Multi-agent-based simulations (MABS) of electric vehicle (EV) home charging ecosystems generate large, complex, and stochastic time-series datasets that capture interactions between households, grid infrastructure, and energy markets. These interactions can lead to unexpected system-level events, such as transformer overloads or consumer dissatisfaction, that are difficult to detect and explain through static post-processing. This paper presents a modular, Python-based dashboard framework, built using Dash by Plotly, that enables efficient, multi-level exploration and root-cause analysis of emergent behavior in MABS outputs. The system features three coordinated views (System Overview, System Analysis, and Consumer Analysis), each offering high-resolution visualizations such as time-series plots, spatial heatmaps, and agent-specific drill-down tools. A case study simulating full EV adoption with smart charging in a Danish residential network demonstrates how the dashboard supports rapid identification and contextual explanation of anomalies, including clustered transformer overloads and time-dependent charging failures. The framework facilitates actionable insight generation for researchers and distribution system operators, and its architecture is adaptable to other distributed energy resources and complex energy systems.

With the rise of distributed energy resources and sector coupling, distributed optimization can be a sensible approach to coordinate decentralized energy resources. Further, district heating, heat pumps, cogeneration, and sharing concepts like local energy communities introduce the potential to optimize heating and electricity output simultaneously. To solve this issue, we tackle the distributed multi-energy scheduling optimization problem, which describes the optimization of distributed energy generators over multiple time steps to reach a specific target schedule. This work describes a novel distributed hybrid algorithm as a solution approach. This approach is based on the heuristics of gossiping and local search and can simultaneously optimize the private objective of the participants and the collective objective, considering multiple energy sectors. We show that the algorithm finds globally near-optimal solutions while protecting the stakeholders' economic goals and the plants' technical properties. Two test cases representing pure electrical and gas-based technologies are evaluated.

The rapid deployment of distributed energy resources (DER) has introduced significant spatio-temporal uncertainties in power grid management, necessitating accurate multilevel forecasting methods. However, existing approaches often produce overly conservative uncertainty intervals at individual spatial units and fail to properly capture uncertainties when aggregating predictions across different spatial scales. This paper presents a novel hierarchical spatio-temporal model based on the conformal prediction framework to address these challenges. Our approach generates circuit-level DER growth predictions and efficiently aggregates them to the substation level while maintaining statistical validity through a tailored non-conformity score. Applied to a decade of DER installation data from a local utility network, our method demonstrates superior performance over existing approaches, particularly in reducing prediction interval widths while maintaining coverage.

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.

Despite their growing popularity, data-driven models of real-world dynamical systems require lots of data. However, due to sensing limitations as well as privacy concerns, this data is not always available, especially in domains such as energy. Pre-trained models using data gathered in similar contexts have shown enormous potential in addressing these concerns: they can improve predictive accuracy at a much lower observational data expense. Theoretically, due to the risk posed by negative transfer, this improvement is however neither uniform for all agents nor is it guaranteed. In this paper, using data from several distributed energy resources, we investigate and report preliminary findings on several key questions in this regard. First, we evaluate the improvement in predictive accuracy due to pre-trained models, both with and without fine-tuning. Subsequently, we consider the question of fairness: do pre-trained models create equal improvements for heterogeneous agents, and how does this translate to downstream utility? Answering these questions can help enable improvements in the creation, fine-tuning, and adoption of such pre-trained models.

Utilidata, an industry leading grid-edge software company, announced today that it is developing a software-defined smart grid chip in collaboration with NVIDIA. The chip will be powered by NVIDIA's AI platform and embedded in smart meters to enhance grid resiliency, integrate distributed energy resources (DERs) -- including solar, storage, and electric vehicles (EVs) -- and accelerate the transition to a decarbonized grid. The U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) will be among the first to test the software-defined smart grid chip as a way to scale and commercialize the lab's Real-Time Optimal Power Flow (RT-OPF) technology, with support from the Solar Energy Technologies Office Technology Commercialization Fund. Originally developed with funding from DOE's Advanced Research Projects – Energy (ARPA-E) program, RT-OPF enables highly localized load control to seamlessly integrate an increasing number of DERs while ensuring stable and efficient grid operations. "To date, the scalability and commercial potential of technologies like RT-OPF have been limited by single-use hardware solutions," said Santosh Veda, Group Manager for Grid Automation and Controls at NREL. "By developing a smart grid chip that can be embedded in one of the most ubiquitous utility assets – the smart meter – this approach will potential enable wider adoption and commercialization of the technology and redefine the role of edge computing for DER integration and resiliency. Enhanced situational awareness and visibility from this approach will greatly benefit both the end customers and the utility."

A moon-scanning method that can automatically classify important lunar features from telescope images could significantly improve the efficiency of selecting sites for exploration. There is more than meets the eye to picking a landing or exploration site on the moon. The visible area of the lunar surface is larger than Russia and is pockmarked by thousands of craters and crisscrossed by canyon-like rilles. The choice of future landing and exploration sites may come down to the most promising prospective locations for construction, minerals or potential energy resources. However, scanning by eye across such a large area, looking for features perhaps a few hundred meters across, is laborious and often inaccurate, which makes it difficult to pick optimal areas for exploration.

Smart grids are part of a growing "smart" phenomenon involving distributed devices that are wirelessly connected and intelligently controlled to automate decisions normally left to people. The Internet of Things (IoT) is the most popular example of this trend, with smart phones, thermostats, fridges, and even cars working in concert to share real-time data and make decisions autonomously. Smart grid technology does the same thing – but for energy. This comprehensive guide explains how smart electrical grids work, why they are important, and how they are helping to revolutionize the electricity landscape – especially as distributed energy sources (DERs) like solar, wind, and battery storage continue to place stress on America's aging power infrastructure. You may also enjoy this brief 30-minute podcast that introduces the challenges of smart grids and highlights some of the benefits of AI to improve energy and utility operations.

The optimal integration of distributed energy resources such as solar, battery storage and smart thermostats becomes an ever-more complex and pressing question. Rahul Kar, general manager and VP for New Energy at AutoGrid Systems looks at the role artificial intelligence can play in smarter energy networks. This article first appeared in Volume 23 of Solar Media's quarterly journal, PV Tech Power, in'Storage & Smart Power', the section of the journal contributed by Energy-Storage.news. The modern electric grid is an engineering marvel and millions depend on it for reliable and on-demand power supply. The grid is becoming greener with the growing retirement of fossil fuel generation and the penetration of renewable energy, energy storage, electric vehicles (EVs), and a variety of other networked distributed energy resources (DERs).

In order to help with an array of challenges facing the digitization of the energy industry, companies, governments and regulatory agencies are looking at ways to make our energy consumption more efficient. One of the key sustainable and reliable solutions is the introduction of the smart grid, which uses a variety of operation and energy measures including smart meters, smart appliances, renewable energy resources and energy efficient resources to provide more data for energy operators, powering better decision-making and resource usage. The vast amount of data captured from operations, whether that be asset performance data, customer data, advanced metering data or geographic information, only continues to grow. Smart grids continuously collect and synthesize huge amounts of data from millions of smart sensors to make timely decisions on how best to allocate energy resources. AI in the energy sector is helping to empower the smart power grid, providing more effective and more profitable power trading and better regulation of power consumption.