Regression-Based Small Language Models for DER Trust Metric Extraction from Structured and Semi-Structured Data

This paper deals with optimal scheduling of networked microgrids (NMGs) considering resilience constraints. The proposed scheme attempts to mitigate the damaging impacts of electricity interruptions by effectively exploiting NMG capabilities. A three-stage framework is proposed. In Stage 1, the optimal scheduling of NMGs is studied through determining the power transaction between the NMGs and upstream network, the output power of distributed energy resources (DERs), commitment status of conventional DERs as well as demand-side reserves. In Stage 2, the decisions made at Stage 1 are realized considering uncertainties pertaining to renewable generation, market price, power consumption of loads, and unintentional islanding of NMGs from the upstream network and resynchronization. Stage 3 deals with uncertainties of unintentional islanding of each MG from the rest of islanded NMGs and resynchronization. The problem is formulated as a mixed-integer linear programming problem and its effectiveness is assured by simulation studies.

The increasing presence of Distributed Energy Resources (DERs) in distribution grids is posing relevant challenges to all electricity operators, particularly concerning the unreliability and intermittency of distributed generators based on Renewable Energy Sources (RESs). The use of Distributed Energy Storage Systems (DESSs) has been recently proposed to cope with these issues, along with the implementation of advanced Demand Response (DR) programs and of ancillary services within Smart Grids. The test of these advanced (and experimental) mechanisms in an operating distribution grid is not usually feasible, both for economic as well as for technical reasons, including security and safety aspects. Conversely, the test in simulation or in Hardware In the Loop (HIL) environments may be limiting, due to the difficulty in reproducing the typically unpredictable behavior of the power demand patterns of end-users. For this reason, a testing facility for the characterization of DESSs coupled with RES generators has been designed and deployed in the eLUX laboratory of the University of Brescia. The system is formed by a 25 kWh DESS coupled with a 64 kWp photovoltaic power plant. The testing facility has been equipped with an experimental supervisory system which allows the remote monitoring and control of both the power installations. Such a system can be used to test and validate different control approaches, including DR requests and ancillary services, under working conditions similar to the real deployment of DESSs in modern power grids.

Networked microgrids (NMGs) are developing as a viable approach for integrating an expanding number of distributed energy resources (DERs) while improving energy system performance. NMGs, as compared to typical power systems, are constructed of many linked microgrids that can function independently or as part of a more extensive network. This allows NMGs to be more flexible, dependable, and efficient. The present study comprehensively investigates architecture, communication, and cybersecurity issues in NMGs. This comprehensive study examines various aspects related to networked microgrids (NMGs). It explores the architecture of NMGs, including control techniques, protection, standards, and the challenges associated with their adoption. Additionally, it investigates communication in NMGs, focusing on the technologies, protocols, and the impact of communication on the functioning of these systems. Furthermore, this study addresses cybersecurity challenges specific to NMGs, such as diverse cyberattack types, detection and mitigation strategies, and the importance of awareness training. The findings of this study offer valuable insights for NMG researchers and practitioners, emphasizing critical aspects that must be considered to ensure the safe and efficient operation of these systems.

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhancing the system performance in terms of reliability, resilience, flexibility, and energy efficiency. With the penetration of MGs, the communication-based distributed control is playing an increasingly important role in NMGs for coordinating a multitude of heterogeneous and spatially distributed DERs, which feature enhanced efficiency, reliability, resilience, scalability, and privacy-preserving as compared with conventional centralized control. This article aims to provide a comprehensive survey of distributed control and communication strategies in NMGs. We provide thorough discussions and elaborate on: 1) Essential merits of MGs and NMGs, and their practical implementations; 2) Distributed communication network characteristics and specific operation objectives of NMGs; 3) Classifications of distributed control strategies in NMGs and their salient features; 4) Communication reliability issues concerning data timeliness, data availability, and data accuracy, and the development of countermeasures; 5) Advancements in several promising communication and computation technologies and their potential applications in NMGs.

We propose a distributed algorithm for online energy management in networked microgrids with a high penetration of distributed energy resources (DERs). A high penetration of DERs introduces high uncertainty of power generation to the microgrids. In general, the state-of-the-art forecasting for non-dispatchable DERs such as solar energy is not sufficiently accurate, which results in inaccurate energy scheduling. To address the high uncertainty issue in the networked microgrids, we propose an online energy management based on the online alternating direction method of multipliers algorithm with the past power generation information from the DERs. The online algorithm provides less conservative schedule than the robust optimization-based approach. The effectiveness of the proposed algorithm is verified by various numerical examples.

Mixed integer linear programming (MILP)–based distributed energy management for networked microgrids embedded modern distribution systems is proposed. Considering the diverse ownership of microgrids, distributed energy resources (DERs) that interface directly with utilities and responsive loads, an alternating direction method of multipliers–based distributed framework was formulated for the scheduling of networked microgrids embedded modern distribution systems by adjusting nodal price signals iteratively. In addition, to make the formulated optimization problems resolvable through more accessible and popular MILP solvers, different linearisation techniques were employed to transform the nonlinear terms into linear or mixed integer linear formats. The proposed MILP‐based distributed method preserves all participants' autonomy (e.g., microgrids, DERs that interface directly with utilities and responsive loads), while incentivising them to actively participate in the distribution system operation with price signals. The proposed method is validated with results of numerical simulation using a modern distribution system consisting of multiple networked microgrids, DERs that interface directly with utilities, as well as responsive loads.

In the Smart City, the Integrated Renewable Energy System (IRES) is playing a crucial role. Integrating the available renewable energy sources is useful in solving energy supply and demand-related issues. For a stable state of energy supply and energy demand, their proper size is needed to adapt to integrated renewable energy sources in the future. To address technical, economic and sizing problems, different algorithms needed to implement the integrated renewable energy scheme, as suggested by various authors. This paper provides a comprehensive review of various topics related to power generation for Smart City based on Integrated Renewable Energy System (IRES). It discusses in detail issues related to the integration of different energy sources, use of smart grids for integration, methods of IRES sizing using software followed by methods of sizing using artificial intelligence algorithms. This article reviews different AI algorithms that focus on the sizing of integrated renewable energy systems in smart cities.

In collaborative networked microgrids (NMs), distributed energy resources (DERs) utilize intelligent Internet of Things (IoT)-based controllers to coordinately support various smart community functions. Meanwhile, privacy and security issues occur when IoT-based controllers interact with each other. This article presents a cryptography-based, programmable control (crypto-control) scheme to provably preserve the privacy of DERs while ensuring fast, flexible distributed control in NMs. Specifically, it makes the following contributions: 1) a programmable crypto-control-based NMs (PCNMs) architecture, where crypto-controllers are fully virtualized, is devised; 2) a novel dynamic encrypted weight addition (DEWA) approach, which integrates an enhanced partial homomorphic encryption and a secret sharing scheme, is devised to ensure privacy preserving of distributed controls; 3) the DEWA privacy-preserving property is mathematically analyzed; and 4) a real-time DEWA-based PCNMs testbed is deployed by incorporating DEWA, real software-defined networking switches, and IoT devices. The deployable crypto-control scheme is interfaced with and thoroughly verified in a Real-Time Digital Simulator environment, and the experimental results validate the effectiveness, benefits, and superiority of DEWA-based PCNMs. The inherent resilience of the DEWA-based PCNMs is validated by small signal stability analysis.

Modern power grid has a fundamental role in the operation of smart cities. However, high impact low probability extreme events bring severe challenges to the security of urban power grid. With an increasing focus on these threats, the resilience of urban power grid has become a prior topic for a modern smart city. A resilient power grid can resist, adapt to, and timely recover from disruptions. It has four characteristics, namely anticipation, absorption, adaptation, and recovery. This paper aims to systematically investigate the development of resilient power grid for smart city. Firstly, this paper makes a review on the high impact low probability extreme events categories that influence power grid, which can be divided into extreme weather and natural disaster, human-made malicious attacks, and social crisis. Then, resilience evaluation frameworks and quantification metrics are discussed. In addition, various existing resilience enhancement strategies, which are based on microgrids, active distribution networks, integrated and multi energy systems, distributed energy resources and flexible resources, cyber-physical systems, and some resilience enhancement methods, including probabilistic forecasting and analysis, artificial intelligence driven methods, and other cutting-edge technologies are summarized. Finally, this paper presents some further possible directions and developments for urban power grid resilience research, which focus on power-electronized urban distribution network, flexible distributed resource aggregation, cyber-physical-social systems, multi-energy systems, intelligent electrical transportation and artificial intelligence and Big Data technology.

The increasing evolution of the global energy landscape has made the adoption of distributed energy resources (DERs) and the emergence of virtual power plants (VPPs) crucial for the development of sustainable and efficient energy systems. This paper delves into the critical aspect of efficient DER coordination within VPPs, especially tailored to real-time market framework. The enhanced pelican optimization algorithm (enhanced POA), which is used in this paper, enhances the VPP's capacity to adjust to rapidly shifting energy demands and market dynamics. This effort attempts to build a complete framework that ensures economic feasibility while maximising efficiency by utilising the combined potential of diverse DERs, including storage systems and renewable energy sources. The paper compares the proposed resource allocation technique with other evolutionary algorithms and evaluate its performance in several case studies using thorough simulation and analysis. With a 8% decrease in energy expenses when compared to other algorithms, the results offer insightful information about optimizing VPP operations and provide a route toward a more adaptable, sustainable, and robust energy management system.

This paper develops a novel distributed iterative event-triggered control scheme for a master-slave-organized dc microgrid network with limited communication bandwidth. The proposed scheme can synchronize the voltage of multiple distributed energy resources (DER) to their desired value. Moreover, the optimal load sharing for their economic operation (e.g., minimize the total generation cost) can be achieved through a low bandwidth communication network. The designed controllers are fully distributed and only triggered at their own event time, which effectively reduces the frequency of controller updates compared with continuous-time feedback control. Eventually, each DER only requires the local voltage and current measurement from its own and some nearest (but not all) neighbors at given event-triggered time through limited-bandwidth communication links. The Lyapunov technique is employed to derive the event-triggered conditions that guarantee the stability. Furthermore, the lower bound of the interevent intervals is captured by the proposed iterative algorithm to exclude Zeno behaviors. Different cases in MATLAB/SimPowerSystems are investigated and results demonstrate the effectiveness and the performance of the proposed approach.

<abstract> <p>This study proposes a centralized control system for an islanded multivariable minigrid to improve its performance, stability and resilience. The integration of renewable energy sources and distributed energy storage systems into microgrid networks is a growing trend, particularly in remote or islanded areas where centralized grid systems are not available. The proposed control system is designed to be implemented at two levels a high-level control system and a low-level control system. Hence, the high-level control system balances energy resources and demand, makes decisions for effective resource utilization and monitors energy transactions within the minigrid. Real-time data from various sources and advanced algorithms are used to optimize energy management and distribution enabling the integration of renewable energy sources and enhancing the resilience of the minigrid against power outages.</p> <p>Moreover, the low-level control system monitors energy parameters such as voltage, current, frequency and mechanical energy. The control system ensures these parameters remain within the specified range, maintaining system stability and ensuring efficient energy distribution. It also protects the minigrid against power outages improving system reliability and security. Finally, the proposed centralized control system offers a promising solution for improving the performance, stability and resilience of microgrid networks. The system provides real-time monitoring, efficient energy management and distribution, and the integration of renewable energy sources. These results have important implications for the development and deployment of microgrid networks in remote or islanded areas.</p> </abstract>

Smart city concept redefines the urban planning and development of the existing and new cities. It drives on economic, social, and environmental sustainability of a city and attracts citizens, professionals, and corporations to build sustainable living. It portrays a city that is operationally optimal and provides a space for innovation. This is achieved through state of the art physical, institutional, and digital infrastructure. This chapter addresses the challenges of the digital aspect of the smart city. Enterprise systems technology is widely used in the organizations and will be utilized in the Smart city systems conceptualization and implementation. Smart city systems definition has been derived by analyzing the smart city requirements. Enterprise systems technology has been explained and the latest ICT trends have been explored to develop the technological foundation of smart city systems. Finally, we introduce partial least square regression, a structural equation modeling method to explore interrelationships between different interdisciplinary constructs and show its application to studying smart city systems. From the digital perspective of smart city, it may be concluded that connectedness leads to integration and integration leads to dynamism and dynamism leads to smartness and cycle continues to realize the best in class smart city.

This comprehensive exploration delves into the innovative field of smart inverter design, with a particular focus on the role of artificial intelligence (AI) in enhancing the integration of Distributed Energy Resources (DERs) into power grids. The rapid evolution of energy systems and the increasing reliance on renewable energy sources have necessitated the development of more sophisticated technologies for power system management. Smart inverters, at the heart of this transformation, are crucial for integrating DERs like solar panels and wind turbines into existing power grids. The integration of AI into smart inverters represents a significant advancement in managing and optimizing these complex systems. This study demonstrates how these advanced technologies can lead to substantial improvements in inverter reliability, power distribution efficiency, and overall management of renewable energy sources. Realistic models highlight the resilience of smart inverters to fluctuating grid conditions and their ability to maintain system reliability and efficiency. Furthermore, the study critically assesses the current challenges facing this field, such as the need for standardized communication protocols, robust cybersecurity measures against digital threats, and the ongoing requirement for innovation to keep pace with rapid technological advancements. Finally, the exploration forecasts future developments and areas that will further enhance smart inverter technology, emphasizing the essential role of continued research and development in fully realizing AI's potential to transform power grids. The examination concludes by highlighting the promising future of AI in smart inverter design, particularly in contributing to the development of more efficient, reliable, and sustainable energy systems.

Data-driven evaluation for quantifying energy resilience in distribution systems with microgrids and P2P energy trading