Carlos Fernández-Peruchena

Kuwait is a country with great potential for concentrated solar power (CSP) due to its high solar resource. The current work analyzes the potential of the application of CSP technology in the country and its particularities. Specifically, the current work examines a CSP plant with parabolic trough collector (PTC) technology and how this technology is affected in its performance depending on the location (inland or coastal) and the refrigeration system (wet, dry, or seawater cooled). The modeling methodology is based on a two-step approach: first, the Rankine steam power cycle is modeled in detail in a state-of-the-art steady-state simulation environment, obtaining the performance of the power cycle for each cooling technology; second, the power cycle performance maps are integrated into a detailed transient system-level simulation model of the complete CSP plant in order to perform annual yield simulations for the different representative locations of the climate of Kuwait. These results provide valuable information on both the potential of this technology for its more immediate application such as electricity generation and other future applications such as the desalination of seawater where the location must necessarily be coastal.

The principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) have been put forward to guide optimal sharing of data. The potential for industrial and social innovation is vast. Domain-specific metadata standards are crucial in this context, but are widely missing in the energy sector. This report provides a collaborative response from the low carbon energy research community for addressing the necessity of advancing FAIR metadata standards. We review and test existing metadata practices in the domain based on a series of community workshops. We reflect the perspectives of energy data stakeholders. The outcome is reported in terms of challenges and elicits recommendations for advancing FAIR metadata standards in the energy domain across a broad spectrum of stakeholders.

Industrial activity concerned with the profitability and safety of investments can be supported and promoted by research through the creation of new mathematical modeling approaches, and the quantification and mitigation of uncertainties. In recent years there has been increasing interest in the adoption of probabilistic approaches to assess sources of uncertainty in solar energy systems to estimate their feasibility, considering yield estimates, investments, operation and maintenance costs, and solar resource. In this context, the synthetic solar irradiance data set approach emerges as a promising tool to emulate the variability inherent to the solar resource in confident designs and feasibility analyses of these systems. Chapter 5 deals with the requirements of the industry with respect to synthetic solar data, and how such requirements are currently addressed during the main stages of development of solar projects. We recap methods for benchmarking the success of generated synthetic irradiance, reviewing statistical indicators for that purpose. We discuss and compare the use of single annual and multiple synthetic annual data sets of solar irradiance in the first stages of solar projects, and present their uses in a case study application in a Concentrating Solar Power (CSP) plant with a similar configuration to a well-known operational Parabolic Trough (PT) plant located in Spain.

The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the adequate solar plant design and reducing the uncertainty of its yield estimates. This work presents a procedure for improving the accuracy of modeled solar irradiance series through site-adaptation with coincident ground-based measurements relying on the use of a regression preprocessing followed by an empirical quantile mapping (eQM) correction. It was tested at nine sites in a wide range of latitudes and climates, resulting in significant improvements of statistical indicators of dispersion, distribution similarity and overall performance: relative bias is reduced on average from −1.8% and −2.3% to 0.1% and 0.3% for GHI and DNI, respectively; relative root mean square deviation is reduced on average from 17.9% and 34.9% to 14.6% and 29.8% for GHI and DNI, respectively; the distribution similarity is also improved after the site-adaptation (KSI is 3.5 and 3.9 times lower for GHI and DNI at hourly scale, respectively). The methodology is freely available as supplementary material and downloadable as R-package from SiteAdapt.