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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
  PubDate: TUE, 22 AUG 2023 14:17:36 -04
  Issue No: Vol. 14, No. 5 (2023)
IEEE Transactions on Smart Grid Information for Authors
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  Pages: C3 - C3
  PubDate: TUE, 22 AUG 2023 14:17:36 -04
  Issue No: Vol. 14, No. 5 (2023)
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  Pages: C4 - C4
  PubDate: TUE, 22 AUG 2023 14:17:36 -04
  Issue No: Vol. 14, No. 5 (2023)
Variable Virtual Impedance-Based Overcurrent Protection for Grid-Forming
Inverters: Small-Signal, Large-Signal Analysis and Improvement
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  Authors: Taoufik Qoria;Heng Wu;Xiongfei Wang;Ilknur Colak;
  Pages: 3324 - 3336
  Abstract: Grid-forming inverters are sensitive to large grid disturbances that may engender overcurrent due to their voltage source behavior. To overcome this critical issue and ensure the safety of the system, current limitation techniques have to be implemented. In this context, the variable virtual impedance (VI) appears as a suitable solution for this problem. The design of the variable virtual impedance basically rests on static considerations, while, its impact on the system stability and dynamics considering both small-signal and large-signal aspects can be significant. This paper proposes small-signal and nonlinear power models to assess the impact of the virtual impedance parameters on the grid current dynamics and on the angle stability. Thanks to the proposed approach, it has been demonstrated that the virtual impedance ratio $sigma _{X_{VI}/R_{VI}}$ has a contradictory effect on the system dynamics and the transient stability, i.e., a resistive virtual impedance results in a well-damped current response but a very limited transient stability margin, while an inductive virtual impedance results in a poorly-damped current response but an acceptable transient stability margin. Based on that, it has been concluded that the conventional virtual impedance cannot cope at once with the current dynamic performances and the transient stability. To overcome this constraint, a Variable Transient Virtual Resistance (VTVR) has been proposed as an additional degree of freedom to vary $sigma _{X_{VI}/R_{VI}}$ . It decreases $sigma _{X_{VI}/R_{VI}}$ in the transient to damp the current response and it increases $sigma _{X_{VI}/R_{VI}}$ in the quasi-static and steady-state to guarantee the m-ximum angle stability margin allowed by the variable virtual impedance. The effectiveness of the proposed control has been proven through time-domain simulations.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Multi-Agent Deep Reinforcement Learning-Based Distributed Optimal
Generation Control of DC Microgrids
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  Authors: Zhen Fan;Wei Zhang;Wenxin Liu;
  Pages: 3337 - 3351
  Abstract: Optimal generation allocation is customarily applied to the DC microgrids to enable optimal operation. Conventionally, the optimization is implemented periodically to obtain the optimal bus voltage or output power references for given operating conditions, which will unavoidably deviate from the actual ones. Since slight disturbances such as load changes will trigger real-time control adjustments, hence, the overall cost will increase due to the disconnect between optimization and real-time control. To overcome this issue, it is preferable to directly apply the optimal control method to render an optimal time path of control actions in real-time. This paper has studied the optimal generation control problem as a constrained non-convex problem with non-linearity. DRL has been successfully applied to solve such problems without mathematically modeling the actual system; the links between states and actions are discovered via ongoing environmental interactions, decreasing the reliance on system parameter information. This paper also showed that TD3-based optimal control could be applied to DC microgrids using a monotonical policy gradient search approach. Furthermore, DRL’s distributed training and execution framework is designed to realize real-time distributed control. The data sampling, storage, and experience buffer initialization strategy are customized to improve learning efficiency. The case study demonstrated its effectiveness.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Finite-Time Synergetic Controller Design for DC Microgrids With Constant
Power Loads
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  Authors: Farahnaz Ahmadi;Yazdan Batmani;Hassan Bevrani;Tianxiao Yang;Chenggang Cui;
  Pages: 3352 - 3361
  Abstract: The negative impedance characteristic of constant power loads (CPLs) can cause DC bus voltage fluctuations or even DC microgrid instability. In this paper, a nonlinear synergetic control method is used to guarantee the DC bus voltage stability of DC microgrids with CPLs. First, to regulate the capacitor voltage to its desired value, a macro-variable is defined using the synergetic control theory. Then, due to the sensitivity of the system to the parameters of the input voltage, the CPL, and the resistive load, and also to enhance the overall system performance, an improved macro-variable is introduced by adding an integral action. Finally, the finite-time synergetic controller is proposed to provide the finite-time convergence for the DC microgrid voltage and also to avoid the chattering phenomenon. The simulation and experimental results demonstrate the acceptable performance of the proposed controller.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
Hierarchical Time-Series Assessment and Control for Transient Stability
Enhancement in Islanded Microgrids
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  Authors: Yang Shen;Yelun Peng;Zhikang Shuai;Quan Zhou;Lipeng Zhu;Z. John Shen;Mohammad Shahidehpour;
  Pages: 3362 - 3374
  Abstract: The grid-forming converters would integrate battery energy storage systems (BESSs) in islanded microgrids for smoothing out the uncertain fluctuations of renewable energy resources. However, gird-forming converters would pose transient instability risks under large disturbances, which could require fast and accurate stability assessment methods in such challenging and uncertain cases. In this work, a hierarchical time-series assessment and control (HTSAC) framework is proposed for assessing the transient stability of grid-forming converters in islanded microgrids. The proposed HTSAC framework offers a gated recurrent unit (GRU) neural network alternative for an intuitive and accurate trajectory prediction in early post-fault stages. Subsequently, an emergency ride-through control (ERC) strategy is proposed which leverages the proposed neural network approach for enhancing the prediction results in real-time assessments of microgrid transient stability. The initial input horizon of GRU is optimized to avoid intense trial-and-error design burdens incurred in conventional data-driven assessment methods. Simulation and experimental results are presented to validate the effectiveness of the proposed HTSAC on an islanded microgrid in south China. The results also point out that the GRU of the prediction layer with a quantile loss function would ensure a timely ERC in the proposed HTSAC approach to renewable energy-based converter operations.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
Safe and Stable Secondary Voltage Control of Microgrids Based on Explicit
Neural Networks
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  Authors: Zixiao Ma;Qianzhi Zhang;Zhaoyu Wang;
  Pages: 3375 - 3387
  Abstract: This paper proposes a novel safety-critical secondary voltage control method based on explicit neural networks (NNs) for islanded microgrids (MGs) that can guarantee any state inside the desired safety bound even during the transient. Firstly, an integrator is introduced in the feedback loop to fully eliminate the steady-state error caused by primary control. Then, considering the impact of secondary control on the stability of the whole system, a set of transient stability and safety constraints is developed. In order to achieve online implementation that requires fast computation, an explicit NN-based secondary voltage controller is designed to cast the time-consuming constrained optimization in the offline NN training phase, by leveraging the local Lipschitzness of activation functions. Specially, instead of using the NN as a black box, the explicit representation of NN is substituted into the closed-loop MG for transferring the stability and safety constraints. Finally, the NN is trained by safe imitation learning, where an optimization problem is formulated by maximizing the imitation accuracy and volume of the stable region while satisfying the stability and safety constraints. Thus, the safe and stable region is approximated that any trajectory initiates within will converge to the equilibrium while bounded by safety conditions. The effectiveness of the proposed method is verified on a prototype MG with detailed dynamics.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
An Integrated Human–Cyber–Physical Framework for Control of
Microgrids
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  Authors: Shuai Feng;Michele Cucuzzella;Thijs Bouman;Linda Steg;Jacquelien M. A. Scherpen;
  Pages: 3388 - 3400
  Abstract: In this paper, to jointly study the energy dynamic behavior of humans and the corresponding physical dynamics of the microgrid, we bridge two disciplines: systems & control and environmental psychology. Firstly, we develop second order motivation-behavior mathematical models inspired by opinion dynamics models for describing and predicting human activities related to the use of energy, where psychological variables and social interactions are considered. Secondly, based on these models, we develop a human-cyber-physical system framework consisting of three layers: (i) human, (ii) cyber and (iii) physical. The first one describes human behavior influenced by behavioral intervention and motivation, which in turn depend on contextual factors, personal values and social norms. The cyber layer solves an optimization problem and embeds load controllers, which are designed to automatically mimic human behavior. Finally, the physical layer represents an AC microgrid. Thus, we formulate a social-physical welfare optimization problem and solve it by designing a distributed primal-dual control scheme, which generates the optimal behavioral intervention (with respect to a given reference) and the control inputs to the microgrid.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Customer-Centered Pricing Strategy Based on Privacy-Preserving Load
Disaggregation
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  Authors: Yuechuan Tao;Jing Qiu;Shuying Lai;Xianzhuo Sun;Yuan Ma;Junhua Zhao;
  Pages: 3401 - 3412
  Abstract: Demand response (DR) is a demand reduction or shift of electricity use by customers to make electricity systems flexible and reliable, which is beneficial under increasing shares of intermittent renewable energy. For residential loads, thermostatically controlled loads (TCLs) are considered as major DR resources. In a price-based DR program, an aggregation agent, such as a retailer, formulates price signals to stimulate the customers to change electricity usage patterns. The conventional DR management methods fully rely on mathematical models to describe the customer’s price responsiveness. However, it is difficult to fully master the customers’ detailed demand elasticities, cost functions, and utility functions in practice. Hence, in this paper, we proposed a data-driven non-intrusive load monitoring (NILM) approach to study the customers’ power consumption behaviors and usage characteristics. Based on NILM, the DR potential of the TCLs can be properly estimated, which assists the retailer in formulating a proper pricing strategy. To realize privacy protection, a privacy-preserving NILM algorithm is proposed. The proposed methodologies are verified in case studies. It is concluded that the proposed NILM algorithm not only reaches a better prediction performance than state-of-art works but also can protect privacy by slightly sacrificing accuracy. The DR pricing strategy with NILM integrated brings more profit and lower risks to the retailer, whose results are close to the fully model-based method with strong assumptions. Furthermore, a NILM algorithm with higher performance can help the retailer earn more benefits and help the grids better realize DR requirements.
  PubDate: TUE, 22 AUG 2023 14:18:36 -04
  Issue No: Vol. 14, No. 5 (2023)
Cyber-Physical Interdependent Restoration Scheduling for Active
Distribution Network via Ad Hoc Wireless Communication
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  Authors: Chongyu Wang;Mingyu Yan;Kaiyuan Pang;Fushuan Wen;Fei Teng;
  Pages: 3413 - 3426
  Abstract: This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Sequential Load Restoration With Soft Open Points and Time-Dependent Cold
Load Pickup for Resilient Distribution Systems
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  Authors: Yifei Wang;Xiaoyun Su;Meng Song;Wei Jiang;Mohammad Shahidehpour;Qingshan Xu;
  Pages: 3427 - 3438
  Abstract: Global changes in climate conditions and increasing number of weather-related disasters in the world have elevated the quest for better load restorations in power distribution systems. This paper proposes a distribution system restoration (DSR) model where non-exponential time-dependent cold load pickup (CLPU) characteristics and soft open point operations are simultaneously addressed. We develop a time-dependent CLPU model for attaining a more realistic and secure DSR in which we demonstrate that the traditional delayed exponential model (DEM) for a long outage duration is a special case of the adopted CLPU model. The current soft open point (SOP) models would typically relax the operation constraints without devising any tightening procedures, which might lead to inaccurate or even infeasible SOP states. In this paper, we analyze the reasons traditional SOP constraints would lead to unsecure operations and present a set of novel SOP operation constraints for recovering bindings. Also, we adopt the sequential second order cone programming (SSOCP) algorithm to establish the sequential framework of DSR and solve the proposed model effectively. Our presented case studies discuss the rationality of the proposed DSR model and algorithm.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Reliability Assessment of Hybrid AC/DC Distribution Systems Using
Independent Contingency States Reduction Technique
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  Authors: Wei Wei;Yuan Cao;Kai Hou;
  Pages: 3439 - 3449
  Abstract: The impact-increment technique is introduced to speed up the reliability assessment of hybrid AC/DC distribution systems (hybrid AC/DC DSs). Based on that conception, two criteria are developed to determine independent contingency states, whose impact-increments are always 0 and can be eliminated. Criterion I is based on total loss of continuity (TLOC), which can identify the minimum granularity subsets of high-order contingency state from the perspective of topology. Criterion II is based on partial loss of continuity (PLOC), which further consider power flow constraints to judge the independence of high-order contingency states. The criteria can be used to accurately screen out most of independent contingency states that do not need the time-consuming optimal load shedding algorithm for evaluation. Therefore, the reliability assessment process will be further accelerated without harming the accuracy. The performance of the proposed approach is verified using an improved IEEE-123 test system. Results show that the proposed method is more accurate and efficient than conventional state enumeration and fault set classification methods.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Dynamic Modelling and Mutual Coordination of Electricity and Watershed
Networks for Spatio-Temporal Operational Flexibility Enhancement Under
Rainy Climates
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  Authors: Yingping Cao;Bin Zhou;Chi Yung Chung;Zhikang Shuai;Zhihao Hua;Yuexin Sun;
  Pages: 3450 - 3464
  Abstract: This paper proposes a watershed-electricity nexus model to unlock the flexibility of watershed networks (WSNs) for supporting the operation of power distribution networks (PDNs) under rainy climates. The proposed model exploits the spatio-temporal flexibility of geographically dispersed pump clusters to provide reserve services to PDNs, and a hyperbolic partial differential function derived from Saint-Venant hydrodynamic equations is formed to describe the dynamic processes of river stream flows. Besides, a flexibility evaluation method based on a composite sensitivity matrix of water levels with respect to power injections is presented to quantify the time-varying adjustable power domain of pump loads. Then, a multi-stage interactive coordinated scheduling strategy is developed for the mutual operation of WSNs and PDNs, where drainage pumps are jointly optimized to provide flexible power reserves, while an optimal PDN economic dispatch is performed to improve the power supply voltage of pump loads. Furthermore, an equivalent mixed-integer linear programming reformulation method is derived to cope with the original nonlinear partial differential optimization problem for computational tractability improvements. Comparative results have validated the effectiveness of the proposed strategy in eliminating voltage violations and shaving peak loads.
  PubDate: TUE, 22 AUG 2023 14:18:36 -04
  Issue No: Vol. 14, No. 5 (2023)
Online State Estimation of the Integrated Electricity and Gas System Based
on the Gaseous Circuit Method
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  Authors: Guanxiong Yin;Dragan Ćetenović;Victor Levi;Hongbin Sun;Vladimir Terzija;
  Pages: 3465 - 3481
  Abstract: In this paper, an online state estimation (SE) scheme of the integrated electricity and gas system (IEGS) is developed. The online SE scheme handles different time resolutions of the electrical power system (EPS) and natural gas system (NGS) by determining the SE model to be applied. When the combined state estimation of the two systems is executed, the proposed Unified State Estimation (USE) model is applied. The USE model is based on the weighted least squares (WLS) estimation within the pre-specified electricity and gas sampling windows. The EPS is modeled via AC power flow because (fast) electromechanical transients die out between two SCADA sampling points. Gas dynamics in pipelines of a natural gas system (NGS) are much slower and can be encapsulated by successive measurements in time. It is proposed to convert the governing model in the form of partial differential equations (PDEs) into algebraic equations in the frequency domain giving the gaseous circuit method (GCM). Time-space coupling constraints are incorporated into the model by considering temporal EPS quantities and NGS frequency components in the time domain. The proposed USE model can adapt to both the NGS’s steady and dynamic operating conditions by considering different frequency components. To analyze the sensitivity to USE parameters and demonstrate the effectiveness and scalability of the proposed model, case studies are developed using one small IEGS and one larger IEGS obtained by integrating the IEEE 39-bus EPS and 20-node Belgium NGS.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Multistage Dynamic Planning of Integrated Hydrogen-Electrical Microgrids
Under Multiscale Uncertainties
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  Authors: Xunhang Sun;Xiaoyu Cao;Bo Zeng;Qiaozhu Zhai;Xiaohong Guan;
  Pages: 3482 - 3498
  Abstract: Integrated Hydrogen-Electrical (IHE) microgrids are desirable testbeds for the practice of carbon-neutral energy supply. This paper studies the IHE microgrids planning (IHEMP) under a dynamic perspective. To keep track with the fast development of hydrogen industry, we propose a multistage stochastic mixed-integer program (MS-MIP) formulation. It comprehensively considers the siting and sizing decisions of IHE microgrids, the dynamic expansion of distributed energy facilities, and the detailed operational model to derive a robust, flexible and profitable investment policy. Moreover, a scenario-tree based sampling strategy is leveraged to capture both the large-scale strategic uncertainties (e.g., the long-term growth of electric loads and hydrogen refueling demands, as well as the cost changes of system components) and fine-scale operating uncertainties (e.g., random variation of renewable energy outputs and loads) under different time scales. As the resulting formulation could be computationally very challenging, we develop a nested decomposition algorithm based on Stochastic Dual Dynamic Integer Programming (SDDiP). Case studies on exemplary IHE microgrids validate the effectiveness of our dynamic planning approach. Also, the customized SDDiP algorithm shows a superior solution capacity to handle large-scale MS-MIPs than the state-of-the-art solver (i.e., Gurobi) and a popular scenario-oriented decomposition method (i.e., progressive hedging algorithm).
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Fully Parallel Optimization of Coordinated Electricity and Natural Gas
Systems on High-Performance Computing
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  Authors: Lin Gong;Yehong Peng;Chenxu Zhang;Yong Fu;
  Pages: 3499 - 3511
  Abstract: Intensified interactions between electric power and natural gas infrastructures raise significant demands to coordinate their system operations. This paper proposes a fully parallel optimization method that can achieve a rapid decision-making on the day-ahead coordinated operation of electricity and natural gas systems on the high-performance computing (HPC) platform. The proposed method can flexibly tailor decomposition strategies to solve the optimization problem according to unique features of problem models in both electric power and natural gas systems. Particularly, the operation problem of power system is split by function and time into numbers of singe-unit subproblems and single-period network subproblems, while the operation problem of natural gas system is decomposed into multiple area subproblems. All the scalable subproblems are solved and coordinated quickly in a fully parallel manner on HPC for improving computational efficiency and tractability of complex electricity-gas co-optimization problem. By optimally coordinate electricity and natural gas systems under different operating conditions, the proposed method could improve the energy economics as well as the system resilience to various outages due to extreme events. Numerical results demonstrate the effectiveness and efficiency of our proposed co-optimization method and its HPC implementation.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Optimal Design and Configuration Strategy for the Physical Layer of Energy
Router Based on the Complex Network Theory
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  Authors: Mingjie Pan;Da Xie;Chenlei Wang;Pengfei Ju;Chenghong Gu;
  Pages: 3512 - 3522
  Abstract: The energy router (ER) is key to realizing the coordinated management and efficient utilization of multiple forms of energy in Energy Internet. This paper proposes a novel design and configuration method for the physical layer of ER by using complex network theory. Firstly, an abstract model of the physical layer of ER is introduced according to its function, and important modules in the model are illustrated in details. Secondly, based on the electrical characteristics of ports, the community structure of the power networks inside ER is analyzed by the improved Girvan-Newman (GN) algorithm to design the independent bus systems (IBSs) and generate the network topology of the physical layer. Then, the optimization model of equipment configuration of the power supply and distribution systems of ER is developed considering the economy, utilization efficiency, and power supply reliability. Finally, two case studies demonstrate that the proposed strategy can effectively accomplish the module-level structure design and device configuration for the physical layer of ER.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Optimal Operation Strategy for Multi-Energy Microgrid Participating in
Auxiliary Service
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  Authors: Yahui Wang;Yong Li;Yijia Cao;Mohammad Shahidehpour;Lin Jiang;Yilin Long;Youyue Deng;Weiwei Li;
  Pages: 3523 - 3534
  Abstract: Since multi-energy microgrid (MEMG) can coordinate various resources to operate as a virtual power plant (VPP), it is an important way to maintain the stable and economic operation of the power systems and decrease the impact of intermittence of distributed energy resources (DERs). However, the potential of MEMG as a VPP has not been thoroughly explored since auxiliary service (AS) market is not fully open for MEMG at present. The relevant challenges include balancing conflict of interests among multiple energy entities, motivating users to adjust flexible loads, integrating multiple flexible resources in energy supply/demand sides and formulating specific policies, etc. To handle these tasks, an optimal operation strategy for MEMG participating in AS is proposed by considering Stackelberg game theory and integrated demand response (IDR). The feasibility of the proposed strategy is validated by a practical MEMG in Hunan, China. The results show that the economic benefits of energy entities are effectively raised and the peak-shaving AS is realized while user satisfaction is also maintained. This work would give reference to the constructor of future AS market to formulate polices about the operation modes and pricing schemes of MEMG.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Privacy-Preserving Regulation Capacity Evaluation for HVAC Systems in
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  Authors: Zhenyi Wang;Peipei Yu;Hongcai Zhang;
  Pages: 3535 - 3549
  Abstract: Heating, ventilation, and air conditioning (HVAC) systems in buildings have great potential to provide regulation capacity that is leveraged to maintain the balance of supply and demand in the power system. In order to make full use of HVAC’s regulation capacity, it is important to accurately evaluate it ahead of time. Because physical model-based approaches are hard to implement and highly personalized for each building, data-driven approaches are preferable for this capacity evaluation. However, given the insufficient data for individual buildings and buildings’ potential unwillingness to share their data because of privacy concerns, it is extremely challenging to build a high-performance data-driven regulation capacity evaluation model. In this paper, we propose a privacy-preserving framework that combines federated learning and transfer learning to evaluate the regulation capacity of HVAC systems in heterogeneous buildings. Specifically, a classified federated learning algorithm is proposed to build capacity evaluation models of HVAC systems for different building types. Each building trains its model locally without sharing data with other buildings to preserve privacy. The algorithm also tackles data insufficiency and achieves high evaluation accuracy. In addition, we design a cross-type transfer learning algorithm to enhance model generalization and further address data deficiency. A protocol is created for the above two algorithms to protect privacy and security. Finally, numerical case studies are conducted to validate the proposed framework.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
An IoT-Based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy
Management
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  Authors: Junlong Li;Chenghong Gu;Xiangyu Wei;Ignacio Hernando Gil;Yue Xiang;
  Pages: 3550 - 3560
  Abstract: The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This dark-grey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Robust Scheduling of Thermostatically Controlled Loads With Statistically
Feasible Guarantees
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  Authors: Wenqian Jiang;Chenbei Lu;Chenye Wu;
  Pages: 3561 - 3572
  Abstract: Flexible resources are increasingly significant for the reliable operation of power grids due to the high penetration of renewable energy. Thermostatically controlled loads (TCLs) are one of the common flexible resources, whose control has been extensively studied. Yet, much can be improved. We investigate the scheduling of TCLs facing uncertain temperatures and dynamic prices. Classical approaches often employ the chance-constrained program or robust optimization to handle such uncertainties. However, these approaches either require specific distribution knowledge or yield too conservative solutions. The distribution knowledge can be rather challenging to obtain especially when the uncertainties from different sources are correlated. To this end, we adopt the notion of statistical feasibility and propose a robust sample-based scheduling scheme for TCLs. Such a sample-based scheme relieves the reliance on the distribution knowledge and is able to characterize the coupling effects of the two uncertainty sources. Besides, through integrating the real-time domain knowledge into uncertainty set reconstruction, we relax the solutions’ conservation by exploring the consumers’ tolerance to the room temperature. Numerical studies highlight the remarkable performance of our proposed scheme. Specifically, our approach is able to simultaneously effectively reduce the electricity bills of consumers and satisfy the consumers’ tolerance to the room temperature.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Stochastic Optimization Framework for Realizing Combined Value Streams
From Customer-Side Resources–Self Service and Distribution System
Operations
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  Authors: Akintonde O. Abbas;Badrul Chowdhury;
  Pages: 3573 - 3583
  Abstract: Customer-side resources are becoming increasingly crucial in grid modernization and transitioning to a sustainable energy future. Although most load-serving entities (LSEs) typically consider customer-side resources for single applications, these resources can be used for multiple applications simultaneously. This paper proposes a stochastic optimization framework to help LSEs capture multiple value streams from customer-side resources within their network. Specifically, we consider self-service applications - peak shaving, energy arbitrage, ramp rate reduction - and distribution system operational applications - loss reduction and voltage management. The framework is also adapted to handle the impacts of the activities of third-party aggregators on the LSE’s network. We also evaluate the performance of two algorithms - decision rule-based and optimal real-time dispatch - for dispatching the customer-side resources in the face of different sources and levels of uncertainty. Simulations were run using modified IEEE test systems within the OpenDSS simulation tool. The results show the value of customer-side resources can be maximized when multiple applications are simultaneously considered, and that value increases with increasing levels of forecast uncertainties.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Demand Management for Peak-to-Average Ratio Minimization via Intraday
Block Pricing
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  Authors: Carolina Cortez;Andreas Kasis;Dimitrios Papadaskalopoulos;Stelios Timotheou;
  Pages: 3584 - 3599
  Abstract: Price based demand response schemes may significantly improve power system efficiency. Additionally, it is desired that such schemes yield improved power operation, by reducing the peak consumption. This paper proposes the Intraday Block Pricing (IBP) scheme, aiming to promote effective demand response among consumers by charging their electricity usage based on intraday time-slots. To design the prices associated with the proposed scheme, we formulate a bilevel optimization problem that aims to minimize the Peak-to-Average Ratio (PAR) and simultaneously benefit the consumers and the utility company. The bilevel problem is converted into a single-level Mathematical Program with Equilibrium Constraints (MPEC). The resulting MPEC is non-convex and includes nonlinear constraints. Hence, to obtain a solution, it is relaxed into a Mixed Integer Linear Program by dealing with all nonlinearities. To evaluate the conservativeness of the proposed approach, a lower bound to the cost of the original bilevel problem is obtained. The applicability of the proposed scheme is demonstrated with simulations on various case studies, which exhibit a significant reduction in PAR and economic gains for the utility company and consumers. Moreover, simulation results show that the solutions of the original and relaxed problems are equivalent, demonstrating the effectiveness of the proposed solution approach. Further simulation results demonstrate significant advantages in the performance of the IBP scheme when compared to existing schemes in the literature.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
Consensus-Based Energy Management of Microgrid With Random Packet Drops
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  Authors: Hongyi Li;Hongxun Hui;Hongcai Zhang;
  Pages: 3600 - 3613
  Abstract: Decentralized energy management using consensus-based algorithm is a vibrant research field since it can promote the local accommodation of the renewable energy generation without raising privacy and scalability issues. Most of the existing methods assume that the communication links are reliable, which might not be true in real-world implementations. This paper focuses on tackling the problem of random packet drops. We first formulate the models of the energy management problem in the microgrid and the information packet drop in the communication network. Based on the models, we conclude that losing the information about incremental electricity cost estimation is tolerable, while losing the information about the power mismatch estimation is not. We propose a novel consensus algorithm that tracks and exchanges the accumulated value of the power mismatch estimation so that the information loss can be recovered. An equivalent form of the proposed method is established by augmenting the communication links with virtual buffering nodes. Based on the augmented communication topology, we theoretically prove the convergence of the proposed algorithm and the optimality of the solution. Several case studies are provided to validate the effectiveness of the proposed algorithm.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Demand Response Potential Evaluation of Aggregated High-Speed Trains
Toward Power System Operation
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  Authors: Haiyue Yu;Chengjin Ye;Yi Ding;Lin Qiu;Youtong Fang;Yonghua Song;
  Pages: 3614 - 3626
  Abstract: The integration of a high proportion of intermittent renewable energy sources and the continuous fluctuation of loads in power systems have created an urgent need for flexible reserves. It has been demonstrated both theoretically and experimentally that high-speed trains (HSTs), with booming electricity consumption, can flexibly change their speed to achieve real-time power climbing, reduction, or even feedback from the train to the grid. In this paper, the flexibility of HSTs is investigated from a power system-oriented perspective, and the demand response (DR) potential of HSTs is exploited based on the kinematics and dynamics equations of trains. The lagging power rebound (LPR) effect after the DR period of HSTs is enclosed for the first time, and a duration-capacity-speed-LPR index system is established to quantify the DR performance of HSTs. Considering the operation requirements of both power and railway systems, a novel operation optimization model for aggregated HSTs is proposed to calculate their maximum DR potential. In the proposed model, the LPR of HSTs is constrained to ensure power system security, and the minimum distance limit between HSTs sharing the same railway is also included to ensure railway safety. In addition, an adaptive solution method based on pseudo-spectral is used to solve the optimization model. This approach balances the requirement for calculation speed in the power system with the need for temporal accuracy in the railway system. Finally, the proposed model and technique are verified using a 3-railway 6-HST system.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
A Time Efficient Factorial Hidden Markov Model-Based Approach for
Non-Intrusive Load Monitoring
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  Authors: Partik Kumar;Abhijit R. Abhyankar;
  Pages: 3627 - 3639
  Abstract: Assessment of energy consumption behaviour plays an important role in designing demand reduction programs by utility companies. Knowledge of appliance activities in a household aids in conducting the energy consumption behaviour assessment for a community load. Non-intrusive load monitoring (NILM) is a tool that can help in identifying the appliance activities. In this paper, a Modified Factorial Hidden Markov Model (MFHMM) based NILM framework is proposed, which models dependencies among appliance operating states and differential appliance operating states by considering differential in power consumption profiles over time. All the appliances are modelled as individual load models using the Hidden Markov Model (HMM). The appliance operating states are obtained with the application of an iterative k-means clustering algorithm. The aggregated power consumption profile is divided into segments using an optimization-based change-point detection (CPD) algorithm. The NILM problem is solved for each of the segments, and the obtained solution is corrected based on the voltage profile at the aggregated load point. The approach of segmentation and efficient identification of appliance operating states make the model less time complex. Simulations are carried out on publicly available datasets named AMPds, REDD, and UK-DALE. The efficacy of the proposed framework over existing frameworks is evident from the simulation results.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Multi-Stage Mobile BESS Operational Framework to Residential Customers in
Planned Outages
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  Authors: Zehua Zhao;Fengji Luo;Jizhong Zhu;Gianluca Ranzi;
  Pages: 3640 - 3653
  Abstract: Battery Energy Storage Systems (BESSs) play an important role in modern energy systems. At present, stationary BESSs have found wide applicability in real-word applications, while there is a growing trend in exploiting the use of mobile BESSs (MBESSs) for serving energy demands at geographical locations that can vary over time. This paper proposes a comprehensive decision-making framework for a MBESS that dispatches multiple carriers to deliver a number of batteries to serve residential houses in a planned grid outage event. The proposed framework includes model representations capable of addressing the following three scenarios: (1) before the start of the outage - during this scenario, the scheduling of the battery delivery to the houses considers the typical energy operations and aims at exploiting the surplus renewable energy for the charging of the batteries; (2) during the outage period - during this scenario, the battery relocation is optimized to minimize the amount of energy demand affected by the outage by enabling the carriers to dynamically transport the batteries and to serve different houses; and (3) after the outage - in this scenario, an optimal path selection model enables the carriers to recycle the batteries from the houses to the MBESS station. Computational approaches are developed to solve the above three models and extensive numerical simulations are conducted to validate the effectiveness of the proposed framework.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Real-Time Incentive Design Under Unknown Demand Response Curves via a
Proportional–Integral Control Frameworks
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  Authors: Xiaotian Wang;Xuan Zhang;Hongbin Sun;
  Pages: 3654 - 3667
  Abstract: Implementing voluntary Demand Response (DR) in real-time or close to real-time is technically feasible because of the deployment of smart grid infrastructures such as smart meters, remote control units, and mobile communication devices. However, most cases require estimating/predicting load demand curves and typically operate from an electricity market clearing perspective. In this work, two frameworks of voluntary incentive-based DR to enhance the resilience and reliability of the system are designed, including a single Proportional-Integral controller based real-time DR framework and a multiple PI controllers cooperation based DR framework, which can be operated in real-time without the requirement of estimating or predicting the load demand curves. Under the proposed frameworks, the overall system can converge to the optimal tracking point to improve resilience/reliability via the cooperation of the different DR devices. Additionally, the corresponding incentive payments can be precisely determined. In particular, both frameworks are proven to be stable. In addition, deep reinforcement learning is adopted to enhance the performance of the proposed frameworks. Because of the generality of the proposed frameworks and the simplicity of the PI controllers, our proposed DR frameworks are promising for practical implementation. Finally, numerical studies verify the effectiveness of the proposed frameworks.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Distributionally Robust Energy Management for Islanded Microgrids With
Variable Moment Information: An MISOCP Approach
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  Authors: Anping Zhou;Ming Yang;Tao Wu;Lun Yang;
  Pages: 3668 - 3680
  Abstract: The ever-increasing penetration of renewable energy sources (RESs) has brought tremendous challenges to power system energy management problems. In this paper, we develop a distributionally robust (DR) joint chance-constrained microgrid energy management model while the uncertainties stemming from RESs are embedded. The proposed framework minimizes the expected cost function and ensures that the DR joint chance constraints (CCs) will satisfy for any distribution over a promising ambiguity set, which is designed based on the variable moments. The employed ambiguity set can describe the uncertainties more accurately compared with the commonly-used moment metric (i.e., fixed moments). By deriving equivalent second-order cone programming (SOCP) reformulations of the expected objective function and introducing effective approximation methods such as the optimized Bonferroni approximation to deal with the DR joint CCs, the proposed model is finally reduced to a tractable mixed-integer SOCP problem that can readily be solved. Case studies are conducted on a representative test system to corroborate the effectiveness of the suggested approach.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Distributed Online Voltage Control With Fast PV Power Fluctuations and
Imperfect Communication
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  Authors: Licheng Wang;Luochen Xie;Yu Yang;Youbing Zhang;Kai Wang;Shi-jie Cheng;
  Pages: 3681 - 3695
  Abstract: Coordinated Volt-Var control methods have demonstrated their techno-economic feasibility in voltage regulation of photovoltaic (PV) rich distribution systems. However, fast fluctuating PV power and imperfect communication networks may significantly challenge the effectiveness of these methods. In this paper, a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real time. With this proposed method, Var saturation and overvoltage issues which tend to occur at downstream buses of PV rich distribution systems are significantly mitigated. To quantitatively analyse the algorithm performance in imperfect communication environments, the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way. On this basis, the state transition process of the whole system is further depicted by a series of row- stochastic matrices, and the ergodic theory is used to analytically derive the algorithm tracking error in an imperfect communication environment. Our proposed method can also be extended to more complex applications, where both Var compensation and PV curtailment (or EV dispatch) are available for system voltage control. Simulation results verify the superiority of our method over traditional ones.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
A Novel Islanding Detection Method for Distributed PV System Based on
μPMUs
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  Authors: Jinlei Xing;Longhua Mu;
  Pages: 3696 - 3706
  Abstract: With the rapid development of distributed PV systems in distribution grids, unintentional islanding is one major concern which maybe cause safety hazards. The existing passive islanding detection methods are mainly based on the voltage and frequency deviations during islanding conditions. Passive methods have relatively large non-detection zone (NDZ) and susceptible to power system faults. Active islanding detection methods applied in inverters have relatively small non-detection zone, but they are usually harmful to power quality. This paper presents a new islanding detection method based on equivalent inter-connection line impedance, which is normally a small value. A measured impedance is calculated with the synchronized phasors from two $mu $ PMUs which are installed in a utility substation and a PV system. Under the grid-connected condition, the measured impedance is no more than the equivalent line impedance. While under the islanding condition, the measured impedance is much larger than the equivalent line impedance. So, an islanding condition can be effectively detected by the comparison of the measured impedance with the equivalent line impedance. The non-detection zone of this proposed method is nearly zero. The detection time is normally less than 250ms. This method is applicable for PV systems connected to the power system with a distribution transformer. It is also applicable for a distribution grid with multiple distributed generation systems.
  PubDate: TUE, 22 AUG 2023 14:18:36 -04
  Issue No: Vol. 14, No. 5 (2023)
Multivariable-Controlled Shunt Compensator for Damping Subsynchronous
Interactions in Electrical Distribution Networks
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  Authors: Pablo Rodríguez-Ortega;Javier Roldán-Pérez;Milan Prodanovic;
  Pages: 3707 - 3718
  Abstract: In recent years, the massive integration of converter-interfaced devices to electrical distribution networks has lead to appearance of poorly-damped subsynchronous interactions in the grid. These oscillation modes amplify electrical disturbances originated from the grid and may cause an interaction between grid elements threatening the system stability. Shunt compensators at the distribution level have already been used to improve the damping of these networks. However, their control system strongly depends on the distribution network dynamics, that is difficult to know at all times. In this paper, a multivariable controller for a shunt compensator is proposed. First, the dynamics of a distribution network with high penetration of electronic interfaces is explored in order to understand the interactions. Then, the controller of the compensator is designed based on an estimated model of the plant so that no previous information of the network is required. This controller is based on a state-feedback controller and a state observer, both designed by using the linear quadratic regulator (LQR) theory. Analytical results and computer simulations are used to verify the effectiveness of the controller. The main findings are validated in a laboratory environment comprising two 15 kW distributed generators, a 15 kW compensator and a 75 kW grid emulator.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Resilient Expansion Planning of Electricity Grid Under Prolonged Wildfire
Risk
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  Authors: Reza Bayani;Saeed D. Manshadi;
  Pages: 3719 - 3731
  Abstract: Public safety power shut-off (PSPS) during potentially dangerous weather conditions is an emergency measure within areas prone to wildfires. While balancing the risk of wildfire ignition and the continuation of energy supply is a short-term operation challenge, this paper explores the optimal long-term resilient expansion planning strategies. With the quantified risk of wildfire ignition, the proposed expansion planning problem maximizes the supplied power to the end-users while limiting the risk of wildfire ignition. The presented scheme provides utility decision-makers with three groups of network expansion decisions, namely addition of new lines, modification of existing lines, and installation of distributed energy resources (DERs). Given the uncertainty of DERs and wildfire risk, a two-stage robust optimization problem is proposed which ensures power system resilience against unfavorable events. The case studies illustrate how the presented model balances shutting off customers, DER installation, and line addition/modification while inhibiting wildfire ignition risk. Besides, the results suggest that, with appropriate transmission switching, DER installation can be the optimal choice for meeting the growing demand while limiting wildfire ignition risk. The implications of different risk level choices are illustrated, and the impacts of uncertainties on the expansion decisions are explored. Finally, the results of applying the proposed model to the IEEE 118-bus test network are illustrated.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Second-Order Cone Programming (SOCP) Model for Three Phase Optimal Power
Flow (OPF) in Active Distribution Networks
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  Authors: Md Mahmud-Ul-Tarik Chowdhury;Biswajit Dipan Biswas;Sukumar Kamalasadan;
  Pages: 3732 - 3743
  Abstract: With the high penetration of distributed generations (DGs), modern distribution systems require novel and efficient optimal power flow (OPF) models. This paper proposes a second-order cone programming (SOCP) based AC-OPF model for three-phase radial power distribution networks. Mutual coupling effects are generally ignored in the existing multi-phase SOCP AC-OPF models. To overcome this critical issue, the proposed SOCP-OPF model introduces a coupling coefficient for the mutual coupling effects on the three-phase unbalanced lines. The derivation of the coupling coefficients has been illustrated with the required proof that the relaxation is tight and the solution from the proposed OPF model is optimal for an unbalanced multi-phase distribution network. The SOCP-OPF model is evaluated on several IEEE standard distribution networks without and with high penetration of Distributed Energy Resources (DERs). It has been shown that the proposed OPF model provides an optimal global solution for convex objective functions. Also, this OPF model is scalable and computationally efficient compared to Nonlinear Programming (NLP) and other convex OPF models.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Optimal Dispatch of Battery Energy Storage in Distribution Network
Considering Electrothermal-Aging Coupling
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  Authors: Yinxiao Li;Yuxuan Gu;Guannan He;Qixin Chen;
  Pages: 3744 - 3758
  Abstract: With the rapid development of distributed generation (DG), battery energy storage systems (BESSs) will play a critical role in supporting the high penetration of renewable DG in distribution networks. The traditional dispatching approach of BESSs commonly adopts linear models with constant operational characteristics and neglects the aging cost. However, the operational characteristics of BESSs are related to operational states, i.e., temperature, state of charge and current. Furthermore, the aging cost is also an important issue to optimize the economic profitability of BESSs, which is affected by the operational states as well. To this end, this paper proposes an optimal dispatch model of BESSs in distribution networks that considers the electrothermal-aging coupling relationship. The nonconvex original model is reformulated as a second-order cone programming problem, which can be effectively solved. Case studies show that the proposed method accurately captures the operational characteristics and synergistically optimizes the operational cost of distribution networks and the aging cost of BESSs. Compared to the traditional dispatching approach, the proposed method largely avoids the deviation between the scheduled power and actual power of BESSs and effectively saves the total cost, leading to a 48% extension of lifetime and a 30% increase in profitability of the BESSs.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Safe Deep Reinforcement Learning for Microgrid Energy Management in
Distribution Networks With Leveraged Spatial–Temporal Perception
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  Authors: Yujian Ye;Hongru Wang;Peiling Chen;Yi Tang;Goran Strbac;
  Pages: 3759 - 3775
  Abstract: Microgrids (MG) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources’ management in distribution networks. In this context, despite deep reinforcement learning (DRL) constitutes a well-suited model-free and data-driven methodological framework, its application to MG energy management is still challenging, driven by their limitations on environment status perception and constraint satisfaction. In this paper, the MG energy management problem is formalized as a Constrained Markov Decision Process, and is solved with the state-of-the-art interior-point policy optimization (IPO) method. In contrast to conventional DRL approaches, IPO facilitates efficient learning in multi-dimensional, continuous state and action spaces, while promising satisfaction of complex network constraints of the distribution network. The generalization capability of IPO is further enhanced through the extraction of spatial-temporal correlation features from original MG operating status, combining the strength of edge conditioned convolutional network and long short-term memory network. Case studies based on an IEEE 15-bus and 123-bus test feeders with real-world data demonstrate the superior performance of the proposed method in improving MG cost effectiveness, safeguarding the secure operation of the network and uncertainty adaptability, through performance benchmarking against model-based and DRL-based baseline methods. Finally, case studies also analyze the computational and scalability performance of proposed and baseline methods.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
PV Inverter-Based Fair Power Quality Control
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  Authors: Aswin Ramesh Vadavathi;Gerwin Hoogsteen;Johann Hurink;
  Pages: 3776 - 3790
  Abstract: Low voltage distribution networks incorporating solar photovoltaic (PV) panels experience overvoltage and voltage unbalance during periods of low load and high PV generation. Resolving overvoltage by active power curtailment (APC) is an effective and cost-efficient solution. However, current APC techniques result in excessive and unfair power curtailment for prosumers at the sensitive parts of the grid that might induce neutral current. In this work, an analytical approach for fair APC and Reactive Power Control is presented for voltage regulation along with neutral current compensation. The desired power quality is maintained by controlling each phase of the PV inverter independently. The proposed algorithm regulates the voltage at the point of common coupling (PCC) within grid limits, eliminates neutral current, and reduces the grid unbalance. Furthermore, the results demonstrate that reducing the neutral current reduces the voltage at the PCC and consequently decreases the power curtailment required for overvoltage regulation.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
Novel Source-Storage Coordination Strategy Adaptive to Impulsive
Generation Characteristic Suitable for Isolated Island Microgrid
Scheduling
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  Authors: Zhongnan Feng;Fanrong Wei;Chuantao Wu;Quan Sui;Xiangning Lin;Zhengtian Li;
  Pages: 3791 - 3803
  Abstract: Wave energy generator (WEG) is a new renewable energy source with prominent advantages and disadvantages. Theoretically, it can move around the sea to harvest wave energy better and will not take up valuable land resources. Normally, WEG collects tiny wave energy continuously and generates electricity intermittently on the minute time-scale to enhance energy harvesting efficiency. Thus, the output of WEG shows prominent impulsive characteristics, which needs to be compensated by the battery energy storage system (BESS) and therefore brings about significant battery life losses. For the purpose of stable and economic operation of the weak power grid connected by WEG, a flexible control method enabling a quantifiable tradeoff between its energy harvesting efficiency and impulsive characteristics is proposed. Then, a novel source-storage coordination strategy and the corresponding energy exchange model of WEG with several optimizable control parameters is formulated to depict the energy flow within the microgrid. Subsequently, an optimization algorithm is proposed to schedule the WEG’s mobile route and optimizable control parameters simultaneously. Simulation results show that the proposed source-storage strategy and the energy management scheme can balance WEG’s energy harvesting and impulsive impact, and utilize its mobility to improve the isolated island microgrid’s operation economy and power quality.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Collaborative Active and Reactive Power Control of DERs for Voltage
Regulation and Frequency Support by Distributed Event-Triggered Heavy Ball
Method
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  Authors: Chuanhao Hu;Xuan Zhang;Qiuwei Wu;
  Pages: 3804 - 3815
  Abstract: The increasing integration of distributed energy resources (DERs) into distribution networks draws great attention to the advanced voltage control. Meanwhile, DERs scattered in distribution networks also show potential to provide frequency support. This paper proposes a novel coordinated active and reactive control strategy of DERs by using a distributed event-triggered heavy ball method, aiming to allow DERs to offer voltage regulation and frequency support in a unified framework. In the meantime, the proposed control strategy can effectively save communication cost and accelerate the convergence rate simultaneously. Specifically, for achieving a faster convergence rate, the dual-based heavy ball method is incorporated into a traditional dual iteration scheme by adding extra momentum terms. To further reduce the communication burden, the control scheme is modified by designing event-triggered conditions on dual variables. The convergence results are provided based on a linearized power flow model to facilitate the controller design and theoretical analysis. The proposed control strategy is also numerically tested on a modified IEEE 123-bus network with a nonlinear power flow model. Finally, the simulation results illustrate that the event-triggered heavy ball approach can greatly reduce the communication cost and achieve an improved convergence rate compared with the existing methods.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Provably Secure and Lightweight Authentication Key Agreement Scheme for
Smart Meters
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  Authors: Sheng Chai;Haotian Yin;Bin Xing;Zhukun Li;Yunyi Guo;Di Zhang;Xin Zhang;Da He;Jie Zhang;Xiaoling Yu;Wei Wang;Xin Huang;
  Pages: 3816 - 3827
  Abstract: Smart Grid is an indispensable part of the Internet of Things (IoT) and has the potential to revolutionise the electricity energy industry. Smart meters, as an important component of Smart Grid, are responsible for the exchange of information between end consumers and service providers. Smart meters and service providers need to use authentication key exchange protocols (AKE) to establish a secure session for secure information transmission. However, The current state-of-the-art lightweight solutions do not support identity anonymity and mostly have high communication costs. This paper proposes a lightweight and secure mutual authentication scheme, which is particularly designed for devices with limited computing capability to provide identity anonymity such as smart meters, based on the Shangyong Mima 2 (SM2) AKE protocol stipulated by the State Cryptography Administration of China. We conducted formal analysis of the security of the proposed scheme and proved its security. In addition, the computational overhead of the protocol was evaluated through performance analysis. Compared with related lightweight protocols, our scheme can reduce up to 30%-40% computational overhead. The results verified that the proposed scheme is indeed suitable for secure communication involving devices with constrained computing capability.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
InFocus: Amplifying Critical Feature Influence on Non-Intrusive Load
Monitoring Through Self-Attention Mechanisms
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  Authors: Jialing He;Zijian Zhang;Liran Ma;Zhouyu Zhang;Meng Li;Bakh Khoussainov;Jiamou Liu;Liehuang Zhu;
  Pages: 3828 - 3840
  Abstract: Non-intrusive load monitoring (NILM) enables extracting individual appliances’ power consumption data from an aggregated power signal in a cost-effective way. The extracted appliance-level power data can greatly facilitate tasks such as malfunction diagnosis and load forecasting, which are of significant importance for efficient energy use. Various neural networks including convolutional neural network (CNN), recurrent neural network (RNN), and transformer (self-attention-based neural network) are employed in the design of NILM solutions since 2015. In particular, CNN is revealed to extract certain critical features such as power level and typical usage duration, thus, achieving superior performance. However, the global features especially the dependency correlations between different positions in a sequence cannot be properly acquired. Accordingly, we devise a novel model incorporating an added attention layer to overcome this limitation. The added self-attention mechanism can automatically assign attention scores/weights to different features outputted by convolutional layers, which amplifies the positive influence of critical knowledge while realizing global reference. Moreover, this model can explicitly extract the appliance’s multi-state information, which endows the model with more interpretability. We further improve our model by substituting the added self-attention mechanism with a lightweight one, which decreases the number of model parameters while maintaining the decomposing accuracy. Experimental results over two real-world datasets, REDD and UK-DALE, demonstrate that our models outperform the state-of-the-art, achieving 6.5%-52% improvement on average for three standard evaluation metrics. Moreover, we offer an extracted NILM solution system architecture incorporating neural networks, aiming to establish a framework to support future research.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
A Learnable Image-Based Load Signature Construction Approach in NILM for
Appliances Identification
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  Authors: Yusen Zhang;Hao Wu;Qing Ma;Qingrong Yang;Yiwen Wang;
  Pages: 3841 - 3849
  Abstract: One of the tasks of Non-Intrusive Load Monitoring (NILM) is load identification, which aims to extract and classify altered electrical signals after switching events are detected. In this subtask, representative and distinguishable load signatures are essential. At present, the literature approach to characterize electrical appliances is mainly based on manual feature engineering. However, the performance of signatures obtained by this way is limited. In this paper, we propose a novel load signature construction method utilizing deep learning techniques. Specifically, three learnable load signatures are presented such as Learnable Recurrent Graph (LRG), Learnable Gramian Matrix (LGM) and Generative Graph (GG). Furthermore, we test different frameworks for learning these signatures and conclude that Temporal Convolutional Networks (TCN) based on residual learning are more suitable for this work than the other schemes mentioned. The results of experiment on the PLAID datasets with submetered and aggregated, WHITED dataset and LILAC dataset confirm that our method outperforms the voltage-current trajectory, Recursive Graph and Gramian Angular Field methods in multiple evaluation metrics.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
An Improved Algorithm for Topology Identification of Distribution Networks
Using Smart Meter Data and Its Application for Fault Detection
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  Authors: Cassidy Flynn;Abu Bakr Pengwah;Reza Razzaghi;Lachlan L. H. Andrew;
  Pages: 3850 - 3861
  Abstract: This paper proposes a new method for estimating the topology of radial distribution networks and a novel data-driven fault detection technique using smart meter data. An improved method to estimate voltage sensitivity coefficients from smart meter data is proposed that takes into account the variability of the transformer voltage. These coefficients are used in both the topology estimation and the fault detection algorithms. In the topology estimation algorithm, an improved graph learning algorithm, which iteratively constructs the network graph, is devised. In the fault detection algorithm, the objective is to look for sudden changes in the estimated voltage sensitivity coefficients. The performance of the proposed methods is first assessed on randomly generated topologies under various noise conditions. Then, the performance of the algorithms is validated using real smart meter measurements obtained from Australian distribution networks. The results show a significant improvement in the accuracy of estimated topologies compared to the state-of-the-art methods while the fault detection technique is successful in detecting faults using real smart meter data.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Detection of Electric Bicycle Indoor Charging for Electrical Safety: A
NILM Approach
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  Authors: Yan Liu;Qingshan Xu;Yongbiao Yang;Weiguo Zhang;
  Pages: 3862 - 3875
  Abstract: The increasing fire accidents caused by electric bicycle (EB) indoor charging have raised great concerns. To ensure residential electrical safety, EB is prohibited from charging indoors by regulations, but efficient monitoring of EB indoor charging behaviour in practice still remains as a challenging problem. From the perspective of non-intrusive load monitoring (NILM), this paper proposes a holistic approach to detect the EB indoor charging behaviour. Our approach utilizes four key elements: (i) a multi-window based cumulative sum algorithm to capture the turning-on event from aggregate power; (ii) a two-stage feature selection method to determine the optimal features to form an effective appliance signature for load identification; (iii) a one-class support vector machine classifier to identify whether the turned-on appliance is EB or other appliances in a semi-supervised way; (iv) a Raspberry-Pi based edge device to implement the overall NILM algorithm. The experiment results verify the effectiveness of our NILM based approach, which can obtain a high detection performance with a low-dimensional feature space, thus providing a cost-effective and high-performance solution for EB indoor charging detection.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
An Efficient Modular Optimization Scheme for Unbalanced Active
Distribution Networks With Uncertain EV and PV Penetrations
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  Authors: Vineeth Vijayan;Abheejeet Mohapatra;Sri Niwas Singh;Chaman Lal Dewangan;
  Pages: 3876 - 3888
  Abstract: Integrated control strategies with Network Reconfiguration (NR), Demand Response (DR), and voltage control can reduce peak demand, energy loss, and system-wide unbalances in modern three-phase active Electric Distribution Networks (EDNs). However, simultaneous handling of these strategies is computationally complex and challenging. This paper presents a stochastic optimization formulation that slices the problem, solves the sub-problems separately, and splices them back to find optimal solutions efficiently. At the outset, all constraint-violating configurations are eliminated using a graph-theory-based edge traversal search developed from the classic Knuth’s Algorithm-S. Subsequently, deploying suitable indices, cyclic NR-DR assignments are performed to find near-optimal topologies and load schedules concerning the minimum loss, peak load, and unbalances. Further, Voltage Regulators (VRs) are set to achieve loss and unbalance reduction with minimal tap operations. The proposed scheme is tested on the modified IEEE 123-node test feeder with extensive EV and PV penetrations. The results show that this modular scheme provides superior solutions with an almost 75% reduction in time than the conventional co-optimization method. Various other case studies illustrate the effectiveness of the proposed scheme.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Demand-Side and Utility-Side Management Techniques for Increasing EV
Charging Load
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  Authors: Salman Sadiq Shuvo;Yasin Yilmaz;
  Pages: 3889 - 3898
  Abstract: Electricity authorities need capacity assessment and expansion plans for efficiently charging the growing Electric vehicle (EV) fleet. Specifically, the distribution grid needs significant capacity expansion as it faces the most impact to accommodate the high variant residential EV charging load. Utility companies employ different scheduling policies for the maintenance of their distribution transformers (hereinafter, XFR). However, they lack scenario-based plans to cope with the varying EV penetration across locations and time. The contributions of this paper are twofold. First, we propose a customer feedback-based EV charging scheduling to simultaneously minimize the peak load for the distribution XFR and satisfy the customer needs. Second, we present a deep reinforcement learning (DRL) method for XFR maintenance, which focuses on the XFR’s effective age and loading to periodically choose the best candidate XFR for replacement. Our case study for a distribution feeder shows the adaptability and success of our EV load scheduling method in reducing the peak demand to extend the XFR life. Furthermore, our DRL-based XFR replacement policy outperforms the existing rule-based policies. Together, the two approaches provide a complete capacity planning tool for efficient XFR maintenance to cope with the increasing EV charging load.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Privacy-Preserving Operation Management of Battery Swapping and Charging
System With Dual-Based Benders Decomposition
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  Authors: Yanni Wan;Jiahu Qin;Yang Shi;Weiming Fu;Dunfeng Zhang;
  Pages: 3899 - 3912
  Abstract: The battery swapping and charging system (BSCS) is an emerging and promising infrastructure to provide the energy refueling service for EVs. However, each operator (i.e., battery swapping module (BSM) operator and battery charging module (BCM) operator) in BSCS has privacy-preserving requirement and individual interest, which brings challenge to the operation management of BSCS. To this end, this paper investigates the privacy-preserving operation management problem (OMP) of BSCS from a social perspective, in which a comprehensive battery-swapping-charging process is modeled. Specifically, we first model the OMP as a constrained mixed-integer programming (MIP), where the evolutionary of the dynamic battery inventory and the SoC of charging batteries are considered simultaneously. Due to the privacy-preserving requirements of different operators and the strong coupling between continuous and binary decision variables, a novel dual-based Benders decomposition (DBD) algorithm is then developed, which has the following two merits: 1) Each BCM and BSM operator can independently solve its own charging and swapping scheduling problem, thus preserving the privacy of individual operator; 2) The combination of dual decomposition and Benders decomposition techniques facilitates a parallel implementation, thus improving the scheduling efficiency and saving the computational time. Finally, simulation results are provided to validate the effectiveness and scalability of the proposed algorithm.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Risk-Aware Operation Modeling for Ride-Hailing Fleet in Order Grabbing
Mode: A Distributional Reinforcement Learning Approach
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  Authors: Yimeng Sun;Zhaohao Ding;Zechun Hu;Wei-Jen Lee;
  Pages: 3913 - 3926
  Abstract: With the development of mobility on-demand and transportation electrification technologies, electric vehicle (EV)-based ride-hailing fleets are playing an increasingly important role in the urban ground transportation system. Due to the stochastic nature of order request arrival and electricity price, there exists decision-making risks for ride-hailing EVs operated in order grabbing mode. It is important to investigate their risk-aware operation and model their impact on fleet charging demand and trajectory. In this paper, we propose a distributional reinforcement learning framework to model the risk-aware operation of ride-hailing EVs in order grabbing mode. First, we develop a risk quantification scheme based on the dual theory of choices under risk. Then, we combine Implicit Quantile Network, distorted quantile sampling, and distributional temporal difference learning methods to capture the intrinsic uncertainties and depict the risk-aware EV operation decisions. The proposed framework can provide a more accurate spatial-temporal portrayal of the charging demand and fleet management results. The real-world data from Haikou city is used to illustrate and verify the effectiveness of the proposed scheme.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
HiCoOB: Hierarchical Concurrent Optimistic Blockchain Consensus Protocol
for Peer-to-Peer Energy Trading Systems
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  Authors: Juhar Abdella;Zahir Tari;Redowan Mahmud;Nasrin Sohrabi;Adnan Anwar;Abdun Mahmood;
  Pages: 3927 - 3943
  Abstract: Hierarchical consensus protocols are one of the most promising approaches considered to improve the performance and scalability of blockchain-based peer-to-peer energy trading (P2P ET) systems. They allow a network to be divided into multiple clusters where each cluster can independently run its own local consensus on local transactions (i.e., transactions that belong to a single cluster) and also cooperate with other clusters for global transactions (i.e., transactions that affect more than one cluster). However, current solutions do not allow the parallel processing of local and global transactions, leading to low throughput and high latency. Moreover, they impose an additional burden on the leader node by assigning to it the responsibility for both local and global consensus. This paper proposes a hierarchical concurrent optimistic blockchain consensus protocol, called HiCoOB, which includes an optimistic local consensus and a deterministic global consensus that enables the concurrent execution of local and global transactions. Additionally, the local and the global consensus are coordinated by two distinct leaders, thereby reducing the overhead on the local leader node. HiCoOB also provides a synchronization method between local and global consensus to avoid the double spending of energy or money that arises from interdependent client transactions. Experimental results indicate that the proposed protocol achieves 87% higher throughput than the existing systems. Furthermore, it reduces the waiting time of the local transactions by a factor of 5x. Finally, HiCoOB has 10 times less overhead on the local leader node in terms of message exchange when compared to state-of-the-art systems.
  PubDate: TUE, 22 AUG 2023 14:18:39 -04
  Issue No: Vol. 14, No. 5 (2023)
A Blockchain Peer-to-Peer Energy Trading System for Microgrids
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  Authors: Jianbin Gao;Kwame Omono Asamoah;Qi Xia;Emmanuel Boateng Sifah;Obiri Isaac Amankona;Hu Xia;
  Pages: 3944 - 3960
  Abstract: Traditional power grids have been the major source of electricity for several households and industries for a number of years. However, with that kind of supply, every member of the grid is affected whenever there is a fault on the transmission line connecting them. With that in mind, microgrids, usually powered by numerous distributed electrical sources, were introduced to curb this problem, and as such, energy users have also become producers themselves. Nonetheless, the generation of power by distributed sources brings about unpredictability on the network and, in essence, problems in energy sharing. Peer-to-peer (P2P) energy trading has several advantages and has been introduced to mitigate energy sharing problems. With networked energy trading comes the issue of trust, as several prosumers are concerned about their privacy and security in such environments. Therefore, this work leverages the advantages of blockchain in proposing a secure energy trading platform for all parties involved. Coupled with certificateless signcryption, an immutable energy trading market is designed, and its use case is applicable in smart cities. A thorough security analysis was performed, and the efficiency of our proposed solution is backed by numerical results.
  PubDate: TUE, 22 AUG 2023 14:18:36 -04
  Issue No: Vol. 14, No. 5 (2023)
Update Scheduling for ADMM-Based Energy Sharing in Virtual Power Plants
Considering Massive Prosumer Access
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  Authors: Cheng Feng;Kedi Zheng;Yangze Zhou;Peter Palensky;Qixin Chen;
  Pages: 3961 - 3975
  Abstract: With the proliferation of distributed energy resources (DERs), electricity consumers in virtual power plants (VPPs) are transitioning into prosumers and are encouraged to share surplus energy with peers. Nevertheless, large-scale energy sharing among thousands of prosumers may encounter communication-related challenges. Communication network congestion may result in a significant increase in the negotiation waiting time to reach a sharing agreement, and potentially risks exceeding the deadline of negotiation before the market gate closes, rendering energy sharing ineffective. This paper proposes an online partial-update algorithm for the alternating direction method of multipliers (ADMM)-based energy sharing. By restricting the update connection between the VPP and the prosumers, the algorithm selects a subset of the prosumers participating in ADMM updates each round, hence eliminating the excessively long waiting time caused by communication congestion. Considering the delay induced by massive prosumer communication access requests, a method for determining the optimal number of prosumers participating in updates is provided. To fully utilize the limited update opportunities, a fair and efficient prosumer update scheduling policy is designed. The VPP schedules the participation of prosumers in updates such that the convergence-critical prosumers receive higher priority, yet every prosumer is granted sufficient update opportunities. Additionally, the extra computation and communication overheads brought by the prosumer scheduling are minimized, allowing the whole algorithm to be executed in real time. Numerical studies are conducted to validate the effectiveness of the algorithm and its performance in reducing the overall convergence time.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
A Dynamic Peer-to-Peer Electricity Market Model for a Community Microgrid
With Price-Based Demand Response
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  Authors: Fayiz Alfaverh;Mouloud Denai;Yichuang Sun;
  Pages: 3976 - 3991
  Abstract: Peer-to-Peer (P2P) energy sharing enables prosumers within a community microgrid to directly trade their local energy resources such as solar photovoltaic (PV) panels, small-scale wind turbines, electric vehicle battery storage among each other based on an agreed cost-sharing mechanism. This paper addresses the energy cost minimization problem associated with P2P energy sharing among smart homes which are connected in a residential community. The contribution of this paper is threefold. First, an effective Home Energy Management System (HEMS) is proposed for the smart homes equipped with local generation such as rooftop solar panels, storage and appliances to achieve the demand response (DR) objective. Second, this paper proposes a P2P pricing mechanism based on the dynamic supply-demand ratio and export-import retail prices ratio. This P2P model motivates individual customers to participate in energy trading and ensures that not a single household would be worse off. Finally, the performance of the proposed pricing mechanism, is compared with three popular P2P sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and bill sharing (BS) considering different types of peers equipped with solar panels, electric vehicle, and domestic energy storage system. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
A False Data Injection Attack Detection Strategy for Unbalanced
Distribution Networks State Estimation
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  Authors: Shuheng Wei;Junjun Xu;Zaijun Wu;Qinran Hu;Xinghuo Yu;
  Pages: 3992 - 4006
  Abstract: With the advance in communication facilities and information technologies, the state estimation (SE) of distribution networks is subject to intensified cybersecurity threats caused by false data injection attacks (FDIAs). To address the issues, this paper proposes a novel FDIA detection strategy for unbalanced distribution networks. The SE model and corresponding general imperfect FDIA are introduced first to emulate the attacking behavior in practice. To achieve FDIA detection, we propose a square-root unscented Kalman filter (SR-UKF) based forecasting-aided SE (FASE) to generate estimation results. By modifying the filtering step of the proposed FASE into a redundant linear regression form, random outliers can be effectively detected and suppressed by leveraging the projection statistics (PS). Afterward, based on the acquired SE results, a generalized likelihood ratio test (GLRT) is designed to detect FDIAs on consecutive snapshots. In the GLRT, the dynamic time warping (DTW) distance between two innovation sequences is set as the test variable, which is compared with the offline determined detection threshold under a specific false alarm rate. The feasibility of the proposed general imperfect FDIA and the effectiveness of the proposed FDIA detection strategy are validated through extensive numerical simulations.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Attack Graph Model for Cyber-Physical Power Systems Using Hybrid Deep
Learning
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  Authors: Alfan Presekal;Alexandru Ştefanov;Vetrivel Subramaniam Rajkumar;Peter Palensky;
  Pages: 4007 - 4020
  Abstract: Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016. However, existing attack detection methods are limited. Most of them are based on power system measurement anomalies that occur when an attack is successfully executed at the later stages of the cyber kill chain. In contrast, the attacks on the Ukrainian power grid show the importance of system-wide, early-stage attack detection through communication-based anomalies. Therefore, in this paper, we propose a novel method for online cyber attack situational awareness that enhances the power grid resilience. It supports power system operators in the identification and localization of active attack locations in Operational Technology (OT) networks in near real-time. The proposed method employs a hybrid deep learning model of Graph Convolutional Long Short-Term Memory (GC-LSTM) and a deep convolutional network for time series classification-based anomaly detection. It is implemented as a combination of software defined networking, anomaly detection in communication throughput, and a novel attack graph model. Results indicate that the proposed method can identify active attack locations, e.g., within substations, control center, and wide area network, with an accuracy above 96%. Hence, it outperforms existing state-of-the-art deep learning-based time series classification methods.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Privacy of Distributed Optimality Schemes in Power Networks
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  Authors: Andreas Kasis;Kanwal Khan;Marios M. Polycarpou;Stelios Timotheou;
  Pages: 4021 - 4034
  Abstract: The increasing participation of local generation and controllable demand units within the power network motivates the use of distributed schemes for their control. Simultaneously, it raises two issues; achieving an optimal power allocation among these units, and securing the privacy of the generation/demand profiles. This study considers the problem of designing distributed optimality schemes that preserve the privacy of the generation and controllable demand units within the secondary frequency control timeframe. We propose a consensus scheme that includes the generation/demand profiles within its dynamics, keeping this information private when knowledge of its internal dynamics is not available. However, the prosumption profiles may be inferred using knowledge of its internal model. We resolve this by proposing a privacy-preserving scheme which ensures that the generation/demand cannot be inferred from the communicated signals. For both proposed schemes, we provide analytic stability, optimality and privacy guarantees and show that the secondary frequency control objectives are satisfied. The presented schemes are distributed, locally verifiable and applicable to arbitrary network topologies. Our analytic results are verified with simulations on a 140-bus system, where we demonstrate that the proposed schemes offer enhanced privacy properties, enable an optimal power allocation and preserve the stability of the power network.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Super-Resolution Perception Assisted Spatiotemporal Graph Deep Learning
Against False Data Injection Attacks in Smart Grid
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  Authors: Jiaqi Ruan;Gang Fan;Yifan Zhu;Gaoqi Liang;Junhua Zhao;Fushuan Wen;Zhao Yang Dong;
  Pages: 4035 - 4046
  Abstract: Developing the deep learning (DL) technique is a promising way to enhance smart grid (SG) cybersecurity. However, previous DL methods require massive attack samples for cyberattack correlation learning, whilst the real-world SG is incapable of providing such a large dataset. Moreover, existing work commonly focuses on extracting temporal features from power grid data for cyberattack detection, while the spatial features are insufficiently investigated. To address these limitations, a spatiotemporal graph deep learning (STGDL)-based scheme is proposed to detect cyberattacks without requiring attack samples. First, the graph convolution and temporal gated convolution are orchestrated to extract spatiotemporal features jointly. Then, a quantile regression training strategy is adopted to give normally operational bounds of state variables in state estimation (SE). It gets rid of limitations on needing attack samples, and the state bounds can indicate cyberattack anomalies. At last, a super-resolution perception (SRP) network is proposed. The SRP network is able to reconstruct the high-frequent data of estimated states from low-frequent SE results, so as to improve the temporal learning ability in the STGDL model. The feasibility and effectiveness of the proposed scheme are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus and 118-bus benchmarks.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Privacy-Preserving Electricity Theft Detection Based on Blockchain
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  Authors: Zhiqiang Zhao;Yining Liu;Zhixin Zeng;Zhixiong Chen;Huiyu Zhou;
  Pages: 4047 - 4059
  Abstract: In most electricity theft detection schemes, consumers’ power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers’ privacy; distributed storage of consumers’ data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers’ privacy.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Experimental Validation of a Remedial Action via Hardware-in-the-Loop
System Against Cyberattacks Targeting a Lab-Scale PV/Wind Microgrid
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  Authors: Ehsan Naderi;Arash Asrari;
  Pages: 4060 - 4072
  Abstract: This paper experimentally validates the effectiveness of a primary/backup framework in preventing/mitigating the impacts of false data injection (FDI) cyberattacks targeting a lab-scale wind/photovoltaic (PV) microgrid. As the primary mechanism, an artificial intelligence-based false data detection algorithm is proposed to forecast the upcoming measurements and alert the operator about the accuracy of sensors’ readings, directly collected from the field devices. Given that some FDI attacks are designed to bypass the utilized detection methods, it is essential to be prepared for such circumstances. Hence, as the backup mechanism, a remedial action scheme (RAS) is also introduced to mitigate the impacts of such malicious cyberattacks and keep the functionality of the microgrid. The proposed framework (i.e., FDI model, detection approach, and remedial action scheme) is developed as a hardware-in-the-loop (HIL) testbed within the cyber-physical structure of the smart microgrid. The experimental results validate 1) the negative impacts of modeled FDI attacks on the lab-scale microgrid, 2) the effectiveness of the developed false data detection technique, and 3) the efficiency of the proposed RAS to keep the normal operation of the targeted microgrid when the detection system fails to recognize the attack.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV
Forecasting
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  Authors: Yiyan Li;Lidong Song;Si Zhang;Laura Kraus;Taylor Adcox;Roger Willardson;Abhishek Komandur;Ning Lu;
  Pages: 4073 - 4085
  Abstract: This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intra-hour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. The framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% - 30% compared with state-of-the-art methods.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid
Learning Algorithms
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  Authors: Tong Wu;Ignacio Losada Carreño;Anna Scaglione;Daniel Arnold;
  Pages: 4086 - 4099
  Abstract: This paper proposes novel architectures for spatio-temporal graph convolutional and recurrent neural networks whose structure is inspired by the physics of power systems. The key insight behind our design consists in deriving the so-called graph shift operator (GSO), which is the cornerstone of Graph Convolutional Neural Network (GCN) and Graph Recursive Neural Network (GRN) designs, from the power flow equations. We demonstrate the effectiveness of the proposed architectures in two applications: in forecasting the power grid state and in finding a stochastic policy for foresighted voltage control using deep reinforcement learning. Since our design can be adopted in single-phase as well as three-phase unbalanced systems, we test our architecture in both environments. For state forecasting experiments we consider the single phase IEEE 118-bus case systems; for voltage regulation, we illustrate the performance of deep reinforcement learning policy on the unbalanced three-phase IEEE 123-bus feeder system. In both cases the physics based GCN and GRN learning algorithms we propose outperform the state of the art.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Faulty-Feeder Detection for Single Phase-to-Ground Faults in Distribution
Networks Based on Waveform Encoding and Waveform Segmentation
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  Authors: Jiawei Yuan;Zaibin Jiao;
  Pages: 4100 - 4115
  Abstract: Faulty feeder detection helps ensure the stability and safety of power grids after single-phase-to-ground (SPG) faults occur in distribution networks. The existing detection techniques identify the faulty feeder by extracting representative fault features, while they fail to show reliable detection performance due to variable fault conditions and complex fault transients. To address these drawbacks, this paper proposes a method based on waveform encoding and waveform segmentation. Since the waveforms have complete fault features in fault signals, it is suitable to recognize the signals on the waveform scale, rather than extracting and fusing several fault features. Firstly, raw sampled zero-sequence voltage (ZSV) and zero-sequence current (ZSC) are processed by using the proposed encoding method, and the ZSV-ZSC image can be generated quickly. Secondly, to learn and understand the waveforms of ZSV and ZSC, a two-path fully convolutional network (FCN) is established to make pixel-wise prediction on the ZSV-ZSC image. Finally, the fault degree of each feeder can be estimated based on the segmented waveform in the ZSV-ZSC image. The performance evaluation is implemented in the NVIDIA Jetson Xavier embedded platform, and the experimental results demonstrate that the proposed method can identify the faulty feeder with high accuracy within 28 ms.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Using Domain-Augmented Federated Learning to Model Thermostatically
Controlled Loads
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  Authors: Attila Balint;Haroon Raja;Johan Driesen;Hussain Kazmi;
  Pages: 4116 - 4124
  Abstract: Optimization of thermostatically controlled loads, such as heat pumps, using data-driven models can significantly reduce domestic energy consumption besides providing critical grid services. However, these data-driven models often require a prohibitive amount of data before reaching sufficient accuracy for individual devices. Centralized or collaborative learning schemes, which aggregate data from many devices, can lower data requirements from individual devices, but at the cost of loss of user privacy (or data leakage). In this paper, we explore federated learning as a modelling alternative to address these concerns, and compare its accuracy against collaborative learning approaches using a real-world dataset. Some important insights emerge from this work. Notably, we show that federated learning, on its own, suffers from several drawbacks when compared against collaborative approaches; including poor convergence in low data availability regimes, as well as a failure to learn causal links even asymptotically. We explore the reasons for these shortcomings, and demonstrate that these issues can be resolved by incorporating domain-informed data augmentation in the learning process, allowing it to converge to a solution that is very close to the baseline collaborative model in terms of both accuracy and interpretability.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Data-Driven Models for Sub-Cycle Dynamic Response of Inverter-Based
Resources Using WMU Measurements
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  Authors: Fatemeh Ahmadi-Gorjayi;Hamed Mohsenian-Rad;
  Pages: 4125 - 4128
  Abstract: Using real-world data from Waveform Measurement Units (WMUs), this letter proposes novel data-driven methods to model the dynamic response of inverter-based resource (IBR) to the high-frequency disturbances that occur in practice in power systems. WMUs are an emerging class of smart grid sensors. They can capture the fast sub-cycle dynamics in power systems, which are overlooked by phasor measurement units (PMUs). After extracting the differential voltage and current waveforms from the raw waveform data, we develop multiple methods that include data-driven model library construction and proper model selection. One class of methods is proposed in frequency domain, which is based on modal analysis. Another class of methods is proposed in time domain, which is based on regression analysis of time-series. Experimental results based on real-world WMU data demonstrate the of performance the proposed methods.
  PubDate: TUE, 22 AUG 2023 14:18:36 -04
  Issue No: Vol. 14, No. 5 (2023)
Data-Driven Inverse Optimization for Modeling Intertemporally Responsive
Loads
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  Authors: Zhenfei Tan;Zheng Yan;Qing Xia;Yang Wang;
  Pages: 4129 - 4132
  Abstract: This letter proposes a novel framework for modeling the response-price relationship of intertemporally responsive loads (IRL) using historical data. This task is cast as a data-driven inverse optimization (DDIO) problem, which trains a surrogate model whose best response to electricity price most closely resembles the observed power trajectory of IRLs. The virtual battery fleet with an adjustable number of elements is used as the surrogate model, which yields a linear modeling result. The DDIO is a bilevel programming problem. To solve it efficiently, a Newton-based algorithm with a grid fitting initialization technique is developed. The accuracy and robustness of the proposed modeling method are validated by numerical tests in comparison with other machine learning regressors.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Energy Management of Multiple Microgrids Considering Missing Measurements:
A Novel MADRL Approach
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  Authors: Sichen Li;Weihao Hu;Di Cao;Sayed Abulanwar;Zhenyuan Zhang;Zhe Chen;Frede Blaabjerg;
  Pages: 4133 - 4136
  Abstract: This paper proposes a novel multi-agent deep reinforcement learning (MADRL) approach for the energy management of multiple microgrids considering the robust voltage control under the missing measurements. Missing measurement control poses challenges to the MADRL. To address the problem, we propose a trajectory history information-utilized opponent modeling-based distributed MADRL to avoid the collapse of control caused by the loss of current time measurement. Simulation results demonstrate that, whether the measurements are complete or not, the proposed approach achieves the ideal results.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)
Dynamic Average Consensus With Anti-Windup Applied to Interlinking
Converters in AC/DC Microgrids Under Economic Dispatch and Delays
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  Authors: Manuel Martinez-Gomez;Marcos E. Orchard;Serhiy Bozhko;
  Pages: 4137 - 4140
  Abstract: This work proposes an application of dynamic average consensus in interlinking converters of AC/DC microgrids with a distributed anti-windup for dealing with steady-state errors from communication delays. The proposed controller consists of a PI control that looks after the power-sharing between interlinking converters while achieving a global incremental cost consensus. The controller uses an observer (by dynamic average consensus) for estimating the average power of the interlinking converter cluster; this method represents an alternative formulation to conventional single-integrator consensus. An anti-windup with reset scheme is proposed to reduce steady-state errors in presence of fixed time delays. Stability analyses are also presented as well as simulations. Both show that the proposed controller successfully balances the power between interlinking converters being comparable with similar approaches in the literature.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
A Stochastic Controller for Primary Frequency Regulation Using ON/OFF
Demand Side Resources
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  Authors: Guanyu Tian;Qun Zhou Sun;
  Pages: 4141 - 4144
  Abstract: Residential ON/OFF devices, such as thermal-controlled loads and smart appliances, have great potential for providing frequency regulation service in the power grid. These devices can be engaged using centralized, distributed, or local control. However, both centralized and distributed controls require significant costs for communication infrastructure, while local controllers may perform poorly due to a lack of coordination. To address the challenges, a stochastic demand-side management controller is proposed. The stochastic controller dynamically adjusts the aggregated response power profile by randomizing the execution of demand response actions using a stochastic filter with no communication required. The system frequency response is analytically proved to be critically damped to the nominal value. Using the stochastic controller on grid-interactive water heaters, test cases are implemented to validate the frequency regulation performance under a power system transient scenario. The proposed controller produces the best system frequency response compared to benchmark controllers.
  PubDate: TUE, 22 AUG 2023 14:18:37 -04
  Issue No: Vol. 14, No. 5 (2023)
Locating Critical Prosumers in P2P Dominant Grids Using State-Sensitivity
Function
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  Authors: Parikshit Pareek;L. P. Mohasha Isuru Sampath;Hung D. Nguyen;Eddy Y. S. Foo;
  Pages: 4145 - 4148
  Abstract: This letter presents the idea of ranking critical prosumers in using a novel state-sensitivity function (SSF). The SSF is designed to work in a given nodal power injection hypercube, and thus the proposed ranking method captures the effect of injection subspace. An element-wise maximum Jacobian is obtained, that quantifies the worst-case sensitivity of each injection on each state. Results from various 33-Bus system configurations highlight the proposed method’s capability in ranking state-injection risk pairs, providing node voltage risk levels and quantifying combined risk levels in peer-to-peer dominant grids.
  PubDate: TUE, 22 AUG 2023 14:18:38 -04
  Issue No: Vol. 14, No. 5 (2023)