Data Analytics and Machine Learning to Enhance the Operational Visibility and Situation Awareness of Smart Grid High Penetration Photovoltaic Systems

Electric utilities have limited operational visibility and situation awareness over grid-tied distributed photovoltaic systems (PV). This will pose a risk to grid stability when the PV penetration into a given feeder exceeds 60% of its peak or minimum daytime load. Third-party service providers offer only real-time monitoring but not accurate insights into system performance and prediction of productions. PV systems also increase the attack surface of distribution networks since they are not under the direct supervision and control of the utility security analysts. Six key objectives were successfully achieved to enhance PV operational visibility and situation awareness: (1) conceptual cybersecurity frameworks for PV situation awareness at device, communications, applications, and cognitive levels; (2) a unique combinatorial approach using LASSO-Elastic Net regularizations and multilayer perceptron for PV generation forecasting; (3) applying a fixed-point primal dual log-barrier interior point method to expedite AC optimal power flow convergence; (4) adapting big data standards and capability maturity models to PV systems; (5) using K-nearest neighbors and random forests to impute missing values in PV big data; and (6) a hybrid data-model method that takes PV system deration factors and historical data to estimate generation and evaluate system performance using advanced metrics. These objectives were validated on three real-world case studies comprising grid-tied commercial PV systems. The results and conclusions show that the proposed imputation approach improved the accuracy by 91%, the estimation method performed better by 75% and 10% for two PV systems, and the use of the proposed forecasting model improved the generalization performance and reduced the likelihood of overfitting. The application of primal dual log-barrier interior point method improved the convergence of AC optimal power flow by 0.7 and 0.6 times that of the currently used deterministic models. Through the use of advanced performance metrics, it is shown how PV systems of different nameplate capacities installed at different geographical locations can be directly evaluated and compared over both instantaneous as well as extended periods of time. The results of this dissertation will be of particular use to multiple stakeholders of the PV domain including, but not limited to, the utility network and security operation centers, standards working groups, utility equipment, and service providers, data consultants, system integrator, regulators and public service commissions, government bodies, and end-consumers

Performance evaluation of optimal photovoltaic-electrolyzer system with the purpose of maximum Hydrogen storage

Power electronics-based electrolyzer systems are prevalently in current use. This paper proposes the more recently employed directly coupled photovoltaic (PV) electrolyzer systems. Equipped with accurate electrical models of the advanced alkaline electrolyzer, PV system and Hydrogen storage tank simulated using MATLAB, the system\u27s performance for a full week is analyzed using Miami, Florida\u27s meteorological data. A multi-level Genetic Algorithm (GA)-based optimization facilitates maximum hydrogen production, minimum excess power generation, and minimum energy transfer loss. The crucial effect of temperature on the overall system performance is also accounted for by optimizing this parameter using GA, maintaining operating conditions close to the Maximum Power Point (MPP) of the PV array. The results of the analysis have been documented to show that the optimal system for a 10 kW electrolyzer can produce, on an average, Hydrogen of 0.0176 mol/s, when the system is operating with 6.3% power loss and 2.4% power transfer loss

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design

Survey on synchrophasor data quality and cybersecurity challenges, and evaluation of their interdependencies

Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of smart grid. With their widespread implementation, significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems, when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks. This paper serves as a starting point for researchers entering the fields of synchrophasor data analytics and security

A Survey of Protocol-Level Challenges and Solutions for Distributed Energy Resource Cyber-Physical Security

The increasing proliferation of distributed energy resources (DERs) on the smart grid has made distributed solar and wind two key contributors to the expanding attack surface of the network; however, there is a lack of proper understanding and enforcement of DER communications security requirements. With vendors employing proprietary methods to mitigate hosts of attacks, the literature currently lacks a clear organization of the protocol-level vulnerabilities, attacks, and solutions mapped to each layer of the logical model such as the OSI stack. To bridge this gap and pave the way for future research by the authors in determining key DER security requirements, this paper conducts a comprehensive review of the key vulnerabilities, attacks, and potential solutions for solar and wind DERs at the protocol level. In doing so, this paper serves as a starting point for utilities, vendors, aggregators, and other industry stakeholders to develop a clear understanding of the DER security challenges and solutions, which are key precursors to comprehending security requirements

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

: Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resilienc

Case study on the effects of partial solar eclipse on distributed PV systems and management areas

Photovoltaic (PV) systems are weather-dependent. A solar eclipse causes significant changes in these parameters, thereby impacting PV generation profile, performance, and power quality of larger grid, where they connect to. This study presents a case study to evaluate the impacts of the solar eclipse of 21 August 2017, on two real-world grid-tied PV systems (1.4 MW and 355 kW) in Miami and Daytona, Florida, the feeders they are connected to, and the management areas they belong to. Four types of analyses are conducted to obtain a comprehensive picture of the impacts using 1 min PV generation data, hourly weather data, real feeder parameters, and daily reliability data. These analyses include: individual PV system performance measurement using power performance index; power quality analysis at the point of interconnection; a study on the operation of voltage regulating devices on the feeders during eclipse peak using an IEEE 8500 test case distribution feeder; and reliability study involving a multilayer perceptron framework for forecasting system reliability of the management areas. Results from this study provide a unique insight into how solar eclipses impact the behaviour of PV systems and the grid, which would be of concern to electric utilities in future high penetration scenarios

Optimal Operation of Combined Photovoltaic Electrolyzer Systems

In this study, the design and simulation of a combination of a photovoltaic (PV) array with an alkaline electrolyzer is performed to maximize the production of hydrogen as a reliable power resource. Detailed electrical model of PV system, as long as thermal and electrochemical model of electrolyzer is used. Since an electrolyzer is a non-linear load, its coupling with PV systems to get the best power transfer is very important. Solar irradiation calculations were done for the region of Miami (FL, USA), giving an optimal surface slope of 25.7° for the PV array. The size of the PV array is optimized, considering maximum hydrogen production and minimum excess power production in a diurnal operation of a system using the imperialistic competitive algorithm (ICA). The results show that for a 10 kW alkaline electrolyzer, a PV array with a nominal power of 12.3 kW The results show that 12.3 kW photvoltaic system can be utilized for supplying a 10 kW electrolyzer. Hydrogen production and Faraday efficiency of the system are 697.21 mol and 0.3905 mol, respectively