JournalTOCs

IEEE Transactions on Smart Grid
Journal Prestige (SJR): 2.854

Citation Impact (citeScore): 9
Number of Followers: 18

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1949-3053
Published by IEEE

[228 journals]

IEEE Transactions on Smart Grid Publication Information
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  Pages: C2 - C2
  Abstract: null
  PubDate: TUE, 22 APR 2025 09:17:16 -04
  Issue No: Vol. 16, No. 3 (2025)
IEEE Transactions on Smart Grid Information for Authors
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  Pages: C3 - C3
  Abstract: null
  PubDate: TUE, 22 APR 2025 09:17:16 -04
  Issue No: Vol. 16, No. 3 (2025)
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  Pages: C4 - C4
  Abstract: null
  PubDate: TUE, 22 APR 2025 09:17:16 -04
  Issue No: Vol. 16, No. 3 (2025)
Two-Stage Planning for Smart Buildings With Flexible Heating Load
Considering Climate Change Induced Heat Waves
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  Authors: Tianyang Zhao;Qianwen Xu;
  Pages: 2012 - 2025
  Abstract: Building energy planning is significantly challenged by climate change, particularly the increasing frequency of heat waves impacting heating and cooling demands. Current planning methodologies neglect the impacts of heat waves on energy consumption and do not accurately model the temperature-dependent performance of heat pumps (HPs). This paper addresses the critical issue of designing energy-efficient and climate-resilient buildings through optimal resource configuration under uncertain weather conditions. A two-stage stochastic optimization model for building energy system planning is proposed. In the first stage, the capacities of energy resources are optimized; in the second stage, operational strategies under various weather scenarios are determined. A novel long-term load forecasting method using morphing techniques is developed to generate scenario trees accounting for both normal conditions and heat waves, capturing the impact of climate change on energy demand. Additionally, a temperature-dependent HP model with finite partial output levels is introduced, improving upon existing fixed coefficient of performance models to reflect practical operational characteristics. Simulation results on a real educational building in Stockholm demonstrate the effectiveness of the approach, showing an 8.33% reduction in heating capacity requirements and a 62.14% decrease in solution time, enhancing both resilience and computational efficiency.
  PubDate: TUE, 28 JAN 2025 09:17:19 -04
  Issue No: Vol. 16, No. 3 (2025)
Adaptive and Decentralized Control Strategy to Support Coordination of
Multiple DC Microgrids Considering Transmission Line Impedance
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  Authors: Chudi Weng;Yonggang Peng;
  Pages: 2026 - 2039
  Abstract: Multiple DC Microgrids (MGs) can be interconnected by interlinking converters (ICs) to support each other through coordinated control strategies. However, the nonnegligible transmission line resistance results in inaccuracies in decentralized coordination at steady state. To address this issue, an adaptive and decentralized control strategy is proposed to eliminate the impact of transmission line resistance and facilitate accurate power sharing among the DC MGs. The proposed strategy injects a perturbance to estimate the transmission line resistance based on the IC signals sampled before and after the perturbance, obviating the need for additional measuring devices on the transmission line. With the estimated results, the proposed strategy compensates for the transmission line resistance through virtual resistance, achieving accurate power sharing among the DC MGs under coordinated control. The compensation and coordination operations rely on the local signals from the IC, ensuring this power coordination in a decentralized manner. Furthermore, the estimation and compensation algorithm is decoupled at each IC port, allowing the easy extension of the proposed strategy. This paper discusses the application of the proposed strategy in three scenarios: two interconnected DC MGs, and several DC MGs interconnected via a multiport IC or multiple ICs. Using the example of two DC MGs with line impedance considered, the system stability is analyzed based on a small-signal model. Lastly, the feasibility of the proposed strategy in these scenarios is validated by the simulation and hardware-in-loop tests.
  PubDate: TUE, 28 JAN 2025 09:17:19 -04
  Issue No: Vol. 16, No. 3 (2025)
Distributed Two-Layer Predictive Control of AC Microgrid Clusters With
Communication Delays
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  Authors: Tao Yang;Jingang Lai;Chang Yu;Xiaoping Wang;Qiang Xiao;
  Pages: 2040 - 2051
  Abstract: This paper proposes a two-layer distributed network predictive control strategy for AC microgrids (MGs) clusters with communication delays. The strategy involves establishing a two-layer communication network to regulate the voltage/frequency of all distributed generators (DGs) within the MG cluster to predefined reference values while ensuring consistency in incremental costs across individual MGs. Furthermore, a multi-step predictive controller is designed, where delay information in the controller is replaced by the latest predictions, enabling proactive compensation for delays. Stability analysis of the closed-loop AC MG clusters is conducted and the response matching condition is derived between the second and tertiary levels. Finally, real-time simulations on an OPAL-RT platform are performed for AC MG clusters, validating the robustness of the proposed control method against communication delays.
  PubDate: WED, 12 FEB 2025 09:16:23 -04
  Issue No: Vol. 16, No. 3 (2025)
Plug and Play Detector Design for DC Microgrids With Unknown-Inputs-Based
FDI Attack
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  Authors: Zhihua Wu;Chen Peng;Engang Tian;Yajian Zhang;
  Pages: 2052 - 2064
  Abstract: DC microgrids, due to their deep integration of control, computing, communication technologies, and physical equipment, are susceptible to cyber-attacks. Consequently, this paper is dedicated to the development of a novel attack-defense framework for generalized DC microgrids. Firstly, an unknown-inputs-based false data injection (FDI) attack strategy is studied from the adversary’s perspective, unlike traditional stealthy attacks requiring non-minimum phase zeros or unstable poles, which conceals the attack signal as false unknown inputs (FUI) to maliciously disrupt current sharing and voltage balancing. Secondly, a comprehensive analysis of the stealthiness and destructiveness of FUI attack is provided, and a dual-observer-based detector is well constructed to detect the FUI attack and isolate the compromised distributed generation units. Then, structured Lyapunov matrix and semidefinite programming are ingeniously employed to solve the distributed observer gains simultaneously. Moreover, plug and play (PnP) performance is also analyzed to ensure the scalability of proposed FUI attack detector. Finally, the destructiveness and stealthiness of proposed FUI attack, as well as the effectiveness of designed detection scheme are demonstrated through simulations using MATLAB/SimPowerSystems Toolbox.
  PubDate: WED, 05 MAR 2025 09:17:31 -04
  Issue No: Vol. 16, No. 3 (2025)
Mode Switching-Induced Instability of Multi-Source Feed DC Microgrid
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  Authors: Shanshan Jiang;Zelin Sun;Jiankun Zhang;Hua Geng;
  Pages: 2065 - 2074
  Abstract: In DC microgrids (DCMGs), DC-bus signaling based control strategy is extensively used for power management, where mode switching plays a crucial role in achieving multi-source coordination. However, few studies have noticed the impact of mode switching and switching strategies on system voltage stability. To fill this gap, this paper aims to provide a general analysis framework for mode switching-induced instability in multi-source DCMGs. First, manifold theory is employed to analyze the stability of the DCMG switched system. Subsequently, the instability mechanism and its physical interpretation are explored. The positive feedback activated by the decreasing DC bus voltage during the switching process leads to instability. Switching strategy may inadvertently contribute to this instability. To improve stability, a novel control method based on mode scheduling is proposed, by adjusting switching strategy and thereby correcting the system trajectory. Finally, both real-time simulations and experimental tests on a DCMG system verify the correctness and effectiveness of theoretical analysis results.
  PubDate: THU, 27 MAR 2025 09:17:53 -04
  Issue No: Vol. 16, No. 3 (2025)
Distribution System Blackstart and Restoration Using DERs and Dynamically
Formed Microgrids
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  Authors: Salish Maharjan;Cong Bai;Han Wang;Yiyun Yao;Fei Ding;Zhaoyu Wang;
  Pages: 2100 - 2114
  Abstract: Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids (MGs), sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated MGs. In DERs-aided blackstart, the continuous operation of these MGs is limited by the finite energy capacity of commonly used blackstart units like battery energy storage (BES)-based grid-forming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between dynamic MGs and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition is developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottom-up blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. The proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.
  PubDate: THU, 30 JAN 2025 09:17:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Sensitivity-Based Heterogeneous Ordered Multi-Agent Reinforcement Learning
for Distributed Volt-Var Control in Active Distribution Network
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  Authors: Xiaodong Zheng;Shixuan Yu;Hui Cao;Tianzhuo Shi;Shuangsi Xue;Tao Ding;
  Pages: 2115 - 2126
  Abstract: As power grids expand, maintaining stable voltage and minimizing losses become increasingly crucial. Meanwhile, the widespread use of heterogeneous devices in modern distribution systems necessitates effective multi-device coordination. This issue is exacerbated by the integration of intermittent renewable sources (e.g., solar and wind), which introduce voltage fluctuations. To tackle these challenges, this paper proposes a novel Sensitivity-based Heterogeneous Ordered Multi-agent Reinforcement Learning (SHOM) method for Volt-Var Control (VVC) in Active Distribution Networks (ADNs). By leveraging voltage-reactive sensitivity to explicitly guide sequential policy updates, SHOM ensures a monotonic improvement in control strategies under heterogeneous, networked constraints. Experimental results on IEEE test feeders demonstrate that the proposed approach achieves superior voltage regulation and lower power losses compared to existing methods.
  PubDate: MON, 10 FEB 2025 09:16:50 -04
  Issue No: Vol. 16, No. 3 (2025)
Single-Pole-to-Earth Fault Section Detection of the MVDC Cables Based on
Variation Mechanism of Grounding Line Currents
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  Authors: Nan Peng;Guangyang Zhou;Rui Liang;Zhisheng Wang;Yudong Hu;Peng Zhang;Zhipeng Zhao;
  Pages: 2127 - 2143
  Abstract: The multiple-conductor structure of the medium-voltage direct current (MVDC) cable results in complex electromagnetic couplings, posing great challenges to analyze variation laws of grounding line currents which can be used to detect the single-pole-to-earth faults (SPTEFs). In this paper, the equivalent circuit models of the MVDC cable in both normal and fault conditions are constructed by considering electromagnetic couplings between multiple conductors of both poles. The variation mechanism of the grounding line current before and after a SPTEF is explained by theoretical analysis. Considering communication methods in practice, two fault section detection criteria are proposed based on variation features of grounding line currents. The experiment model of a MVDC cable system is established by RTDS real time simulators. The method is only validated by hardware in the loop simulation. The simulation results show that the method is applicable to both ordinary faults and high-impedance ones with $3000\Omega $ . The fault detection accuracies can reach 99% with 20dB noises while they are no less than 95% with ±10% measurement errors. The shortest time for implementing the method is only 0.1ms. The comparison work shows the advantages of the method.
  PubDate: TUE, 04 MAR 2025 09:17:14 -04
  Issue No: Vol. 16, No. 3 (2025)
Deep Learning and Projection Neural Network With Finite-Time Convergence
for Energy Management of Multi-Energy System
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  Authors: Xueying Liu;Xing He;Chaojie Li;Tingwen Huang;
  Pages: 2156 - 2168
  Abstract: In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES.
  PubDate: TUE, 28 JAN 2025 09:17:19 -04
  Issue No: Vol. 16, No. 3 (2025)
Privacy-Enhanced Safe Reinforcement Learning for the Dispatch of a Local
Energy Community
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  Authors: Haoyuan Deng;Ershun Du;Yi Wang;
  Pages: 2169 - 2183
  Abstract: Local Energy Community (LEC) has emerged as a viable community-focus framework to enhance local reliability and energy efficiency by integrating different energy sectors and managing local distributed energy resources (DERs). However, the difficulties associated with handling model complexity, along with privacy concerns arising from interactions between energy operators within the LEC, pose challenges for traditional algorithms in achieving coordinated dispatch. To this end, we develop a novel privacy-enhanced, safe, coordinated dispatch framework that integrates reinforcement learning (RL), the perturbation module, and the safety module. The private states of each energy sector within the LEC are concealed by the independent perturbation module before sharing. A central RL agent is then trained on the concealed state space to learn the optimal policy for coordinated dispatch under the complex and uncertain environment. Furthermore, dispatch actions are evaluated and refined by the safety module before the operators execute them. In this way, we can obtain an optimal policy without disclosing any sector’s private state while ensuring the safe operation of the LEC. Extensive experiments are carried out to validate the superior performance and scalability of the proposed method.
  PubDate: THU, 13 FEB 2025 09:18:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Industrial Energy Management and Production Decision Making via
Lyapunov-Guided Learning
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  Authors: Dafeng Zhu;Bo Yang;Lei Li;Yu Wu;Haoran Deng;Zhaoyang Dong;Kai Ma;Xinping Guan;
  Pages: 2184 - 2196
  Abstract: Energy-intensive industries have to reduce fossil fuel consumption while scheduling production for cost efficiency. It poses the question that how to coordinate renewable energy generation, storage, heat recovery and energy cascade utilization in real time to deal with the low energy efficiency and continuous production problems existing in complex dynamic coupled process production. This question is further complicated while facing difficulties in collaborative modeling and online control by the underlying stochastic process without accurate statistic knowledge. To characterize the above issues, a non-convex operation optimization problem is formulated for coupling production and energy joint scheduling. To obtain a simple online solution with provable performance, a method by combining Lyapunov optimization and actor-critic deep reinforcement learning is proposed. The former is used to decouple the original problem into small-size non-convex subproblems for each time slot and guarantee the long-term constraints. The latter aims at the non-convex part by using model information of the former to obtain accurate evaluations of production actions for fast convergence and high robustness with low computational complexity. The simulation shows that the proposed method can achieve the online optimal benefit while ensuring production tasks and system stability with high scalability.
  PubDate: THU, 20 MAR 2025 09:16:19 -04
  Issue No: Vol. 16, No. 3 (2025)
A Statistical-Based Approach for Decentralized Demand Response Toward
Primary Frequency Support: A Case Study of Large-Scale 5G Base Stations
With Adaptive Droop Control
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  Authors: Peng Bao;Yaoqiang Wang;Xin Jiang;Yucui Wang;Chutong Wang;
  Pages: 2208 - 2221
  Abstract: Due to the rapidity required for primary frequency control (PFC), the decentralized responses of distributed energy resources (DERs) are often uncoordinated and cannot be aggregated into a desired outcome. This paper proposes a novel approach to consolidate the decentralized responses of ON/OFF DERs as an organized droop control. For a DER population, a dynamic response threshold can be calculated based on its statistical distribution, enabling DERs to autonomously respond to PFC according to local states. The aggregation of their responses can align with any preset droop curve (linear or non-linear). The approach is demonstrated by a novel DER, 5G base stations (gNBs) and their backup energy storage systems (BESSs), marking the first attempt of gNBs for PFC support, to the authors’ knowledge. In this regard, gNB dormancy model, based on user equipment (UE) access, and dynamic BESS response model, derived from experimental data, are developed. An adaptive droop control scheme is designed to enable gNBs within multiple radio access networks (RANs) to provide differentiated responses based on disturbances degree and RANs busyness level. Simulations based on real device data show that the proposed approach enables decentralized gNBs to perform accurate response, providing PFC support without affecting communication network operation.
  PubDate: TUE, 28 JAN 2025 09:17:19 -04
  Issue No: Vol. 16, No. 3 (2025)
Two-Timescale Online Optimization of Behind-the-Meter Battery Storage for
Stacked Revenue by Providing Multi-Services
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  Authors: Shibo Chen;Suhan Zhang;Shangyang He;Haosen Yang;
  Pages: 2222 - 2233
  Abstract: Behind-the-meter (BTM) battery energy storage systems (BESS) are becoming increasingly important in the power system with the proliferation of intermittent distributed renewable energy sources. Stacked revenue can be achieved by providing multi-services to the power grid, justifying the substantial upfront cost of BTM BESS and promoting their future adoption. This paper focuses on optimizing the operation strategy of BTM BESS to maximize the time average stacked revenue obtained from multiple service markets, including energy arbitrage, frequency regulation, photovoltaic (PV) power smoothing and reactive power compensation. Challenges arise from the coupling of operation decisions both among multiple services and over the temporal dimension, considering the different decision timescales of service markets as well as the battery dynamics. The uncertainty of stochastic parameters further complicates the optimization process. To address these challenges, this paper proposes a novel two timescale online optimization scheme based on the Lyapunov optimization framework. The uncertainties are tackled by making decisions online, and the computation complexity is highly relieved by relaxing the temporal coupling with a drift-plus-penalty technique. Theoretic analyses are conducted to prove that the solution of this relaxed online decision problem is always feasible for the original one, and it can achieve near-optimum with a constant optimality gap. Extensive simulations utilizing the energy and frequency regulation data from the real market validate the effectiveness of our proposed scheme.
  PubDate: THU, 13 FEB 2025 09:18:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Occupant-Centric Demand Response for Thermostatically-Controlled Home
Loads
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  Authors: Roshan L. Kini;Alex Vlachokostas;Michael R. Brambley;Austin Rogers;
  Pages: 2234 - 2245
  Abstract: Efficiently managing energy usage to balance supply and demand on the electric grid is crucial, especially with the widespread deployment of distributed variable renewable electricity generation. This paper introduces two duty-cycle control methods for heating systems, adjusting thermostat setpoints to limit and shift electricity demand. The control approaches employ innovative techniques, such as adaptive duty cycling, to prioritize household thermal comfort while reducing peak demand. These control methods can respond to signals from the electric grid, including demand targets and time-of-use tariffs, and were tested physically on an electric furnace and heat pump in a test home during winter conditions in 2021 and 2022. The results are given as average demand reductions and energy use impacts with respect to the average indoor-outdoor temperature difference during the control period. For heat pumps, demand limiting control reduced power by 18.5% and 23.3% for indoor-outdoor temperature differences of 30°F and 40°F. Preheating-based demand shifting achieved reductions of 34.8% and 33.2% for the same temperature differences. Electric furnace tests showed demand reductions of 33.8% and 25.3% for demand limiting, and 56.1% and 45.7% for preheating-based demand shifting. These findings highlight the potential for innovative control methods to enhance grid efficiency and reduce energy consumption.
  PubDate: MON, 10 FEB 2025 09:16:50 -04
  Issue No: Vol. 16, No. 3 (2025)
A “Smart Model-Then-Control” Strategy for the Scheduling of
Thermostatically Controlled Loads
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  Authors: Xueyuan Cui;Boyuan Liu;Yehui Li;Yi Wang;
  Pages: 2246 - 2260
  Abstract: Model predictive control (MPC) has been widely adopted for indoor temperature control and building energy management. There are two steps in traditional MPC: 1) modeling thermal dynamics as the state space function to represent the temperature variation influenced by thermostatically controlled loads (TCLs); 2) formulating an optimization problem for optimal scheduling of TCLs within the control horizon. However, such a “model-then-control” strategy could result in biased control because of the unaligned modeling error and control cost, i.e., minimization of model errors may not necessarily lead to minimal costs against actual thermal dynamics in buildings. To tackle this problem, we advocate for a “smart model-then-control” (SMC) strategy that incorporates thermal dynamics modeling into the temperature control task. In particular, instead of using mean squared errors (MSE), we adopt the control objective as the task-specific loss function to guide the model training. We further formulate an Input Convex Neural Network (ICNN)-based surrogate loss function, which is differentiable and convex for effective training. In this way, the objectives of both model training and temperature control in MPC are well-aligned to obtain cost-effective decisions. We validate the performance of the SMC strategy in single-zone and multi-zone buildings. The simulation results show that it can reduce control costs by 5.97% and 2.10% respectively when compared with traditional MPC.
  PubDate: MON, 17 FEB 2025 09:16:33 -04
  Issue No: Vol. 16, No. 3 (2025)
An Adaptive MARL Large Model for Dispatch Strategy Generation in
Logistics-Energy Spatiotemporal Coordination of Container Seaports
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  Authors: Yiwen Huang;Wentao Huang;Ran Li;Tao Huang;Canbing Li;Nengling Tai;
  Pages: 2261 - 2277
  Abstract: Logistics-energy coordination significantly enhances energy efficiency in electrified seaports. However, daily changes in environment data necessitate the re-implementation of optimization procedures, causing huge computational burdens. This paper proposes an adaptive multi-agent reinforcement learning (MARL) large model for logistics-energy spatiotemporal coordination of container seaports. The well-trained large model can directly generate optimal policy for each operating day from environment data without re-solving. To achieve this, a comprehensive logistics-energy coordination model is first established considering the spatial and temporal constraints of all-electric ships (AESs), quay cranes (QCs), auto guided vehicles (AGVs), and the seaport power distribution network (SPDN). The model is formulated as a Markov Decision Process (MDP). Then a MARL large model is developed, involving a hypernetwork mapping environment data to optimal policy, and special structures for both hypernetwork and agent policy networks to adapt to any number of daily arrival AESs. Additionally, a cascading action modification layer is designed to ensure correct action outputs within complex spatiotemporal constraints. A tailored training method with two acceleration strategies are developed for the large model. Case studies illustrate that the large model after training can automatically generate optimal policies with little to no fine-tuning, outperforming existing methods that require extensive solution time.
  PubDate: THU, 06 MAR 2025 09:16:55 -04
  Issue No: Vol. 16, No. 3 (2025)
Harmonic Stability Analysis of Microgrids With Converter-Interfaced
Distributed Energy Resources, Part I: Modeling and Theoretical Foundations
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  Authors: Johanna Kristin Maria Becker;Andreas Martin Kettner;Mario Paolone;
  Pages: 2316 - 2326
  Abstract: This paper proposes a method for the Harmonic Stability Assessment (HSA) of power systems with a high share of Converter-Interfaced Distributed Energy Resources (CIDERs). To this end, the Harmonic State-Space (HSS) model of a generic power system is formulated by combining the HSS models of the resources and the grid in closed-loop configuration. The HSS model of the resources is obtained from the Linear Time-Periodic (LTP) models of the CIDER components transformed to frequency domain using Fourier theory and Toeplitz matrices. Notably, the HSS of a CIDER is capable of representing the coupling between harmonic frequencies in detail. The HSS model of the grid is derived from the dynamic equations of the individual branch and shunt elements. The system matrix of the HSS models on power-system or resource level is employed for eigenvalue analysis in the context of HSA. A sensitivity analysis of the eigenvalue loci w.r.t. changes in model parameters, and a classification of eigenvalues into control-design variant, control-design invariant, and design invariant eigenvalues is proposed. A case of harmonic instability is identified by the HSA and validated via Time-Domain Simulations (TDS) in Simulink.
  PubDate: MON, 27 JAN 2025 09:17:36 -04
  Issue No: Vol. 16, No. 3 (2025)
Harmonic Stability Analysis of Microgrids With Converter-Interfaced
Distributed Energy Resources, Part II: Case Studies
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  Authors: Johanna Kristin Maria Becker;Andreas Martin Kettner;Mario Paolone;
  Pages: 2327 - 2337
  Abstract: In Part I of this paper a method for the Harmonic Stability Assessment (HSA) of power systems with a high share of Converter-Interfaced Distributed Energy Resources (CIDERs) was proposed. Specifically, the Harmonic State-Space (HSS) model of a generic power system is derived through combination of the components’ HSS models. The HSS models of CIDERs and grid are based on Linear Time-Periodic (LTP) models, capable of representing the coupling between different harmonics. In Part II, the HSA of a grid-forming, and two grid-following CIDERs (i.e., ex- and including the DC-side modelling) is performed. More precisely, the classification of the eigenvalues, the impact of the maximum harmonic order on the locations of the eigenvalues, and the sensitivity curves of the eigenvalues w.r.t. to control parameters are provided. These analyses allow to study the physical meaning and origin of the CIDERs’ eigenvalues. Additionally, the HSA is performed for a representative example system derived from the CIGRÈ low-voltage benchmark system. A case of harmonic instability is identified through the system eigenvalues, and validated with Time-Domain Simulations (TDS) in Simulink. It is demonstrated that, as opposed to stability analyses based on Linear Time-Invariant (LTI) models, the HSA is suitable for the detection of harmonic instability.
  PubDate: MON, 27 JAN 2025 09:17:36 -04
  Issue No: Vol. 16, No. 3 (2025)
AC and DC Interaction Analysis and Transient Control Strategy for Soft
Open Points in Unbalanced Distribution Networks
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  Authors: Xinquan Chen;Zejie Huang;Xin Yin;
  Pages: 2338 - 2347
  Abstract: Soft open points (SOPs) can play a key role in hybrid AC/DC distribution networks (DNs) due to their flexible power control ability. However, the sequence-domain behavior of SOPs in unbalanced DNs cannot be ignored from the perspective of system stability and safety. Therefore, this paper first investigates the interaction between AC and DC components in unbalanced DNs by considering SOPs with the typical $V_{dc}$ /Q and P/Q control strategies in medium-voltage DNs. The analytical results indicate $2^{\mathrm { nd}}$ frequency oscillations in q-axis voltage, power, and DC voltage are present in unbalanced DNs. This deteriorates the transient dynamics and synchronization of SOPs and may result in the disconnection of loads and resources. To address this, a robust sequence-decomposed control strategy is proposed for SOP. To ride through unbalanced DNs, an adaptive virtual impedance-based control is activated in the positive sequence to improve restoration capability, while the negative-sequence currents are suppressed by the inner control. The DC synchronization loop is implemented by using the transferred DC power and internal energy for frequency stability enhancement. The case studies in a 40-node medium-voltage DN indicate that the proposed control enables SOPs to ride through both unbalanced load and unbalanced fault conditions in a short transient time.
  PubDate: TUE, 28 JAN 2025 09:17:19 -04
  Issue No: Vol. 16, No. 3 (2025)
Accelerated Dynamic Event-Triggered Algorithm for Online Power System
Optimization by Using Powerball Technique
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  Authors: Chuanhao Hu;Xuan Zhang;Qiuwei Wu;
  Pages: 2348 - 2360
  Abstract: Nowadays, online optimization for power systems has gained increasing attention due to many time-varying scenarios in practical applications. This paper proposes a novel feedback-based online algorithm for power system optimization problems by combining the powerball technique and a dynamic event-triggered mechanism, aiming to achieving a faster convergence rate and high communication efficiency simultaneously. In particular, a measurement-based prima-dual approach is utilized to solve the considered time-varying optimization problem. Taking the constrained resource issue, a more flexible triggering condition with a dynamic threshold is designed for dual variables to reduce communication burden. By further employing the powerball strategy, the primal-dual iteration is modified with an improved convergence rate. Finally, numerical experiments on test systems are conducted to demonstrate the effectiveness of the proposed online algorithm. It is shown that the proposed approach has superior convergence property especially in the initial process. Meanwhile, a better trade-off between the satisfactory system performance and communication resources consumption can be achieved.
  PubDate: THU, 06 FEB 2025 09:17:56 -04
  Issue No: Vol. 16, No. 3 (2025)
Multi-Time Scale Frequency Regulation Control of Virtual Power Plant Based
on Fuzzy Sets
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  Authors: Lili Mo;Junkun Lan;Zhizhong Chen;Hao Yang;Xin Liao;Haoyong Chen;Sibei Chen;
  Pages: 2361 - 2374
  Abstract: With the continuous development of the power system, in the face of the frequency deviation caused by the randomness and volatility of renewable energy sources such as photovoltaic and wind power, considering the use of air conditioning, electric vehicle charging piles, and energy storage to form a distributed resource cluster that takes into account economy and efficiency to participate in frequency regulation auxiliary services to reduce frequency deviation. In the process of a virtual power plant (VPP) participating in frequency regulation auxiliary service, a multi-time scale frequency regulation control strategy of VPP is proposed, which can cope with frequency deviation on different time scales. By establishing a three-stage frequency regulation process, the collaborative optimization of distributed resources is realized. The priority principle is adopted to prioritize the scheduling of higher-value resources to participate in frequency regulation, which helps to allocate resources. In the process of tertiary frequency regulation, the fuzzy sets are used to optimize the frequency regulation strategy of energy storage, which reduces the switching times of energy storage and improves the stability of frequency regulation. The effectiveness of the proposed algorithm and the feasibility of distributed resources participating in frequency regulation are verified by a case.
  PubDate: THU, 13 FEB 2025 09:18:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Model-Based Safe Reinforcement Learning for Active Distribution Network
Scheduling
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  Authors: Yuxiang Guan;Wenhao Ma;Liang Che;Mohammad Shahidehpour;
  Pages: 2375 - 2388
  Abstract: Data-driven methods, especially reinforcement learning (RL), are adept at addressing uncertainties but are poor at ensuring safety, which is a critical requirement in active distribution networks (DNs). To address the problem of active DN scheduling and to overcome RL’ most critical drawback—security risk, this paper proposes a model-based safe RL framework that embeds a model-based safety module (MBSM) in the RL’s loop. The proposed framework can guarantee that the agent’s actions (real/reactive power outputs of controllable distributed energy resources (DERs)) strictly satisfy the DN’s operational security constraints. It does not rely on any expert knowledge and is suitable for application in large-scale systems. Comparative studies against existing Safe RL (SRL) and classic optimization methods verify that the proposed method achieves the best performance in terms of DERs operating cost and renewable energy consumption while strictly satisfying the DN’s operational security constraints.
  PubDate: MON, 17 MAR 2025 09:18:37 -04
  Issue No: Vol. 16, No. 3 (2025)
Leveraging Time-Causal State Variable Aggregation for Real-Time Schedule
of Massive Air Conditioners
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  Authors: Jingguan Liu;Xiaomeng Ai;Shichang Cui;Xizhen Xue;Shengshi Wang;Jiakun Fang;Jinyu Wen;Yang Shi;
  Pages: 2389 - 2403
  Abstract: Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP) algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.
  PubDate: TUE, 04 MAR 2025 09:17:14 -04
  Issue No: Vol. 16, No. 3 (2025)
Model-Free Aggregation for Virtual Power Plants Using Input Convex Neural
Networks
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  Authors: Wei Lin;Yi Wang;Jianghua Wu;Fei Feng;
  Pages: 2404 - 2415
  Abstract: The virtual power plant (VPP) has been advocated as a promising way to aggregate massive distributed energy resources (DERs) in a distribution system (DS) for their participation in transmission-level operations. This requires identifying the feasible set of VPP power transfers fulfilling DS operational constraints. The identification task is completed using the existing methods by relying on constraint parameters (e.g., line impedance). However, the constraint parameters in a DS may be inaccurate and even missing in practice. Consequently, this paper develops a model-free aggregation method for VPPs. The proposed method first develops an input convex neural network (ICNN)-based surrogate for the feasible set of VPP power transfers. Our ICNN-based surrogate can be determined in a model-free manner by historical data. Furthermore, it is proven by leveraging the convexity and epigraph relaxation of an ICNN that our ICNN-based surrogate can be reformulated as a linear programming model without binary variables. This allows efficiently embedding our ICNN-based surrogate in transmission-level operations so that numerous VPPs can be efficiently coordinated at the transmission level. The proposed method is verified by numerical experiments in the IEEE 33-bus and IEEE 136-bus test systems.
  PubDate: FRI, 07 MAR 2025 10:41:39 -04
  Issue No: Vol. 16, No. 3 (2025)
Anomaly Identification of Synchronized Voltage Waveform for Situational
Awareness of Low Inertia Systems
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  Authors: He Yin;Wei Qiu;Yuru Wu;Wenpeng Yu;Jin Tan;Andy Hoke;Cameron J. Kruse;Brad W. Rockwell;Yilu Liu;
  Pages: 2416 - 2428
  Abstract: Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. To achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.
  PubDate: MON, 10 MAR 2025 09:16:41 -04
  Issue No: Vol. 16, No. 3 (2025)
Predictive Health Management of Smart Meters: Daily Measurement Error
Forecasting Under Complex Environmental Conditions
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  Authors: Junfeng Duan;Qiu Tang;Ning Li;Wei Qiu;Wenxuan Yao;
  Pages: 2429 - 2438
  Abstract: Daily measurement error (ME) forecasting is critical for the health management of smart meters (SMs) under complex environmental conditions. This paper proposes a tailored short-term ME prediction framework employing Gaussian Process Regression (GPR) enhanced by a Weighted Automatic Relevance Determination (WARD) kernel and time-frequency feature augmentation. A dual constraint screening mechanism using Pearson Correlation Analysis (PPMCC) and Pareto Smoothed Importance Sampling (PSIS) is introduced to optimize input features. To further improve predictive capabilities, an Adaptive S-transform (AST) decomposes ME, capturing time-frequency information for GPR input enhancement. Experimental validation with real-world SM data under extreme conditions demonstrates that the proposed AST-MKGPR(WARD) model achieves superior interpretability and predictive accuracy compared to state-of-the-art approaches, offering a robust solution for daily SM health assessments.
  PubDate: MON, 17 FEB 2025 09:16:33 -04
  Issue No: Vol. 16, No. 3 (2025)
Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for
Electricity Theft Detection With Imbalanced and Missing Data
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  Authors: Honggang Yang;Cheng Lian;Bingrong Xu;Ruijin Ding;Pengbo Zhao;Zhigang Zeng;
  Pages: 2439 - 2450
  Abstract: The widespread adoption of advanced metering infrastructure has provided abundant data, enabling the integration of deep learning techniques into smart grids. However, it has also led to more sophisticated and concealed methods of electricity theft. Due to the challenges posed by data imbalance and missing values caused by device malfunctions and communication issues, existing deep learning models often perform poorly. To address these issues, this paper proposes a multi-step training framework named DING, which incorporates diffusion generation, self-supervised pre-training, normalized condition imputation, and generation-balanced fine-tuning. First, sufficient balanced smart meter data is generated using a diffusion model. Second, a pre-trained encoder is trained on the generated data, extracting unbiased low-dimensional features that can be used for downstream classification tasks and as conditions to guide the training of the imputation model. Next, an imputation model is trained based on a diffusion state-space equation. Finally, fine-tuning is performed on the balanced data. Experiments on a real dataset from the State Grid Corporation of China demonstrate that the proposed method outperforms previous models for both electricity theft detection and imputation tasks.
  PubDate: MON, 03 MAR 2025 09:17:37 -04
  Issue No: Vol. 16, No. 3 (2025)
A Unified Model for Smart Meter Data Applications
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  Authors: Zhenyi Wang;Hongcai Zhang;Geert Deconinck;Yonghua Song;
  Pages: 2451 - 2463
  Abstract: Making adequate utilization of smart meter data is conducive to improving the energy efficiency of the power system from demand side, especially with booming artificial intelligence (AI) technology. However, most existing AI-based methods are highly incompatible to each other due to unique designs based on their respective tasks. Low compatibility will lead to duplicate modeling among similar tasks and skyrocketing implementation costs, which is not suitable for diverse and changing demand-side tasks. Although large language models provide a promising way to build the general-purpose models, they either need substantial resources for pre-training or case-by-case design for fine-tuning. Hence, there are practically rare task-generic models available for power systems. In this paper, we propose a novel unified model for smart meter data applications. Specifically, we first propose a unified model with mixture-of-expert layers to ensure sufficient model capacity in a cost-effective manner, which makes the training from scratch affordable. Then, we design an information bottleneck-based training scheme to facilitate the unified model to efficiently learn the generic knowledge. Moreover, we develop a general framework based on pre-training paradigm to formulate a uniform objective function and provide a consistent workflow for different tasks. Finally, the effectiveness and superiority of our proposed method are validated on public datasets, where the proposed unified model can be applied to load forecasting, data imputation as well as anomaly detection, and realizes comparable performance to state-of-the-art task-specific methods.
  PubDate: FRI, 21 MAR 2025 09:16:35 -04
  Issue No: Vol. 16, No. 3 (2025)
Online Menu-Based Scheduling of Electric Vehicle Charging
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  Authors: Angeliki Mathioudaki;Georgios Tsaousoglou;Emmanouel Varvarigos;Dimitris Fotakis;
  Pages: 2478 - 2491
  Abstract: This paper addresses the problem of efficiently scheduling of EV charging requests in a single Charging Station (CS), also taking into consideration the inability of EV users to express their preferences in closed-form utility functions. We consider a paradigm where a menu of possible charging intervals, each associated with an urgency price, is generated online. By letting the EV users pick their most preferable menu option, the proposed algorithm commits on each EV’s charging completion time upon its arrival, achieves a near optimal total weighted charging completion time, and prevents the users from strategically misreporting their preferences, while offering a practical and implementable solution to the problem of EVs - charging station interaction. Our experimental evaluation demonstrates that our algorithmic approach performs nearly optimal in a variety of practically relevant scenarios.
  PubDate: FRI, 07 FEB 2025 09:16:50 -04
  Issue No: Vol. 16, No. 3 (2025)
Implicit Enhanced Distributed Heavy-Ball Energy Management Strategy for
Microgrids With Time-Varying Social Welfare and Delay
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  Authors: Mingqi Xing;Dazhong Ma;Kai Ma;Qiuye Sun;
  Pages: 2492 - 2503
  Abstract: Diverse energy sources and fluctuating energy demands highlight the criticality of microgrid energy management (MEM). This paper develops a general model of social welfare maximization in the presence of time-varying social welfare with quadratic transmission losses, which implies that the fitting coefficients of the transmission losses, cost function, and utility function may vary over time. A significant difference with existing works is the dependence of the problem’s optimal solution on time variation, making higher requirements on the algorithm’s performance, especially the convergence rate. To address these challenges, we extend the conventional heavy-ball method and propose a novel implicit enhanced distributed heavy-ball algorithm. The algorithm incorporates multiple heavy-ball terms to accelerate convergence. Notably, the second heavy-ball term is implicitly implemented via an acceleration term that contains historical information and consensus error. Furthermore, the algorithm obviates the necessity for the sharing of additional auxiliary variables, thereby reducing the communication overhead. We demonstrate that the algorithm converges asymptotically to the neighborhood of the time-varying optimal solution even with arbitrarily large but bounded communication delays. Finally, detailed case studies illustrate that the algorithm can improve the convergence rate by 15.02% over conventional method, followed by the validation of its scalability and the discussion of the negative impact of delay.
  PubDate: MON, 10 MAR 2025 09:16:41 -04
  Issue No: Vol. 16, No. 3 (2025)
Network Topology Flexibility-Aware Robust Electricity Trading for
Distribution System Survivability Enhancement
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  Authors: Mingze Xu;Shunbo Lei;Canqi Yao;Weimin Wu;Cheng Ma;Chong Wang;
  Pages: 2504 - 2517
  Abstract: Extreme weather events significantly threaten power system security and market operations, causing either substantial load fluctuations or grid line failures. Current distribution-level electricity market mechanisms often prove inadequate during such events. Furthermore, limited research has explored electricity market mechanisms for improving distribution system survivability. To fill this gap, this study proposes a double-auction mechanism to facilitate distribution-level transactions of non-utility distributed energy resources through market incentives for proactive system resilience enhancement. This mechanism aims to utilize a market-driven approach to achieve the highest system survivability in disasters. This trading mechanism is divided into two stages to address the direct changes in the tradability caused by line failures: clearing and delivery. During market clearing, the DSO operates as an auctioneer to concurrently optimize social welfare and prior-event network topology. The pricing rule employs an enhanced Vickrey-Clarke-Groves mechanism to ensure truthful bidding and incentive compatibility. During delivery, the DSO performs redispatch to mitigate economic losses and compensates for energy curtailments through established settlement protocols. This market-driven resilience enhancement method formulates a bi-level two-stage robust optimization problem, solved using a customized column-and-constraint generation algorithm. Modified IEEE 13-node and 123-node systems are used to verify the effectiveness of the proposed approach.
  PubDate: TUE, 11 MAR 2025 09:16:47 -04
  Issue No: Vol. 16, No. 3 (2025)
Network Intrusion Detection for Modern Smart Grids Based on Adaptive
Online Incremental Learning
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  Authors: Qiuyu Lu;Kexin An;Jun’e Li;Jin Wang;
  Pages: 2541 - 2553
  Abstract: New and evolving cyber attacks against smart grids are emerging. This necessitates that the network intrusion detection systems (IDSs) have online learning ability. However, most existing methods struggle to handle new and evolving attacks while retaining old attack knowledge, and many of them employ deep models requiring long update periods. Therefore, we propose an IDS based on adaptive online incremental learning (AdaOIL-IDS). 1) A class-correlated broad learning system (CC-BLS) is proposed for intrusion detection. A weighted CC-factor derived from intra- and inter-class correlations is introduced in CC-BLS to improve classification accuracy. CC-incremental learnings are designed to quickly add new inputs and additional nodes without retraining. The CC-factor for new inputs is adjusted based on correlations of new and old classes, which enables simultaneous adaptation to new attacks and new observations of old attacks while retaining more old knowledge. 2) An adaptive learning framework is proposed for online-offline combined learning of models. Online learning and offline retraining are adaptive switched based on the real-time loss to achieve efficient lifelong learning. Experiment results show that CC-BLS has better performance than selected state-of-the-art incremental broad and deep models, and the proposed adaptive learning framework behaviors better effectiveness and efficiency than selected existing frameworks.
  PubDate: THU, 13 FEB 2025 09:18:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Cyber-Physical Interdependence for Power System Operation and Control
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  Authors: Ioannis Zografopoulos;Ankur Srivastava;Charalambos Konstantinou;Junbo Zhao;Amir Abiri Jahromi;Astha Chawla;Bang Nguyen;Bu Siqi;Chendan Li;Fei Teng;Goli Preetham;Juan Ospina;Mohammad Asim Aftab;Mohammadreza Arani;Ömer Sen;Panayiotis Moutis;Pudong Ge;Qinglai Guo;Subham Sahoo;Subhash Lakshminarayana;Tuyen Vu;Zhaoyuan Wang;
  Pages: 2554 - 2573
  Abstract: This paper summarizes the technical endeavors undertaken by the Task Force (TF) on Cyber-Physical Interdependence for Power System Operation and Control. The TF was established to investigate the cyber-physical interdependence of critical power system elements and their influence on the operation and control of energy systems. State-of-the-art analysis techniques, including co-simulation and digital twin technologies, are employed to address various layers of interdependence between cyber and physical systems, facilitating the identification of potential threats and vulnerabilities. The paper examines prospective trajectories for resilient cyber-physical systems and outlines the educational and workforce training imperatives for addressing cybersecurity threats in contemporary power systems. Furthermore, concluding remarks and future recommendations are provided to mitigate the inherent vulnerabilities within the extensively interoperable grid infrastructure.
  PubDate: MON, 03 FEB 2025 09:16:27 -04
  Issue No: Vol. 16, No. 3 (2025)
Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage
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  Authors: Ming Yi;Saud Alghumayjan;Bolun Xu;
  Pages: 2574 - 2586
  Abstract: This paper presents a novel decision-focused framework integrating the physical energy storage model into machine learning pipelines. Motivated by the model predictive control for energy storage, our end-to-end method incorporates the prior knowledge of the storage model and infers the hidden reward that incentivizes energy storage decisions. This is achieved through a dual-layer framework, combining a prediction layer with an optimization layer. We introduce the perturbation idea into the designed decision-focused loss function to ensure the differentiability over linear storage models, supported by a theoretical analysis of the perturbed loss function. We also develop a hybrid loss function for effective model training. We provide two challenging applications for our proposed framework: energy storage arbitrage, and energy storage behavior prediction. The numerical experiments on real price data demonstrate that our arbitrage approach achieves the highest profit against existing methods. The numerical experiments on synthetic and real-world energy storage data show that our approach achieves the best behavior prediction performance against existing benchmark methods, which shows the effectiveness of our method.
  PubDate: THU, 06 MAR 2025 09:16:54 -04
  Issue No: Vol. 16, No. 3 (2025)
A Lightweight Framework for Measurement Causality Extraction and FDIA
Localization
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  Authors: Shengyang Wu;Chen Yang;Jingyu Wang;Dongyuan Shi;
  Pages: 2587 - 2598
  Abstract: False Data Injection Attack (FDIA) has become a growing concern in modern cyber-physical power systems. Many learning-based approaches have utilized the statistical correlation patterns between measurements to facilitate the detection and localization of FDIA. However, these correlation patterns are susceptible to the distribution drift of measurement data, which can be induced by changes in system operating points or variations in attack strength, leading to degraded model performance. Causal inference serves as a promising solution to this problem, as it can embed the physical relationship between measurements as causal patterns that are robust against data distribution drifts. However, causal inference is also computationally demanding. To leverage its advantages and address the computational cost issue, this paper proposes a lightweight framework based on causal inference and Graph Attention Networks (GATs) to extract causal patterns between measurements and locate FDIAs. The proposed framework consists of two levels. The lower level uses an X-learner algorithm to estimate the causality strength between measurements and generate Measurement Causality Graphs (MCGs). The upper level then applies a GAT to identify the anomaly patterns in the MCGs. Since the extracted causality patterns are intrinsically related to the measurements, it is easier for the upper level model to identify the attacked nodes than the existing FDIA localization approaches. A physical neighbor masking strategy is implemented to cut down the computational cost of both levels. The performance of the proposed framework is evaluated on the IEEE 39-bus and 118-bus systems. Experimental results show that the causality-based FDIA localization mechanism provides a lightweight solution to interpretable measurement causality extraction and robust FDIA localization.
  PubDate: FRI, 14 MAR 2025 09:16:46 -04
  Issue No: Vol. 16, No. 3 (2025)
S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
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  Authors: Minh Tran;Adrian De Luis;Haitao Liao;Ying Huang;Roy McCann;Alan Mantooth;Jackson Cothren;Ngan Le;
  Pages: 2611 - 2623
  Abstract: As the negative impact of climate change escalates, the global necessity to transition to sustainable energy sources becomes increasingly evident. Renewable energies have emerged as a viable solution for users, with Photovoltaic (PV) technology being a favored choice for small installations due to its high reliability, competitive market and increasing efficiency. Accurate mapping of PV installations is crucial in improving grid management, facilitating the integration of renewable energy, encouraging active participation from prosumers, and optimizing the economic performance of decentralized energy markets. To meet this need, S3Former is introduced, which is designed to segment solar panels from aerial imagery and provide size and location information critical for analyzing the impact of such installations on the grid. Although computer vision has become a preferred choice for such implementations, solar panel identification is challenging due to factors such as time-varying weather conditions, different roof characteristics, Ground Sampling Distance (GSD) variations and lack of appropriate initialization weights for optimized training. To tackle these complexities, S3Former features a Masked Attention Mask Transformer incorporating a self-supervised learning pretrained backbone. Specifically, the model leverages low-level and high-level features extracted from the backbone and incorporates an instance query mechanism incorporated on the Transformer architecture to enhance the localization of solar PV installations. Moreover, a self-supervised learning (SSL) phase (pretext task) is introduced to fine-tune the initialization weights on the backbone of S3Former, leading to a noticeable improvement on the results. To rigorously evaluate the performance of S3Former, diverse datasets are utilized, including GGE (France), IGN (France), and USGS (California, USA), across different GSDs. Our extensive experiments consistently demonstrate that the proposed model either matches or surpasses state-of-the-art models (SOTA) and validate the benefit of using the SSL method to improve the segmentation architecture. Source code is available upon acceptance.
  PubDate: WED, 05 FEB 2025 09:17:45 -04
  Issue No: Vol. 16, No. 3 (2025)
A Hybrid LSTM-Transformer Model for Power Load Forecasting
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  Authors: Vasileios Pentsos;Spyros Tragoudas;Jason Wibbenmeyer;Nasser Khdeer;
  Pages: 2624 - 2634
  Abstract: This paper introduces a novel optimized hybrid model combining Long Short-Term Memory (LSTM) and Transformer deep learning architectures designed for power load forecasting. It leverages the strengths of both LSTM and Transformer models, ensuring more accurate and reliable forecasts of power consumption while considering geographic factors, user behavioral factors, and time constraints for the training time. The model is modified to forecast the total power load for consecutive future time instances rather than the next time instance. We have tested the models using residential power consumption data, and the findings reveal that the optimized hybrid model consistently outperforms existing methods.
  PubDate: THU, 13 FEB 2025 09:18:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Integrated Framework of Multisource Data Fusion for Outage Location in
Looped Distribution Systems
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  Authors: Liming Liu;Yuxuan Yuan;Zhaoyu Wang;Yiyun Yao;Fei Ding;
  Pages: 2635 - 2646
  Abstract: Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. The framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.
  PubDate: TUE, 11 FEB 2025 09:16:40 -04
  Issue No: Vol. 16, No. 3 (2025)
Synchro-Waveform-Based Event Identification Using Multi-Task
Time-Frequency Transform Networks
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  Authors: Wei Qiu;He Yin;Yuqing Dong;Xiang Wei;Yilu Liu;Wenxuan Yao;
  Pages: 2647 - 2658
  Abstract: Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. Results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.
  PubDate: THU, 27 FEB 2025 09:16:55 -04
  Issue No: Vol. 16, No. 3 (2025)
Spike Talk: Genesis and Neural Coding Scheme Translations
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  Authors: Subham Sahoo;
  Pages: 2659 - 2670
  Abstract: Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, Spike Talk basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does Spike Talk simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.
  PubDate: TUE, 04 MAR 2025 09:17:14 -04
  Issue No: Vol. 16, No. 3 (2025)
A Trustworthy Cloud-Edge Collaboration Framework for Scheduling
Distributed Energy Resources in Distribution Networks
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  Authors: Qiangang Jia;Wenshu Jiao;Sijie Chen;Zheng Yan;Haitao Sun;
  Pages: 2691 - 2694
  Abstract: Integration of distributed energy resources, such as photovoltaics, has expanded rapidly within power distribution networks in recent years. Existing management architectures face great challenges in balancing data security and communication efficiency. To address this issue, the paper proposes a cloud-edge collaborative framework that caters to managing multiple distributed energy resources. Firstly, a two-stage structural design method is introduced to determine the optimal configuration of edge nodes for variable reduction. Secondly, a credit-based data interaction scheme considering inherent uncertainty of distributed energy resources is proposed to ensure trustworthy cloud-edge collaborative optimizations. The above work is expected to facilitate the in-depth application of cloud-edge collaboration in the energy scheduling field.
  PubDate: TUE, 25 FEB 2025 09:16:47 -04
  Issue No: Vol. 16, No. 3 (2025)
Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse
Optimization
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  Authors: Ruike Lyu;Hongye Guo;Goran Strbac;Chongqing Kang;
  Pages: 2695 - 2698
  Abstract: The intricate mixed-integer constraints in industrial load models not only pose challenges for their direct integration into economic dispatch or market clearing processes but also render current analytical dimension-reduction methods ineffective. We propose a novel data-driven dimension-reduction approach for industrial load modeling, which uses the optimal energy usage data from industrial loads to train a dimension-reduced model that best fits the original constraints. Our approach, implemented by the adjustable load fleet model, outperformed analytical methods across three industrial load datasets.
  PubDate: MON, 24 FEB 2025 09:16:33 -04
  Issue No: Vol. 16, No. 3 (2025)
Learning the Reluctance of Demand-Side Resources From Equilibrium in
Price-Based Demand Response
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  Authors: Xiaotian Sun;Haipeng Xie;Dawei Qiu;Yunpeng Xiao;Goran Strbac;Zhaohong Bie;
  Pages: 2699 - 2702
  Abstract: The reluctance of demand-side resources (DSRs) in demand response (DR) is not directly accessible, yet, significantly impacts the DR performance. This work aims to estimate DR reluctance from observed DR equilibrium outcomes by inverse variational inequality (VI). First, the definition and properties of DR reluctance are introduced. Then, the equivalent generalized Nash equilibrium condition in DR is derived by strong duality. Based on inverse VI technique, a data-driven linear-programming (LP) for learning DR reluctance is formulated. Finally, the proposed method is validated through a toy example and larger-scale cases, showing its effectiveness and scalability.
  PubDate: THU, 27 FEB 2025 09:16:55 -04
  Issue No: Vol. 16, No. 3 (2025)
A Progressive Polyhedral Approximation Method for Nonlinear
PDE-Constrained Electricity-Water Nexus Dispatch
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  Authors: Zhihao Hua;Bin Zhou;Ka Wing Chan;Cong Zhang;Yingping Cao;Pengcheng Wang;Mingchao Xia;
  Pages: 2703 - 2706
  Abstract: This letter proposes an efficient progressive polyhedral approximation (PA) method to tackle the high nonlinearity and nonconvexity of optimal electricity-water nexus (EWN) dispatch caused by hyperbolic nonlinear partial differential equations (HNPDEs). In this method, the HNPDE-constrained EWN dispatch model can be reformulated into a tractable mixedinteger linear programming (MILP) problem by tailored adaptive discretization and piecewise PA. Furthermore, a progressive approximation refinement technique is developed to dynamically strengthen the MILP for efficient convergence to a near-optimal solution. Comparative studies have validated the effectiveness of the proposed method in reducing decision-making time for the EWN dispatch.
  PubDate: FRI, 21 MAR 2025 09:16:35 -04
  Issue No: Vol. 16, No. 3 (2025)
TechRxiv: Share Your Preprint Research With the World!
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  Pages: 2707 - 2707
  Abstract: null
  PubDate: TUE, 22 APR 2025 09:17:17 -04
  Issue No: Vol. 16, No. 3 (2025)
Introducing IEEE Collabratec
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  Pages: 2708 - 2708
  Abstract: null
  PubDate: TUE, 22 APR 2025 09:17:16 -04
  Issue No: Vol. 16, No. 3 (2025)

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