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This paper captures an engaging—and at times heated—Power-Globe (PG) discussion of evolving definitions of smart grid technologies. The exchange took place between December 2024 and January 2025. The primary objective of this paper is to clarify some of the ambiguities surrounding the term “smart grid” over the past two decades, as highlighted in the spirited PG debate. “Smart grids” have sometimes been advocated as a panacea to resolve the tension between competing objectives for the provision of electricity (specifically, making it reliable, clean, and affordable). This paper examines the term “smart grid” in terms of raw technical functionalities, applications, and use cases, some of which may get closer than others to meeting the aspirational promises. While smart technology should expand our menu of options, it will not absolve us of the need to make hard decisions.
This paper provides an overview of the application potential of artificial intelligence (AI) in power systems and points towards prospective developments in the fields of AI that are promised to play a transformative role in the evolution of power systems. Among the basic requirements, also imposed by regulation in some places, are trustworthiness and interpretability. Large language models, foundation models, as well as neuro-symbolic and compound AI models, appear to be the most promising emerging AI paradigms. Finally, the trajectories along which the future of AI in power systems might evolve are discussed, and conclusions are drawn.
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 high-lights 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.
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.
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.
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.
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.
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.
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.
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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Xiyuan Zhou, Yan Xu, Junhua Zhao, Rui Zhang
2026,14(3):773-790, DOI: 10.35833/MPCE.2025.000760
Abstract:
Modern power systems are evolving due to increasing penetration of renewable energy sources, deeper participation of the demand side, and widespread deployment of advanced information and digital technologies. As a result, system operation and control are becoming increasingly challenging. Large language models (LLMs), with their advanced capabilities in semantic understanding and knowledge reasoning, offer a promising tool to support the operation, control, analysis, and decision-making of power systems. This paper provides a comprehensive review of LLM applications in power systems, encompassing four representative application domains: power grid, power equipment, demand side, and electricity market and policy-making. Based on the functional roles and implementations of LLMs, four major application strategies are identified: model adaptation, capability enhancement, multimodality integration, and multi-agent coordination. In addition, the core functions, representative methods, and evolving trends of LLM applications are reviewed across different domains. Finally, key challenges in applying LLMs to power systems are discussed, and future research directions are outlined with regard to ensuring physical feasibility, enhancing data efficiency and privacy, and improving interpretability and rationality.
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Pengfei Zhao, Weihao Hu, Di Cao, Zhaochen Dong, Yaqi Zeng, Qi Huang, Zhe Chen
2026,14(3):791-809, DOI: 10.35833/MPCE.2025.000740
Abstract:
Electrical load forecasting (ELF) plays a critical role in the planning and operation of modern power systems. As energy demand patterns grow more complex, deep learning (DL) techniques, and more recently, foundation models (FMs), have emerged as powerful tools for modeling temporal dynamics and integrating heterogeneous inputs. In practice, the effectiveness of these models depends not only on their architectures but also on the learning paradigms that determine how they are trained, adapted, and deployed. However, most existing surveys focus solely on network architectures, with limited attention to the underlying paradigms. To this end, we survey the DL-based ELF from perspectives of learning paradigms and FMs. It organizes the literature into four orthogonal paradigms: task-tuned offline learning, adaptive DL, collaborative DL, and general-purpose DL. This paradigm-centric perspective enables a unified understanding of how DL methods evolve to meet the challenges of ELF. It also provides a natural framework to incorporate FMs as the latest advancement in this trajectory. Finally, key challenges are provided, and research opportunities are highlighted to inform future directions.
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Jieyi Xu, Hui Qin, Wenkai Dong, Juan Chen, Xiaorong Xie
2026,14(3):810-820, DOI: 10.35833/MPCE.2025.000449
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High-frequency oscillations (HFOs) in modular multilevel converter based high-voltage direct current (MMC-HVDC) connected to AC networks have aroused concern in recent years. This paper reveals a new HFO phenomenon, where modular multilevel converter (MMC) exhibits impedance characteristics different from those in conventional HFO studies. To investigate this issue, a high-frequency dynamic model of MMC incorporating delay compensation is developed under different outer-loop control strategies. In addition, an AC line modeling method accounting for frequency-dependent parameters is proposed. Mechanism analysis demonstrates that the new HFO phenomenon arises from the adverse interaction between capacitive impedance of MMC and inductive impedance of AC network. Further, the causes of two types of capacitive regions in the high-frequency impedance of MMC are theoretically explained, which are linked to the relatively high proportional gains of current and power controls, as well as delay compensation. The effects of the frequency dependence of AC line parameters on HFO analysis are also demonstrated. Finally, the potential multiple HFOs and possible mitigation approaches are discussed for further study.
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Angelo Maurizio Brambilla, Davide del Giudice, Daniele Linaro, Federico Bizzarri
2026,14(3):821-832, DOI: 10.35833/MPCE.2025.000055
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Distributed generation by converter-interfaced renewable energy sources connected to distribution feeder nodes has been increasingly penetrating power grids, along with their expected contribution to frequency and voltage support services. This will change the perspective with which stability and dynamic behavior of power systems have been analyzed/simulated to date. Until a few years ago, only transmission systems were simulated in detail, while each distribution feeder was replaced by an aggregated load. This model reduction allows minimizing the CPU time of simulations and is deemed acceptable in the past. However, the ever-increasing share of distributed generation requires adopting accurate models of both transmission systems and active distribution feeders, which leads to a staggering increase in the total number of nodes/equations and CPU time. We propose a numerical method that is up to two orders of magnitude faster than existing methods in performing integrated transient stability simulations of transmission systems and distribution feeders in the three-phase frame. We show the numerical properties and efficiency of the proposed method by simulating a well-known transmission system connected to several distribution feeders with a high penetration of inverter-based resources.
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Tianyu Jin, Yuke Lu, Linquan Bai, Xinyu Chen, Jinyu Wen, Yuxin Zhang
2026,14(3):833-844, DOI: 10.35833/MPCE.2025.000306
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As the rapid growth of renewable energy sources (RESs) with fluctuating and uncertain power generation, reserve shortages become increasingly severe, limiting the further integration of RESs and making it essential to exploit additional reserve sources. The fast-growing data centers offer significant potential to provide reserves due to their spatial and temporal dispatch flexibility. However, large quantities of jobs in the data centers have different computing requirements, making it challenging to estimate the availability of data centers for providing reserves. Meanwhile, the regional resource mix leads to uneven reserve distribution and mismatches between reserve supply and demand. The cross-region reserve supply has the capability to mitigate such mismatches as it allows different regions to provide reserve to each other. This paper enriches the reserve sources in multi-region system dispatch to enhance renewable energy integration by exploiting the reserve supply capabilities of data centers and cross-region systems. The model for available reserves of data centers is proposed via analyzing the envelope of computing constraints of aggregated jobs. In addition, a comprehensive cross-region reserve supply model is presented by considering reserve supply and demand limitations and reserve transmission capability. Case studies are performed and the results show that the data centers can provide a substantial amount of reserves. Both the migration of jobs among data centers and the cross-region reserve supply transfer reserve resources among multiple regions. Simulations on a real-world power grid system further demonstrate that unlocking the reserve supply potential of data centers can reduce operating costs by 0.4% and the curtailment of RESs by 1.7%.
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Miguel Toro, Juan Segundo-Ramírez, Emanuel Rosas, Ramón Daniel Rodriguez-Soto, Aaron Esparza, Emilio Barocio
2026,14(3):845-857, DOI: 10.35833/MPCE.2025.000297
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This paper presents an industrial-grade hardware-in-the-loop (HIL) validation method for a wide-area monitoring system designed to detect electromechanical oscillations in power systems. The proposed method leverages dynamic mode decomposition (DMD) to extract spatiotemporal patterns from synchronized phasor measurements, enabling accurate identification of oscillation modes. Traditional methods are widely used but face limitations in accurately capturing complex system dynamics. Despite the improved processing capabilities of modern controllers, advanced data-driven methods such as DMD remain underutilized due to concerns about computational cost and implementation complexity. This paper demonstrates the feasibility of integrating DMD into industrial-grade controllers by employing efficient algorithms such as singular value decomposition and QR decomposition. A comparative analysis with the Prony method across multiple test systems, along with industrial-grade hardware-in-the-loop validation, confirms the accuracy and computational efficiency of DMD for real-time applications. Results show that DMD reliably identifies local modes, inter-area oscillations, multimodal behavior, and mode shapes. These findings support the integration of spatiotemporal methods into industrial-grade controllers to improve the performance of real-time monitoring on power system stability.
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Jian Xu, Zhonghao He, Longwen Jia, Siyang Liao, Yaokun Zou
2026,14(3):858-870, DOI: 10.35833/MPCE.2025.000486
Abstract:
The emergency control strategy for mitigating cascading failures plays an important role in the safe and stable operation of power systems with high integration of renewable energy sources (RESs). To mitigate the high control costs associated with active splitting, this paper proposes a novel tree-partitioning based emergency control strategy. Initially, a coherency grouping model for power system integrated with wind turbines is established, incorporating an improved phase motion equation to facilitate the identification of unit homology. Furthermore, a modified fuzzy c-means (FCM) clustering algorithm is proposed to achieve the grouping of the generators. Then, the cluster partitioning issue is converted into an eigenvalue solution problem. This allows for a rapid and accurate cluster partitioning of the power system, taking into account both the random fluctuations of renewable energy output and the unit homology constraints. Based on the modified Prim’s algorithm, the tree-partitioning method is used to select the optimal bridge, whereby other bridges are disconnected and the inter-cluster power imbalances remain stable. A power adjustment measure is proposed to determine the power adjustment amount within each cluster based on the power flow tracing. Simulations on the IEEE 118-bus system and the actual case system demonstrate that the proposed strategy reduces control costs by 55.60% and 43.26% compared with active splitting, while also accelerating the post-fault recovery. These results highlight the potential of the proposed strategy for mitigating cascading failures in real-world applications. Index Terms—Cascading failure, emergency control, tree-par‐ titioning, cluster partitioning, coherency grouping, spectral clus‐ tering.
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Debargha Brahma, Abhinav Kumar Singh, Abdul Saleem Mir, Nilanjan Senroy, Bikash C. Pal
2026,14(3):871-883, DOI: 10.35833/MPCE.2025.000195
Abstract:
The significance of system inertia, especially its non-uniform spatial distribution, is becoming paramount in the power system. The scope of inertia estimation has traditionally been the estimation of overall or total system inertia. However, as frequency dynamics become increasingly localized with the increasing penetration level of inverter-based resources (IBRs), the need for higher spatial resolution (geographically localized estimation) and faster temporal resolution (online or continuous estimation) in inertia estimation becomes paramount. This paper proposes an analytical method to estimate the spatial inertia distribution down to the transmission bus level, i.e., nodal inertia. Depending on data availability, the proposed method is flexible and can be used in two ways ①
to estimate nodal inertia under any given operating condition (or snapshot), or ②to continuously estimate nodal inertia under both ambient and transient conditions using available measurements from local phasor measurement units (PMUs). The novelty of the proposed method lies in its analytical formulation, which does not require rate of change of frequency (ROCOF) measurements or rate of change of power injections, making it immune to the noise associated with the estimation of these derived quantities. Additionally, the proposed method does not require defining near-zero ROCOF thresholds, which is a system-specific and non-trivial problem. The proposed method is mode-agnostic, which makes it more general than the dominant mode-based linearized methods. The applicability of the proposed method is demonstrated through simulation studies performed on the IEEE 39-bus and IEEE 68-bus test systems with varying penetration levels of IBRs. The robustness of the proposed method is numerically assessed against modeling and measurement uncertainties. - 1
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2026,14(3):884-895, DOI: 10.35833/MPCE.2025.000512
Abstract:
The integration of renewable energy sources into power grids may introduce wideband oscillation risks, challenging the stability of modern power systems. Traditional artificial neural network-based data-driven impedance identification methods face significant limitations due to the black-/gray-box characteristics of wind power units (WPUs) and the scarcity of impedance measurement data. To address these challenges, this paper proposes few-shot data-driven online impedance identification and stability assessment for wind-integrated modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems. By enhancing the backpropagation neural network (BPNN) with adversarial domain adaptation (ADA), the proposed online impedance identification leverages transfer learning to develop wideband impedance identification models for WPUs and MMCs, enabling online impedance identification with minimal data requirements and achieving the direct model transfer from WPUs to onshore MMC. A comprehensive case study of the Rudong offshore project in China demonstrates the effectiveness of the proposed few-shot data-driven online impedence identification and stability assessment, showing a 95% reduction in data requirements and significant improvements in model transferability compared with conventional methods.
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Julio Cesar Stacchini de Souza, Milton Brown Do Coutto Filho, Marcio Andre Ribeiro Guimaraens
2026,14(3):896-906, DOI: 10.35833/MPCE.2025.000295
Abstract:
Advanced network analysis and control tools in energy management systems depend on a reliable real-time database provided by power system state estimation (SE). The processing of gross errors—
bad data (BD) —is considered an essential SE step in such a context. It is widely recognized that the adverse conditions with weak network observability hinder the identification and correction of multiple BD, a process notorious for its time-consuming nature. This paper proposes a method for facilitating the processing of BD in supervisory control and data acquisition (SCADA) measurements using phasor measurement units (PMUs). The main objective is to strategically allocate a limited number of PMUs to allow the phasor-aided state estimation (PHASE), which offers the advantage of effectively handling single and multiple BD. Tests with the IEEE 14-, 30-, and 118-bus systems are conducted to demonstrate the practical application of handling BD, including adverse conditions such as network spots with low redundant measurements. Index Terms—Bad data, gross error, state estimation, energy management system (SCADA), phasor measurement unit , supervisory control and data acquisition (PMU).- 1
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Jianzhong Xu, Yiyang Zhu, Yifan Liu, Zhaoxuan Tian, Chengyong Zhao, Gen Li
2026,14(3):907-919, DOI: 10.35833/MPCE.2025.000301
Abstract:
The timely detection of internal faults in permanent magnet synchronous generators (PMSGs), which are the key components of direct-drive or semi-direct-drive systems, is crucial for ensuring the long-term stable operation of wind turbines. Large-scale experiments are impractical for acquiring sufficient fault data, whereas simulation can effectively provide such data. Furthermore, the power electronic devices in wind turbines exhibit microsecond-level dynamic characteristics, necessitating electromagnetic transient (EMT) simulation. Moreover, the black-box models provided by commercial EMT simulation software do not support internal fault simulation. Additionally, existing modeling methods for internal faults in PMSGs can only simulate the generator itself, making it difficult to generate nodal equivalent circuits and preventing direct interfacing with external components such as converters in wind turbine systems. This paper proposes the EMT modeling and simulation method capable of representing various types of internal faults in PMSGs. By integrating two state variables, a nodal quivalent circuit is developed, effectively avoiding the calculation of time-varying partial derivatives. The proposed method can directly interface with converters and grid connections, enabling the fault characteristics to be reflected in the wind turbine system. A unified EMT model encompassing multiple fault types is developed through a standardized modeling procedure. The proposed method is implemented in PSCAD/EMTDC and compared with results obtained from MATLAB. The results demonstrate that the proposed method can accurately reflect the characteristics of internal faults, validating its effectiveness.
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Hai Xie, Jun Yao, Wenwen He, Dong Yang, Linsheng Zhao
2026,14(3):920-931, DOI: 10.35833/MPCE.2025.000411
Abstract:
The virtual synchronous generator (VSG) control is increasingly adopted in multi-paralleled photovoltaic generation systems (MP-PGSs) due to its enhanced grid-support capability. While the transient stability of VSG-controlled photovoltaic generation systems (PGSs) under symmetrical grid faults is well-studied, instability mechanisms under asymmetrical grid faults (AGFs) remain underexplored. This paper establishes a multiple coupling analysis model for transient stability analysis of VSG-controlled MP-PGSs under AGFs. Based on this model, the impacts of coupling dynamics including sequence coupling and mutual coupling on the transient stability of VSG-controlled MP-PGSs are investigated. Furthermore, the influence laws of key parameters on the transient stability are analyzed. To improve the low voltage ride-through capability of MP-PGSs under AGFs, a multi-objective stabilization control method is proposed, which satisfies both the grid codes and current limitation requirements. Finally, simulation results validate the correctness of the theoretical analysis and the effectiveness of the proposed control method.
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Renshun Wang, Yuzhong Gong, Guangchao Geng, Quanyuan Jiang
2026,14(3):932-944, DOI: 10.35833/MPCE.2025.000261
Abstract:
The increasing penetration of renewable energy sources will impose even more stress on the operational flexibility at multiple timescales. Energy storage (ES) is a promising option to provide multiple services, while various energy storage systems (ESSs) exhibit diverse economic performances at different timescales. However, efficiently and economically combining multi-timescale ESSs to meet flexibility requirements is challenging due to the gap between coarse-grained ESS representations and multi-timescale flexibility requirements. This paper presents a wavelet packet decomposition (WPD) based multi-timescale operational flexibility quantification method. Such requirements are clustered and then satisfied by an ES planning model covering multiple timescales from intra-hourly to seasonal using representative scenarios, while considering both short-term (operational) and long-term (technology cost) uncertainties. An empirical analysis of a provincial power grid in East China is performed to obtain the planning results in 2030 and 2060. Numerical results demonstrate the effectiveness of the multi-timescale ES planning model as well as its computational performance and economic advantages.
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Runze Zhang, Rui Wang, Ming-Jia Li, Qiuye Sun, Pinjia Zhang, Yibo Wang, Peng Wang
2026,14(3):945-955, DOI: 10.35833/MPCE.2025.000426
Abstract:
Although virtual asynchronous machine (VAM) control has been proposed for virtual energy storage systems (VESSs), research into its secondary control applications is still limited. Thus, a distributed secondary frequency restoration control strategy based on VAMs is presented for VESSs. First, the VAM control is introduced, and a detailed electro-thermal coupling model of the VESS is developed. This model includes indoor-outdoor temperature differences, heat transfer through building envelope (walls, windows, and roof), solar radiations, ventilation losses, and electric boiler dynamics. It effectively captures the coupling between indoor temperature regulation and grid power balancing. Next, a distributed secondary frequency restoration control strategy based on VAM is proposed. It addresses parameter heterogeneity within a nonlinear multi-agent framework among VESSs. The nonlinear dynamics are converted into a linear reference model, which simplifies controller design and stability analysis. Using only local and neighboring information, the proposed strategy restores frequency and ensures active power sharing. Furthermore, the proposed strategy coordinates thermal power regulation to maintain indoor temperature balancing across VESSs within seasonal thermal comfort ranges. This improves thermal comfort without compromising dynamic response. Finally, the stability of the proposed strategy is verified using Lyapunov method, and simulation results from an islanded microgrid (MG) test system under parameter variations, communication imperfections, and winter/summer operating scenarios validate the effectiveness and robustness of the proposed strategy.
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Ning Zhang, Yanbo Chen, Zhi Zhang, Haoxin Tian
2026,14(3):956-967, DOI: 10.35833/MPCE.2025.000508
Abstract:
Energy storage and generalized energy storage (GES) such as electric vehicles (EVs) and heating, ventilation, and air conditioning (HVAC) systems play a critical role in enhancing the flexibility of power systems. The shared system architecture can improve utilization rates of energy storage and reduce configuration costs. However, the heterogeneity and inherent uncertainty of GES pose significant challenges to the rational configuration and operation of energy storage. To address this, we employ a two-stage distributionally robust capacity configuration method for shared energy storage considering the flexibility and uncertainty of distributed resources. First, by integrating the operational characteristics of both physical and virtual energy storage, a shared system architecture is proposed for the GES. Second, to overcome the limitation of existing EV aggregation models, which are typically tailored for ideal battery behaviors, a more accurate aggregation model is introduced based on the parameter planning, termed the GES model. An uncertainty probability set modeling approach for demand-side resources is derived based on this model. Finally, considering the flexibility and uncertainty associated with EVs and HVAC systems, a distributionally robust optimization model for shared energy storage capacity configuration is proposed. Simulation results demonstrate that the proposed method increases the revenue of operators and the utilization of energy storage resources, while also reducing the configuration capacity.
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Wenhao Wang, Jiehui Zheng, Zhaoxi Liu, Wei Yao, Yuekuan Zhou, Zhigang Li, Qinhua Wu
2026,14(3):968-979, DOI: 10.35833/MPCE.2025.000281
Abstract:
With high integration of distributed generators (DGs) and diverse topology reconfigurations, the dynamic states of active distribution networks (ADNs) become more complex, which poses great challenges to the dynamic equivalence of ADNs. In this paper, motivated by the similarities between Newton Raphson power flow calculation (NRPFC) and computation of convolution network (CNN), and based on the capability of recurrent neural network (RNN) to represent the differential algebraic equations (DAEs) of loads, we propose a multimodal machine learning based equivalent modeling method in order to track the dynamic behaviors of ADNs. First, the multimodal machine learning is divided into two modules, one of which is gated recurrent unit
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Gengchen Li, Hao Wu, Rufeng Zhang, Kai Hou, Hongjie Jia, Houhe Chen
2026,14(3):980-990, DOI: 10.35833/MPCE.2025.000631
Abstract:
The dispatch of demand-side resources is becoming increasingly essential for enhancing the resilience of distribution systems against extreme weather events. Traditional studies rely primarily on direct load curtailment to mitigate power shortages, thereby neglecting the power demand of different customers. To bridge this gap, a novel coordination method of transactive demand response (DR) and rolling outage management of diverse loads is proposed. First, the DR program is designed to incentivize voluntary load adjustments by coordinating participation of private consumers. Considering the diversity and characteristics of customers during DR, detailed models of diverse loads are established, including an energy-material flow model of industrial loads (ILs), an adjustable load model of commercial loads (CLs), and an outage-sensitive model of residential loads (RLs). When the supply-demand imbalance exceeds the adjustment capacity of DR, rolling outage measures are integrated into the proposed method to reduce losses incurred by load shedding. The coordination of transactive DR and rolling outage management is formulated as a bilevel optimization problem from system operators and responsive load, which is solved by the Stackelberg game-theoretic approach. Finally, the proposed method is tested on the modified IEEE 33-node distribution system. The results show that the proposed method can effectively ensure customer profit and reduce load interruption loss by dispatching demand-side resources during restoration.
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Yincheng Zhao, Jianghao Wu, Guozhou Zhang, Sen Zhang, Lingling Wang, Chuanwen Jiang, Weihao Hu
2026,14(3):991-1001, DOI: 10.35833/MPCE.2024.001227
Abstract:
With the continuous development of renewable energy technologies and expansion of their system scales, a large number of renewable energy sources are being integrated into distribution networks. These renewable energy sources are increasingly managed in the framework of active distribution networks (ADNs). However, the expansion of the system scale and stochastic nature of user behaviors produce dynamic changes in the active and reactive power flows of these ADNs, which generate unpredictable operational stability problems such as voltage deviations. To address this challenge, a novel active and reactive power coordination control strategy is proposed for voltage control in ADNs. The strategy is based on a deep reinforcement learning framework, while introducing an attention mechanism to train an agent model for voltage control in an ADN in response to the system dynamics. To this end, this study first formulates an optimization problem for mapping between the active-reactive power and bus voltages in a system. Then, this problem is reformulated as a Markov decision process and solved using a local cross-channel interaction network-based soft actor-critic (LCCIN-SAC) algorithm. Simulation results on a modified IEEE 69-bus system demonstrate that the proposed strategy can successfully improve voltage deviation produced by rapid dynamic changes in a distribution network. The proposed strategy effectively ensures the stable and reliable operation of a distribution network with a high penetration of renewable energy sources.
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Anton Hinneck, Mathias Duckheim, Michael Metzger, Stefan Niessen
2026,14(3):1002-1013, DOI: 10.35833/MPCE.2024.001212
Abstract:
Distribution system reconfiguration (DSR) uses switching actions, which are available to distribution system operators (DSOs) without regulatory changes, to optimize the grid topology. Although distribution systems often support meshed operation, they are typically operated radially to simplify fault isolation and improve reliability. The minimization of active power losses not only improves efficiency but also helps prevent voltage and current limit violations. However, the DSR problem is computationally difficult due to its combinatorial and non-convex nature. This paper proposes a cycle-basis-informed heuristic method for radial DSR, aiming to reduce active power losses and stabilize the voltage. The proposed heuristic method works directly with the non-convex AC load flow model, considering switching actions as the only degrees of freedom. A large neighborhood search framework is used, constructing restricted mixed-integer nonlinear programming (MINLP) subproblems of controllable complexity. Radiality is ensured via a graph-theoretic method based on the cycle basis of the system, enabling systematic exploration of feasible configurations. The proposed heuristic method is evaluated on benchmark systems with varying load profiles, demonstrating robust performance, effective loss reduction, improved voltage profiles, and practical computation time. These results confirm its applicability to real-world DSR in radial AC systems.
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Zhuoxu Chen, Zechun Hu, Yujian Wan, Junsong Li
2026,14(3):1014-1026, DOI: 10.35833/MPCE.2025.000131
Abstract:
Extreme weather conditions, characterized as high-impact low-probability (HILP) events, pose significant threats to distribution network (DN). Soft open points (SOPs) have emerged for precise power flow regulation and voltage establishment, and hence can be used for post-fault load restoration to improve the DN resilience, incorporating scenarios generated from weather profiles. In this paper, an extreme weather risk-averse planning method for SOPs is proposed. A two-stage scenario-based stochastic programming (SBSP) model is established to minimize expectation and conditional value-at-risk (CVaR) of load shedding and network loss. Due to the integer variables introduced by DN reconfiguration constraints in the operational stage, the classic Benders decomposition algorithm is inapplicable. To address this challenge, we develop a novel generalized Benders decomposition (GBD)-based solution algorithm, designed to improve the computational efficiency on large-scale cases. Lift-and-project (L&P) cutting plane is employed to derive Benders cuts through the convex hull of mixed-integer second-order cone programming (MISOCP) subproblems. Finally, the effectiveness of the proposed method is demonstrated by numerical experiences on 33- and 123-node test DNs.
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Zhinong Wei, Peng Liu, Sheng Chen, Jingtao Zhao, Shu Zheng, Guoqiang Sun
2026,14(3):1027-1038, DOI: 10.35833/MPCE.2025.000427
Abstract:
To address the limited adjustable capacity of distribution networks (DNs) under the large-scale integration of flexible resources into microgrid (MG), an MG flexible operation region (MGFOR) model is proposed to quantify the adjustability of MG under aggregated regulation cost constraints. A coordinated dispatch framework of DN and MG is then developed based on MGFOR to enable cooperative flexibility allocation. The original boundary of MGFOR is determined through a radial iterative linear search, and its construction is completed using a convex-hull fitting approach. The simulation is conducted on the modified IEEE 33-node test system. It is demonstrated that MGFOR explicitly quantifies adjustable power boundaries, yielding 2.2% lower total operation cost for the proposed coordinated dispatch framework of DN and MG compared with hierarchical dispatch framework of DN and MG. The voltage safety margins at noon are enhanced by up to 56.46% at distribution feeder terminals. Furthermore, the impact of operation modes of distributed photovoltaic units on node voltage in DN is comparatively analyzed.
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Shweta Meena, Ayman AlZawaideh, Hao Tu, Srdjan Lukic
2026,14(3):1039-1051, DOI: 10.35833/MPCE.2024.001359
Abstract:
The growing integration of renewable energy sources is driving microgrids (MGs) toward 100% inverter-based architectures, whose system stability and dynamic performance are tightly coupled with both the MG plant (i.e., resource type, number, and placement) and inverter control strategies. However, the existing MG design approaches typically overlook this coupling and fail to assess the dynamic performance during the design phase explicitly. Therefore, they often produce suboptimal configurations that do not satisfy the dynamic performance requirements once the design process is complete. To address this challenge, this paper introduces a plant-control co-design approach for 100% inverter-based MGs, which simultaneously optimizes the MG plant design and its control to minimize the system costs while ensuring the stable operation and enhanced dynamic performance. The proposed co-design approach systematically explores the feasible plant designs, evaluating their transient responses under disturbances using the optimal controllers. The MG plant design is formulated as a mixed-integer linear program, while MG dynamics are captured through nonlinear differential-algebraic equations. Electromagnetic transient simulation is used to validate the stability, compliance with IEEE 1547 standard, and dynamic performance, including power tracking in the grid-connected mode and disturbance rejection in the islanded mode. Case studies demonstrate the impacts of the number and placement of battery energy storage systems (BESSs) on the dynamic performance of 100% inverter-based MGs.
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Minglin Xu, Gangquan Si, Detao Fan, Wenhan Tong, Xin Wang
2026,14(3):1052-1063, DOI: 10.35833/MPCE.2025.000738
Abstract:
With the increasing penetration of distributed energy resources, alternating current (AC) microgrids face persistent challenges in maintaining frequency and voltage stability, as well as achieving active and reactive power sharing under renewable intermittency and load variations. To address these issues, this paper proposes a predefined-time distributed secondary control (PTDSC) strategy that ensures fast and accurate frequency and voltage recovery as well as active and reactive power sharing within a user-specified convergence time, independent of the initial conditions and complex tuning of gain. A key component of the proposed strategy is a predefined-time distributed average-voltage observer (PTDAVO), which enables fully distributed observation of the global average voltage. By incorporating the observed average voltage into the secondary control, the proposed strategy achieves coordinated voltage recovery and reactive power sharing within a distributed predefined-time framework. Stability results are rigorously established by the Lyapunov stability theory. Extensive simulation validates the effectiveness, flexibility, and plug-and-play capability of the proposed strategy, along with its adaptability to time-varying communication topologies and superiority over existing strategies.
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Jiashun Lin, Xiaodong Yuan, Chenyu Zhang, Ruihuang Liu, Shi Chen, Wei Jiang
2026,14(3):1064-1075, DOI: 10.35833/MPCE.2025.000351
Abstract:
Dynamic microgrids (MGs) possess flexible system topologies and power flow control capabilities, enabling adaptive responses to outages caused by extreme events. This paper proposes a multi-region and multi-stage coordinated control method for the resilience enhancement of dynamic AC/DC MGs, aiming to address limitations arising from rigid electrical boundaries and insufficient coordination of heterogeneous resources during distribution network restoration. The proposed method introduces smart switches (SSWs) to establish dynamic electrical boundaries, thereby partitioning the system into multiple self-operable mini-AC/DC MGs. A novel multi-mode coordinated control scheme is designed to govern grid-forming (GFM) distributed generators (DGs), grid-following (GFL) DGs, and coupling converters (CCs). Specifically, GFM DGs operate in voltage-source mode, maintaining system frequency and voltage via droop control; GFL DGs operate in current-source mode, actively contributing to system regulation through inverse droop control; and CCs manage bidirectional power transfer between AC and DC subgrids based on inverse droop characteristics. Furthermore, a distributed secondary control layer enables the coordinated operation of GFM DGs, GFL DGs, and CCs across multiple regions and stages, ensuring zero-deviation restoration of voltage and frequency, proportional load sharing, and seamless topology reconfiguration. Notably, GFL DGs and CCs remain actively engaged throughout the entire restoration process, thereby maximizing the utilization of available resources and alleviating the capacity burden on GFM DGs. Simulation studies conducted on a 12-bus hybrid AC/DC test system validate the effectiveness of the proposed method in enhancing the resilience and adaptability of hybrid AC/DC MGs under extreme operating conditions.
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Vijaya Kumar Dunna, Eluri NVDV Prasad, Kumar Pakki Bharani Chandra, Pravat Kumar Rout, Binod Kumar Sahu
2026,14(3):1076-1086, DOI: 10.35833/MPCE.2025.000319
Abstract:
The integration of renewable energy sources and the rising load demand has introduced significant challenges to the secure operation of interconnected power systems. To address these issues, a novel grid-connected microgrid control strategy based on higher-order sliding mode observer (HOSMO) and fractional-order sliding mode controller (FOSMC) is proposed for a grid-connected microgrid incorporating photovoltaic (PV) system, wind energy conversion system (WECS), and battery energy storage system (BESS). The proposed control strategy ensures effective DC bus voltage regulation by optimally managing the PV, WECS, and battery converter units. The HOSMO is designed to accurately estimate the current of PV or WECS converter, enhancing system robustness, while the FOSMC minimizes the chattering in DC bus voltage deviations. The performance of FOSMC is rigorously evaluated through extensive MATLAB/Simulink simulation under various uncertainties, including generation fluctuations, DC load variations, and DC faults. Stability analysis and comparative studies have been presented to demonstrate the superiority of the proposed control strategy. For comparative analysis, advanced control techniques such as finite time disturbance observer (FTDO)-based fixed time terminal sliding mode controller (FTTSMC) and nonlinear disturbance observer (NDO)-based back-stepping sliding mode controller (BSMC) are considered. Additionally, real-time validation of the proposed FOSMC is executed using the OPAL-RT (OP4510) platform to confirm the feasibility and reliability under practical operation conditions.
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Yang Yang, Yanjun Liu, Yuanhang Yang, Yang Li, Wenchao Zhu, Changjun Xie
2026,14(3):1087-1099, DOI: 10.35833/MPCE.2025.000381
Abstract:
Hydrogen -
electricity coupled DC microgrids (HE-DCMGs) represent a promising and sustainable solution for off-grid power supply. However, achieving high economic performance while ensuring DC bus voltage stability is a critically challenging task. This study proposes a hierarchical energy management and control strategy for HE-DCMGs that integrates an adaptive mutation Harris hawks optimization (AMHHO) algorithm at the system level with a fractional-order sliding mode controller (FOSMC) at the device level. A multi-objective optimization problem is formulated to minimize hydrogen consumption and reduce degradation of proton exchange membrane fuel cells and lithium-ion batteries. The AMHHO algorithm, augmented with differential evolution and Lévy flight mechanism, determines the optimal power allocation among distributed sources, while the FOSMC provides robust DC bus voltage regulation. The proposed strategy is validated on a 750 V HE-DCMG experimental platform capable of 168 hours of off-grid operation. Experimental results show that the proposed strategy reduces long-term operating costs and improves energy-utilization efficiency, achieving an overall system efficiency of 80.49%-97.37%. The DC bus voltage is maintained with a response time of 0.02 s, a low overshoot of 3.7%, and a voltage fluctuation rate of 3.08%, all of which comply with the requirements of IEEE Std 1547‒2018. - 1
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Yibo Ding, Xianzhuo Sun, Yuhong Zhao, Cheng Lyu, Junyu Chen, Xudong Li, Wenzhuo Shi, Jiaqi Ruan, Zhao Xu
2026,14(3):1100-1112, DOI: 10.35833/MPCE.2025.000388
Abstract:
In the landscape of low-carbon transition in the power system, it is imperative for the system operator (SO) to implement electricity-carbon joint management. Currently, the increasing integration of renewable energy sources (RESs) is facilitating the achievement of emission reduction targets. However, the inherent uncertainties of RES power output pose challenges on power system operation, highlighting the needs for developing a power prediction model that serves as the prerequisite of better scheduling decisions. Nevertheless, existing accuracy-oriented prediction may not necessarily guarantee better decisions. Besides, due to the inevitable prediction errors, SOs have to adjust power outputs of thermal generators (TGs) during the intraday redispatching, leading to unexpected emission variations for each generation companies (GENCOs). Under the current centralized emission trading scheme (ETS), GENCOs with lower emissions are unable to fully utilize their emission allowances, while those exceeding their limits may face high penalties. However, these two groups of GENCOs exhibit inherent complementarity in terms of emission allowance consumption. To address the above challenges, this study proposes a novel multi-stage electricity-carbon joint management framework, where the power prediction model is decision-oriented to focus more on cost-saving. Moreover, bilateral trading contracts for emission allowances among GENCOs are incorporated into the proposed framework to promote the sufficient utilization of allocated emission allowances and prevent emission exceedances, thereby enhancing the total social welfare. Extensive simulations on a modified IEEE 30-bus system statistically verify the effectiveness of the developed decision-oriented predict-then-optimize method in terms of reducing operation cost. The welfare improvement of GENCOs brought by the designed bilateral trading contracts is also verified through simulation studies.
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Haiyue Yu, Yusheng Xue, Minglei Bao, Hengyu Hui, Dongliang Xie, Yi Ding
2026,14(3):1113-1125, DOI: 10.35833/MPCE.2025.000134
Abstract:
Power systems are undergoing a policy-driven transition to decarbonization, which increases uncertainty and fluctuation due to the high penetration of renewable energy. Concurrently, advancements in communication and control technologies are unlocking greater demand-side flexibility. However, existing conventional long-term resource planning models for power systems do not adequately integrate demand response (DR) resources, operation simulations that account for uncertainty, and social factors such as government policy. To address these shortcomings, this paper introduces a robust planning framework for power system decarbonization pathways, specifically considering DR load as a critical flexible resource along with other techniques and impacting factors. Firstly, the long-term impacts of government policies and other social factors on system resource investment costs and development constraints are quantitatively considered, and the adjustable and transferable DR loads as key flexible resources are incorporated to smooth the fluctuations caused by renewable energy generation. Then, a three-stage robust planning model is developed by integrating long-term development planning, day-ahead unit commitment simulation, and intra-day power dispatch simulation to obtain the optimal solution with the lowest total cost over the planning period. Moreover, the column and constraint generation algorithm is modified to solve the planning model, specifically designed to address the decision-dependent uncertainty arising from new asset investments. Case study based on a provincial power system shows that the proposed framework enhances the integration of renewable energy by applying DR resources in line with real social development trends. The combination of long-term system planning, refined short-term operation simulation, and strategic DR integration ensures that the resulting decarbonization pathway is not only economically feasible but also highly robust and reliable.
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Yiyang Song, Jianxiao Wang, Yi Wang
2026,14(3):1126-1137, DOI: 10.35833/MPCE.2025.000570
Abstract:
Virtual power plants (VPPs) promote the high-level integration of distributed energy resources (DERs) through complementation and aggregation. To avoid the privacy leakage and enhance the market efficiency, this study aims to achieve the non-iterative market participation of VPPs by accurately characterizing their external bidding functions. To this end, an improved multi-parametric linear programming (MPLP) method is developed to derive the bidding function representation of VPPs. The proposed method projects the detailed internal model of VPPs onto the point of common coupling (PCC). An algorithm for determining a precise initial parameter space (IPS) is proposed, thereby avoiding the redundant solutions that typically occur in traditional MPLP method. The IPS is then partitioned into several critical regions (CRs), within each of which the marginal cost remains constant, facilitating a piecewise linear mapping between the trading power and bidding price. Case studies on modified IEEE 33-bus and IEEE 123-bus systems demonstrate that the proposed method accurately characterizes the bidding functions of VPPs and substantially improves the efficiency of their non-iterative market participation while ensuring data privacy.
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Shuai He, Ming Yang, Nian Liu, Xiaohe Yan, Jianpei Han
2026,14(3):1138-1150, DOI: 10.35833/MPCE.2024.000934
Abstract:
Multi-energy retailer (MER) is a new type of retailer participating in both the electricity and gas markets. In a cooperative and competitive market environment, how to find the optimal coalition structure (OCS) model for MERs is an unsolved problem. Thus, this paper proposes an OCS model for MERs in electricity and gas markets. First, an MER optimization model is built to maximize profit, and the market-clearing constraints are considered. A coalition game model for MERs including coalition value and OCS model is introduced, which describes the behavior of forming a coalition among MERs considering coalition cost and scale limitation. Moreover, a hybrid algorithm including the diagonalization algorithm (DGA) and dynamic programming algorithm (DPA) is designed to solve the OCS model. Finally, the case studies are performed on a system with IEEE 14-bus electric network and 7-bus gas network. The numerical results show that the proposed model can effectively obtain OCS.
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Volume 14, Issue 3, 2026
>Review
>Original Paper
More+Virtual Issue
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Ricardo J. Bessa, Spyros Chatzivasileiadis, Ning Zhang, Chongqing Kang, Nikos Hatziargyriou
2026,14(1):23-36, DOI: 10.35833/MPCE.2025.000990
Abstract:
This paper provides an overview of the application potential of artificial intelligence (AI) in power systems and points towards prospective developments in the fields of AI that are promised to play a transformative role in the evolution of power systems. Among the basic requirements, also imposed by regulation in some places, are trustworthiness and interpretability. Large language models, foundation models, as well as neuro-symbolic and compound AI models, appear to be the most promising emerging AI paradigms. Finally, the trajectories along which the future of AI in power systems might evolve are discussed, and conclusions are drawn.
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2026,14(1):1-6, DOI: 10.35833/MPCE.2025.001004
Abstract:
This paper tries to summarize the attempts to apply artificial intelligence (AI) to power systems, particularly power system planning and operations which require significant computer analysis. Although the term AI was coined earlier, this paper considers the beginning to be in the 1980s when the first expert systems were applied to power engineering. Of course, many of the analytical techniques applied can be traced to earlier statistical analysis and pattern recognition. The concept of expert systems was very much in line with the concept of AI. The various methods for applying AI to power systems are traced here. The historical journey in this paper closes with the great explosion of AI applications in the last decade when almost all power system analysis is trying to utilize AI techniques to help the transformation of the power system into a more efficient and carbon-free system. This proliferation of research in the application of AI is covered in the other papers in this series.
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Om Malik, Jay Liu, Marcelo Simões, Chris Dent, Kai Strunz, Jeffrey Wischkaemper, Vladimiro Miranda, Trevor Gaunt, Math Bollen, Mladen Kezunovic, Daniel Kirschen, Antonio Gomez-Exposito, Robin Podmore, Harold Kirkham, Panayiotis Moutis, Anjan Bose, Ian Hiskens, Gene Preston, Canbing Li, Hasala Dharmawardena, Alexandra von Meier, Leigh Tesfatsion, Paulo Ribeiro
2025,13(6):1845-1853, DOI: 10.35833/MPCE.2025.000807
Abstract:
This paper captures an engaging— and at times heated—Power-Globe (PG) discussion of evolving definitions of smart grid technologies. The exchange took place between December 2024 and January 2025. The primary objective of this paper is to clarify some of the ambiguities surrounding the term “ smart grid” over the past two decades, as highlighted in the spirited PG debate. “ Smart grid” has sometimes been advocated as a panacea to resolve the tension between competing objectives for the provision of electricity (specifically, making it reliable, clean, and affordable). This paper examines the term “ smart grid” in terms of raw technical functionalities, applications, and use cases, some of which may get closer than others to meeting the aspirational promises. While smart technology should expand our menu of options, it will not absolve us of the need to make hard decisions. - 1
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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
Abstract:
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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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
Abstract:
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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Mingyu Yang, Yusheng Xue, Bin Cai, Feng Xue
2025,13(5):1481-1494, DOI: 10.35833/MPCE.2024.001135
Abstract:
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. - 1
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Francisco S. Fernandes, Ricardo J. Bessa, João Peças Lopes
2025,13(4):1113-1126, DOI: 10.35833/MPCE.2024.000478
Abstract:
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global interpretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.
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Zhiyuan Meng, Xiangyang Xing, Xiangjun Li, Jiadong Sun
2025,13(3):1064-1077, DOI: 10.35833/MPCE.2024.000404
Abstract:
The virtual synchronous generator (VSG), utilized as a control strategy for grid-forming inverters, is an effective method of providing inertia and voltage support to the grid. However, the VSG exhibits limited capabilities in low-voltage ride-through (LVRT) mode. Specifically，the slow response of the power loop poses challenges for VSG in grid voltage support and increases the risk of overcurrent, potentially violating present grid codes. This paper reveals the mechanism behind the delayed response speed of VSG control during the grid faults. On this basis, a compound compensation control strategy is proposed for improving the LVRT capability of the VSG, which incorporates adaptive frequency feedforward compensation (AFFC), direct power angle compensation (DPAC), internal potential compensation (IPC), and transient virtual impedance (TVI), effectively expediting the response speed and reducing transient current. Furthermore, the proposed control strategy ensures that the VSG operates smoothly back to its normal control state following the restoration from the grid faults. Subsequently, a large-signal model is developed to facilitate parameter design and stability analysis, which incorporates grid codes and TVI. Finally, the small-signal stability analysis and simulation and experimental results prove the correctness of the theoretical analysis and the effectiveness of the proposed control strategy.
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Zhe Chen, Zhihao Li, Da Lin, Changjun Xie, Zhewei Wang
2025,13(3):904-914, DOI: 10.35833/MPCE.2024.000606
Abstract:
Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems (IESs). Although the optimal scheduling of IES has been widely studied, few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale. Combined with hybrid energy storage, the comprehensive use of various uncertainty optimization methods under different time scales will be promising. This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties. Firstly, the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established. Then, an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage. On this basis, a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min, respectively. The results show that ①
the proposed method improves the economic benefits, and the intra-day and real-time scheduling costs are reduced, respectively; ②by adjusting the uncertainty budget in the model, a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved; ③reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions, thus reducing the cost of system scheduling. - 1
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Jalal Sahebkar Farkhani, Özgür Çelik, Kaiqi Ma, Claus Leth Bak, Zhe Chen
2025,13(3):840-851, DOI: 10.35833/MPCE.2023.000925
Abstract:
Traditional protection methods are not suitable for hybrid (cable and overhead) transmission lines in voltage source converter based high-voltage direct current (VSC-HVDC) systems. Accordingly, this paper presents the robust fault detection, classification, and location based on the empirical wavelet transform-Teager energy operator (EWT-TEO) and artificial neural network (ANN) for hybrid transmission lines in VSC-HVDC systems. The operational scheme of the proposed protection method consists of two loops ①
an EWT-TEO based feature extraction loop, ②and an ANN-based fault detection, classification, and location loop. Under the proposed protection method, the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform (EWT) method. The energy content extracted by the EWT is fed into the ANN for fault detection, classification, and location. Various fault cases, including the high-impedance fault (HIF) as well as noises, are performed to train the ANN with two hidden layers. The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB, respectively. The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave (TW) based protection method. The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems, where a mean percentage error of approximately 0.1% is achieved. - 1
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Linguang Wang, Xiaorong Xie, Wenkai Dong, Yong Mei, Aoyu Lei
2025,13(3):747-756, DOI: 10.35833/MPCE.2024.000630
Abstract:
With the rapid integration of renewable energy, wide-band oscillations caused by interactions between power electronic equipment and grids have emerged as one of the most critical stability issues. Existing methods are usually studied for local power systems with around one hundred nodes. However, for a large-scale power system with tens of thousands of nodes, the dimension of transfer function matrix or the order of characteristic equation is much higher. In this case, the existing methods such as eigenvalue analysis method and impedance-based method have difficulty in computation and are thus hard to utilize in practice. To fill this gap, this paper proposes a novel method named the smallest eigenvalues based logarithmic derivative (SELD) method. It obtains the dominant oscillation modes by the logarithmic derivative of the k-smallest eigenvalue curves of the sparse extended nodal admittance matrix (NAM). An oscillatory stability analysis tool is further developed based on this method. The effectiveness of the method and the tool is validated through a local power system as well as a large-scale power system.
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Wenping Qin, Xiaozhou Li, Xing Jing, Zhilong Zhu, Ruipeng Lu, Xiaoqing Han
2025,13(2):675-687, DOI: 10.35833/MPCE.2024.000118
Abstract:
The virtual power plant (VPP) facilitates the coordinated optimization of diverse forms of electrical energy through the aggregation and control of distributed energy resources (DERs), offering as a potential resource for frequency regulation to enhance the power system flexibility. To fully exploit the flexibility of DER and enhance the revenue of VPP, this paper proposes a multi-temporal optimization strategy of VPP in the energy-frequency regulation (EFR) market under the uncertainties of wind power (WP), photovoltaic (PV), and market price. Firstly, all schedulable electric vehicles (EVs) are aggregated into an electric vehicle cluster (EVC), and the schedulable domain evaluation model of EVC is established. A day-ahead energy bidding model based on Stackelberg game is also established for VPP and EVC. Secondly, on this basis, the multi-temporal optimization model of VPP in the EFR market is proposed. To manage risks stemming from the uncertainties of WP, PV, and market price, the concept of conditional value at risk (CVaR) is integrated into the strategy, effectively balancing the bidding benefits and associated risks. Finally, the results based on operational data from a provincial electricity market demonstrate that the proposed strategy enhances comprehensive revenue by providing frequency regulation services and encouraging EV response scheduling.
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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:
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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Shengren Hou, Edgar Mauricio Salazar, Peter Palensky, Qixin Chen, Pedro P. Vergara
2025,13(2):597-608, DOI: 10.35833/MPCE.2024.000391
Abstract:
The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.
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Wang Xiang, Mingrui Yang, Jinyu Wen
2025,13(2):452-461, DOI: 10.35833/MPCE.2024.000229
Abstract:
Conventional offshore wind farm (OWF) integration systems typically employ AC cables to gather power to a modular multilevel converter (MMC) platform, subsequently delivering it to onshore grids through high-voltage direct current (HVDC) transmission. However, scaling up the capacity of OWFs introduces significant challenges due to the high costs associated with AC collection cables and offshore MMC platforms. This paper proposes a diode rectifier (DR)-MMC hub based hybrid AC/DC collection and HVDC transmission system for large-scale offshore wind farms. The wind farms in proximity to the offshore converter platform utilize AC collection, while distant wind farms connect to the platform using DC collection. The combined AC/DC power is then transmitted to the offshore DR-MMC hub platform. The topology and operation principle of the DR-MMC hub as well as the integration system are presented. Based on the operational characteristics, the capacity design method for DR-MMC hub is proposed. And the control and startup strategies of the integration system are designed. Furthermore, an economic comparison with the conventional MMC-HVDC based offshore wind power integration system is conducted. Finally, the technical feasibility of the proposed integration scheme is verified through PSCAD/EMTDC simulation with the integration scale of 2 GW.
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Wei Kong, Kai Sun, Jinghong Zhao
2025,13(1):276-288, DOI: 10.35833/MPCE.2023.001027
Abstract:
The hydrogen energy storage system (HESS) integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty. To address the above issues, a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems (CIESs) with power to hydrogen and heat (P2HH) capability. It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads. First, the refined operation model of HESS is established, and its operation model is linearized according to the operation domain of HESS, which simplifies the difficulty of solving the optimization problem under the premise of maintaining high approximate accuracy. Next, considering the flexible start-stop of alkaline electrolyzer (AEL) and the avoidance of multiple energy conversions, the operation sequences of HESS are formulated. Finally, a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established, and the model is simulated and verified using the source-load prediction data of typical days in each season. The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14% while maintaining similar operating cost to the optimal day-ahead economic optimization scheduling. Furthermore, by formulating the operation sequences of HESS, the operating cost of CIES is reduced by up to about 4.4%.
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2025,13(1):179-189, DOI: 10.35833/MPCE.2023.000624
Abstract:
With photovoltaic (PV) sources becoming more prevalent in the energy generation mix, transitioning grid-connected PV systems from grid-following (GFL) mode to grid-forming (GFM) mode becomes essential for offering self-synchronization and active support services. Although numerous GFM methods have been proposed, the potential of DC voltage control malfunction during the provision of the primary and inertia support in a GFM PV system remains insufficiently researched. To fill the gap, some main GFM methods have been integrated into PV systems featuring detailed DC source dynamics. We conduct a comparative analysis of their performance in active support and DC voltage regulation. AC GFM methods such as virtual synchronous machine (VSM) face a significant risk of DC voltage failure in situations like alterations in solar radiation, leading to PV system tripping and jeopardizing local system operation. In the case of DC GFM methods such as matching control (MC), the active support falls short due to the absence of an accurate and dispatchable droop response. To address the issue, a matching synchronous machine (MSM) control method is developed to provide dispatchable active support and enhance the DC voltage dynamics by integrating the MC and VSM control loops. The active support capability of the PV systems with the proposed method is quantified analytically and verified by numerical simulations and field tests.
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Francisco Jesús Matas-Díaz, Manuel Barragán-Villarejo, José María Maza-Ortega
2025,13(1):102-114, DOI: 10.35833/MPCE.2024.000316
Abstract:
The integration of converter-interfaced generators (CIGs) into power systems is rapidly replacing traditional synchronous machines. To ensure the security of power supply, modern power systems require the application of grid-forming technologies. This study presents a systematic small-signal analysis procedure to assess the synchronization stability of grid-forming virtual synchronous generators (VSGs) considering the power system characteristics. Specifically, this procedure offers guidance in tuning controller gains to enhance stability. It is applied to six different grid-forming VSGs and experimentally tested to validate the theoretical analysis. This study concludes with key findings and a discussion on the suitability of the analyzed grid-forming VSGs based on the power system characteristics.
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Yanqiu Jin, Zheren Zhang, Zheng Xu
2025,13(1):87-101, DOI: 10.35833/MPCE.2024.000432
Abstract:
This study analyzes the stability and reactive characteristics of the hybrid offshore wind farm that includes grid-forming (GFM) and grid-following (GFL) wind turbines (WTs) integrated with a diode rectifier unit (DRU) based high-voltage direct current (HVDC) system. The determination method for the proportion of GFM WTs is proposed while considering system stability and optimal offshore reactive power constraints. First, the small-signal stability is studied based on the developed linear model, and crucial factors that affect the stability are captured by eigenvalue analysis. The reactive power-frequency compensation control of GFM WTs is then proposed to improve the reactive power and frequency dynamics. Second, the relationship between offshore reactive power imbalance and the effectiveness of GFM capability is analyzed. Offshore reactive power optimization methods are next proposed to diminish offshore reactive load. These methods include the optimal design for the reactive capacity of the AC filter and the reactive power compensation control of GFL WTs. Third, in terms of stability and optimal offshore reactive power constraints, the principle and calculation method for determining the proportion of GFM WTs are proposed, and the critical proportion of GFM WTs is determined over the full active power range. Finally, case studies using a detailed model are conducted by time-domain simulations in PSCAD/EMTDC. The simulations verify the theoretical analysis results and the effectiveness of the proposed determination method for the proportion of GFM WTs and reactive power optimization methods.
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Hang Shuai, Buxin She, Jinning Wang, Fangxing Li
2025,13(1):79-86, DOI: 10.35833/MPCE.2023.000882
Abstract:
This study investigates a safe reinforcement learning algorithm for grid-forming (GFM) inverter based frequency regulation. To guarantee the stability of the inverter-based resource (IBR) system under the learned control policy, a model-based reinforcement learning (MBRL) algorithm is combined with Lyapunov approach, which determines the safe region of states and actions. To obtain near optimal control policy, the control performance is safely improved by approximate dynamic programming (ADP) using data sampled from the region of attraction (ROA). Moreover, to enhance the control robustness against parameter uncertainty in the inverter, a Gaussian process (GP) model is adopted by the proposed algorithm to effectively learn system dynamics from measurements. Numerical simulations validate the effectiveness of the proposed algorithm.
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Ghazala Shafique, Johan Boukhenfouf, François Gruson, Frédéric Colas, Xavier Guillaud
2025,13(1):66-78, DOI: 10.35833/MPCE.2024.000822
Abstract:
Grid-forming (GFM) converters are recognized for their stabilizing effects in renewable energy systems. Integrating GFM converters into high-voltage direct current (HVDC) systems requires DC voltage control. However, there can be a conflict between GFM converter and DC voltage control when they are used in combination. This paper presents a rigorous control design for a GFM converter that connects the DC-link voltage to the power angle of the converter, thereby integrating DC voltage control with GFM capability. The proposed control is validated through small-signal and transient-stability analyses on a modular multilevel converter (MMC)-based HVDC system with a point-to-point (P2P) GFM-GFM configuration. The results demonstrate that employing a GFM-GFM configuration with the proposed control enhances the stability of the AC system to which it is connected. The system exhibits low sensitivity to grid strength and can sustain islanding conditions. The high stability limit of the system with varying grid strength using the proposed control is validated using a system with four voltage source converters.
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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:
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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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:
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
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Haiyu Zhao, Hongyu Zhou, Wei Yao, Qihang Zong, Jinyu Wen
2025,13(1):3-14, DOI: 10.35833/MPCE.2024.000722
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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Sheng Chen, Jingchun Zhang, Zhinong Wei, Hao Cheng, Si Lv
2024,12(6):1697-1709, DOI: 10.35833/MPCE.2023.000887
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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Xiaoyu Zhang, Yushuai Li, Tianyi Li, Yonghao Gui, Qiuye Sun, David Wenzhong Gao
2024,12(5):1472-1483, DOI: 10.35833/MPCE.2023.000351
Abstract:
The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.
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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:
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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Jingtao Zhao, Zhi Wu, Huan Long, Huapeng Sun, Xi Wu, Chingchuen Chan, Mohammad Shahidehpour
2024,12(5):1333-1344, DOI: 10.35833/MPCE.2023.000372
Abstract:
With the large-scale integration of distributed renewable generation (DRG) and increasing proportion of power electronic equipment, the traditional power distribution network (DN) is evolving into an active distribution network (ADN). The operation state of an ADN, which is equipped with DRGs, could rapidly change among multiple states, which include steady, alert, and fault states. It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs. In this paper, the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states. The multi-state identification indicators and identification methods are summarized in detail. The multi-state regulation capacity quantification methods are analyzed considering controllable resources, quantification indicators, and quantification methods. A detailed survey of optimal operation control strategies, including multiple state operations, is presented, and key problems and outlooks for the expansion of ADN are discussed.
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Qifan Chen, Siqi Bu, Chi Yung Chung
2024,12(4):1003-1018, DOI: 10.35833/MPCE.2023.000526
Abstract:
To tackle emerging power system small-signal stability problems such as wideband oscillations induced by the large-scale integration of renewable energy and power electronics, it is crucial to review and compare existing small-signal stability analysis methods. On this basis, guidance can be provided on determining suitable analysis methods to solve relevant small-signal stability problems in power electronics-dominated power systems (PEDPSs). Various mature methods have been developed to analyze the small-signal stability of PEDPSs, including eigenvalue-based methods, Routh stability criterion, Nyquist/Bode plot based methods, passivity-based methods, positive-net-damping method, lumped impedance-based methods, bifurcation-based methods, etc. In this paper, the application conditions, advantages, and limitations of these criteria in identifying oscillation frequencies and stability margins are reviewed and compared to reveal and explain connections and discrepancies among them. Especially, efforts are devoted to mathematically proving the equivalence between these small-signal stability criteria. Finally, the performance of these criteria is demonstrated and compared in a 4-machine 2-area power system with a wind farm and an IEEE 39-bus power system with 3 wind farms.
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Jie Xu, Hongjun Gao, Renjun Wang, Junyong Liu
2024,12(3):886-899, DOI: 10.35833/MPCE.2023.000213
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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Zhoujun Ma, Yizhou Zhou, Yuping Zheng, Li Yang, Zhinong Wei
2024,12(3):852-862, DOI: 10.35833/MPCE.2023.000204
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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- MPCE Online Forum: Artificial Intelligence in Power Engineering: Challenges, Prospects, and Promises will be held on Mar. 25, 2026
- 15 Papers Awarded as "MPCE Best Papers"
- 2025 MPCE Editorial Board Meeting Held in Austin, TX, USA
- 2024 MPCE Editorial Board Meeting Held in Seattle, WA, USA
- [Extended to July 31] Special Section on Dynamic Performance and Flexibility Enhancement of RES-dominated Power Systems with Grid-forming Converters
- Report Abstracts for Invited Speakers on MPCE 10th Anniversary Forum
- MPCE 10th Anniversary Forum on Resilience and Flexibility of Modern Power Systems will be held on Dec. 15, 2023
- 2023 MPCE Editorial Board Meeting Held in Orlando, FL, USA
- JCR Q1! MPCE 2022 Impact factor is 6.3
- [Extended to September 30] Special Section on Battery Energy Storage Systems for Net-zero Power Systems and Markets
