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The modern bulk power system operation is complex and dynamic, with rapidly increasing inverter-based resources and active distribution systems. Therefore, high-speed monitoring is required to operate the power system reliably and efficiently. Transmission network topology processing (TNTP) is vital in power system control. Today’s TNTP is based on supervisory control and data acquisition (SCADA) system monitoring of relay signals (SMRS). Due to the slow data communication rate, SMRS cannot efficiently support the modern bulk power system’s energy management system (EMS) functions. In this study, a physics-based hierarchical TNTP (H-TNTP) approach based solely on node voltages and branch currents measurements is proposed utilizing artificial intelligence algorithms. H-TNTP includes the identification of substation configuration. The reliability of the H-TNTP is guaranteed by the design with inherent verification. If required, H-TNTP is capable of operating concurrently with the TNTP-SMRS. A power system with solar photovoltaic (PV) plants is utilized as a test system to illustrate the proposed H-TNTP approach. Results indicate that H-TNTP is fast with synchrophasor measurements. Furthermore, to demonstrate the application of the reliable and fast TNTP approach in EMSs, fast automatic generation control (AGC) during contingencies is studied. Typical results show that fast reconfiguration of AGC modes and dispatch factors leads to better frequency regulation. 

Amidst the challenges posed by the high penetration of distributed energy resources (DERs), particularly a number of distributed photovoltaic plants (DPVs), in modern electric power distribution systems (MEPDS), the integration of new technologies and frameworks become crucial for addressing operation, management, and planning challenges. Situational awareness (SA) and situational intelligence (SI) over multi-time scales is essential for enhanced and reliable PV power generation in MEPDS. In this paper, data-driven digital twins (DTs) are developed using AI paradigms to develop actual and/or virtual models of DPVs, These DTs are then applied for estimating and forecasting the power outputs of physical and virtual PV plants. Virtual weather stations are used to estimate solar irradiance and temperature at user-selected locations in a localized region, using inferences from physical weather stations. Three case studies are examined based on data availability: physical PV plant, hybrid PV plants, and virtual PV plants, generating realtime estimations and short-term forecasts of PV power production that can support distribution system studies and decision-making. 

Renewable energy generation sources (RESs) are gaining increased popularity due to global efforts to reduce carbon emissions and mitigate effects of climate change. Planning and managing increasing levels of RESs, specifically solar photovoltaic (PV) generation sources is becoming increasingly challenging. Estimations of solar PV power generations provide situational awareness in distribution system operations. A digital twin (DT) can replicate PV plant behaviors and characteristics in a virtual platform, providing realistic solar PV estimations. Furthermore, neural networks, a popular paradigm of artificial intelligence may be used to adequately learn and replicate the relationship between input and output variables for data-driven DTs (DD-DTs). In this paper, DD-DTs are developed for Clemson University’s 1 MW solar PV plant located in South Carolina, USA to perform realistic solar PV power estimations. The DD-DTs are implemented utilizing multilayer perceptron (MLP) and Elman neural networks. Typical practical results for two DD-DT architectures are presented and validated.