Volume 13, Issue 5, 2025

>Original Paper

Co-optimization of Carbon Reduction and Carbon Sequestration in Power Sector Toward Carbon Neutrality

Mingyu Yang, Yusheng Xue, Bin Cai, Feng Xue

2025,13(5):1481-1494, DOI: 10.35833/MPCE.2024.001135

Abstract: (8) HTML (2) PDF (1.79 MKB) (22)

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Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal, spatial, and cross-domain factors. A novel framework is proposed, grounded in the cyber-physical-social system in energy (CPSSE) and whole-reductionism thinking (WRT), incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector. Using the carbon peaking and carbon neutrality transition of China as a case study, clustering method is employed to construct a diverse set of strategically distinct carbon trajectories. For each trajectory, the evolution of the generation mix and the deployment pathways of carbon capture and storage (CCS) technologies are analyzed, identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs. Further, by comparing non-fossil energy substitution and CCS retrofitting in thermal power, the analysis highlights the potential for co-optimization of carbon reduction and carbon sequestration. The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality, providing insights for integrated decarbonization strategies in power system planning.

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Fast-converging Deep Reinforcement Learning for Optimal Dispatch of Large-scale Power Systems Under Transient Security Constraints

Tannan Xiao, Ying Chen, Han Diao, Shaowei Huang, Chen Shen

2025,13(5):1495-1506, DOI: 10.35833/MPCE.2024.000624

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Power system optimal dispatch with transient security constraints is commonly represented as transient security-constrained optimal power flow (TSC-OPF). Deep reinforcement learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converging DRL method for optimal dispatch of large-scale power systems under transient security constraints is proposed in this paper. The Markov decision process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved deep deterministic policy gradient algorithm with curriculum learning, parallel exploration, and ensemble decision-making (DDPG-CL-PE-ED) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.

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Physics-guided Safe Policy Learning with Enhanced Perception for Real-time Dynamic Security Constrained Optimal Power Flow

Yujian Ye, Yizhi Wu, Jianxiong Hu, Hao Hu, Siqi Qian, Xi Zhang, Qiong Wang, Goran Strbac

2025,13(5):1507-1519, DOI: 10.35833/MPCE.2024.001219

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Driven by increasing penetration of intermittent renewable energy generation, modern power systems are promoting the integration of energy storage (ES) and advocating high-resolution dynamic security constrained optimal power flow (DSCOPF) models to exploit ES time-shifting flexibility against contingencies and respond promptly to more frequent variations in the system operating status. While pioneering research works explore different methods to solve security constrained optimal power flow (SCOPF) problems at individual time steps, real-time implementation of DSCOPF still faces challenges associated with uncertainty adaptation, complex constraint satisfaction， and computational efficiency. This paper proposes a physics-guided safe policy learning method, featuring an analytical evaluation model to provide both accurate safety and cost-efficiency evaluations. A primal-dual-based learning procedure is developed to guide policy learning, fostering prompt convergence. A spatial-temporal graph neural network is constructed to enhance perception on the spatial-temporal uncertainties and leverage policy generalization. Case studies validate the effectiveness and scalability of the proposed method in safety, cost-efficiency, and computational performance and highlight the value of enhanced perception on IEEE 39-bus and 118-bus test systems.

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Distributed Hierarchical Transactive Energy Management to Exploit Flexibilities in Transmission Systems

Ali Alizadeh, Mahmoud A. Allam, Moein Esfahani, Innocent Kamwa

2025,13(5):1520-1531, DOI: 10.35833/MPCE.2024.000859

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Advanced management algorithms are required in modern power systems to sustain energy supply with the highest availability and lowest cost. These algorithms need to be capable of not only maintaining scalability, tractability, and privacy, but also enabling the utilization of grid-edge aggregated flexibilities in transmission systems. This paper proposes a distributed hierarchical transactive energy management (TEM) scheme to manage peak load and line congestion problems using connected and aggregated flexibilities. In the scheme, resource owners can privately solve their respective preference problems and send their scheduled power to the corresponding node operator (NO). Afterward, NOs solve a coordination problem to harmonize the actions of resource owners at the same node. Meanwhile, the independent system operator (ISO) updates control signals to steer the scheduled power to a feasible and optimal point. To accomplish all these, a hybrid decomposition approach is further proposed based on consensus+exchange alternating direction method of multipliers (CE-ADMM) and dual decomposition (DD) (CE-ADMM+DD). Besides, a dynamically constrained cutting plane (DC-CP) update algorithm is evolved to control the feasibility condition and minimize sensitivity to initialization. The proposed hybrid decomposition approach is verified and its performance is compared with other reported approaches. Application to various networks verifies its scalability, enhanced accuracy, and convergence speed.

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Transmission Expansion Planning with High Surge Impedance Loading Lines at Reduced Voltage Levels

Bhuban Dhamala, Mona Ghassemi

2025,13(5):1532-1544, DOI: 10.35833/MPCE.2024.001149

Abstract: (3) HTML (1) PDF (3.16 MKB) (19)

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Transmission expansion planning (TEP) addresses the intricate task of optimizing new transmission infrastructure within an existing grid to meet system objectives. As a critical strategy in power system development, TEP significantly influences the long-term efficiency, reliability, and scalability of the network, with enduring effects on overall system performance. This paper explores the application of unconventional high surge impedance loading (HSIL) lines as a cost-effective alternative to conventional extra high-voltage (EHV) transmission lines. By optimizing the geometry of subconductors, HSIL designs could achieve higher power delivery capacities while operating at reduced voltage levels, addressing the increased demand for sustainable energy infrastructure. Two 500 kV HSIL line configurations are analyzed for their feasibility in replacing the conventional 765 kV transmission lines for the TEP to integrate the large-scale wind energy sources located in far remote areas. The analysis is carried out within the 23-bus EHV test system. This study reveals that both HSIL line configurations successfully meet the technical constraints of the TEP problem, ensuring reliable system operation even under contingency conditions. Therefore, the HSIL lines offer significant cost savings due to infrastructure and accessories at reduced voltage levels with much smaller right of way (ROW) than conventional counterparts. This underscores the potential of unconventional HSIL lines to contribute to more sustainable and cost-effective grid planning strategies for integrating large-scale renewable energy sources.

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Deep-learning-based Short-term Voltage Stability Assessment with Topology-adaptive Voltage Dynamic Feature and Domain Transfer

Xin Chen, Long Huo, Chengqian Sun

2025,13(5):1545-1555, DOI: 10.35833/MPCE.2024.000507

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Short-term voltage stability (STVS) assessment is a critical monitoring technology in modern power systems. During daily operations, transmission lines may switch on or off due to scheduled maintenance or unexpected faults, which poses challenges to the STVS assessment under varying topology change conditions. To adapt the STVS assessment to the system topology changes, we propose a deep-learning-based STVS assessment model with the topology-adaptive voltage dynamic feature and the fine-tuning domain transfer for power systems with changing system topologies. The topology-adaptive voltage dynamic feature, extracted from streaming time-series data of phasor measurement units (PMUs), is used to characterize transient voltage stability. The voltage dynamic features depend on the balance of reactive power flow and system topology, effectively revealing both spatio-temporal patterns of post-disturbance system dynamics. The simulation results based on large disturbances in the New England 39-bus power system demonstrate that the proposed model achieves superior STVS assessment performance, with an accuracy of 99.65% in predicting voltage stability compared with the existing deep learning methods. The proposed model also performs well when applied to the larger IEEE 145-bus power system. The fine-tuning domain transfer of the proposed model adapts very well to system topology changes in power systems. It achieves an accuracy of 99.50% in predicting the STVS for the New England 39-bus power system with the transmission line alternation. Furthermore, the proposed model demonstrates strong robustness to noisy and missing data.

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Reducing Blackout Risk by Segmenting European Power Grid with HVDC Lines

Damià Gomila, Benjamín A. Carreras, José-Miguel Reynolds-Barredo, María Martínez-Barbeito, Pere Colet, Oriol Gomis-Bellmunt

2025,13(5):1556-1567, DOI: 10.35833/MPCE.2024.000768

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The utilization of high-voltage direct current (HVDC) lines for the segmentation of the European power grid has been demonstrated to be a highly effective strategy for the mitigation of the risk of cascading blackouts. In this study, an accurate and efficient method for determining the optimal power flow through HVDC lines is presented, with the objective of minimizing load shedding. The proposed method is applied to two distinct scenarios: first, the segmentation of the power grid along the Pyrenees, with the objective of segmenting the Iberian Peninsula from the rest of Europe; and second, the segmentation of the power grid into Eastern and Western Europe, approximately in half. In both scenarios, the method effectively reduces the size of blackouts impacting both sides of the HVDC lines, resulting in a 46% and 67% reduction in total blackout risk, respectively. Furthermore, we have estimated the cost savings from risk reduction and the expenses associated with converting conventional lines to HVDC lines. Our findings indicate that segmenting the European power grid with HVDC lines is economically viable, particularly for segmenting the Iberian Peninsula, due to its favorable cost-risk reduction ratio.

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Optimal Preventive Generation Rescheduling for Improving Small- and Large-disturbance Rotor Angle Stabilities of Power Systems Considering Wind Power Uncertainty

Heling Yuan, Yan Xu

2025,13(5):1568-1579, DOI: 10.35833/MPCE.2024.000853

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The widespread penetration of wind power has introduced challenges in managing the rotor angle stability characteristics of the power system, affecting both small- and large-disturbance rotor angle stabilities due to its uncertain steady-state power output and inverter-based grid interfacing. Traditionally, the two stability criteria are separately analyzed and improved via preventive control, e.g., generation rescheduling. However, they may have conflicting relationship during the preventive control optimization. Therefore, this paper firstly integrates both small- and large-disturbance rotor angle stabilities and proposes an optimization model for preventive generation rescheduling to simultaneously improve them while considering wind power uncertainty. The stability constraints are linearized using trajectory sensitivity analysis, while the wind power fluctuation is represented by employing a scenario-based Taguchi’s orthogonal array testing (TOAT) method. An iterative solution method is proposed to efficiently solve the optimization model. The proposed optimization model is established on the New England 10-machine 39-bus system and a large Nordic system, demonstrating its robustness and effectiveness in addressing wind power fluctuations.

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Oscillation Stability Control Based on Equipment-level and Farm-level Cooperative Optimization for Power System Connected with Direct-drive PMSG-based Wind Farms

Jing Ma, Yawen Deng, Honglu Xu, Yufeng Zhao

2025,13(5):1580-1592, DOI: 10.35833/MPCE.2024.001021

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Existing sub-/super-synchronous oscillation stability control methods are primarily focused on specific operating conditions at discrete frequencies, limiting their adaptation to varying oscillation ​scenarios in the power system connected with direct-drive permanent magnet synchronous generator (PMSG)-based wind farms. Based on supplementary dissipation compensation, this paper proposes an oscillation stability control method incorporating equipment-level and farm-level cooperative optimization ​to enhance the system-level stability. First, the effects of dynamic self-dissipation and dynamic coupled dissipation on system stability are analyzed, ​establishing the foundational principle of supplementary dissipation compensation. Subsequently, the optimal locations for supplementary dissipation compensation are identified based on critical control designed to enhance the dynamic self-dissipation effect and suppress the dynamic coupled dissipation effect. Furthermore, by considering energy requirements under the combined wind farm-grid interaction and inter-PMSG interactions and balancing the wind farm-grid interaction dissipation energy with inter-PMSG interaction dissipation energy distribution, an equipment-level control parameter optimization algorithm and a farm-level power cooperative optimization algorithm are established. Finally, the simulation results demonstrate that dynamic coupled dissipation constitutes the ​root cause of oscillation inception and progression. Through equipment-level and farm-level cooperative optimization, the proposed method can reliably compensate dynamic dissipation energy, while adapting to the variation of oscillation frequency and the oscillation scenario. It can maximize the energy dissipation effect of the interconnected system, achieving rapid suppression of sub-/super-synchronous oscillations.

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Revisiting Capacity Value of Variable Renewable Energy Generation in Power Systems with High Renewable Energy Penetration

Yanghao Yu, Haiyang Jiang, Ning Zhang, Pei Yong, Fei Teng, Jiawei Zhang, Yating Wang, Goran Strbac

2025,13(5):1593-1603, DOI: 10.35833/MPCE.2024.001101

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Adequacy is a key concern of power system planning, which refers to the availability of sufficient facilities to meet demand. The capacity value (CV) of variable renewable energy (VRE) generation represents its equivalent contribution to system adequacy, in comparison to conventional generators. While VRE continues to grow and increasingly dominates the generation portfolio, its CV is becoming non-negligible, with the corresponding impact mechanisms becoming more complicated and nuanced. In this paper, the concept of CV is revisited by analyzing how VRE contributes to power system balancing at a high renewable energy penetration level. A generalized loss function is incorporated into the CV evaluation framework considering the adequacy of the power system. An analytical method for the CV evaluation of VRE is then derived using the statistical properties of both hourly load and VRE generation. Through the explicit CV expression, several critical impact factors, including the VRE generation variance, source-load correlation, and system adequacy level, are identified and discussed. Case studies demonstrate the accuracy and effectiveness of the proposed method in comparison to the traditional capacity factor-based methods and convolution-based methods. In the IEEE-RTS79 test system, the CV of a 2500 MW wind farm (with 40% renewable energy penetration level) is found to be 6.8% of its nameplate capacity. Additionally, the sensitivity of CV to various impact factors in power systems with high renewable energy penetration is analyzed.

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Active Damping Control of High-frequency Resonances Based on Virtual Admittance for DFIG System Connected to Weak Grid

Mohammad Javad Jalilian, Behrooz Vahidi, Seyed Fariborz Zarei, Gholam Hossein Riahy Dehkordi

2025,13(5):1604-1616, DOI: 10.35833/MPCE.2023.000872

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The mutual impedance between doubly-fed induction generator (DFIG) system and weak grid may cause a resonance, which yields to undesirable distortions and harmonics. The equivalent impedance of DFIG systems is high, which creates high-frequency resonance (HFR) in interaction with weak grids. Although several studies are conducted to mitigate HFRs, more improvements are needed in terms of damping and phase-margin. Accordingly, an active damping control strategy based on virtual admittance is proposed, which properly mitigates the disturbances. The proposed strategy is accurate as it considers the dynamic high-frequency model of DFIG system to effectively reduce the HFR. The performance of the proposed strategy is verified by using different case studies on a 2 MW DFIG system with time-domain simulations in MATLAB/Simulink environment.

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Energy Management of Photovoltaic-Battery Energy Storage System for Stable Frequency Support Based on Flexible Power Reserve Considering SOC Recovery

Tianqi Liu, Yulong Shi, Qiao Peng

2025,13(5):1617-1629, DOI: 10.35833/MPCE.2024.000725

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The reduced frequency regulation capability in low-inertia power systems necessitates enhanced frequency support from photovoltaic (PV) systems. However, the regulation capability of PV system under conventional control scheme is limited, which requires flexible power control and support from battery energy storage systems (BESSs). This paper proposes an energy management strategy of PV-BESS to provide stable frequency support to the grid. The proposed strategy initially develops a maximum power point tracking (MPPT)-based power reserve control (PRC) for PV power reserve. At this stage, the BESS is manipulated to buffer the power fluctuations caused by MPPT execution and environmental variability. To enhance the regulation capability of BESS and mitigate battery aging, the proposed strategy incorporates a flexible PRC for state of charge (SOC) recovery. Subsequently, the battery can be rationally charged or discharged during the PRC process to maintain SOC. Simulation results obtained from MATLAB/Simulink and experimental results from the StarSim platform validate that the proposed strategy achieves stable PV power reserve, mitigates fluctuations induced by environmental uncertainties, enables efficient SOC recovery, and improves grid frequency quality. Overall, the proposed strategy ensures stable operation of PV-BESS both under steady-state conditions and during frequency events.

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Complex Frequency-based Control for Inverter-based Resources

Rodrigo Bernal, Federico Milano

2025,13(5):1630-1641, DOI: 10.35833/MPCE.2024.000907

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This paper proposes a novel control scheme for inverter-based resources (IBRs) based on the complex frequency (CF) concept. The control objective is to maintain a constant CF of voltage at the terminals of IBR by adjusting its current reference. This current is imposed based on the well-known power flow equation, the dynamics of which are calculated through estimating the CFs for the voltages of adjacent buses. The performance is evaluated by analyzing the local variations in frequency and voltage magnitude, as well as the frequency of center of inertia (CoI), and then compared with conventional frequency droop, proportional-integral (PI) voltage controllers, and virtual inertia. The case study utilizes a modified version of WSCC 9-bus system and a 1479-bus model of the Irish transmission grid and considers various contingencies and sensitivities such as the impact of current limiters, delays, noise, R/X ratio, and electromagnetic transient (EMT) dynamics. Results show that the proposed control scheme consistently outperforms the conventional controllers, leading to significant improvements in the overall dynamic response of the system.

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Impedance Dataset Optimization Method for Data-driven Modeling of Renewable Power Generation Equipment Considering Multi-operation Conditions

Han Li, Heng Nian, Bin Hu, Zhen He

2025,13(5):1642-1652, DOI: 10.35833/MPCE.2024.000967

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The data-driven approaches have been extensively developed for multi-operation impedance modeling of the renewable power generation equipment (RPGE). However, due to the black box of RPGE, the dataset used for establishing impedance model lacks theoretical guidance for data generation, which reduces data quality and results in a large amount of data redundancy. To address this issue, this paper proposes an impedance dataset optimization method for data-driven modeling of RPGE considering multi-operation conditions. The objective is to improve the data quality of the impedance dataset, thereby reflecting the overall impedance characteristics with a reduced data amount. Firstly, the impact of operation conditions on impedance is evaluated to optimize the selection of operating points. Secondly, at each operating point, the frequency distribution is designed to reveal the impedance characteristics with fewer measurement points. Finally, a serial update method for measured datasets and the multi-operation impedance model is developed to further refine the dataset. The experiments based on control-hardware-in-loop (CHIL) are conducted to verify the effectiveness of the proposed method.

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Reshaping Reactive Power Control Loop to Suppress Sub-synchronous Oscillation of Grid-forming Converters at Low Power Levels

Jinhao Wang, Chao Wu, Qianchen Sun, Yakun Liu, Yong Wang

2025,13(5):1653-1663, DOI: 10.35833/MPCE.2024.000903

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This paper demonstrates a new type of sub-synchronous oscillation of the grid-forming (GFM) converter, which occurs at low rather than high power levels. To reveal the intrinsic mechanism, a simplified analytical small-signal impedance model of the GFM converter is derived. It is found that the reactive power control loop (RPCL) can have a significant impact on the system stability. In particular, the voltage matrix introduced by the RPCL is the key factor causing instability in the GFM grid-connected system at low operating points. Therefore, this paper proposes a control strategy that reshapes the RPCL to counteract the negative effect of the voltage matrix by introducing a q-axis current feedforward, ensuring stable operation at any operating point. Finally, experimental results validate the correctness and effectiveness of the proposed control strategy.

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Interpretable Distributionally Robust Optimization for Battery Energy Storage System Planning

Qian Wang, Xueguang Zhang, Ying Xu, Zhongkai Yi, Dianguo Xu

2025,13(5):1664-1676, DOI: 10.35833/MPCE.2024.000974

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A mathematical programming approach rooted in distributionally robust optimization (DRO) provides an effective data-driven strategy for battery energy storage system (BESS) planning. Nevertheless, the DRO paradigm often lacks interpretability in its results, obscuring the causal relationships between data distribution characteristics and the outcomes. Furthermore, the current approach to battery type selection is not included in traditional BESS planning, hindering comprehensive optimization. To tackle these BESS planning problems, this paper presents a universal method for BESS planning, which is designed to enhance the interpretability of DRO. First, mathematical definitions of interpretable DRO (IDRO) are introduced. Next, the uncertainties in wind power, photovoltaic power, and loads are modeled by using second-order cone ambiguity sets (SOCASs). In addition, the proposed method integrates selection, sizing, and siting. Moreover, a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems. Finally, the effectiveness of the proposed method is demonstrated through case studies, offering planners richer decision-making insights.

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Coordinating Multiple Geo-distributed Data Centers for Enhanced Participation in Frequency Regulation Services Under Uncertainty

Bin Zou, Ge Chen, Hongcai Zhang, Yonghua Song

2025,13(5):1677-1688, DOI: 10.35833/MPCE.2024.001044

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Data centers are promising demand-side flexible resources that can provide frequency regulation services to power grids. While most existing studies focus on individual data centers, coordinating multiple geo-distributed data centers can significantly enhance operational flexibility and market participation. However, the inherent uncertainty in both data center workloads and regulation signals pose significant challenges to maintaining effective operations, let alone determining regulation capacity offerings. To address these challenges, this paper proposes a coordinated bidding strategy for electricity purchases and regulation capacity offerings for multiple geo-distributed data centers in electricity markets. This strategy expands the feasible region of operational decisions, including workload dispatch, server activation, and cooling behaviors. To enhance the participation of data centers in frequency regulation services under uncertainty, chance-constrained programming is adopted. This paper presents explicit models for these uncertainties involved, starting with the Poisson-distributed workloads and then addressing the unpredictable regulation signals. Numerical experiments based on real-world datasets validate the effectiveness of the proposed strategy compared with state-of-the-art strategies.

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Harmonic Blocking Based Differential Relay Protection Considering Neutral Stray Currents from DC Metro Systems

Haoran Fan, Sheng Lin, Aimin Wang, Qi Zhou, Hongbo Cheng

2025,13(5):1689-1700, DOI: 10.35833/MPCE.2024.000440

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Stray currents from DC metro systems intrude into the grounded neutrals of large power transformers, posing a major threat to the differential relay protection of transformer. In this paper, the performance of harmonic blocking based differential relay protection considering neutral stray currents (NSCs) from DC metro systems is thoroughly investigated. The findings reveal that relays may fail to clear internal faults in some scenarios because they are blocked due to NSC-induced harmonic currents. To improve the reliability of differential relay protection, a method for preventing incorrect operation is proposed using a skewness-based criterion to detect the presence of NSCs. Then, the relay is unblocked when an internal fault is simultaneously detected by the novel internal fault detection block. The proposed method is resistant to current transformer saturation and accounts for NSC fluctuations. Various time-domain simulations conducted in PSCAD/EMTDC verify the effectiveness of the proposed method.

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Operational Reliability Evaluation and Risk Mitigation of Asynchronous Grids Coupled Through Flexible HVDC Systems

Qing Sun, Junjie Tang, Sui Peng, Weijie Zhong, Liu Zhu, Yuan Zhao, Wenyuan Li

2025,13(5):1701-1713, DOI: 10.35833/MPCE.2024.000677

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This paper constructs a synthetic framework for the operational reliability evaluation and risk mitigation of asynchronous grids coupled through flexible high-voltage DC (HVDC) systems (AGs-FDCSs). First of all, an analytical model for the unavailability of DC units is reformulated to refine and facilitate the reliability modeling of such flexible HVDC systems considering their time-dependent features as well as the impacts of converter station configurations. Subsequently, the operational risk associated with the redispatch procedure is extended to the reliability evaluation of composite power system, and the risk is mitigated through an optimal power flow (OPF) based short-term state assessment model. In addition, some new reliability indices like expected DC transmission power (EDCTP) and DC terminal outage probability (DCTOP) are defined to quantify the impact of the reliability of flexible HVDC systems on the entire grid. The effectiveness of the proposed framework on a modified IEEE RTS-79 system is validated with the elaborate discussions on the time-dependent reliability of AGs-FDCSs as well as the impacts of the converter station configurations.

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A Distributionally Robust Optimization Scheduling Considering Distribution of Tie-line Endpoints

Minghao Guo, Hongjun Gao, Haifeng Qiu, Junyong Liu

2025,13(5):1714-1725, DOI: 10.35833/MPCE.2024.000747

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Abstract:

As power systems scale up and uncertainties deepen, traditional centralized optimization approaches impose significant computation burdens on large-scale optimization problems, introducing new challenges for power system scheduling. To address these challenges, this study formulates a distributionally robust optimization (DRO) scheduling model that considers source-load uncertainty and is solved using a novel distributed approach that considers the distribution of tie-line endpoints. The proposed model includes a constraint related to the transmission interface, which consists of several tie-lines between two subsystems and is specifically designed to ensure technical operation security. In addition, we find that tie-line endpoints enhance the speed of distributed computation, leading to the development of a power system partitioning approach that considers the distribution of these endpoints. Further, this study proposes a distributed approach that employs an integrated algorithm of column-and-constraint generation (C&CG) and sub-gradient descent (IACS) to address the proposed model across multiple subsystems. A case study of two IEEE test systems and a practical provincial power system demonstrates that the proposed model effectively ensures system security. Finally, the scalability and effectiveness of the distributed approach in accelerating problem-solving are confirmed.

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Offline-training Online-execution Framework for Volt-var Control in Distribution Networks

Shu Zheng, Zhi Wu, Xiao Zhang, Wei Gu, Jingtao Zhao, Zhihua Xu

2025,13(5):1726-1737, DOI: 10.35833/MPCE.2024.000887

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With the increasing integration of uncertain distributed renewable energies (DREs) into distribution networks (DNs), communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning (DRL). To address these issues, this paper proposes an offline-training online-execution framework for volt-var control in DNs. In the offline-training phase, a graph convolutional network (GCN)-based denoising autoencoder (DAE), referred to as the deep learning (DL) agent, is designed and trained to capture spatial correlations among limited physical quantities. This agent predicts voltage values for nodes with missing measurements using historical load data, DRE outputs, and global voltages from simulations. Furthermore, the dual-timescale voltage control problem is formulated as a multi-agent Markov decision process. A DRL agent employing the multi-agent soft actor-critic (MASAC) algorithm is trained to regulate the tap position of on-load tap changer (OLTC) and reactive power output of photovoltaic (PV) inverters. In the online-execution phase, the DL agent supplements the limited measurement data, providing enhanced global observations for the DRL agent. This enables precise equipment control based on improved system state estimation. The proposed framework is validated on two modified IEEE test systems. Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid, and accurate voltage control even under severe measurement deficiencies.

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Mixed Taguchi-based Method for Probabilistic Short Circuit Analysis of Low-voltage Distribution Systems with Photovoltaic Systems

Antonio Bracale, Pierluigi Caramia, Guido Carpinelli, Pasquale De Falco, Paola Verde

2025,13(5):1738-1751, DOI: 10.35833/MPCE.2024.000772

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The probabilistic short circuit analysis provides relevant information for power system planning and power quality assessment tasks. Traditional Monte Carlo methods (TMCMs) are usually applied to consider the randomness affecting short circuit operating conditions, but they require numerous iterations to properly characterize the network conditions. This paper proposes a mixed Taguchi-based method (MTBM) as a new alternative tool to account for the uncertainties affecting the inputs of probabilistic short circuit analysis. The MTBM significantly reduces the number of iterations required to properly address the randomness of inputs (environmental conditions, pre-fault conditions, fault characteristics), and allows diversifying the representation of inputs through a quantile-based selection of their levels. The proposed method is applied to unbalanced three-phase four-wire low-voltage (LV) distribution systems with photovoltaic systems (PVSs) operating in low voltage ride- through (LVRT) during the fault. Numerical applications related to a test system are presented, and the proposed MTBM is compared with the TMCM, the unmixed Taguchi-based method (UTBM), and the point estimate method (PEM). The proposed MTBM returns values very close to those of the TMCM (with average deviations ranging from 0.01% to 3.12%) and enables a fast and accurate analysis of faulted LV distribution systems with PVSs operating in LVRT.

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Collaborative Recovery Method for Cyber-physical Distribution System Considering Multiple Coupling Constraints

Jiani Lu, Chao Qin, Yuan Zeng, Guilian Wu, Hao Chen

2025,13(5):1752-1762, DOI: 10.35833/MPCE.2024.000925

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In cyber-physical distribution systems (CPDSs), the complex coupling between cyber and physical components poses significant challenges to system resilience. When extreme weather disasters occur, these coupling relationships greatly increase the complexity of recovery decisions, which prolongs recovery time and increases recovery costs. In this paper, a collaborative recovery method for CPDS considering multiple coupling constraints is proposed to avoid large-scale outages. First, a fictitious flow based model is established to describe the functional availability of cyber nodes. Second, three typical components are analytically modeled to describe the energy-control coupling relationships. Then, a collaborative recovery method is proposed for post-disaster crew dispatch, network reconfiguration, and fault repair to restore critical loads, considering both the cyber availability constraints and cyber-physical coupling constraints. Finally, the effectiveness of the proposed recovery method is verified by the DCPS-160 test system.

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Non-intrusive Hybrid Two-stage Detection of Dynamic Attacks in Wide-area Damping Controller Using Autoencoder and Unscented Kalman Filter with Unknown Input Estimation

Abhishek Saini, Hussain M. Mustafa, Pratyasa Bhui, Anurag K.ivastava

2025,13(5):1763-1775, DOI: 10.35833/MPCE.2024.000946

Abstract: (4) HTML (3) PDF (10.06 MKB) (18)

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Wide-area damping controllers (WADCs) help in damping poorly damped inter-area oscillations (IAOs) using wide-area measurements. However, the vulnerability of the communication network makes the WADC susceptible to malicious dynamic attacks. Existing cyber-resilient WADC solutions rely on accurate power system models or extensive simulation data for training the machine learning (ML) model, which are difficult to obtain for large-scale power system. This paper proposes a novel non-intrusive hybrid two-stage detection framework that mitigates these limitations by eliminating the need for real-time access to large system data or attack samples for training the ML model. In the first stage, an autoencoder is deployed at the actuator location to detect dynamic attacks with sharp gradient variations, e.g., triangular, saw-tooth, pulse, ramp, and random attack signals. In the second stage, an unscented Kalman filter with unknown input estimation at the control center identifies smoothly varying dynamic attacks by estimating the control signal received by the actuator using synchrophasor measurements. A modified cosine similarity (MCS) metric is proposed to compare and quantify the similarity between the estimated control signal and the control signal sent by the WADC placed at the control center to detect any dynamic attacks. The MCS is designed to differentiate between events and dynamic attacks. The performance of the proposed framework has been validated on a hardware-in-the-loop (HIL) cyber-physical testbed built by using the OPAL-RT simulator and industry-grade hardware.

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Smart Inverter Enabled Meter Encoding for Detecting False Data Injection Attacks in Distribution System State Estimation

Hang Zhang, Bo Liu, Hongyu Wu

2025,13(5):1776-1786, DOI: 10.35833/MPCE.2024.000882

Abstract: (6) HTML (3) PDF (2.72 MKB) (9)

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Meter encoding, as a side-effect-free scheme, has been proposed to detect false data injection (FDI) attacks without significantly affecting the operation of power systems. However, existing meter encoding schemes either require encoding lots of measurements from different buses to protect a substantial proportion of a power system or are unhidden from alert attackers. To address these issues, this paper proposes a smart inverter enabled meter encoding scheme for detecting FDI attacks in distribution system state estimation. The proposed scheme only encodes the measurements from the existing programmable smart inverters. Meanwhile, this scheme can protect all the downstream buses from the encoded inverter bus. Compared with existing schemes, the proposed scheme encodes fewer meters when protecting the same number of buses, which decreases the encoding cost. In addition, by following the physical power flow laws, the proposed scheme is hidden from alert attackers who can implement the state estimation-based bad data detection (BDD). Simulation results from the IEEE 69-bus distribution system demonstrate that the proposed scheme can mislead the attacker’s state estimation on all the downstream buses from the encoded bus without arousing the attacker’s suspicion. FDI attacks that are constructed based on the misled estimated state are very likely to trigger the defender’s BDD alarm.

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A Decentralized Peer-to-peer Framework for Integrated Electricity-Heat-Carbon Sharing Among Multiple Microgrids

Jie Wang, Hongjie Jia, Xiaolong Jin, Xiaodan Yu, Yunfei Mu, Kai Hou, Wei Wei, Jiarui Zhang, He Meng

2025,13(5):1787-1799, DOI: 10.35833/MPCE.2024.000618

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The increasing focus on carbon neutrality has led to heightened interest in multiple microgrids (MGs) due to their potential to significantly reduce emissions by the integrated electricity-heat-carbon sharing among them. In this paper, a decentralized peer-to-peer (P2P) framework for integrated electricity-heat-carbon sharing is proposed to optimize the trading process of multi-energy and carbon among multiple MGs. The proposed framework considers certified emission reductions (CERs) of photovoltaic (PV) systems in each MG, and carbon allocation and trading among multiple MGs. The P2P trading behaviors among multiple MGs are modelled as a non-cooperative game. A decentralized optimization method is then developed using a price-based incentive scheme to solve the non-cooperative game and optimize the transactions of the electricity-heat-carbon jointly. The optimization problem is solved using sub-gradient in a decentralized manner. And the Nash equilibrium of the non-cooperative game is proven to exist uniquely, ensuring the convergence of the model. Furthermore, the proposed decentralized optimization method safeguards the private information of the MGs. Numerical results show that the total operational cost of the MGs and the carbon emissions can be reduced significantly.

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Distributed Multi-scale Attention and Predictor-based Control for AC Microgrids with Time Delays and Cyber Failures

Yutong Li, Ningxuan Guo, Lili Wang, Jian Hou, Yinan Wang, Gangfeng Yan

2025,13(5):1800-1812, DOI: 10.35833/MPCE.2024.000685

Abstract: (2) HTML (2) PDF (3.51 MKB) (18)

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Distributed secondary control has been proposed to maintain frequency/voltage synchronization and power sharing for distributed energy sources in AC microgrids (MGs). The cyber layer is susceptible to time delays and cyber failures and thus, a distributed resilient secondary control should be investigated. This paper proposes a distributed multi-scale attention and predictor-based control (DMAPC) strategy to address false data injection attacks and packet loss failures with time delays. The multi-scale attention mechanism enables the system to selectively focus on neighbors ’ states with higher confidence evaluated in different time scales, while the data-driven predictor compensates for lost neighbors’ states in the nonlinear controller. The DMAPC does not impose strict limitations on the number of false communication links or upper bound for false data. Besides, the DMAPC is formulated as an uncertain system with time delays and is proven to be uniformly ultimately bounded. Extensive experiments on a hardware-in-the-loop MG testbed have validated the effectiveness of DMAPC, which successfully relaxes restrictions on cyber failures compared to existing strategies.

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A Joint Electricity-reserve Trading Model for Virtual Power Plants to Mitigate Naked Selling

Yingjun Wu, Runrun Chen, Yuyang Chen, Xuejie Chen, Jiangfan Yuan, Hengchao Mao, Juefei Wang

2025,13(5):1813-1822, DOI: 10.35833/MPCE.2024.000211

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Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.

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Two-stage Bidding Strategy with Dispatch Potential of Electric Vehicle Aggregators for Mitigating Three-phase Imbalance

Chengwei Lou, Chen Li, Lu Zhang, Wei Tang, Jin Yang, Jake Cunningham

2025,13(5):1823-1835, DOI: 10.35833/MPCE.2024.001067

Abstract: (4) HTML (1) PDF (2.64 MKB) (17)

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The proliferation of electric vehicles (EVs) introduces transformative opportunities and challenges for the stability of distribution networks. Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid, while regulated EV charging will alleviate such imbalance. To systematically address this challenge, this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators (EVAs). By constructing a coordinated framework that integrates the day-ahead and real-time markets, the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system, with a particular focus on dynamic compensation for deviations between scheduled and real-time operations. A bi-level Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance, with Karush-Kuhn-Tucker (KKT) conditions and mixed-integer second-order cone programming (MISOCP) ensuring feasible solutions. The proposed coordinated framework is validated with different bidding modes includes independent bidding, full price acceptance, and cooperative bidding modes. The proposed two-stage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.

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Data-driven Peer-to-peer Energy Trading Based on Prosumer-driven Carbon-aware Distribution Locational Marginal Price

Xingyu Liu, Yunting Yao, Tianran Li, Yening Lai, Qi Wang, Zhenya Ji

2025,13(5):1836-1848, DOI: 10.35833/MPCE.2024.000548

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Peer-to-peer (P2P) energy trading enables an efficient regulation of distributed renewable energy among prosumers, implicitly promoting low-carbon operation. This study proposes a novel P2P energy trading scheme with coupled electricity-carbon (E/C) market that co-optimizes both power and carbon emission flows. To facilitate the low-carbon operations in the market, we introduce a prosumer-driven carbon-aware distribution locational marginal price (PDC-DLMP) to serve as a pricing signal for the distribution system operator (DSO). To efficiently determine the optimal trading solutions, we adopt a two-layer data-driven approach. The first layer employs a reinforcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient (MATD3); the second layer uses a deep neural network (DNN) driven surrogate model, which is designed to map the PDC-DLMP signals, thereby eliminating the need for direct DSO intervention during market operation. This approach protects the physical model parameters of the distribution network and ensures multi-level privacy protection. Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market, demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.

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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: (89) HTML (59) PDF (1.50 MKB) (414)

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  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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• 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: (91) HTML (46) PDF (2.09 MKB) (414)

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

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• 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: (56) HTML (53) PDF (3.67 MKB) (788)

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  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: (68) HTML (31) PDF (3.37 MKB) (731)

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  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: (43) HTML (54) PDF (6.88 MKB) (952)

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  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: (48) HTML (68) PDF (2.47 MKB) (777)

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  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: (56) HTML (28) PDF (7.71 MKB) (877)

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  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: (48) HTML (39) PDF (4.23 MKB) (825)

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  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: (61) HTML (29) PDF (3.61 MKB) (923)

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  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: (110) HTML (51) PDF (3.02 MKB) (889)

  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: (93) HTML (40) PDF (1.67 MKB) (360)

  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: (119) HTML (146) PDF (2.66 MKB) (1861)

  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: (120) HTML (42) PDF (4.96 MKB) (1259)

  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: (223) HTML (85) PDF (1.12 MKB) (1254)

  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: (243) HTML (115) PDF (3.80 MKB) (2507)

  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: (158) HTML (43) PDF (4.37 MKB) (640)

  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: (130) HTML (47) PDF (2.80 MKB) (622)

  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.

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

  Abdelfatah Ali, Hossam H. H. Mousa, Mostafa F. Shaaban, Maher A. Azzouz, Ahmed S. A. Awad

  2024,12(3):675-694, DOI: 10.35833/MPCE.2023.000107

  Abstract: (261) HTML (156) PDF (7.11 MKB) (1991)

  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: (595) HTML (94) PDF (4.50 MKB) (1278)

  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: (831) HTML (96) PDF (3.53 MKB) (1063)

  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: (640) HTML (84) PDF (3.46 MKB) (1233)

  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: (891) HTML (88) PDF (595.82 KKB) (918)

  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.

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

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

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

  Abstract: (1098) HTML (128) PDF (4.42 MKB) (1958)

  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: (835) HTML (147) PDF (2.13 MKB) (1908)

  Abstract:

  As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.

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

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

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

  Abstract: (849) HTML (96) PDF (3.23 MKB) (2127)

  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: (853) HTML (190) PDF (2.79 MKB) (1789)

  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: (491) HTML (20) PDF (2.16 MKB) (537)

  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: (528) HTML (17) PDF (2.57 MKB) (651)

  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: (796) HTML (34) PDF (2.95 MKB) (1568)

  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: (249) HTML (55) PDF (2.00 MKB) (907)

  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: (618) HTML (90) PDF (1.33 MKB) (1157)

  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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