41 research outputs found

    Control and Limit Enforcements for VSC Multi-Terminal HVDC in Newton Power Flow

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    This paper proposes a novel method to automatically enforce controls and limits for Voltage Source Converter (VSC) based multi-terminal HVDC in the Newton power flow iteration process. A general VSC MT-HVDC model with primary PQ or PV control and secondary voltage control is formulated. Both the dependent and independent variables are included in the propose formulation so that the algebraic variables of the VSC MT-HVDC are adjusted simultaneously. The proposed method also maintains the number of equations and the dimension of the Jacobian matrix unchanged so that, when a limit is reached and a control is released, the Jacobian needs no re-factorization. Simulations on the IEEE 14-bus and Polish 9241-bus systems are performed to demonstrate the effectiveness of the method.Comment: IEEE PES General Meeting 201

    Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

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    Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.Comment: 14 pages, 4 figures, published on IEEE Transaction on Smart Grid 2022 Nov 15. See: https://ieeexplore-ieee-org.utk.idm.oclc.org/stamp/stamp.jsp?arnumber=995140

    DiME and AGVIS A Distributed Messaging Environment and Geographical Visualizer for Large-scale Power System Simulation

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    This paper introduces the messaging environment and the geographical visualization tool of the CURENT Large-scale Testbed (LTB) that can be used for large-scale power system closed-loop simulation. First, Distributed Messaging Environment (DiME) implements an asynchronous shared workspace to enable high-concurrent data exchange. Second, Another Grid Visualizer (AGVis) is presented as a geovisualization tool that facilitates the visualization of real-time power system simulation. Third, case studies show the use of DiME and AGVis. The results demonstrate that, with the modular structure, the LTB is capable of not only federal use for real-time, large-scale power system simulation, but also independent use for customized power system research.Comment: 5 pages, 7 figures, conferenc

    Improving Virtual Synchronous Generator Control in Microgrids using Fuzzy Logic Control

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    Virtual synchronous generators (VSG) are designed to mimic the inertia and damping characteristics of synchronous generators (SG), which can improve the frequency response of a microgrid. Unlike synchronous generators whose inertia and damping are restricted by the physical characteristics of the SG, VSG parameters can be more flexibly controlled to adapt to different disturbances. This paper therefore proposes a fuzzy logic controller designed to adaptively set the parameters of the VSG during a frequency event to ensure an improved frequency nadir and rate of change of frequency (ROCOF) response. The proposed control method is implemented and tested on the power inverter for the battery energy storage system of the Banshee Microgrid Feeder 2 test case system using MATLAB/SIMULINK. The effectiveness of the adaptive control scheme is validated by comparing its performance with a constant parameter VSG, a virtual inertia only fuzzy controller, and an inertial-less inverter control

    Virtual Inertia Scheduling (VIS) for Real-Time Economic Dispatch of IBRs-Penetrated Power Systems

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    A New Concept Called Virtual Inertia Scheduling (VIS) is Proposed to Efficiently Handle the Increasing Penetration of Inverter-Based Resources (IBRs) in Power Systems. VIS is an Inertia Management Framework that Targets Security-Constrained and Economy-Oriented Inertia Scheduling and Generation Dispatch with a Large Scale of Renewable Generations. Specifically, It Determines the Appropriate Power Setting Points and Reserved Capacities of Synchronous Generators and IBRs, as Well as the Control Modes and Control Parameters of IBRs to Provide Secure and Cost-Effective Inertia Support. First, a Uniform System Model is Employed to Quantify the Frequency Dynamics of the IBRs-Penetrated Power Systems after Disturbances. Leveraging This Model, the s-Domain and Time-Domain Analytical Responses of IBRs with Inertia Support Capability Are Derived. Then, VIS-Based Real-Time Economic Dispatch (VIS-RTED) is Formulated to Minimize Generation and Reserve Costs, with Full Consideration of Dynamic Frequency Constraints and Derived Inertia Support Reserve Constraints. the Virtual Inertia and Damping of IBRs Are Formulated as Decision Variables. a Deep Learning-Assisted Linearization Approach is Further Employed to Address the Non-Linearity of Dynamic Constraints. Finally, VIS-RTED is Demonstrated on a Two-Machine System and a Modified IEEE 39-Bus System. a Full-Order Time-Domain Simulation is Performed to Verify the Scheduling Results and Ensure their Feasibility

    Decentralized and Coordinated Vf Control for Islanded Microgrids Considering DER Inadequacy and Demand Control

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    This paper proposes a decentralized and coordinated voltage and frequency (Vf) control framework for islanded microgrids, with full consideration of the limited capacity of distributed energy resources (DERs) and Vf dependent load. First, the concept of DER inadequacy is illustrated with the challenges it poses. Then, a decentralized and coordinated control framework is proposed to regulate the output of inverter based generations and reallocate limited DER capacity for Vf control. The control framework is composed of a power regulator and a Vf regulator, which generates the supplementary signals for the primary controller. The power regulator regulates the output of grid forming inverters according to the real time capacity constraints of DERs, while the Vf regulator improves the Vf deviation by leveraging the load sensitivity to Vf. Next, the static feasibility and small signal stability of the proposed method are rigorously proven through mathematical formulation and eigenvalue analysis. Finally, a MATLAB Simulink simulation demonstrates the functionalities of the control framework. A few goals are fulfilled within the decentralized and coordinated framework, such as making the best use of limited DERs capacity, enhancing the DC side stability of inverter based generations, and reducing involuntary load shedding

    Virtual Synchronous Generator Control Using Twin Delayed Deep Deterministic Policy Gradient Method

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    This paper presents a data-driven approach that adaptively tunes the parameters of a virtual synchronous generator to achieve optimal frequency response against disturbances. In the proposed approach, the control variables, namely, the virtual moment of inertia and damping factor, are transformed into actions of a reinforcement learning agent. Different from the state-of-the-art methods, the proposed study introduces the settling time parameter as one of the observations in addition to the frequency and rate of change of frequency (RoCoF). In the reward function, preset indices are considered to simultaneously ensure bounded frequency deviation, low RoCoF, fast response, and quick settling time. To maximize the reward, this study employs the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. TD3 has an exceptional capacity for learning optimal policies and is free of overestimation bias, which may lead to suboptimal policies. Finally, numerical validation in MATLAB/Simulink and real-time simulation using RTDS confirm the superiority of the proposed method over other adaptive tuning methods
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