francisco Jesús Matas-Díaz, Manuel Barragán-Villarejo, José María Maza-Ortega

        2025,13(1):102-114, DOI: 10.35833/MPCE.2024.000316

        Abstract: (49) HTML (18) PDF (3.37 MKB) (535)

        Abstract:

        The integration of converter-interfaced generators (CIGs) into power systems is rapidly replacing traditional synchronous machines. To ensure the security of power supply, modern power systems require the application of grid-forming technologies. This study presents a systematic small-signal analysis procedure to assess the synchronization stability of grid-forming virtual synchronous generators (VSGs) considering the power system characteristics. Specifically, this procedure offers guidance in tuning controller gains to enhance stability. It is applied to six different grid-forming VSGs and experimentally tested to validate the theoretical analysis. This study concludes with key findings and a discussion on the suitability of the analyzed grid-forming VSGs based on the power system characteristics.

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      • Proportion of Grid-forming Wind Turbines in Hybrid GFM-GFL Offshore Wind Farms Integrated with Diode Rectifier Unit Based HVDC System

        Yanqiu Jin, Zheren Zhang, Zheng Xu

        2025,13(1):87-101, DOI: 10.35833/MPCE.2024.000432

        Abstract: (31) HTML (45) PDF (6.88 MKB) (706)

        Abstract:

        This study analyzes the stability and reactive characteristics of the hybrid offshore wind farm that includes grid-forming (GFM) and grid-following (GFL) wind turbines (WTs) integrated with a diode rectifier unit (DRU) based high-voltage direct current (HVDC) system. The determination method for the proportion of GFM WTs is proposed while considering system stability and optimal offshore reactive power constraints. First, the small-signal stability is studied based on the developed linear model, and crucial factors that affect the stability are captured by eigenvalue analysis. The reactive power-frequency compensation control of GFM WTs is then proposed to improve the reactive power and frequency dynamics. Second, the relationship between offshore reactive power imbalance and the effectiveness of GFM capability is analyzed. Offshore reactive power optimization methods are next proposed to diminish offshore reactive load. These methods include the optimal design for the reactive capacity of the AC filter and the reactive power compensation control of GFL WTs. Third, in terms of stability and optimal offshore reactive power constraints, the principle and calculation method for determining the proportion of GFM WTs are proposed, and the critical proportion of GFM WTs is determined over the full active power range. Finally, case studies using a detailed model are conducted by time-domain simulations in PSCAD/EMTDC. The simulations verify the theoretical analysis results and the effectiveness of the proposed determination method for the proportion of GFM WTs and reactive power optimization methods.

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      • Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee

        Hang Shuai, Buxin She, Jinning Wang, Fangxing Li

        2025,13(1):79-86, DOI: 10.35833/MPCE.2023.000882

        Abstract: (32) HTML (54) PDF (2.47 MKB) (602)

        Abstract:

        This study investigates a safe reinforcement learning algorithm for grid-forming (GFM) inverter based frequency regulation. To guarantee the stability of the inverter-based resource (IBR) system under the learned control policy, a model-based reinforcement learning (MBRL) algorithm is combined with Lyapunov approach, which determines the safe region of states and actions. To obtain near optimal control policy, the control performance is safely improved by approximate dynamic programming (ADP) using data sampled from the region of attraction (ROA). Moreover, to enhance the control robustness against parameter uncertainty in the inverter, a Gaussian process (GP) model is adopted by the proposed algorithm to effectively learn system dynamics from measurements. Numerical simulations validate the effectiveness of the proposed algorithm.

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      • DC Voltage Control with Grid-forming Capability for Enhancing Stability of HVDC System

        Ghazala Shafique, Johan Boukhenfouf, François Gruson, Frédéric Colas, Xavier Guillaud

        2025,13(1):66-78, DOI: 10.35833/MPCE.2024.000822

        Abstract: (36) HTML (16) PDF (7.71 MKB) (659)

        Abstract:

        Grid-forming (GFM) converters are recognized for their stabilizing effects in renewable energy systems. Integrating GFM converters into high-voltage direct current (HVDC) systems requires DC voltage control. However, there can be a conflict between GFM converter and DC voltage control when they are used in combination. This paper presents a rigorous control design for a GFM converter that connects the DC-link voltage to the power angle of the converter, thereby integrating DC voltage control with GFM capability. The proposed control is validated through small-signal and transient-stability analyses on a modular multilevel converter (MMC)-based HVDC system with a point-to-point (P2P) GFM-GFM configuration. The results demonstrate that employing a GFM-GFM configuration with the proposed control enhances the stability of the AC system to which it is connected. The system exhibits low sensitivity to grid strength and can sustain islanding conditions. The high stability limit of the system with varying grid strength using the proposed control is validated using a system with four voltage source converters.

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      • Grid Strength Assessment Method for Evaluating Small-signal Synchronization Stability of Grid-following and Grid-forming Converters Integrated Systems

        Qianhong Shi, Wei Dong, Guanzhong Wang, Junchao Ma, Chenxu Wang, Xianye Guo, Vladimir Terzija

        2025,13(1):55-65, DOI: 10.35833/MPCE.2024.000759

        Abstract: (31) HTML (28) PDF (4.23 MKB) (627)

        Abstract:

        Oscillations caused by small-signal instability have been widely observed in AC grids with grid-following (GFL) and grid-forming (GFM) converters. The generalized short-circuit ratio is commonly used to assess the strength of GFL converters when integrated with weak AC systems at risk of oscillation. This paper provides the grid strength assessment method to evaluate the small-signal synchronization stability of GFL and GFM converters integrated systems. First, the admittance and impedance matrices of the GFL and GFM converters are analyzed to identify the frequency bands associated with negative damping in oscillation modes dominated by heterogeneous synchronization control. Secondly, based on the interaction rules between the short-circuit ratio and the different oscillation modes, an equivalent circuit is proposed to simplify the grid strength assessment through the topological transformation of the AC grid. The risk of sub-synchronization and low-frequency oscillations, influenced by GFL and GFM converters, is then reformulated as a semi-definite programming (SDP) model, incorporating the node admittance matrix and grid-connected device capacities. The effectiveness of the proposed method is demonstrated through a case analysis.

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      • Hybrid Frequency-domain Modeling and Stability Analysis for Power Systems with Grid-following and Grid-forming Converters

        Ni Liu, Hong Wang, Weihua Zhou, Jie Song, Yiting Zhang, Eduardo Prieto-Araujo, Zhe Chen

        2025,13(1):15-28, DOI: 10.35833/MPCE.2023.000842

        Abstract: (43) HTML (13) PDF (3.61 MKB) (698)

        Abstract:

        With the increase of the renewable energy generator capacity, the requirements of the power system for grid-connected converters are evolve, which leads to diverse control schemes and increased complexity of systematic stability analysis. Although various frequency-domain models are developed to identify oscillation causes, the discrepancies between them are rarely studied. This study aims to clarify these discrepancies and provide circuit insights for stability analysis by using different frequency-domain models. This study emphasizes the limitations of assuming that the transfer function of the self-stable converter does not have right half-plane (RHP) poles. To ensure that the self-stable converters are represented by a frequency-domain model without RHP poles, the applicability of this model of grid-following (GFL) and grid-forming (GFM) converters is discussed. This study recommends that the GFM converters with ideal sources should be represented in parallel with the P / Q - θ / V admittance model rather than the V - I impedance model. Two cases are conducted to illustrate the rationality of the P / Q - θ / V admittance model. Additionally, a hybrid frequency-domain modeling framework and stability criteria are proposed for the power system with several GFL and GFM converters. The stability criteria eliminates the need to check the RHP pole numbers in the non-passive subsystem when applying the Nyquist stability criterion, thereby reducing the complexity of stability analysis. Simulations are carried out to validate the correctness of the frequency-domain model and the stability criteria.

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      • Dynamic Analysis of Uniformity and Difference for Grid-following and Grid-forming Voltage Source Converters Using Phasor and Topological Homology Methods

        Haiyu Zhao, Hongyu Zhou, Wei Yao, Qihang Zong, Jinyu Wen

        2025,13(1):3-14, DOI: 10.35833/MPCE.2024.000722

        Abstract: (57) HTML (29) PDF (3.02 MKB) (651)

        Abstract:

        Grid-following voltage source converter (GFL-VSC) and grid-forming voltage source converter (GFM-VSC) have different dynamic characteristics for active power-frequency and reactive power-voltage supports of the power grid. This paper aims to clarify and recognize the difference between grid-following (GFL) and grid-forming (GFM) frequency-voltage support more intuitively and clearly. Firstly, the phasor model considering circuit constraints is established based on the port circuit equations of the converter. It is revealed that the voltage and active power linearly correspond to the horizontal and vertical axes in the phasor space referenced to the grid voltage phasor. Secondly, based on topological homology, GFL and GFM controls are transformed and mapped into different trajectories. The topological similarity of the characteristic curves for GFL and GFM controls is the essential cause of their uniformity. Based on the above model, it is indicated that GFL-VSC and GFM-VSC possess uniformity with regard to active power response, type of coupling, and phasor trajectory. They differ in synchronization, power coupling mechanisms, dynamics, and active power-voltage operation domain in the quasi-steady state. Case studies are undertaken on GFL-VSC and GFM-VSC integrated into a four-machine two-area system. Simulation results verify that the dynamic uniformity and difference of GFL-VSC and GFM-VSC are intuitively and comprehensively revealed.

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      • Towards Renewable-dominated Energy Systems: Role of Green Hydrogen

        Sheng Chen, Jingchun Zhang, Zhinong Wei, Hao Cheng, Si Lv

        2024,12(6):1697-1709, DOI: 10.35833/MPCE.2023.000887

        Abstract: (80) HTML (32) PDF (1.67 MKB) (203)

        Abstract:

        Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strengthened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.

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      • 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: (107) HTML (130) PDF (2.66 MKB) (1677)

        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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      • Two-stage Transient-stability-constrained Optimal Power Flow for Preventive Control of Rotor Angle Stability and Voltage Sags

        Jorge Uriel Sevilla-Romero, Alejandro Pizano-Martínez, Claudio Rubén Fuerte-Esquivel, Reymundo Ramírez-Betancour

        2024,12(5):1357-1369, DOI: 10.35833/MPCE.2023.000461

        Abstract: (86) HTML (9) PDF (4.96 MKB) (1085)

        Abstract:

        In practice, an equilibrium point of the power system is considered transiently secure if it can withstand a specified contingency by maintaining transient evolution of rotor angles and voltage magnitudes within set bounds. A novel sequential approach is proposed to obtain transiently stable equilibrium points through the preventive control of transient stability and transient voltage sag (TVS) problems caused by a severe disturbance. The proposed approach conducts a sequence of non-heuristic optimal active power re-dispatch of the generators to steer the system toward a transiently secure operating point by sequentially solving the transient-stability-constrained optimal power flow (TSC-OPF) problems. In the proposed approach, there are two sequential projection stages, with the first stage ensuring the rotor angle stability and the second stage removing TVS in voltage magnitudes. In both projection stages, the projection operation corresponds to the TSC-OPF, with its formulation directly derived by adding only two steady-state variable-based transient constraints to the conventional OPF problem. The effectiveness of this approach is numerically demonstrated in terms of its accuracy and computational performance by using the Western System Coordinated Council (WSCC) 3-machine 9-bus system and an equivalent model of the Mexican 46-machine 190-bus system.

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      • Optimal Operation Control Strategies for Active Distribution Networks Under Multiple States: A Systematic Review

        Jingtao Zhao, Zhi Wu, Huan Long, Huapeng Sun, Xi Wu, Chingchuen Chan, Mohammad Shahidehpour

        2024,12(5):1333-1344, DOI: 10.35833/MPCE.2023.000372

        Abstract: (197) HTML (68) PDF (1.12 MKB) (1054)

        Abstract:

        With the large-scale integration of distributed renewable generation (DRG) and increasing proportion of power electronic equipment, the traditional power distribution network (DN) is evolving into an active distribution network (ADN). The operation state of an ADN, which is equipped with DRGs, could rapidly change among multiple states, which include steady, alert, and fault states. It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs. In this paper, the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states. The multi-state identification indicators and identification methods are summarized in detail. The multi-state regulation capacity quantification methods are analyzed considering controllable resources, quantification indicators, and quantification methods. A detailed survey of optimal operation control strategies, including multiple state operations, is presented, and key problems and outlooks for the expansion of ADN are discussed.

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      • 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: (225) HTML (103) PDF (3.80 MKB) (2328)

        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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      • Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

        Jie Xu, Hongjun Gao, Renjun Wang, Junyong Liu

        2024,12(3):886-899, DOI: 10.35833/MPCE.2023.000213

        Abstract: (136) HTML (31) PDF (4.37 MKB) (409)

        Abstract:

        The increasing integration of intermittent renewable energy sources (RESs) poses great challenges to active distribution networks (ADNs), such as frequent voltage fluctuations. This paper proposes a novel ADN strategy based on multi-agent deep reinforcement learning (MADRL), which harnesses the regulating function of switch state transitions for the real-time voltage regulation and loss minimization. After deploying the calculated optimal switch topologies, the distribution network operator will dynamically adjust the distributed energy resources (DERs) to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm. Owing to the model-free characteristics and the generalization of deep reinforcement learning, the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments. Additionally, integrating parameter sharing (PS) and prioritized experience replay (PER) mechanisms substantially improves the strategic performance and scalability. This framework has been tested on modified IEEE 33-bus, IEEE 118-bus, and three-phase unbalanced 123-bus systems. The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

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      • Distributed Robust Optimal Dispatch of Regional Integrated Energy Systems Based on ADMM Algorithm with Adaptive Step Size

        Zhoujun Ma, Yizhou Zhou, Yuping Zheng, Li Yang, Zhinong Wei

        2024,12(3):852-862, DOI: 10.35833/MPCE.2023.000204

        Abstract: (120) HTML (38) PDF (2.80 MKB) (467)

        Abstract:

        This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system (RIES). Our model regards the distribution network and each energy hub (EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic (PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers (ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

        • 1
      • 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: (225) HTML (143) PDF (7.11 MKB) (1791)

        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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      • Virtual Transmission Solution Based on Battery Energy Storage Systems to Boost Transmission Capacity

        Matías Agüero, Jaime Peralta, Eugenio Quintana, Victor Velar, Anton Stepanov, Hossein Ashourian, Jean Mahseredjian, Roberto Cárdenas

        2024,12(2):466-474, DOI: 10.35833/MPCE.2023.000729

        Abstract: (581) HTML (86) PDF (4.50 MKB) (1056)

        Abstract:

        The increasing penetration of variable renewable energy (VRE) generation along with the decommissioning of conventional power plants in Chile, has raised several operational challenges in the Chilean National Power Grid (NPG), including transmission congestion and VRE curtailment. To mitigate these limitations, an innovative virtual transmission solution based on battery energy storage systems (BESSs), known as grid booster (GB), has been proposed to increase the capacity of the main 500 kV corridor of the NPG. This paper analyzes the dynamic performance of the GB using a wide-area electromagnetic transient (EMT) model of the NPG. The GB project, composed of two 500 MVA BESS units at each extreme of the 500 kV corridor, allows increasing the transmission capacity for 15 min during N - 1 contingencies, overcoming transmission limitations under normal operation conditions while maintaining system stability during faults. The dynamic behavior of the GB is also analyzed to control power flow as well as voltage stability. The results show that the GB is an effective solution to allow greater penetration of VRE generation while maintaining system stability in the NPG.

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      • Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

        Xiao Xu, Ziwen Qiu, Teng Zhang, Hui Gao

        2024,12(2):440-453, DOI: 10.35833/MPCE.2023.000742

        Abstract: (811) HTML (82) PDF (3.53 MKB) (881)

        Abstract:

        The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

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      • Optimal Bidding Strategy for PV and BESSs in Joint Energy and Frequency Regulation Markets Considering Carbon Reduction Benefits

        Jing Bian, Yuheng Song, Chen Ding, Jianing Cheng, Shiqiang Li, Guoqing Li

        2024,12(2):427-439, DOI: 10.35833/MPCE.2023.000707

        Abstract: (625) HTML (74) PDF (3.46 MKB) (1018)

        Abstract:

        Photovoltaic (PV) and battery energy storage systems (BESSs) are key components in the energy market and crucial contributors to carbon emission reduction targets. These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading. This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets, with a specific focus on carbon reduction benefits. A two-stage bidding framework that optimizes the profit of PV and BESSs is presented. In the first stage, the day-ahead energy market takes into account potential real-time forecast deviations. In the second stage, the real-time balancing market uses a rolling optimization method to account for multiple uncertainties. Notably, a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control (AGC). This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market. This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error (ACE) occurs. The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.

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      • Optimal Offering of Energy Storage in UK Day-ahead Energy and Frequency Response Markets

        Makedon Karasavvidis, Andreas Stratis, Dimitrios Papadaskalopoulos, Goran Strbac

        2024,12(2):415-426, DOI: 10.35833/MPCE.2023.000737

        Abstract: (875) HTML (78) PDF (595.82 KKB) (749)

        Abstract:

        The offering strategy of energy storage in energy and frequency response (FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors (deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

        • 1
      • 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: (1083) HTML (115) PDF (4.42 MKB) (1765)

        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: (799) HTML (135) PDF (2.13 MKB) (1739)

        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.

        • 1
      • 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: (814) HTML (82) PDF (3.23 MKB) (1911)

        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: (813) HTML (177) PDF (2.79 MKB) (1599)

        Abstract:

        We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i.e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.

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      • Electricity Theft Detection Method Based on Ensemble Learning and Prototype Learning

        Xinwu Sun, Jiaxiang Hu, Zhenyuan Zhang, Di Cao, Qi Huang, Zhe Chen, Weihao Hu

        2024,12(1):213-224, DOI: 10.35833/MPCE.2022.000680

        Abstract: (481) HTML (14) PDF (2.16 MKB) (367)

        Abstract:

        With the development of advanced metering infrastructure (AMI), large amounts of electricity consumption data can be collected for electricity theft detection. However, the imbalance of electricity consumption data is violent, which makes the training of detection model challenging. In this case, this paper proposes an electricity theft detection method based on ensemble learning and prototype learning, which has great performance on imbalanced dataset and abnormal data with different abnormal level. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are employed to obtain abstract feature from electricity consumption data. After calculating the means of the abstract feature, the prototype per class is obtained, which is used to predict the labels of unknown samples. In the meanwhile, through training the network by different balanced subsets of training set, the prototype is representative. Compared with some mainstream methods including CNN, random forest (RF) and so on, the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5% and 1.25% of normal data. The results show that the proposed method outperforms other state-of-the-art methods.

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      • Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

        Bingjing Yan, Pengchao Yao, Tao Yang, Boyang Zhou, Qiang Yang

        2024,12(1):41-51, DOI: 10.35833/MPCE.2022.000524

        Abstract: (512) HTML (9) PDF (2.57 MKB) (455)

        Abstract:

        Electric power grids are evolving into complex cyber-physical power systems (CPPSs) that integrate advanced information and communication technologies (ICTs) but face increasing cyberspace threats and attacks. This study considers CPPS cyberspace security under distributed denial of service (DDoS) attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources. Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks. Different resource allocation strategies are adopted by attackers and defenders under the three cases of attack-free, failed attack, and successful attack, which lead to a corresponding consumption of resources. A multidimensional node value analysis is designed to introduce physical and cybersecurity indices. Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.

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      • Improved Subsynchronous Oscillation Parameter Identification with Synchrophasor Based on Matrix Pencil Method in Power Systems

        Xiaoxue Zhang, Fang Zhang, Wenzhong Gao, Jinghan He

        2024,12(1):22-33, DOI: 10.35833/MPCE.2022.000766

        Abstract: (715) HTML (22) PDF (2.95 MKB) (1343)

        Abstract:

        The subsynchronous oscillations (SSOs) related to renewable generation seriously affect the stability and safety of the power systems. To realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method (MPM), this paper proposes an improved MPM-based parameter identification with synchrophasors. The MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coefficients of the matrix pencil to ensure the accuracy of the identified parameters, since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation modes. Then, the identification and recovery of bad data are proposed by utilizing the difference in temporal continuity of the synchrophasors before and after noise reduction. The proposed parameter identification is verified with synthetic, simulated, and actual measured phase measurement unit (PMU) data. Compared with the existing MPM, the improved MPM achieves better accuracy for parameter identification of each component in SSOs, better real-time performance, and significantly reduces the effect of bad data.

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      • 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: (235) HTML (47) PDF (2.00 MKB) (748)

        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.

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      • 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: (601) HTML (79) PDF (1.33 MKB) (995)

        Abstract:

        The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short- and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.

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