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1. Application of a multi-port solid state transformer for volt-VAR control in distribution systems

   https://doi.org/10.1109/PESGM.2017.8274365  

   Rashidi, Mohammad ; Bani-Ahmed, Abedalsalam ; Nasiri, Adel ( July 2017 , 2017 IEEE Power & Energy Society General Meeting)

   Distribution systems need significant voltage support with growing penetration of distributed generations especially intermittent renewable energy resources and smart loads. This paper introduces the application of the Multi-Port Solid State Transformer (MPSST) as an effective tool to support grid voltage at distribution level while integrating distributed energy resources. The solid state transformer replaces the conventional transformer between two voltage zones of distribution systems. Matlab/Simulink environment is used to simulate the IEEE 14 bus test system with an MPSST as a case study. The simulation results prove the effectiveness of the MPSST supporting the distribution system at local level in a fast and efficient manner in response to disturbances caused by load variations. 

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2. Learning Control for Voltage and Frequency Regulation of an Infinite Bus System

   https://doi.org/10.1109/ICSTCC50638.2020.9259638  

   Duffaut Espinosa, Luis A. ; Gray, W. Steven ; Venkatesh, G. S. ( October 2020 , Proc. 24st International Conference on System Theory, Control and Computing)

   null (Ed.)

   A new learning methodology in terms of a discretization of a so-called Chen-Fliess series of a control affine nonlinear system was recently proposed, in part, for the purpose of systematically including system structure and expert knowledge into control strategies. The main objective of this paper is to appropriately embed this learning unit as a supporting predictive controller for power dynamical systems. In particular, an infinite bus system is used for the prototype design of a smart and active control policy to regulate voltage and frequency. It is demonstrated by simulation how a controller employing a Chen-Fliess learning unit can recover from a fault and address modeling mismatch. 

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3. The University of Michigan Implements a Hub-and-Spoke Design to Accommodate Social Distancing in the Campus Bus System Under COVID-19 Restrictions

   https://doi.org/10.1287/inte.2022.1131  

   Chen, Gongyu ; Fei, Xinyu ; Jia, Huiwen ; Yu, Xian ; Shen, Siqian ( November 2022 , INFORMS Journal on Applied Analytics)

   The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools and communities during the pandemic, requiring policy makers to ensure both safety and operational feasibility. In this paper, we develop mixed-integer programming models and simulation tools to redesign routes and bus schedules for operating a real university campus bus system during the COVID-19 pandemic. We propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and bus stops to be used in the new routes. To reduce disease transmission via expiratory aerosol, we design new bus routes that are shorter than 15 minutes to travel and operate using at most 50% seat capacity and the same number of buses before the pandemic. We sample a variety of scenarios that cover variations of peak demand, social distancing requirements, and bus breakdowns to demonstrate the system resiliency of the new routes and schedules via simulation. The new bus routes were implemented and used during the academic year 2020–2021 to ensure social distancing and short travel time. Our approach can be generalized to redesign public transit systems with a social distancing requirement to reduce passengers’ infection risk. History: This paper was refereed. This article has been selected for inclusion in the Special Issue on Analytics Remedies to COVID-19. Funding: This work was supported by the National Science Foundation [Grant CMMI-2041745] and the University of Michigan, College of Engineering. 

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4. A Test System for Transmission Expansion Planning Studies

   https://doi.org/10.3390/electronics13030664  

   Dhamala, Bhuban ; Ghassemi, Mona ( February 2024 , Electronics)

   This paper introduces a 17-bus 500 kV test system intended for transmission expansion planning (TEP) studies. The overhead lines used in the system are based on an actual 500 kV transmission line geometry. Although several test systems have been developed for various forms of power system analysis, few are specifically tailored for TEP studies at the transmission voltage level, as opposed to the distribution voltage level. Current test systems for TEP studies are limited to single loading conditions only for normal operating conditions, and the majority of these systems are intertwined with issues related to the energy market or devised specifically for integrating new generations and loads into the existing power systems. However, ensuring a test system satisfies both voltage drop and line loading criteria during both normal and all single contingency operations is crucial in TEP studies, and addressing these issues under contingency conditions poses notable challenges. Moreover, practical TEP scenarios involve varied loadings, including peak load and dominant loading (60% of peak load) scenarios, while the existing test systems are configured solely for single loading conditions. To address these technical gaps, this paper introduces the 17-bus test system operating at a transmission voltage level of 500 kV, meeting technical requirements under normal and all single contingency operations for both peak load and dominant load scenarios. Detailed specifications of the proposed test system and load flow analysis at both normal and contingency conditions for different loading conditions are presented. This test system serves as an invaluable resource for TEP studies. 

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5. Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow

   https://doi.org/10.1109/LCSYS.2023.3289435  

   Feng, Jie ; Cui, Wenqi ; Cortés, Jorge ; Shi, Yuanyuan ( June 2023 , IEEE Control Systems Letters)

   Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their real-world deployment. This letter constructs a decentralized RL-based controller for inverter-based real-time voltage control in distribution systems. It features two components: a transient control policy and a steady-state performance optimizer. The transient policy is parameterized as a neural network, and the steady-state optimizer represents the gradient of the long-term operating cost function. The two parts are synthesized through a safe gradient flow framework, which prevents the violation of reactive power capacity constraints. We prove that if the output of the transient controller is bounded and monotonically decreasing with respect to its input, then the closed-loop system is asymptotically stable and converges to the optimal steady-state solution. We demonstrate the effectiveness of our method by conducting experiments with IEEE 13-bus and 123-bus distribution system test feeders. 

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