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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.
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.
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.
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.
The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.
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.
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.
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.
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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2026,14(1):1-6, DOI: 10.35833/MPCE.2025.001004
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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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Angelos Vlachos, Anastasia Poulopoulou, Christina Giannoula, Georgios Goumas, Nectarios Koziris
2026,14(1):7-22, DOI: 10.35833/MPCE.2025.001111
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Recent progress in artificial intelligence (AI) is powered by three key elements: algorithmic innovations, specialized chips and hardware, and a rich ecosystem of software and data toolboxes. This paper provides an analysis of these three key elements, tracing the evolution of AI from symbolic systems and small, labeled benchmarks to today’s large-scale, generative, and agentic models trained on web-scale corpora. We review the hardware trajectory from central processing units (CPUs) to graphics processing units (GPUs), tensor processing units (TPUs), and custom accelerators, and show how the co-design of chips and models has unlocked improvements in throughput and cost by orders of magnitude. On the algorithmic side, we cover the deep learning revolution, scaling laws, pretraining and fine-tuning paradigms, and multimodal and agentic architectures. We map the modern software stacks, i.e., open-source AI frameworks, end-to-end toolchains, and community datasets, that make model development reproducible and widely accessible. Given the environmental and infrastructural impact of scale, we emphasize the trade-offs in energy, datacenter, and governance. Finally, we identify emerging trends that reshape how AI is developed and deployed.
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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
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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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Qi Zhou, Yiming Zhang, Yanggan Gu, Yuanyi Wang, Zhaoyi Yan, Zhen Li, Chi Yung Chung, Hongxia Yang
2026,14(1):37-49, DOI: 10.35833/MPCE.2025.000973
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Large language models (LLMs) have achieved remarkable progress in recent years. Nevertheless, the prevailing centralized paradigm for training generative artificial intelligence (AI) is increasingly approaching its structural limits. First, the concentration of large-scale graphics processing unit (GPU) clusters restricts the access to the pre-training stage, confining the fundamental model development to a small number of resource-rich institutions. Second, the economic and energy costs associated with operating massive data centers render this paradigm progressively less sustainable. Third, the hardware gatekeeping narrows the participation to computer science specialists, limiting the involvement of domain experts who are essential for high-impact applications. Finally, small- and medium-sized enterprises remain dependent on expensive application programming interface (APIs) or shallow fine-tuning methods that are insufficient to modify the core knowledge of a model. Together, these constraints impede innovation and hinder equitable access to next-generation AI systems. Model fusion offers a scalable alternative by integrating multiple specialized models without retraining from scratch. This paper analyzes the current landscape of model fusion, outlining the strengths and limitations of existing methods and discussing future directions. We highlight recent advances such as InfiFusion, InfiGFusion, and InfiFPO, which improve the alignment and scalability through techniques like top-K logit selection, graph-based distillation, and preference optimization. These techniques demonstrate substantial efficiency and reasoning gains, pointing toward a more accessible and resource-aware paradigm for large-scale model development. Finally, we discuss the practical applicability of model fusion, using the energy domain as an illustrative example.
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Yuheng Cheng, Wenxuan Liu, Yusheng Xue, Jie Huang, Junhua Zhao, Fushuan Wen
2026,14(1):50-62, DOI: 10.35833/MPCE.2025.000639
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An electricity market is a complex, dynamically operated network encompassing multiple participants under defined rules, thereby ensuring real-time supply-demand balance and system reliability. However, the inherent complexity and dynamism of the electricity market pose significant challenges to conventional modelling approaches, which often rely on expert knowledge and manual processes informed by market regulations. This reliance frequently leads to inefficiencies and elevated risks of error. To address these limitations, this paper proposes a framework for automated electricity market modelling and simulation centered on a large language model based agent, termed the modelling and simulation system agent (MSS-Agent) framework. The proposed MSS-Agent framework employs the hierarchical chain-of-thought (HCoT) method to more accurately extract essential information from relevant documents, thereby enhancing modelling fidelity. Moreover, it integrates tool usage and reflexive debugging to optimize the code generation process, ensuring reliability in automated electricity market modelling and simulation. Experimental results demonstrate that the proposed MSS-Agent framework significantly improves both mathematical model extraction accuracy and code execution reliability. Consequently, the proposed MSS-Agent framework not only increases simulation efficiency but also provides more precise and dependable tools for informed decision-making in electricity markets.
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Minhang Liang, Qingquan Luo, Tao Yu, Peiwei Kuang, Zhaotao Li, Zhenning Pan
2026,14(1):63-81, DOI: 10.35833/MPCE.2025.000794
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The modern power systems face challenges, including high proportions of uncertain renewable energy, rapid dynamics of power electronics, and decentralized control among multiple entities. Digital development has enabled power grids to integrate numerous edge devices equipped with sensing and computing capabilities, aiming to exploit edge data to enhance grid observability, controllability, and resilience. However, much of potential value of edge data remains unexploited with traditional architecture and methods. Therefore, we explore the potential of leveraging large language models (LLMs) to fully exploit edge data in modern power systems. An intelligent, scalable, and efficient three-layer architecture is proposed to align the capabilities of LLMs with the constraints of edge scenarios. Supporting technologies are reviewed for each layer, including multimodal data fusion, lightweight collaborative inference, and closed-loop control. To validate the proposed architecture, we provide three representative scenarios for preliminary exploration: virtual power plant (VPP) dispatch, intelligent substation inspection, and contingency management, illustrating how LLMs can unlock the value of edge data. We conclude by identifying key technical challenges and outlining future research directions for building modern power systems by LLM-based exploitation of edge data.
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Xiaoyu Peng, Feng Liu, Peng Yang, Beisi Tan, Pengfei Gao, Zhaojian Wang
2026,14(1):82-94, DOI: 10.35833/MPCE.2024.001298
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In Part I of this paper, we have proposed the new concept of generalized voltage damping (GVD) and derived the system-wise GVD (sGVD) index for the global assessment of voltage stability and system strength. Part II of this paper extends this concept to develop a port-wise index for quantifying the voltage damping characteristics locally. To this end, we decompose the sGVD index into individual ports (or buses), thereby forming the port-wise GVD (pGVD) index, which can be computed using local measurements. By inheriting the interpretation of the system-wise index, we further prove that the average of pGVD indices across all ports is approximately identical to the sGVD index. Moreover, it exhibits favorable properties absent in existing indices based on the maximum Lyapunov exponents (MLEs) of terminal voltages, empowering its application as an assessment metric for the supportive capability of devices to short-term voltage stability. The model-independent feature enables the assessment considering the complex and nonlinear dynamics of inverter-based resources (IBRs) such as wind turbines, photovoltaics (PVs), and battery energy storages. Experimental simulations conducted on a heterogeneous IEEE 39-bus system and two practical power systems with massive renewable resource integration confirm the theoretical results. The influence of voltage control strategies of IBR, control parameters, integration locations, and active power control parameters are also analyzed, providing a new perspective for understanding the individual support of devices for short-term voltage stability.
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Qili Ding, Xinggan Zhang, Zifeng Li, Xiangxu Wang, Weidong Li
2026,14(1):95-107, DOI: 10.35833/MPCE.2025.000010
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The existing minimum demand inertia (MDI) assessment methods based on time-domain simulation of system frequency response are complex in modeling and time-consuming in computation. If incorporating the load-side resources, it will lead to further computation inefficiency. This paper proposes a fast assessment method (FAM) for MDI in power systems. A full-response analytical model (FRAM) of a multi-resource system considering the load-side inertia is developed. The analytical expression of the mapping relationship between the maximum frequency deviation and system inertia is derived, thus realizing the fast solution of the system MDI under frequency security constraints. Case studies based on the modified IEEE RTS-79 test system and a provincial power grid in China demonstrate that the proposed FAM can solve the MDI in milliseconds without being affected by the system scale while maintaining high accuracy. This can provide an accurate and rapid analytical tool for sensing inertia security boundary in grid inertia resource planning and operation scheduling.
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2026,14(1):108-120, DOI: 10.35833/MPCE.2025.000175
Abstract:
Under weak grid conditions, oscillation in wind power integrated system occurs frequently. However, existing oscillation suppression methods face challenges in effectively coordinating control parameters and fail to guarantee excellent dynamic performance. Therefore, this paper proposes an uncertainty and disturbance estimator-based feedback linearization sliding mode control (UDE-FLSMC) method, which can reduce the negative damping region of the impedance phase of wind power integrated system. Firstly, the feedback linearization process of multi-input multi-output (MIMO) systems is derived. Then, the uncertainty and disturbance estimator (UDE) is used to estimate the disturbance in sliding mode control, and the UDE-FLSMC method is proposed. Secondly, the control structure and impedance model of wind power grid-side converter (GSC) are established. The impact of control parameters on the impedance characteristics of the converter is analyzed. It is demonstrated that the impedance phase in sub/supersynchronous frequency band maintains within a significant positive damping region under different operating conditions. Then, a hardware-in-loop experimental platform is constructed to verify the dynamic performance of the proposed UDE-FLSMC method, which is compared with proportional integral (PI) control and phase margin frequency division compensation (PM-FDC) control. The results show that the proposed UDE-FLSMC method exhibits superior oscillation suppression ability and faster response characteristics, which can significantly improve the stability of wind power integrated system under weak grid conditions.
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Gustavo Gonçalves dos Santos, Matheus Rosa Nascimento, João Pedro Peters Barbosa, Maiara Camila Oliveira, Ahda Pionkoski Grilo Pavani, Rodrigo Andrade Ramos
2026,14(1):121-131, DOI: 10.35833/MPCE.2024.001077
Abstract:
The massive integration of intermittent renewable generation and the increasing variability of demand raise concerns about the high level of uncertainty in the security assessment of power systems. In this context, the main contribution of this study is the proposal of a new definition of contingency criticality, which is based on the violation probability of the voltage security margin (VSM) while considering correlated uncertainties in both system loads and wind power generation. From this new definition, a contingency ranking can be derived and used to determine preventive control actions. To calculate this probability for each contingency, a new approach based on the cross-entropy (CE) method is developed and applied. The CE method is well-suited to handle high levels of uncertainty, as it typically provides faster and more accurate results compared with Monte Carlo simulation, particularly for cases with low violation probabilities of the VSM. Another innovative feature of this approach is the consideration of correlated uncertainties through the use of multivariate normal distributions and Gaussian copulas. Furthermore, the proposed definition is implemented using a formulation that is capable of detecting either saddle-node or limit-induced bifurcations to accurately identify the maximum loadability point. A proof of concept is presented for a comprehensive explanation of the proposed definition, followed by an application of this definition to the IEEE 118-bus test system. The findings of this paper highlight the need to carefully select critical contingencies for voltage security assessment in the context of increasing uncertainties.
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Haoyang Yin, Dong Liu, Jiaming Weng
2026,14(1):132-144, DOI: 10.35833/MPCE.2024.001216
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The uncertainty and variability of advancing wildfires present significant challenges to the resilience of power systems. This paper proposes a hierarchical dispatch strategy of multi-type virtual power plants (VPPs) for enhancing resilience of power systems under wildfires, which encompass geographically distributed VPPs (GDVPPs) based on Internet data centers (IDCs) and geographically concentrated VPPs (GCVPPs) that aggregate flexible loads (FLs). The proposed strategy enhances resistance to wildfire-induced uncertainties by facilitating coordinated operations between these two types of VPPs. At the upper level, an improved maximum flow model is introduced to quantify the dynamic changes in the workload transfer capability of IDC (WTCI) under wildfire conditions, and stochastic model predictive control (SMPC) is employed to perform rolling optimization of generator outputs, IDC workload transfers, and load shedding, thereby minimizing the total regulation costs. Based on the load shedding instructions from the upper level, the lower level integrates GCVPPs to provide load curtailment services, effectively offsetting the load shedding power. Subsequently, the lower level feeds back the load rebound (LR) resulting from these load curtailment services to the upper-level strategy, serving as a basis for its rolling optimization. The SMPC integrates an event-driven deductive model to address the fine-grained modeling of the operational state, effectively overcoming challenges posed by discrepancies in simulation time steps arising from power system cascading failures, variations in IDC adjustment capacity, and LR effects. Finally, a modified 39-bus power system, integrated with an 8-bus IDC network, is used as a case study to validate the effectiveness of the proposed strategy.
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2026,14(1):145-157, DOI: 10.35833/MPCE.2024.000697
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Wide-area measurement systems enable the transmission of measurement and control signals for wide-area damping controllers (WADCs) in smart grids. However, the vulnerability of the communication network makes the WADC susceptible to malicious cyber attacks, such as false data injection (FDI) attack and denial of service (DoS) attack. Researchers develope numerous supervised machine-learning and model-based solutions for attack detection. However, the partially labeled attack data, skewed class distributions, and the need for precise mathematical models present significant challenges for real-world attack detection. This paper introduces the cyber attack-resilient wide-area damping controller (CyResWadc) system framework to address these challenges. The proposed framework leverages semi-supervised generative adversarial network (SSGAN) model to handle partially labeled attack data. It utilizes the support vector machine-based synthetic minority oversampling technique (SVM-SMOT) for data oversampling to manage skewed class distributions. Furthermore, probing signals are used to stimulate the power system, facilitating the generation of synthetic attack scenarios under different operational conditions. If any attack is detected, an alternate pair of measurement and control signals is used for attack mitigation. The performance is validated on a developed hardware-in-the-loop (HIL) cyber-physical testbed built using the open parallel architecture laboratory-real time (OPAL-RT) simulator, industry-grade hardware, Network Simulator 3 (NS-3), and open platform for data collection (OpenPDC).
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Mostafa Ansari, Mohsen Ghafouri, Amir Ameli, Ulas Karaagac, Ilhan Kocar
2026,14(1):158-173, DOI: 10.35833/MPCE.2024.001368
Abstract:
The recent growing integration of wind farms (WFs), particularly variable speed wind turbines (WTs), results in several operational challenges to power grids integrated with WFs, such as low grid inertia and the reduced performance of measurement-based fast frequency response. To deal with such challenges, grid operators use WF active power controllers (WFAPCs) to enhance frequency control support from WTs and improve the frequency stability of the grid. However, the operation of WFAPC relies on measurements received through communication networks and cyber layers of WFs, which consequently makes them prone to cyber threats, e.g., false data injection (FDI). On this basis, firstly, this paper analyzes the cybersecurity vulnerabilities of WFAPCs and the possible impacts of exploiting cybersecurity vulnerabilities on the frequency response of WF and frequency stability of the grid. Then, based on the knowledge of intruders, two attacks, i.e., white-box and black-box FDI attacks, are developed against WFAPCs. Afterward, to detect these attacks, a novel bi-level detection and mitigation technique based on support vector machine (SVM)-based technique and long short-term memory (LSTM)-based technique is developed, which is implemented at the control center of the WF (primary detector) and at the dispatch center of the power grid (secondary detector), respectively. These detectors classify real-time measurements into attack and normal operation. Additionally, a hierarichical mitigation technique is proposed to counter the developed cyber attacks by replacing the active power reference signal of WF with new values obtained based on the droop control theory. The impacts of the attacks and the effectiveness of the proposed bi-level technique are evaluated using the modified 39-bus benchmark.
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Changming Chen, Yunchu Wang, Shunjiang Yu, Bing Chen, Zikang Shen, Ze Li, Hongtao Wang, Zhenzhi Lin
2026,14(1):174-186, DOI: 10.35833/MPCE.2025.000034
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Among disasters that may lead to large-scale blackouts of power systems, wind storms introduce spatio-temporal variations in restoration security risks, making large-scale power system restoration more difficult. Power system restoration during wind storms requires coordinated efforts among the regional independent system operators, transmission system operators, and distribution system operators. However, existing research mainly focuses on the coordination between transmission system operators and distribution system operators, which limits its applicability to large-scale blackouts caused by wind storms. Therefore, a spatio-temporal coordinated restoration method based on restoration security risk assessment for multi-voltage-level power systems (MVLPSs) is proposed in this paper. Typhoons, known for their high wind speeds and destructive power, are considered as the disaster scenario. First, a spatio-temporal restoration security risk assessment approach is proposed to reduce additional control costs caused by restoration security risks. Then, a spatio-temporal coordinated restoration framework for MVLPSs is established, and a triple-level optimization model for the spatio-temporal coordinated restoration of MVLPSs is proposed to maximize the net restoration benefits of MVLPSs during the full-stage restoration process. Finally, case studies on an actual 379-bus MVLPS in China are conducted to verify that the proposed method can achieve higher net restoration benefits compared with existing restoration methods.
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Xinyu Liu, Maosheng Gao, Juan Yu, Zhifang Yang, Wenyuan Li
2026,14(1):187-198, DOI: 10.35833/MPCE.2024.001249
Abstract:
Operational reliability assessment (ORA), which evaluates the risk level of power systems, is hindered by accumulated computational burdens and thus cannot meet the demands of real-time assessment. Recently, data-driven methods with fast calculation speeds have emerged as a research focus for online ORA. However, the diverse contingencies of transformers, power lines, and other components introduce numerous topologies, posing significant challenges to the learning capabilities of neural networks. To this end, this paper proposes a multi-kernel collaborative graph convolution neural network (GCNN) for ORA considering varying topologies. Specifically, a physics law-informed graph convolution kernel derived from the Gaussian-Seidel iteration is introduced. It effectively aggregates node features across different topologies. By integrating additional advanced graph convolution kernels with a novel self-attention mechanism, the multi-kernel collaborative GCNN is constructed, which enables the extraction of diverse features and the construction of representative node feature vectors, thereby facilitating high-precision reliability assessments. Furthermore, to enhance the robustness of multi-kernel collaborative GCNN, the inherent pattern of the load-shedding model is analyzed and utilized to design a specialized supervised loss function, which allows the neural network to explore a broader feature space. Compared with the existing data-driven methods, the multi-kernel collaborative GCNN, combined with supervised exploration, can accommodate a wider range of contingencies and achieve superior assessment accuracy.
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Bin Li, Zhongrun Xie, Jiawei He, Mingyu Shao, Haiji Wang, Zepeng Hu
2026,14(1):199-211, DOI: 10.35833/MPCE.2024.000536
Abstract:
Time-domain distance protection shows superior performance for transmission lines integrated with renewable energy sources (RESs). However, in 35-110 kV renewable power transmission systems, the inhomogeneity of the mixed overhead lines (OHLs) and underground cables (UGCs) negatively affects the feasibility of distance protection. This paper proposes a robust algorithm of time-domain distance protection for renewable power transmission system with the mixed OHLs and UGCs. First, based on the time-domain mathematical model, the accuracy and robustness of the conventional algorithm under inhomogeneous line parameters are evaluated. To solve the “
0/0 ”problem caused by weak signals, the singular value decomposition-based least squares method (SVD-LSM) is adopted to avoid calculation outliers and improve the protection reliability. Meanwhile, a weighting method based on Euclidean norm is designed to overcome the problem of computational non-convergence. It also ensures the protection operation speed by using a short time window. In addition, a distance correction method is designed for mixed lines to improve the accuracy of fault location. On the basis, a prototype of the protection device is developed, and extensive hardware-in-the-loop (HIL) tests are performed to verify its feasibility and superiority. In addition, the prototype of the protection device has been applied to actual renewable power transmission systems. - 1
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Shuaifeng Wang, Sheng Huang, Juan Wei, Qiuwei Wu, Wenbo Tang, Lu Zhou, Shoudao Huang
2026,14(1):212-223, DOI: 10.35833/MPCE.2024.001323
Abstract:
High-reliability double-sided ring collector systems have been widely implemented in offshore wind farms (OWFs). It is challenging to achieve a globally optimal network topology and a cable capacity rating for the OWF collector system (CS) simultaneously. This paper proposes an optimal collector system planning (CSP) method for OWF with double-sided ring topology based on bidirectional flow conservation method to minimize cable costs and total power losses. By analyzing the power flow direction after faults, all fault scenarios are summarized into two fault conditions. The bidirectional flow conservation method is developed to reveal the matching mechanism between different cable sequence positions and their optimal ratings, considering the minimal rating requirements. The complex high-dimensional CSP problem, which involves the coupling characteristics of different cable parameters and system power flows, is convexified by equivalent alternative methods into a mixed-integer quadratic programming (MIQP) to guarantee a global optimal solution within feasible computation time, improving the solvability and practicality. The effectiveness of the proposed optimal CSP method has been validated in MATLAB.
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Yi Yang, Ping Tang, Can Wang, Nan Yang, Hui Ma, Zhuoli Zhao
2026,14(1):224-236, DOI: 10.35833/MPCE.2025.000113
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Integrated energy system (IES) integrates various energy subsystems such as electricity, natural gas, heat, and the dynamic characteristics of different energy networks differ significantly. To realize the coordinated operation of heterogeneous energy flow network of electricity, natural gas, and heat, in this paper, a multi-spatial-temporal-scale coordinated optimal scheduling method of IES considering frequency support ability is presented. The method divides the IES into three layers on the spatial scale and divides IES optimal scheduling into three stages: day-ahead, intra-day and real-time on the temporal scale. In the day-ahead stage, the most economical day-ahead scheduling plan is developed. In the intra-day stage, considering the different response characteristics of the device, the slow, medium, and fast subsystem layers are divided for control, and the device output related to cold, heat, electricity, and natural gas is controlled hierarchically based on distributed model predictive control. In the real-time stage, the supporting effect of IES on power grid frequency is fully explored, and an IES active-frequency-support control method considering frequency regulation cost is proposed. Case studies show that the devices can be fully utilized with different response ability to perform the scheduling plans of each layer, effectively reducing the system operation cost and improving the frequency quality.
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Shunjiang Lin, Xuan Sheng, Yue Pan, Weikun Liang, Mingbo Liu
2026,14(1):237-249, DOI: 10.35833/MPCE.2024.001260
Abstract:
The offshore-onshore integrated energy system (OOIES) comprises offshore gas production platforms, wind farms, and onshore gas-fired combined heat and power plants, facilitating the integrated operation of multiple energy sources. To address the challenge of optimally configuring the device capacities in carbon capture and power to gas (CC-P2G) amid stochastic fluctuations in offshore gas and wind power outputs, this study proposes a multi-objective approximate dynamic programming algorithm. This algorithm solves the multi-objective stochastic optimal configuration for the device capacities in CC-P2G in OOIES by simultaneously optimizing investment and operation costs, wind power curtailment, and carbon emissions. By leveraging value function matrices for multiple objectives to solve the extended Bellman equation, the multi-objective multi-period model is decomposed into a series of multi-objective single-period optimization problems, which are solved recursively. Additionally, a weighted Chebyshev function is introduced to obtain the compromise optimal solution for multi-objective optimization model during each period. A case study of an OOIES confirms the effectiveness and efficiency of the proposed algorithm.
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Xihai Zhang, Shaoyun Ge, Yue Zhou, Hong Liu, Shida Zhang, Changxu Jiang
2026,14(1):250-260, DOI: 10.35833/MPCE.2024.001198
Abstract:
The proliferation of distributed energy resources and time-varying network topologies in active distribution networks presents unprecedented challenges for network operators. While reinforcement learning (RL) has shown promise in addressing network-constrained energy scheduling, it faces difficulties in managing the complexities of dynamic topologies and discrete-continuous hybrid action spaces. To address these challenges, a graph-based safe RL approach is proposed to learn dynamic optimal power flow under time-varying network topologies. This proposed approach leverages graph convolution operators to handle network topology changes, while safe RL with parameterized action ensures policy development. Specifically, the graph convolution operator abstracts key characteristics of the network topology, enabling effective power flow management in non-stationary environments. Besides that, a parameterized action constrained Markov decision process is employed to handle the hybrid action space and ensure compliance with physical network constraints, thereby accelerating the deployment of safe policy for hybrid action spaces. Numerical results demonstrate that the proposed approach efficiently navigates the discrete-continuous decision space while accounting for the constraints imposed by the dynamic nature of power flow in time-varying network topologies.
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Ali S. Aljumah, Mohammed H. Alqahtani, Ahmed R. Ginidi, Abdullah M. Shaheen
2026,14(1):261-272, DOI: 10.35833/MPCE.2024.001380
Abstract:
The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family. We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribution networks to maximize energy efficiency. Three loading levels (low, medium, and high) per day are investigated. The proposed IAHA is evaluated on the IEEE 33-bus distribution network (DN) and 69-bus DN. The proposed IAHA demonstrates notable improvements in cost savings and voltage profile compared with the conventional artificial hummingbird algorithm (AHA). In addition, it enhances energy savings across various loading conditions and outperforms the conventional AHA in both best and average performance metrics. Although raising the compensation limit initially increases cost savings, the benefits decrease beyond a threshold, highlighting the importance of balancing the compensation levels for maximum efficiency.
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Guangxiao Zhang, Gaoxi Xiao, Xinghua Liu, Yan Xu, Peng Wang
2026,14(1):273-285, DOI: 10.35833/MPCE.2024.001299
Abstract:
This paper proposes a robust faulted line-section location method based on the normalized quantile Hausdorff distance (NQHD) algorithm for detecting single-phase-to-ground faults in distribution networks. The faulted line section is determined according to the characteristic differences between the zero-sequence currents on the faulted and healthy line sections. Specifically, the zero-sequence currents at both ends of a healthy line section are highly similar to each other, while such is generally not the case on a faulted line section. The NQHD algorithm can disregard extremes or outliers while also providing a normalized scaling in different scenarios. Thus, it can be applied to calculate the robust waveform similarity of zero-sequence current waveforms at both ends of different line sections for identifying reliably the faulted line section even under the interference of outliers. The results demonstrate the good performance of the proposed method in detecting single-phase-to-ground faults under different fault conditions. Comparative tests with the existing methods confirm the advantageous robustness of the proposed method against the impacts of outliers and noises.
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Jiahui Jin, Graduate, Guoqiang Sun, Sheng Chen, Yaping Li, Yingqi Liao, Wenbo Mao, Lu Shen
2026,14(1):286-297, DOI: 10.35833/MPCE.2025.000035
Abstract:
The coordination of power distribution networks (PDNs) and microgrids (MGs) is challenging due to the abundant resources and their dispersed geographical distribution, making centralized computation inefficient. To address this issue, we propose a coordination framework with single leader and multiple followers that allows limited information exchange. In this framework, the PDN operators act as leaders, while the MG operators act as followers. However, variations in load and renewable energy during MG scheduling intervals can cause variability in power transactions between PDNs and MGs. This variability can reduce the net revenue of MGs and increase the operation costs of PDNs, which makes it essential to consider the worst-case fluctuations. We introduce a multi-agent robust deep reinforcement learning (MARDRL) approach for coordination of PDNs and MGs, accounting for the worst-case scenarios. The numerical results on the test systems verify the effectiveness of the proposed approach in enhancing the coordination of PDNs and MGs.
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Mohammadreza Najafi, Hossein Aliamooei-Lakeh, Hessam Kazari, Mohammadreza Toulabi
2026,14(1):298-309, DOI: 10.35833/MPCE.2024.001254
Abstract:
The increasing integration of inverter-based renewable energy sources (RESs) has significantly reduced the power grid inertia, leading to challenges in maintaining frequency stability. Virtual synchronous generators (VSGs), which emulate the behavior of synchronous generators (SGs), can help address this issue by providing synthetic inertia and improving system stability during disturbances. The paralleled operation of VSGs and SGs is particularly important in islanded microgrids, where small SGs are commonly used for power generation. This paper presents a comprehensive dynamic model of a paralleled VSG-SG system and proposes a model predictive control (MPC) strategy for VSG to enhance disturbance rejection and improve dynamic performance. Additionally, an adaptive delay compensator (ADC) is introduced to manage communication delays between the control center and system. Simulation results in MATLAB/Simulink demonstrate the effectiveness of the MPC-based VSG control method in improving frequency control in various disturbance scenarios.
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Bo Wang, Zhehan Jia, Xingying Chen, Lei Gan, Haochen Hua, Kun Yu, Jun Shen
2026,14(1):310-321, DOI: 10.35833/MPCE.2025.000456
Abstract:
Liquefied natural gas (LNG), recognized as the primary form for natural gas transportation, can release substantial cold energy during gasification. To make efficient use of this cold energy, this paper proposes a data-driven stochastic robust (DDSR) energy management method for the multi-stage cascade utilization of LNG cold energy in a multi-energy microgrid (MEMG) of an LNG receiving terminal. Firstly, a general scheduling model considering the flexible coupling between adjacent stages, energy losses, and electric power consumption for the cascade utilization of LNG cold energy is introduced. This model is applied to carbon capture, cryogenic power generation, and direct cooling, which are sequentially associated with the deep, medium, and shallow cooling zones of LNG cold energy, respectively. Moreover, a two-stage energy management framework is proposed to coordinate the cascade utilization of LNG cold energy with other energy resources in the MEMG. To tackle the uncertainties of renewable energy generation and various loads, a DDSR-based solution method is developed, aiming to achieve both economic benefits and solution robustness by identifying the worst-case scenarios and the corresponding worst-case probability. Accordingly, a Benders decomposition-based solution algorithm is proposed to divide the original problem into a master problem and a slave problem, which are solved iteratively. The simulation results verify the effectiveness and high efficiency of the proposed DDSR energy management method for multi-stage cascade utilization of LNG cold energy.
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Lester Marrero, Daniel Sbárbaro, Luis García-Santander
2026,14(1):322-333, DOI: 10.35833/MPCE.2024.001304
Abstract:
The growing electricity demand, combined with the increasing integration of photovoltaic (PV) generation into the distribution system, requires higher flexibility from the demand side. This paper proposes a customized scheduling approach for demand response (DR) of customers with dispatchable inverters in distribution-level PV facilities. Based on the Chilean context, the proposed approach enables these energy resources to provide flexibility in the technical and economic management of the distribution system operator (DSO). Specifically, a bi-level optimization model is introduced. At the upper level, the DSO minimizes distribution system costs by determining daily price signals for customers based on their response profile classes (RPCs) and active and reactive power set points for PV facilities. At the lower level, customers aim to reduce their electricity bills. In addition, the proposed approach ensures the reliable operation of the distribution system with high probability by addressing uncertainty through chance constraints (CCs). Incorporated CCs in the distribution system modeling include the squared magnitude of nodal voltage, complex power flow in lines, and apparent power of inverters. Finally, two case studies are presented, involving 420 residential and commercial Chilean customers with two distribution-level PV facilities using real-world market prices and daily consumption profiles on the IEEE 37-node test feeder. Results demonstrate how the proposed model enables the customized scheduling of customers and PV facilities, highlighting its effectiveness over the uniform price scheme.
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Swodesh Sharma, Apeksha Ghimire, Shashwot Shrestha, Rachana Subedi, Sushil Phuyal
2026,14(1):334-346, DOI: 10.35833/MPCE.2024.001153
Abstract:
Accurate load profile data are essential for optimizing energy systems. However, real-world datasets often suffer from low resolution and significant missing values. To address these challenges, this paper introduces physics-informed loss generative adversarial network (PIL-GAN), a model that combines generative adversarial networks (GANs) with physics-informed losses (PILs) derived from physics-informed neural networks (PINNs) that are integrated directly into the generator. High-resolution load profiles are generated that not only fill in missing data but also ensure that the generated profiles adhere to physical laws governing the energy systems, such as energy conservation and load fluctuations. By embedding domain-specific physics into the generation process, the proposed model significantly enhances data quality and resolution for low-quality datasets. The experimental results demonstrate notable gains in data accuracy, resolution, and consistency, making PIL-GAN an effective tool for energy management systems. The PIL-GAN also has broader applicability in other fields such as generating and inpainting high-resolution datasets for energy systems, industrial processes, and any domain in which data must comply with real-world physical laws or operational requirements.
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Qiangang Jia, Wenshu Jiao, Sijie Chen, Jian Ping, Zheng Yan, Haitao Sun
2026,14(1):347-356, DOI: 10.35833/MPCE.2024.001191
Abstract:
Distributed photovoltaic (PV) entities can be coordinated to provide reactive power for voltage regulation in distribution networks. However, integrating large-scale distributed PV entities into reactive power optimization makes it difficult to balance the individual benefit of each PV entity with the overall economic efficiency of the system. To address this challenge, we propose a market-oriented two-stage reactive power regulation method. At the first stage, a long-term multi-layer reactive power capacity market is created to incentivize each PV entity to provide reactive power capacity, while ensuring their financial interests are guaranteed. At the second stage, a real-time multi-layer reactive power dispatch mechanism is introduced to manage the reactive power generation of distributed PV entities, prioritizing the dispatch of lower-cost PV entities to maximize system-wide economic efficiency. Simulation results based on a real Finnish radial distribution network demonstrate the effectiveness of the proposed method in optimizing reactive power for large-scale distributed PV entities.
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Hanwen Wang, Yang Wang, Xianyong Xiao, Zhiquan Ma, Qunwei Xu
2026,14(1):357-367, DOI: 10.35833/MPCE.2024.000628
Abstract:
The transformer inrush current has been a potential threat in wind farms connected modular multilevel converter based high-voltage direct current (WF-MMC-HVDC) system due to the low overcurrent capability of power electronic devices. To investigate this issue, this paper develops a complete harmonic state space (HSS) model of the WF-MMC-HVDC system containing saturable transformers. The severity of the inrush current is investigated under different transformer configurations and the result is compared with EMTP simulations. More importantly, key factors that influence inrush current characteristics in a WF-MMC-HVDC system are studied using the single-input single-output impedance model derived from the linearized HSS model. The results indicate that wind farms have a minor impact on the inrush current characteristics, whereas V/F controlled modular multilevel converter (MMC) reduces its output voltage during transformer energization, thereby mitigating the severity of the inrush current. The severity of the inrush current largely depends on the resonance point determined by the transmission line. In the case of offshore WF-MMC-HVDC system, long submarine cables may cause severe harmonic amplifications and even do not attenuate for a long time.
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Marzio Barresi, Davide del Giudice, Davide de Simone, Samuele Grillo
2026,14(1):368-382, DOI: 10.35833/MPCE.2024.001154
Abstract:
Modular multilevel converters (MMCs) have emerged as a promising solution for integrating renewables. In case of photovoltaic (PV) systems, PV arrays can be integrated at the submodule (SM) level, and the distributed maximum power point tracking (DMPPT) can be achieved through AC and DC circulating current control and perturb and observe (P&O) methods. However, this implementation is hindered by the need for numerous measurements, since the voltage and current of all PV arrays in each SM must be known. To address this issue, we propose a three-phase reduced-sensor MMC with distributed MPPT for PV integration based on an extended Kalman filter (EKF). For each MMC arm, the EKF estimates the voltage and irradiance of each SM by exploiting their gate signals and duty cycles as well as the arm current and voltage. This solution is compatible with uniform and non-uniform irradiance conditions both under the steady-state and transient conditions and uses significantly fewer sensors than other strategies employed in similar-purpose MMCs, while achieving comparable efficiency. Moreover, by exploiting the PV array characteristics, it allows performing DMPPT more directly, without using P&O methods. These features are confirmed by simulations of an MMC-based PV system with 12 SMs per arm.
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Volume 14, Issue 1, 2026
>Featured Theme: Artificial Intelligence in Power Engineering: Challenges, Prospects, and Promises
>Original Paper
More+Virtual Issue
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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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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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Abdelfatah Ali, Hossam H. H. Mousa, Mostafa F. Shaaban, Maher A. Azzouz, Ahmed S. A. Awad
2024,12(3):675-694, DOI: 10.35833/MPCE.2023.000107
Abstract:
Electric vehicles (EVs) are becoming more popular worldwide due to environmental concerns, fuel security, and price volatility. The performance of EVs relies on the energy stored in their batteries, which can be charged using either AC (slow) or DC (fast) chargers. Additionally, EVs can also be used as mobile power storage devices using vehicle-to-grid (V2G) technology. Power electronic converters (PECs) have a constructive role in EV applications, both in charging EVs and in V2G. Hence, this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications. It examines PECs from the point of view of their classifications, configurations, control approaches, and future research prospects and their impacts on power quality. These can be classified into various topologies: DC-DC converters, AC-DC converters, DC-AC converters, and AC-AC converters. To address the limitations of traditional DC-DC converters such as switching losses, size, and high-electromagnetic interference (EMI), resonant converters and multiport converters are being used in high-voltage EV applications. Additionally, power-train converters have been modified for high-efficiency and reliability in EV applications. This paper offers an overview of charging topologies, PECs, challenges with solutions, and future trends in the field of the EV charging station applications.
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Virtual Transmission Solution Based on Battery Energy Storage Systems to Boost Transmission Capacity
Matías Agüero, Jaime Peralta, Eugenio Quintana, Victor Velar, Anton Stepanov, Hossein Ashourian, Jean Mahseredjian, Roberto Cárdenas
2024,12(2):466-474, DOI: 10.35833/MPCE.2023.000729
Abstract:
The increasing penetration of variable renewable energy (VRE) generation along with the decommissioning of conventional power plants in Chile, has raised several operational challenges in the Chilean National Power Grid (NPG), including transmission congestion and VRE curtailment. To mitigate these limitations, an innovative virtual transmission solution based on battery energy storage systems (BESSs), known as grid booster (GB), has been proposed to increase the capacity of the main 500 kV corridor of the NPG. This paper analyzes the dynamic performance of the GB using a wide-area electromagnetic transient (EMT) model of the NPG. The GB project, composed of two 500 MVA BESS units at each extreme of the 500 kV corridor, allows increasing the transmission capacity for 15 min during
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Xiao Xu, Ziwen Qiu, Teng Zhang, Hui Gao
2024,12(2):440-453, DOI: 10.35833/MPCE.2023.000742
Abstract:
The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.
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Jing Bian, Yuheng Song, Chen Ding, Jianing Cheng, Shiqiang Li, Guoqing Li
2024,12(2):427-439, DOI: 10.35833/MPCE.2023.000707
Abstract:
Photovoltaic (PV) and battery energy storage systems (BESSs) are key components in the energy market and crucial contributors to carbon emission reduction targets. These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading. This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets, with a specific focus on carbon reduction benefits. A two-stage bidding framework that optimizes the profit of PV and BESSs is presented. In the first stage, the day-ahead energy market takes into account potential real-time forecast deviations. In the second stage, the real-time balancing market uses a rolling optimization method to account for multiple uncertainties. Notably, a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control (AGC). This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market. This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error (ACE) occurs. The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.
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Makedon Karasavvidis, Andreas Stratis, Dimitrios Papadaskalopoulos, Goran Strbac
2024,12(2):415-426, DOI: 10.35833/MPCE.2023.000737
Abstract:
The offering strategy of energy storage in energy and frequency response (FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors (deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.
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Pavitra Sharma, Krishna Kumar Saini, Hitesh Datt Mathur, Puneet Mishra
2024,12(2):381-392, DOI: 10.35833/MPCE.2023.000761
Abstract:
The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These prosumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.
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Jianlin Li, Zhijin Fang, Qian Wang, Mengyuan Zhang, Yaxin Li, Weijun Zhang
2024,12(2):359-370, DOI: 10.35833/MPCE.2023.000345
Abstract:
As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.
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Hongchao Gao, Tai Jin, Guanxiong Wang, Qixin Chen, Chongqing Kang, Jingkai Zhu
2024,12(2):346-358, DOI: 10.35833/MPCE.2023.000762
Abstract:
The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, wherein energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear reconstruction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.
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Mubarak J. Al-Mubarak, Antonio J. Conejo
2024,12(2):323-333, DOI: 10.35833/MPCE.2023.000306
Abstract:
We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i.e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.
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- 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
- MPCE 10th Anniversary Forum will be held on Feb. 27, 2023
