The news: In the biggest mass-market AI launch yet, Google is rolling out Gemini, its family of large language models, across almost all its products, from Android to the iOS Google app to Gmail to Docs and more. A new subscription plan will also give users access to Gemini Ultra, the most powerful version of the model, for the first time. Why it matters: ChatGPT, released by Microsoft-backed OpenAI just 14 months ago, changed people's expectations of what computers could do. Google has been racing to catch up ever since and unveiled its Gemini family of models in December. By baking Gemini into its ubiquitous tools, it will be hoping to make up any lost ground, and even overtake its rival.

Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and facilitating their participation in wholesale energy markets. These VPP deployments have been fueled by the Federal Energy Regulatory Commission's Order 2222, which makes DERs and VPPs competitive across market segments. However, the diversity and decentralized nature of DERs present significant challenges to the scalable coordination of VPP assets. To address efficiency and speed bottlenecks, this paper presents a novel machine learning-assisted distributed optimization to coordinate VPP assets. Our method, named LOOP-MAC(Learning to Optimize the Optimization Process for Multi-agent Coordination), adopts a multi-agent coordination perspective where each VPP agent manages multiple DERs and utilizes neural network approximators to expedite the solution search. The LOOP-MAC method employs a gauge map to guarantee strict compliance with local constraints, effectively reducing the need for additional post-processing steps. Our results highlight the advantages of LOOP-MAC, showcasing accelerated solution times per iteration and significantly reduced convergence times. The LOOP-MAC method outperforms conventional centralized and distributed optimization methods in optimization tasks that require repetitive and sequential execution.

Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced predictive control techniques under uncertainty to ensure long-term economics and decarbonization. In this paper, we propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep learning-based forecasting and stochastic optimization, where these two stages are connected by the uncertainty estimation at multiple temporal resolutions; (ii) An efficient online data augmentation scheme, jointly involving model pre-training and online fine-tuning stages. In this way, the proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process, and finally to realize an optimal and robust dispatch solution. The proposed framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain. In addition, comprehensive experiments are conducted to interpret its effectiveness in the real-life scenario of smart building energy management.

Artificial Intelligence is on everyone's lips right now. It is the fastest growing branch of the high-tech industry. The German government sees AI as a key strategy for mastering some of the greatest challenges of our time, such as climate change and pollution. It is difficult to establish a clear differentiation of Artificial Intelligence or even a precise definition. AI is often used in connection or sometimes even synonymous with the terms machine learning, big data, or deep learning.

Around the world, and particularly in Australia, energy traders are trying to get their minds, and their algorithms, around the complexities of trading in variable wind and solar projects and super-fast battery storage installations. Maybe they should give up now, and hand it over to artificial intelligence. US-based software-as-a-service platform provider AMS says automated trading systems for batteries and renewable energy projects using deep learning and artificial intelligence can out-compete the best human traders, by around a factor of five. With the deployment of large-scale energy storage systems occurring at an ever-increasing rate, this is critical – not just for the ability to make money out of the markets, but also for the ongoing operation of the National Electricity Market itself. Traditional generators only need to maximise their generation during periods of sufficiently high energy prices.

The U.S. solar revolution has been a terrific boon to customer choice, the economy and climate policy planning. But solar panels alone can't achieve the full value of solar generation or the aggressive goals of greenhouse gas reductions. Moreover, solar developers face a wave of changes that is challenging their continued growth. Energy markets are shifting, supply chains are becoming more competitive, electric and solar rates are changing and customers' interest in controlling their energy destiny is increasing. As a result, the economics of distributed solar projects are getting skinnier and riskier for the solar developer.