The photovoltaics (PV) technology landscape is evolving rapidly. To predict the potential and scalability of emerging PV technologies, a global understanding of these systems' performance is essential. Traditionally, experimental and computational studies at large national research facilities have focused on PV performance in specific regional climates. However, synthesizing these regional studies to understand the worldwide performance potential has proven difficult. Given the expense of obtaining experimental data, the challenge of coordinating experiments at national labs across a politically-divided world, and the data-privacy concerns of large commercial operators, however, a fundamentally different, data-efficient approach is desired. Here, we present a physics-guided machine learning (PGML) scheme to demonstrate that: (a) The world can be divided into a few PV-specific climate zones, called PVZones, illustrating that the relevant meteorological conditions are shared across continents; (b) by exploiting the climatic similarities, high-quality monthly energy yield data from as few as five locations can accurately predict yearly energy yield potential with high spatial resolution and a root mean square error of less than 8 kWhm$^{2}$, and (c) even with noisy, heterogeneous public PV performance data, the global energy yield can be predicted with less than 6% relative error compared to physics-based simulations provided that the dataset is representative. This PGML scheme is agnostic to PV technology and farm topology, making it adaptable to new PV technologies or farm configurations. The results encourage physics-guided, data-driven collaboration among national policymakers and research organizations to build efficient decision support systems for accelerated PV qualification and deployment across the world.

This paper studies the synergy of solar-battery energy storage system (BESS) and develops a viable strategy for the BESS to unlock its economic potential by serving as a backup to reduce solar curtailments while also participating in the electricity market. We model the real-time bidding of the solar-battery system as two Markov decision processes for the solar farm and the BESS, respectively. We develop a novel deep reinforcement learning (DRL) algorithm to solve the problem by leveraging attention mechanism (AC) and multi-grained feature convolution to process DRL input for better bidding decisions. Simulation results demonstrate that our AC-DRL outperforms two optimization-based and one DRL-based benchmarks by generating 23%, 20%, and 11% higher revenue, as well as improving curtailment responses. The excess solar generation can effectively charge the BESS to bid in the market, significantly reducing solar curtailments by 76% and creating synergy for the solar-battery system to be more viable.

A floating solar farm off the coast of Thailand, a $34 million project, will be monitored by Percepto drones. Percepto announced that it has completed a proof-of-concept (POC) with the Electric Generating Authority of Thailand (EGAT) to monitor a 250-acre floating solar farm. The farm is the size of 70 soccer fields and is located 350 m from the nearest shoreline. Percepto's AIM software and drone-in-a-box solution will autonomously perform routine inspections of panels and other equipment to detect anomalies and ensure everything is operating properly. The drones will provide regular operations and maintenance reports, map the location of the panels; and perform inspections of substations, transformers, floating fences and solar floaters, which hold the solar panels above water.

New research from the University of Georgia suggests a novel approach to safeguarding one possible target of a cyberattack – the nation's solar farms. In a study published in IEEE Transactions on Smart Grid, a team in UGA's College of Engineering introduced a sensor system that monitors a key electrical component of solar farms for signs of cyber-intrusion in real time. "A growing concern is that hackers may exploit the converters that connect solar farms with the power grid," said WenZhan Song, the Georgia Power Mickey A. Brown Professor in Engineering and the study's lead investigator. "In modern grid-connected solar farms, power electronics converters can be remotely controlled, but this internet connection also expands the potential for cyberattacks." In general, power electronics use semiconductor switching devices to control and convert electrical power flow from one form to another. This technology has revolutionized modern life by streamlining manufacturing processes, increasing product efficiencies and improving the delivery of reliable power from utilities.

The use of artificial intelligence (AI) is growing in almost every area of business, from banking to transport to healthcare, as the true potential of the technology becomes increasingly recognised. Within our industry, AI has the potential to transform the way solar projects are operated and managed. When aggregated, enormous datasets from thousands of solar farms can help predict output by analysing trends in cloud cover, radiance and more, while more sophisticated imaging and assessment techniques are improving our understanding of module performance and degradation over time. Although still nascent in the solar space, AI approaches will almost certainly change the industry as we know it. Those who embrace the technology early may well gain a competitive advantage.

In a highly competitive market, all energy generators rely on highly accurate predictions of how much electricity they'll be able to make. Australian researchers have figured out a way to improve these predictions for wind and solar farms, using artificial intelligence. The National Energy Market – "the grid" – requires automatic forecasts every five minutes from electricity generators. This ensures that electricity generation meets demand. It can be very costly if those five-minute forecasts prove to be incorrect.

All the sessions from Transform 2021 are available on-demand now. Duke Energy's AI journey began because the utility company had a business problem to solve, Duke Energy chief information officer Bonnie Titone told VentureBeat's head of AI content strategy Hari Sivaraman at the Transform 2021 virtual conference on Thursday. Duke Energy was facing some significant challenges, such as the growing issue of climate change and the need to transition to clean energy in order to reach net zero emissions by 2050. Duke Energy is considered an essential service, as it supplies 25 million people with electricity daily, and everything the utility company does revolves around a culture of safety and reliability. The variables together was a catalyst for exploring AI technologies, Titone said, because whatever the company chose to do, it had to support the clean energy transition, deliver value to customers, and find a way for employees to work and improve safety.

Humanity uses a lot of energy, and while solar power here on Earth is doing a reasonable job of contributing to the power mix, scientists have long hypothesized that solar power gathered from space itself would be an altogether more effective scenario. And now China says it's going to be the first to do exactly that, announcing plans to build a solar power station that will orbit the Earth at 36,000 kilometers. According to China's state-backed Science and Technology Daily, Chinese scientists plan to build and launch small power stations into the stratosphere between 2021 and 2025, with a megawatt-level station planned for 2030 and a gigawatt-level facility before 2050. Without interference from the atmosphere or seasonal and night time loss of sunlight, these space-based solar farms could provide an inexhaustible source of clean energy, with the China Academy of Space Technology Corporation claiming such a set-up could reliably supply 99 percent of time at six times the intensity of solar plants on Earth. There are, of course, numerous challenges associated with this sci-fi-sounding plan.

In his new job as a commercial drone pilot, former Army helicopter pilot Tony Zimlich directs a drone-powered field site inspection of a Pennsylvania solar farm. In his new job as a commercial drone pilot, former Army helicopter pilot Tony Zimlich directs a drone-powered field site inspection of a Pennsylvania solar farm. On a recent sunny afternoon at a solar farm outside Philadelphia, Pa., commercial drone pilots Tony Zimlich and Gunner Goldie are preparing for flight. Dressed in hard hats and matching yellow vests, they run through a series of safety and equipment checks, and survey the surrounding terrain and airspace, before picking up what looks like a pair of oversized video game controllers. Then, with a streak of beeps and whirs, their drone -- about the size of a milk crate -- rises steadily into the sky above.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.