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Due to the growing proportion of wind energy in Great Britain’s energy mix, prolonged periods of low wind power generation have become a significant challenge for decarbonising the electricity system. As such, characterising drought severity and duration is important for ensuring the reliability of the electricity system. Employing concepts derived from hydrology, an extreme value analysis was carried out on wind drought events in Great Britain based on 72 years of ERA5 reanalysis data. The application of pooling procedures was found to be beneficial in robustly identifying wind droughts in cases where the capacity factor is not constantly below an arbitrary threshold. The sequent peak algorithm pooling was found to have particular relevance for electricity systems where energy storage technologies are used to compensate for low wind power generation. The Pearson-III distribution was identified as a suitable model to represent extreme wind droughts, while the Lognormal and Generalised Pareto distributions are also viable alternatives. Sustained periods of low wind power generation with a duration of 14 days were estimated to have a return period of five years and the longest event on record of approximately 26 days is expected to occur once every 100 years. The investigation of these wind droughts from a hydrological perspective has thus shown that they may not be particularly rare occurrences.

Due to the growing proportion of wind energy in Great Britain’s energy mix, prolonged periods of low wind power generation have become a significant challenge for decarbonising the electricity system. As such, characterising drought severity and duration is important for ensuring the reliability of the electricity system. Employing concepts derived from hydrology, an extreme value analysis was carried out on wind drought events in Great Britain based on 72 years of ERA5 reanalysis data. The application of pooling procedures was found to be beneficial in robustly identifying wind droughts in cases where the capacity factor is not constantly below an arbitrary threshold. The sequent peak algorithm pooling was found to have particular relevance for electricity systems where energy storage technologies are used to compensate for low wind power generation. The Pearson-III distribution was identified as a suitable model to represent extreme wind droughts, while the Lognormal and Generalised Pareto distributions are also viable alternatives. Sustained periods of low wind power generation with a duration of 14 days were estimated to have a return period of five years and the longest event on record of approximately 26 days is expected to occur once every 100 years. The investigation of these wind droughts from a hydrological perspective has thus shown that they may not be particularly rare occurrences.

Testing photovoltaic generators performance is complicated. This is due to the influence of a variety of interactive parameters related to the environment such as solar irradiation and temperature in addition to solar cell material (mono-crystalline, poly-crystalline, amorphous and thin films). This paper presents a computer-based instrumentation system for the characterization of the photovoltaic (PV) conversion. It based on a design of a data acquisition system (DAQS) allowing the acquisition and the drawing of the characterization measure of PV modules in real meteorological test conditions.

Testing photovoltaic generators performance is complicated. This is due to the influence of a variety of interactive parameters related to the environment such as solar irradiation and temperature in addition to solar cell material (mono-crystalline, poly-crystalline, amorphous and thin films). This paper presents a computer-based instrumentation system for the characterization of the photovoltaic (PV) conversion. It based on a design of a data acquisition system (DAQS) allowing the acquisition and the drawing of the characterization measure of PV modules in real meteorological test conditions.

Abstract Renewable energy resource inventories show that ocean waves are one of the most energy dense untapped resources in the world, and present an opportunity to generate significant quantities of electricity. To accurately assess the levels of usable energy over long periods, a parametric representation of the raw wave resource is required. This study investigates the variability across four wave energy assessment methods, and two input data sources, to quantify the uncertainties in WEC power production assessments. Two conventional methods were tested: a time-series method and a standard spectral method with a generic spectral shape. Two higher fidelity techniques were additionally studied; an aggregate spectral and a partitioned spectral method. Annual WEC energy production assessments varied between 472 MWh and 543 MWh, a difference of 15%. The partitioned spectral method is shown to minimize prediction uncertainties, yet results in a 14% reduction in annual WEC energy production and increasing power variability. Spectral shape has limited impact on power estimates and energy production assessments, while the numerical wave model data can underestimate annual energy estimates by up to 13%. These uncertainties significantly impact the feasibility of wave energy developments and need to be accounted for as the industry matures.

Abstract Renewable energy resource inventories show that ocean waves are one of the most energy dense untapped resources in the world, and present an opportunity to generate significant quantities of electricity. To accurately assess the levels of usable energy over long periods, a parametric representation of the raw wave resource is required. This study investigates the variability across four wave energy assessment methods, and two input data sources, to quantify the uncertainties in WEC power production assessments. Two conventional methods were tested: a time-series method and a standard spectral method with a generic spectral shape. Two higher fidelity techniques were additionally studied; an aggregate spectral and a partitioned spectral method. Annual WEC energy production assessments varied between 472 MWh and 543 MWh, a difference of 15%. The partitioned spectral method is shown to minimize prediction uncertainties, yet results in a 14% reduction in annual WEC energy production and increasing power variability. Spectral shape has limited impact on power estimates and energy production assessments, while the numerical wave model data can underestimate annual energy estimates by up to 13%. These uncertainties significantly impact the feasibility of wave energy developments and need to be accounted for as the industry matures.

Abstract A method for the monitoring of a wind turbine generator is proposed, based on its power curve and using control charts. Exponentially Weighted Moving Average (EWMA) and Generally Weighted Moving Average (GWMA) control charts are used to detect underperformances such as blade surface erosion. These variations in production amount to a few percent per year. The reference power curve is modeled using the bin method. A validation bench using simulated shifts on data from an MW-class wind turbine generator is used to assess the performance of the proposed method. Results show great potential, with both the EWMA and GWMA control charts able to detect a 1% per year underperformance inside 300 days of operation, based on simulated data. A short example is also given of an application using data involving a real case of underperformance: this example illustrates both the applicability and potential of this method. In this case, a shift of 3.4% in annual energy production over a period of five years could have been detected in time to plan proper maintenance. The rate of false alarms observed is one for every 667 points, which demonstrate the method's robustness.

Abstract A method for the monitoring of a wind turbine generator is proposed, based on its power curve and using control charts. Exponentially Weighted Moving Average (EWMA) and Generally Weighted Moving Average (GWMA) control charts are used to detect underperformances such as blade surface erosion. These variations in production amount to a few percent per year. The reference power curve is modeled using the bin method. A validation bench using simulated shifts on data from an MW-class wind turbine generator is used to assess the performance of the proposed method. Results show great potential, with both the EWMA and GWMA control charts able to detect a 1% per year underperformance inside 300 days of operation, based on simulated data. A short example is also given of an application using data involving a real case of underperformance: this example illustrates both the applicability and potential of this method. In this case, a shift of 3.4% in annual energy production over a period of five years could have been detected in time to plan proper maintenance. The rate of false alarms observed is one for every 667 points, which demonstrate the method's robustness.

Today, the use of renewable energies in buildings represents one of the main ways to reach a sustainable world. Whilst present buildings are still often energivorous systems, in the near future they will have to be converted to (or replaced by) zero energy buildings, also capable to export green energy (produced on-site by renewables) towards other buildings and/or users. This review article focuses on a selection of research papers, presented at the 16th International Conference on Building Simulation (BS 2019), regarding renewable energy applications, energy saving and comfort techniques for buildings. BS 2019 conference was organized in collaboration with the International Building Performance Simulation Association (IBPSA) and it was held at the Angelicum Congress Centre (San Tommaso d’Aquino Pontifex University) in Rome, Italy, during September 2-4, 2019. The conference was attended by 912 researchers and experts, with 660 presented research papers. The above-mentioned selection of papers is included in a dedicated Special Issue of the Renewable Energy - An International Journal (RENE), titled “Renewable energies: simulation tools and applications”. Reported studies are mostly dedicated to models, simulations, and optimization procedures of renewable energy devices. Specifically, photovoltaic systems, building integrated photovoltaic collectors, hybrid photovoltaic/thermal systems, solar thermal collectors as well as other energy efficiency tools are analysed through different simulation approaches and suitable optimization procedures. Attention is also paid to specific case studies related to innovative combinations of renewable energy devices and innovative envelope materials in different building typologies and weather zones. In some papers, solar energy is exploited for space heating and cooling purposes, while in other articles renewables or other energy tools are studied to achieve comfort targets, low grid dependencies, smart building/communities, and mainly the zero energy building goal.