/pdf/cost-minimized-hydrogen-from-solar-and-wind-production-and-1o1gzvtt.pdf

Cost minimized hydrogen from solar and wind – Production and supply in the European catchment area

This review critically updates the state of the art on the uncertainties associated with reanalysis data directly used for wind resource assessment. Performances were analysed from 15 (9 global and 6 regional) reanalysis products applied on 322 very heterogeneous locations worldwide. Reanalysis scores were assessed by location type (offshore, coastal, inland and mountainous) and height above the ground (10–300 m). The results confirmed that reanalyses can predict wind resource pattern over time, while they are not similarly able over space. Reanalysis data (particularly by ERA5) are sufficiently reliable on offshore and flat onshore locations. Uncertainties are greater on mountainous and coastal sites, where wind speed and energy yield are significantly under- and over-estimated, respectively. On these locations, the use of higher-resolution regional products should be preferred over global datasets as they are better suited to resolving detailed terrain features as well as roughness and topographical discontinuities. Comparison between older and newer reanalysis versions revealed that the latter do not always outperform the former. In many cases, however, improvements by the newer products were found – rather than in accuracy itself – in pushing a given accuracy towards higher elevations, thus more suitably meeting the needs of modern wind power industry. In perspective, it is desirable that advances in forecasting models will enable current problems in assimilating surface land wind observations by global reanalyses to be overcome. Refinements in global datasets could also positively reflect on regional products. • Comparison of 15 reanalysis products applied on 322 locations within 40 articles. • Wind speed and AEY are quite reliably estimated on offshore and flat onshore sites. • Uncertainties are greater on mountainous and particularly on coastal locations. • Newer products improvements often lie in pushing accuracy towards higher elevations. • Future advances were detected that could be implemented in global reanalysis. 

Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review

Although complementarity achieved by combining multiple renewable energy sources (RES) is an important method to increase shares of RES, it is often overlooked in policy prescriptions supporting an energy transition. Complementarity can be implemented by multiple actors, however there has been little attention to which actors are involved, and their roles. A systematic review was conducted to provide an overview of the state of academic literature on the topic of combinations of multiple RES and the involvement of multiple associated actors. The sample included 78 articles using a range of methodologies to analyze varying combinations of wind, solar, bioenergy, hydro, geothermal, and ocean energy, alongside combinations of traditional, new, and supporting energy actors. Studies included contextualized (location specific) agent-based, techno-economic, economic, business model, and qualitative analyses, and decontextualized reviews, agent-based, and optimization models. Multi-actor complementarity is being addressed by diverse disciplines in diverse contexts globally, across a range of geographic scales. The majority of studies focus on solar-wind, although more diverse RES combinations were found in contextualized studies. New actors usually participate alongside traditional system actors. More attention to supporting actors is required. Findings highlight the need for further research beyond the technical benefits of combining multiple RES, to explore the roles of various actors. This can be accomplished by incorporating more context in studies, for example, using the substantial existing body of data and research, and by including a greater range of RES combinations, and incorporating more perspectives of associated actors. 

Many actors amongst multiple renewables: A systematic review of actor involvement in complementarity of renewable energy sources

https://doi.org/10.1016/j.softx.2022.101003

highRES-Europe: The high spatial and temporal Resolution Electricity System model for Europe

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

/pdf/a-novel-wind-speed-interval-prediction-system-based-on-2lycev9z.pdf

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

&amp;lt;p&amp;gt;Brazil is the global frontrunner in the production of sugarcane ethanol. Strong national biofuels policies, a consolidated internal demand for ethanol for transportation purposes, and a global growing demand for sugar and ethanol have supported this development. The sugarcane ethanol industry has contributed to economic growth, technological progress, job creation and is among the key strategies for mitigating CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emissions in Brazil. However, the industry is also responsible for a wide range of undesirable impacts on land. Biodiversity loss, structural soil degradation, pollution, and depletion of water sources can result from the associated direct and indirect land-use change. We therefore assess the potential of a carbon capture and utilization pathway to increase the fuel production of this industry in a land-neutral way.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The pathway combines the almost clear surplus CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;-stream from the ethanol fermentation process with H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; produced using wind and solar power to synthesize methanol. The change of use of land from sugarcane production to renewable electricity generation is an intensification step which allows to spare significant amounts of land.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To understand the implications of this pathway in terms of land-use and cost, we develop a spatio-temporal model to determine the cost-optimal system configuration, the resulting land effciency, and consequently the land sparing potential. The core of the model consists of a techno-economic optimization model that minimizes cost for a system that includes variable renewable electricity generation (wind and solar power), storage (electricity, CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;), electrolyzers and methanol synthesis installations for each one of the sugarcane ethanol production plants in the country. The optimization model relies crucially on two time-series which we derived specifically for each Brazilian ethanol plant based on a consolidated spatially explicit data set of sugarcane ethanol installations: first, individual time series of the CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;-streams from ethanol fermentation, and second multi-year time series of wind and solar power in hourly temporal resolution using ERA5 and ERA5-land reanalysis data. Furthermore, we extensively review costs of individual system components and derive footprints of Brazilian solar and wind power plants from satellite imagery.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The proposed pathway leads to a combined amount of ethanol and methanol that represents an increase of&amp;amp;#160; 43%-49% compared to the current output of the ethanol industry in energetic terms. This amounts to around 100 TWh of methanol that would be sufficient to cover the projected growth in Brazil biofuel demand until 2030. In contrast, if the same amount of energy would be provided by sugarcane ethanol, produced at the current average Brazilian sugarcane-to-ethanol land-use efficiency, an additional 23,000 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; - 27,000 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; of land would be required. This underlines the significant land sparing potential of the proposed pathway.&amp;amp;#160;&amp;lt;/p&amp;gt; 

Significant land-sparing potentials from implementing carbon capture and utilization for the Brazilian sugarcane ethanol industry

&amp;lt;p&amp;gt;Climate data sets are widely used for renewable power simulation. While previous generations of global reanalysis data including MERRA-2 and ERA-Interim have been widely assessed for their suitability to simulate variable renewable power systems, more recent datasets such as ERA5 and ERA5-Land lack validation, particularly in regions outside of Europe.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we assess the accuracy and bias of wind power simulation using ERA5 wind speeds in Brazil and New Zealand as well as solar photovoltaic power simulation accuracy using ERA5-Land solar radiation and temperature data in Chile. We compare the performance of ERA5 and ERA5-Land to MERRA-2 based renewable power generation. The reference data sets are capacity factors derived from data measured at individual installations in each country and the performance indicators include the pearson&amp;amp;#8217;s correlation coefficient, mean bias error (MBE) and root mean square error (RMSE). For wind power simulation, we also assess a bias correction method using the Global Wind Atlas.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Since models applying the resulting datasets are based on different spatial and temporal scales, we also aim at finding a relation between the spatial and temporal resolution and simulation quality. We assess the simulation results applying spatial aggregation ranging from individual installations to the country level and temporal aggregation varying from hours to months. This aids to evaluate the reliability of the simulated renewable power generation time series on various spatiotemporal scales for future simulation efforts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Overall, we find that both datasets, ERA5 and ERA5-Land, perform well in wind and solar photovoltaic power simulations. For wind power simulation, ERA5 shows improved performance compared to MERRA-2 based wind power simulation, while for solar photovoltaic the improvements of ERA5-Land compared to MERRA-2 are minor. Correlation of wind power generation is around 0.8 without correction and MBEs around -0.1. Mean bias correction with the Global Wind Atlas does not consistently improve simulation results. For the solar photovoltaic power simulation, we find correlations above 0.75, while the MBE is between -0.05 and 0.1.&amp;lt;/p&amp;gt; 

PV and wind power simulation with ERA5 and ERA5-land &amp;#8211; a multi-country analysis

&amp;lt;p&amp;gt;From a superficial reading of the literature on land-use requirements for renewable energy systems, one may conclude that the community has a very clear understanding of how much land is necessary to deploy renewable energy generation technologies. In particular for solar PV and wind power (VRES), the technologies with the highest growth potentials in the coming decades, abundant literature is available. However, a systematic overview is lacking, in particular, concerning approaches used for estimating the requirements, underlying factors that affect the results, and the regional differences.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;There is no standard metric that is applied for estimating the land area necessary to accommodate a certain amount of renewable energy capacity. Several different metrics are used for this purpose, including land-use efficiency, land-use intensity, power density, and land occupation, to name a few. E.g., land-use efficiency in one paper can refer to the land area needed to install a certain capacity for energy generation, whereas another paper considers land-use efficiency as the area needed to generate a certain amount of electricity. Therefore, estimates of land-use requirements quantified with the same metrics vary greatly. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The variability of the land-use requirements is also rooted in the underlying assumptions regarding the area, power-related component, and time of the chosen metrics. However, a systematized review of all aforementioned assumptions on land-use by renewable energies is absent.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Land-use requirements are often derived from the existing VRES facilities. Hence, their values are influenced by such location-specific factors as climatic conditions, orography, land ownership, among others. As land-use requirements are often integrated into the research that estimates potentials for the future deployment of renewable energy, the influence of those factors is implicitly integrated as well. Hence, the potentials for one region may be estimated by applying the data from another region. Although the application of region-specific land-use requirements could reduce the introduced inconsistencies, known land-use requirements are in its vast majority estimated for the US. Therefore, a compilation that in particular gathers literature on land-use of renewable energies in underrepresented world regions is of high importance.&amp;lt;/p&amp;gt; 

Yu Wu

Papers

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Significant land-sparing potentials from implementing carbon capture and utilization for the Brazilian sugarcane ethanol industry

PV and wind power simulation with ERA5 and ERA5-land &#8211; a multi-country analysis

An overview of land-use requirements for solar PV and wind power

Yu Wu

Papers

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Significant land-sparing potentials from implementing carbon capture and utilization for the Brazilian sugarcane ethanol industry

PV and wind power simulation with ERA5 and ERA5-land &amp;#8211; a multi-country analysis

An overview of land-use requirements for solar PV and wind power

PV and wind power simulation with ERA5 and ERA5-land – a multi-country analysis