For the third time in a decade, solar energy pricing records are tumbling in the Persian Gulf. As each previous wave of new records was met with incredulity, only for these prices to become the new normal around the world within a few years, it would be unwise to once again dismiss low prices as unrepresentative outliers. In this study, we show how local conditions and global macroeconomic factors have conspired to bring solar energy into a new regime of extreme affordability in the region and argue that the Gulf market, especially the United Arab Emirates and Saudi Arabia, represents the leading edge of the global learning curve and therefore offers a window into the likely near future of large‐scale photovoltaics around the world. As this future increasingly appears to be one of previously unimaginably cheap energy, we conclude with a discussion of the ways in which the region and the world can seek to meet the opportunities and challenges of the coming renewable revolution.

Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available.

As the scaling of silicon PV cells and module manufacturing has driven solar energy penetration up and costs down, concentrator photovoltaic technologies, originally conceived as a cost-saving measure, have largely been left behind. The loss of market share by CPV is being locked in even as solar energy development encounters significant obstacles related to space constraints in many parts of the world. The inherently higher collection efficiency enabled by the use of concentrators could substantially alleviate these challenges, but the revival of CPV for this purpose requires substantial reinvention of the technology to actually capture the theoretically possible efficiency gains, and to do so at market-friendly costs. This article will discuss recent progress in key areas central to this reinvention, including miniaturization of cells and optics to produce compact, lightweight “micro-CPV” systems; hybridization of CPV with thermal, illumination and other applications to make use of unused energy streams such as diffuse light and waste heat; and the integration of sun-tracking into the CPV module architecture to enable greater light collection and more flexible deployment, including integration into built structures. Applications showing particular promise include thermal applications such as water heating, industrial processes and desalination; agricultural photovoltaics; building-integrated photovoltaics with dynamic daylighting capabilities; and chemical processes including photocatalysis and hydrogen production. By appropriately tailoring systems to the available solar resource and local energy demand, we demonstrate how CPV can finally achieve real-world efficiencies, or solar resource utilization factors, far higher than those of standard silicon-based PV systems. This makes the argument for sustained development of novel CPV designs that can be applied to the real-world settings where this efficiency boost will be most beneficial.

While concentrator photovoltaics have long promised high power densities compared to standard photovoltaics, in practice, the requirement of sun tracking and the loss of the diffuse component of the solar resource have prevented this from being realized, especially in space-constrained settings such as building integration where it would have most value. Schemes for tracking integration, in which sun-tracking capability is incorporated into the CPV module, rather than being supplied by an external tracker, have thus far failed to change this state of affairs, due to the limited tracking range of practical integrated tracking systems, and the higher diffuse fraction on a fixed-tilt as opposed to a sun-oriented surface. Recently proposed systems promise to finally resolve this situation by combining tracking integration (TI) with partial transparency of the CPV module, which allows the module to simultaneously collect sunlight for illumination without degrading electricity production. This concept, which is currently undergoing commercialization, has substantial application potential in building-integrated and agricultural settings. In order to adequately evaluate the technical and economic potential of these technologies, a straightforward method for estimating their performance is required. Standard photovoltaic modeling tools are of limited value for this emerging and unique class of technologies, although significant development is ongoing. In this manuscript, we present a simple, semi- empirical method for performance modeling of semi-transparent tracking-integrated concentrator photovoltaics based on the outputs of a standard photovoltaic system model and experimental characterizations of actual modules. Optical and electrical and performances are considered in an easily-implemented performance model that avoids resorting to detailed module-level simulations. Based on the model, the economic viability of TI-CPVs is discussed. We have previously used this modeling approach to evaluate module performance in several target applications, which will be summarized, and we furthermore discuss ongoing work to increase the rigor and precision of the modeling approach while retaining simplicity of use.

In this article we discuss an emerging concept for non-mechanical solar tracking that can have a significant impact for the design of next generation concentrator photovoltaics systems. Based on the modification of the optical properties of the concentrator elements instead of their mechanical rearrangement, self-tracking concentrators, with recently demonstrated prototypes, could make the mechanical trackers redundant expanding the scope of application of CPV systems. We propose here a new approach to a reactive-tracking system, analyze its underlying physics and discuss initial experimental and simulation results towards the development of a prototype.