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The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

The integration of variable distributed generations (DGs) and loads in microgrids (MGs) has made the reliance on communication systems inevitable for information exchange in both control and protection architectures to enhance the overall system reliability, resiliency and sustainability. This communication backbone in turn also exposes MGs to potential malicious cyber attacks. To study these vulnerabilities and impacts of various cyber attacks, testbeds play a crucial role in managing their complexity. This research work presents a detailed study of the development of a real-time co-simulation testbed for inverter-based MGs. It consists of a OP5700 real-time simulator, which is used to emulate both the physical and cyber layer of an AC MG in real time through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human–machine interface (HMI) is used for local/remote monitoring and control. The creation and management of HMI is carried out in ACSELERATOR Diagram Builder SEL-5035 software. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established, which map the interaction among the corresponding nodes of cyber-physical layers and also synchronizes data transmission between the systems. The testbed not only provides a real-time co-simulation environment for the validation of the control and protection algorithms but also extends to the verification of various detection and mitigation algorithms. Moreover, an attack scenario is also presented to demonstrate the ability of the testbed. Finally, challenges and future research directions are recognized and discussed.

Quantum key distribution (QKD) has often been hailed as a reliable technology for secure communication in cyber–physical microgrids. Even though unauthorised key measurements are not possible in QKD, attempts to read them can disturb quantum states leading to mutations in the transmitted value. Further, inaccurate quantum keys can lead to erroneous decryption producing garbage values, destabilising microgrid operation. QKD can also be vulnerable to node‐level manipulations incorporating attack values into measurements before they are encrypted at the communication layer. To address these issues, this paper proposes a secure QKD protocol that can identify errors in keys and/or nodal measurements by observing violations in control dynamics. Additionally, the protocol uses a dynamic adjacency matrix‐based formulation strategy enabling the affected nodes to reconstruct a trustworthy signal and replace it with the attacked signal in a multi‐hop manner. This enables microgrids to perform nominal operations in the presence of adversaries who try to eavesdrop on the system causing an increase in the quantum bit error rate (QBER). We provide several case studies to showcase the robustness of the proposed strategy against eavesdroppers and node manipulations. The results demonstrate that it can resist unwanted observation and attack vectors that manipulate signals before encryption. This paper proposes a secure QKD protocol that can identify errors in keys and nodal measurements by observing violations in control dynamics. Additionally, the protocol uses a dynamic adjacency matrix‐based formulation strategy enabling the affected nodes to reconstruct a trustworthy signal and replace it with the affected signal in a multi‐hop manner. This enables microgrids to perform nominal operations in the presence of adversaries who try to eavesdrop on the system causing an increase in the quantum bit error rate (QBER).

Traditional protection systems for microgrids, which rely on high fault currents and continuous communication, struggle to keep up with the changing dynamics and cybersecurity concerns of decentralized networks. In this study, we introduce a biologically inspired protection system based on neuromorphic principles, where each distributed energy resource (DER) functions as a simple neuron. These neurons process local changes in voltage and current signals, and convert them into spike patterns that represent the severity of disturbances. Just as neurons communicate via synapses in biological systems, we exploit transmission cables to coordinate between DERs, enabling them to share information and respond to faults collectively. Fault detection and circuit breaker activation are driven by a First-To-Spike (FTTS) mechanism, similar to the concept of traveling wave protection, but without needing GPS synchronization or communication links. A key innovation is the ability to use the timing of spikes to locally determine the nature of a fault, offering an intelligent, adaptive response to disturbances. Performance shows tripping latency of 10–58 ms, surpassing conventional relays and even traveling-wave methods (60 ms), while maintaining detection accuracy above 98% and spatial selectivity over 97%, enabling real-time, communication-free, scalable protection for plug-and-play microgrids. Saurabh Prabhakar and colleagues propose a spike-based neuromorphic protection framework for inverter-dominated microgrids using local voltage, current, and power disturbances. The approach enables fast, communication-free fault detection and selective isolation.

This paper reviews and discusses the state of the art of integrated switched-capacitor and integrated inductive power converters and provides a perspective on progress towards the realization of efficient and fully integrated DC–DC power conversion. A comparative assessment has been presented to review the salient features in the utilization of transistor technology between the switched-capacitor and switched inductor converter-based approaches. First, applications that drive the need for integrated switching power converters are introduced, and further implementation issues to be addressed also are discussed. Second, different control and modulation strategies applied to integrated switched-capacitor (voltage conversion ratio control, duty cycle control, switching frequency modulation, Ron modulation, and series low drop out) and inductive converters (pulse width modulation and pulse frequency modulation) are then discussed. Finally, a complete set of integrated power converters are related in terms of their conditions and operation metrics, thereby allowing a categorization to provide the suitability of converter technologies.

In this modern era, cloud computing is not enough to meet today’s intelligent society’s data processing needs, so edge computing has emerged. In contrast to computation in the cloud, it elaborates user proximity and proximity to the data source. To store local, small sized, and processed data on the edges of the network is more effective. The edge paradigm, intended to be a leading computation due to its low latency, also faces many challenges due to computational capabilities and resource availability. Edge computing allows edge devices to release heavy loads and computational operations on the remote server. This allows us to take full advantage of the server-side computing and storage in edge devices. However, the offload of all highly compressed computing operations on a remote server at the same time may become overcrowded, leading to intensive processing delays for many computing operations and unexpectedly elevated power usage. Instead of that, it is possible that spare edge resources may need to be utilized effectively and the access to expensive cloud resources would be restricted. As a result, it is important to investigate the collaborative planning process (scheduling) for the edge servers with a cloud server based on task features, development objectives, and system status. It can assist in performing all the computing functions efficiently and effectively. This paper analyzes and summarizes computing conditions for the edge computing context and classifies the computation of tasks into various edge-cloud computing scenarios. At the end, based on the problem structure, various collaborative planning methods for computational functions are presented.

Federated learning (FL) is a cutting-edge machine learning platform that protects user privacy while enabling collaborative learning across various devices. It is particularly relevant in the current environment when massive volumes of data are generated at the edge of networks by developing technologies like social networking, cloud computing, edge computing, and the Internet of Things. FL reduces the possibility of unauthorized access by third parties by allowing data to stay on local devices, hence mitigating any privacy breaches. The integration of FL in Cloud, Edge, and hybrid Edge-Cloud settings are some of the computing paradigms that this study investigates. We highlight the salient features of FL, go over the main obstacles to its implementation and use, and make recommendations for future study directions. Furthermore, we assess how FL, by facilitating safe and cooperative data sharing among vehicles, can improve service quality in the Internet of Vehicles (IoV). Our study findings are intended to offer practical insights and suggestions that may have an impact on a variety of computing technology research topics.

Diffusion models have recently emerged as a powerful alternative to autoregressive (AR) models for generative modeling, with strong results in continuous domains such as images and video. In the discrete setting of language, however, diffusion models still lag behind AR approaches in both likelihood and sampling efficiency. This thesis investigates the foundations of diffusion language models and asks whether they can be made competitive with traditional AR language models. First, we revisit the role of the forward noising process. Contrary to the prevailing theory that likelihood is invariant to the noise schedule, we prove that this invariance holds only for univariate schedules. We introduce context-adaptive, multivariate noise processes that are learned from data and show that they strictly improve likelihood and sample quality. Second, we propose a simple and unified framework for discrete diffusion that encompasses masked and Uniform-state processes. Within this framework, we derive Rao–Blackwellized variational bounds that are tighter and exhibit lower variance than existing ELBO formulations. Using these bounds, our MDLM models achieve state-of-the-art diffusion perplexities, approach AR perplexities on standard language benchmarks, and surpass AR models on several likelihood evaluations, while enabling efficient conversion of BERT-style encoders into generative models. Third, we establish a discrete–continuous duality that links Uniform-state diffusion in discrete space with Gaussian diffusion in continuous space. This connection allows us to transfer efficient parameterizations, improved training objectives, and distillation-based fast samplers to the discrete domain. Leveraging this, we obtain a twofold speedup in training convergence and two orders of magnitude faster sampling, and we introduce the first few-step discrete diffusion samplers via discrete consistency distillation. Collectively, these contributions show that diffusion language models can substantially close the gap to AR models in both quality and speed, while providing a principled foundation for future advances in discrete diffusion, fast sampling, and controllable language generation.

Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, Spike Talk basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does Spike Talk simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.

Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, Spike Talk basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does Spike Talk simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.