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This paper presents a methodology for optimal design of diesel/PV/wind/battery hybrid renewable energy system (HRES) for the electrification of residential buildings in rural areas. Contrary to previous work, in this study, the effects of climate diversity and building energy efficiency on the size optimization of HRES are investigated. First, a multi-criteria spatial analysis trough a common geographical information system tool (ArcGIS 10.2) is undertaken to develop the renewable energy potential map for Algeria. Then, particle swarm optimization algorithm and ε-constraint method were used to solve the multi-objective problem, which was formulated to minimize the cost of energy (COE) as the primary objective, while maximizing system reliability and a renewable fraction (RF). According to the resulting renewable potential map, seven zones are identified, and then seven locations have been selected (one from each zone) to execute the optimization of the proposed HRES. By considering low efficient buildings, photovoltaic/wind/diesel/battery is found the best configuration for Adrar and Tindouf, while photovoltaic/diesel/battery is obtained the best for the other locations. However, in the case of high-performance buildings, another optimal HRES configurations are obtained. The better one is acquired in Biskra and Tamenrast, which includes PV-Battery (100% renewable energy) and fulfilling COE of 0.21 $/kWh. Image 1 • Hybrid Renewable Energy Systems. • Techno-Economic and Feasibility Study. • Building Energy Efficiency. • Multi-Objective Particle Swarm Optimization. • Geographical information system. [ABSTRACT FROM AUTHOR]

Good knowledge of future wind energy resources is crucial for sitting and planning studies of wind farms. The simulation results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a proposed new downscaling method based on the bidirectional gated recurrent unit (BiGRU) are both used in this paper to study future offshore wind energy resources in China. The proposed new downscaling method is validated and compared to two traditional methods. It is found that the spatial patterns of downscaled wind speed are highly consistent with the reference data, and biases are significantly reduced by the new method, especially in coastal and shallow water areas. Using the new method, we downscale the CMIP6 future projected simulation results and generate a new dataset of offshore wind speeds in China for the period of 2021–2100 with a resolution of 0.25°. Multi-model ensemble (MME) results project small decreases in the offshore wind speed and wind power density over the East China Sea and increases in those parameters over the South China Sea, for the middle and end of the 21st Century (2041–2060 and 2081–2100) under two representative scenarios (SSP2-4.5 and SSP5-8.5). • The first work on projection of future offshore wind energy resources in China. • A new downscaling method based on deep learning network was developed. • Global climate models' simulations were downscaled to high-resolution grids of 0.25°. • Small changes in future offshore wind speed and power density were projected in China. [ABSTRACT FROM AUTHOR]

This paper analyzes the cross-quantile dependence and causality between non-ferrous metals and clean energy indices by employing data from November 2003 to May 2019. Specifically, we utilize the time-varying copulas to examine the asymmetric connectedness among the assets. Based on the assessed dependence, we utilize the time-static and time-varying cross-quantilogram approach to evaluate the asymmetric dependence across different quantiles. Finally, we employ a Granger-causality in quantiles analysis to assess the causal relationship across different quantiles of the return distributions of the underlying assets. By utilizing time-varying copulas, we report that the conditional dependence between the assets is time-varying and asymmetric with the potential for tail dependence. Our results from the cross-quantilogram analysis provide further evidence that the interconnectedness is asymmetric across quantiles, and it increases with the increase in lags. In addition, we report that extreme market conditions positively influence the dependence structure. Finally, our findings from Granger-causality in quantiles indicate bidirectional causality among assets that intensifies with the increase in lag order. These findings are important for governmental policies that aim at mitigating the impact of climate change by transforming the global energy landscape towards clean and renewable energy sources. • Assets considered are non-ferrous metals and clean energy indices. • Time-varying copulas, Cross-Quantile dependence and causality in quantiles applied. • The assets are time-varying and asymmetric having tail dependence. • Bidirectional causality established which intensifies for higher lag orders. • Non-ferrous metals are essential for the development of clean energy assets. [ABSTRACT FROM AUTHOR]

Renewable energies are deployed worldwide to mitigate climate change and push power systems towards sustainability. Nevertheless, the weather-dependent nature and variability of renewable energy sources often hinders their integration to national grids. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. This paper introduces a novel method for quantifying total temporal energetic complementarity between three different variable renewable sources, based on well-known mathematical techniques: correlation coefficients and compromise programming. It has the major advantage of allowing the simultaneous assessment of partial and total complementarity, as well as allowing for a linear assessment of complementarity. The method is employed to study the complementarity of wind, solar and hydropower generation on different temporal scales in a region of Poland. Results show that timescale selection has a determinant impact on the estimated value of the total temporal complementarity index. • A method for simultaneous evaluation of partial and total energetic complementarity. • Method combines known metrics: compromise programming and correlations. • Timescale has a direct impact on the value of the complementarity index. [ABSTRACT FROM AUTHOR]