A multimodal approach to chaotic renewable energy prediction using meteorological and historical information

Leveraging food waste for electricity: A low-carbon approach in energy sector for mitigating climate change and achieving net zero emission in Hong Kong (China)

In recent years, food waste has been a global concern that contributes to climate change. To deal with the rising impacts of climate change, in Hong Kong, food waste is converted into electricity in the framework of low-carbon approach. This work provides an overview of the conversion of food waste into electricity to achieve carbon neutrality. The production of methane and electricity from waste-to-energy (WTE) conversion are determined. Potential income from its sale and environmental benefits are also assessed quantitatively and qualitatively. It was found that the electricity generation from the food waste could reach 4.33 × 109 kWh annually, avoiding equivalent electricity charge worth USD 3.46 × 109 annually (based on US' 8/kWh). An equivalent CO2 mitigation of 9.9 × 108 kg annually was attained. The revenue from its electricity sale in market was USD 1.44×109 in the 1st year and USD 4.24 ×109 in the 15th year, respectively, according to the projected CH4 and electricity generation. The modelling study indicated that the electricity production is 0.8 kWh/kg of landfilled waste. The food waste could produce electricity as low as US' 8 per kW ∙ h. In spite of its promising results, there are techno-economic bottlenecks in commercial scale production and its application at comparable costs to conventional fossil fuels. Issues such as high GHG emissions and high production costs have been determined to be resolved later. Overall, this work not only leads to GHG avoidance, but also diversifies energy supply in providing power for homes in the future.

Energy modeling to compensate for the seasonal lack of electrical and thermal energy depending on the different climates of Iran

Renewable energy sources are in focus for environment-friendly power generation when compared to non-renewable sources. Modeling an energy system of a statistical population can shed light on the possibilities and potential of using renewable resources. In this study, energy modeling of 4 provinces of Iran with different climates is done for 2020 and 2032. The lack of energy caused by seasonal climatic impacts is compensated for by using renewable energy systems. The modeling of three different scenarios is considered to indicate different policies in each energy system strategy. The energy system's past data is gathered and analyzed to predict future data, and then the 2032 energy system is modeled using EnergyPLAN. The results show that there will be a shortage of electrical energy in summers in hot & humid and hot & dry climates, while the energy shortage for cold and temperate & humid climates is the heating demand in winters. Three scenarios of business as usual (BAU), using maximum possible renewable energy (S1), and changing the structure of the energy supply system (S2) are considered with their specification. The results indicate that by using S1, 61.42 TWh of primary energy sources (PES), and by using S2, 136.7 TWh of PES consumption is reduced. Also, for the same scenarios, 29.98 Mt less CO2 is emitted for all climates. The climatic analysis illustrates that using solar in hot & humid and hot & dry, wind and geothermal in cold, and hydropower in hot & humid and temperate & humid climates produce the most amount of renewable potential which not only compensates the lack of seasonal energy but also replace 8% of the total energy needed, previously supplied by fossil fuels. Totally for the 4 provinces, 3250 MW of hydropower, 5625 MW of solar, 650 MW of wind, and 100 MW of geothermal energy are considered while other provinces with the same climate could benefit too based on their geographical specification.

Forecasting of non-renewable and renewable energy production in India using optimized discrete grey model

Renewable energy delivers reliable power supplies and fuel diversification, enhancing energy security and lowering fuel spill risk. Renewable energy also helps conserve the nation's natural resources. Solar and other renewable energy sources have become increasingly prominent in recent years. India has achieved the 20 GW capacity solar energy production target before 2022. It is presently producing the lowest-cost solar power at the global level. Thermal energy has dominated the energy market. Countries have decided on energy generation from renewable sources and adopting green energy. This study forecasted non-renewable and renewable energy from multiple sources (hydropower, solar, wind and bioenergy) using grey forecasting model DGM (1,1,α). The comparative analyses with the classical models DGM (1,1) and EGM (1,1) revealed the superiority of the DGM (1,1,α). We also used CAGR for 2009-2019 to compare the actual and predicted data growth rate. The results show that non-renewable and renewable energy production is expected to increase. However, renewable energy generation wind sources continue to increase faster than hydropower, solar and bioenergy.

A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories

The intermittent and uncertain properties of wind power have presented enormous obstacles to the planning and steady operation of power systems. In this context, as an effective technique to study wind power uncertainty, the development of an accurate wind speed scenario generation method is of great significance for evaluating the impact of wind power in the power system. In the case of several wind farms, accurate scenario generation involves precise acquisition of the correlation between wind speeds and the greatest retention of statistical properties of wind speed data. Under this goal, this research provided a new method for scenario development based on principle component (PC) and R-vine copula theories that incorporates the spatiotemporal correlation of wind speeds. By integrating with PC theory, this strategy avoids the dimension disaster induced by employing R-vine copula alone while taking benefit of its flexibility. The simulation results utilizing the historical wind speeds of three adjacent wind farms as samples showed that the method described in this article could effectively preserve the statistical properties of wind speed data. Eight evaluation indicators covering three facets of the scenario generation method were used to compare the proposed method holistically to two other commonly used scenario generation methods. The results indicated that this method’s accuracy was increased further. Additionally, the validity and necessity of applying R-vine copula in this model was demonstrated through comparisons to C-vine and D-vine copulas.