Title: Evaluating wind and solar complementarity in China: considering climate change and source-load matching dynamics

Journal: Energy

DOI: https://doi.org/10.1016/j.energy.2024.133485

Abstract: Changes in wind and solar energy due to climate change may reduce their complementarity, thus affecting the stable power supply of the power system. This paper investigates the wind and solar complementarity in China under climate change from the perspective of source-load matching. First, the ability of the PRECIS model to simulate the wind and solar complementarity characteristics at different time scales (hourly, daily, and monthly scales) over China is verified. At the hourly scale, the complementarity shows an increasing trend from east to west, with Qinghai, Yunnan and Xinjiang exhibiting the most pronounced complementarity. The southeastern region exhibits smaller net load peak-to-valley differences and volatility, signifying a diminished requirement for system flexibility in this area, while northern and northwestern China exhibit a higher demand for system flexibility. Then, the changes of wind and solar energy complementarity and net load fluctuation are predicted in the 2030s and 2060s under the SSP2-4.5 and SSP5-8.5 scenarios. Overall, climate change is anticipated to have a negative impact on the future complementarity of wind and solar energy. In the 2060s, on an hourly scale, the complementary characteristic () shows a downward trend in most regions, particularly notable in eastern and central China, where it decreased by about 0.05 and 0.04. Furthermore, there is an escalation in the peak-valley difference and fluctuation of net load in most areas of China, particularly under the SSP5-8.5 scenario. The peak-valley difference of net load in the central and southwest regions projects a marked increase of 22.4% and 18.7% in the 2060s, suggesting that climate change is anticipated to augment the demand for power system flexibility, necessitating increased investments in flexible and adjustable resources such as energy storage.

Title: Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada

Journal: Environmental Research Communications

DOI: https://doi.org/10.1088/2515-7620/ad85c5

Abstract: Accurate prediction of potato yield is essential for informed agricultural decision-making, ensuring food security, and supporting farmers’ livelihoods. This is particularly critical in regions like Prince Edward Island (PEI), where potato production is not only a staple of local agriculture but also a cornerstone of the regional economy, accounting for a significant proportion of agricultural revenue and employment. Although machine learning algorithms have been extensively applied in agricultural yield prediction, previous studies have not fully leveraged the potential of capturing both short- and long-term dependencies. This research highlights the efficacy of integrating these temporal dependencies into machine learning models to enhance the accuracy of potato yield predictions. The methodology adopted in this research, including data collection, model selection, and scenario-based projections, can be applied to other regions and crops. Our projections for PEI toward the end of the century indicate a substantial decline in potato yields across different climate scenarios. Under the high-emission SSP5-8.5 scenario, our models predict a potential potato yield reduction of up to 70%. In contrast, the SSP1 and SSP2 scenarios suggest a more moderate decline in potato yield, ranging from 4% to 15%. These findings underscore the urgent need for reducing greenhouse gas emissions to mitigate the adverse impacts on potato production. Furthermore, they highlight the importance of implementing adaptive farming practices to sustain potato yield in the face of climate change.