With the continuous promotion of the national energy clean transformation development strategy, the scale of new energy development will continue to expand. Many regions in China will show the characteristics of new energy with high installed capacity, high proportion and high permeability. The instability of trans provincial output curve caused by the intermittency, randomness and volatility of new energy will become a common problem. Based on the analysis of clean energy Big data, this paper explores the potential of complementation and mutual aid of water, fire and scenery in the Northwest Power Grid, Research the mechanism of bundling and exporting new and conventional energy, scientifically determine the bundling ratio, rationalize the price formation mechanism, fully leverage the role of market-oriented transactions in discovering value, and ensure that market entities achieve a win-win situation among multiple parties.

The prediction of ocean ambient noise is crucial for protecting the marine ecosystem and ensuring communication and navigation safety, especially under extreme weather conditions such as typhoons and strong winds. Ocean ambient noise is primarily caused by ship activities, wind waves, and other factors, and its complexity makes it a significant challenge to effectively utilize limited data to observe future changes in noise energy. To address this issue, we have designed a multi-modal linear model based on a “decomposition-prediction-modal trend fusion-total fusion” framework. This model simultaneously decomposes wind speed data and ocean ambient noise data into trend and residual components, enabling the wind speed information to effectively extract key trend features of ocean ambient noise. Compared to polynomial fitting methods, single-modal models, and LSTM multi-modal models, the average error of the relative sound pressure level was reduced by 1.3 dB, 0.5 dB, and 0.3 dB, respectively. Our approach demonstrates significant improvements in predicting future trends and detailed fittings of the data.

Recently, the Statistical Communiqué of the People’s Republic of China on the 2022 National Economic and Social Development (hereinafter referred to as "Communiqués") was published by the National Bureau of Statistics. According to the Communiqués, China’s total primary energy consumption in 2022 amounted to 5.41 billion tons of standard coal. It is projected that by 2025, the total energy consumption will reach approximately 5.84-6.04 billion tons of standard coal. In order to meet the target of non-fossil energy consumption accounting for 20% of total energy consumption by 2025, the installed new energy capacity within operating areas of State Grid Corporation of China is anticipated to exceed the previous estimate by 100-140 million kilowatts. From a risk assessment standpoint, the research team has constructed an evaluation model to gauge China’s new energy development trend, taking into account the accomplishment of the consumption proportion target for non-fossil energy. They delve into the issues brought about by the unexpected development of new energy and put forth pertinent measures to tackle them.

As one of the major energy-consuming and high-emission industries, the refineries account for 10% of global industrial carbon emissions, of which about 25% is emitted by fluid catalytic cracking (FCC) process. Therefore, CO2 capture from FCC flue gas create the possibility for low-carbon refineries. We developed a novel CO2 utilization process to methanol production modeling using Aspen Plus. Meanwhile, organic Rankine Cycle (ORC) power generation technology was coupled to recover the waste heat of the system. A five-million-ton FCC unit in China was selected as a case study. We conducted some analysis for the process, the results show that the developed system boosts the energy efficiency of the FCC unit by 2.8%. The annual capacity of the waste heat power generation unit is 4.8 GWh, with a thermal efficiency of 5.9%.

Reduction of greenhouse gas (GHG) emissions for mitigating climate changes is globally necessary. As a major GHG emitter, refinery sector is responsible for 14.1% of China’s CO2 emission in 2019. The flue gas associated with petroleum refining has high CO2 content, presenting potentials for methanol and electricity co-production when the methane and hydrogen in dry gas are considered. To unlock the green opportunity for refineries, in this study we employed Aspen Plus to develop the process of methanol synthesis by recovering CO2 in the flue gas of fluid catalytic cracking (FCC) unit and the methane and hydrogen in dry gas. The results show that the new co-production process increases the energy efficiency of the FCC unit by 2.4%. Compared with traditionally natural gas-based methanol production, the developed process enables annual CO2 mitigation of 1.3 million tons.