With the acceleration of the green and low-carbon transformation of the power system, it is very important to calculate and analyze the carbon emissions of the urban power systems. In order to effectively grasp the carbon emission distribution of power systems and reduce the carbon emission of power system, this paper proposes a systematic carbon emission measurement method for regional power systems. Firstly, the quantitative analysis model of driving factors for regional power system carbon emissions is constructed, and the direction and measures of low-carbon transformation and green collaborative development of regional power systems are proposed. Secondly, energy consumption scenarios under different constraints are established to support the collaborative control path of CO₂. It provides key data and a theoretical basis for the low-carbon development of the power industry. Finally, through the analysis of the arithmetic example and the combination of the three scenarios, it is concluded that under the 2020–2030 Tianjin baseline scenario, the Tianjin power sector cannot reach the peak before 2030, and under the low carbon scenario and the ultra-low carbon scenario, the total carbon emissions of the power sector are expected to peak in 2024, with a peak range of 55.83–55.9 million tons, which is only a slight increase of 210,000–280,000 tons compared to 2020 in emissions, showing the potential for effective carbon emission control. The validity of the methodology proposed in this paper is verified, and the effective path for future carbon emission reduction in electric power is analyzed, providing empirical support and strategic recommendations for the green and low-carbon transformation of the electric power system.

The remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach (including an improved machine learning approach) has poor generalization performance due to stochasticity, and the combined prediction approach lacks sufficient theoretical support at the same time. In this paper, we first analyze the prediction principles and applicability of models such as long and short-term memory networks and random forests, and then propose a method for predicting the RUL of batteries based on the integration of multiple-model, and finally validate the proposed model by using experimental data. The experimental results show that (1) for the proposed model, in the best case, the root-mean-square error (RMSE) does not exceed 0.14%, which has a stronger generalization; (2) for the comparison with the single model used, the average RMSE is reduced by 46.2%, 43.7%, and 80.6%, which has a better fitting performance. These results show that the model has good prediction accuracy and application prospects for predicting the RUL of energy storage batteries.

High-temperature geothermal well resource exploration faces high-temperature and high-pressure environments at the bottom of the hole. The all-metal turbodrill has the advantages of high-temperature resistance and corrosion resistance and has good application prospects. Multistage hydraulic components, consisting of stators and rotors, are the key to the turbodrill. The purpose of this paper is to provide a basis for designing turbodrill blades with high-density drilling fluid under high-temperature conditions. Based on the basic equation of pseudo-fluid two-phase flow and the modified Bernoulli equation, a mathematical model for the coupling of two-phase viscous fluid flow with the turbodrill blade is established. A single-stage blade performance prediction model is proposed and extended to multi-stage blades. A Computational Fluid Dynamics (CFD) model of a 100-stage turbodrill blade channel is established, and the multi-stage blade simulation results for different fluid properties are given. The analysis confirms the influence of fluid viscosity and fluid density on the output performance of the turbodrill. The research results show that compared with the condition of clear water, the high-viscosity and high-density conditions (viscosity 16 mPa∙s, density 1.4 g/cm³) will increase the braking torque of the turbodrill by 24.2%, the peak power by 19.8%, and the pressure drop by 52.1%. The results will be beneficial to the modification of the geometry model of the blade and guide the on-site application of the turbodrill to improve drilling efficiency.

With the deepening reform of the power system, power sales companies need to adopt new power sales strategies to provide customers with better economic marketing solutions. Customer-side configuration of an energy storage system (ESS) can participate in power-related policies to reduce the comprehensive cost of electricity for commercial and industrial customers and improve customer revenue. For power sales companies, this can also attract new customers, expand sales and quickly capture the market. However, most of the ESS evaluation models studied so far are based on historical data configuration of typical daily storage capacity and charging and discharging scheduling instructions. In addition, most models do not adequately consider the performance characteristics of the ESS and cannot accurately assess the economics of the energy storage model. This study proposes an intelligent power sales strategy based on load forecasting with the participation of optimal allocation of ESS. Based on long short-term memory (LSTM) artificial neural network for predictive analysis of customer load, we evaluate the economics of adding energy storage to customers. Based on the premise of the two-part tariff, the ESS evaluation model is constructed with the objective of minimizing the annual comprehensive cost to the user by considering the energy tariff and the savings benefits of the basic tariff, assessing the annualized cost of ESS over its entire life cycle, and the impact of battery capacity decay on economics. The particle swarm optimization (PSO) algorithm is introduced to solve the model. By simulating the arithmetic example for real customers, their integrated electricity costs are significantly reduced. Moreover, this smart power sales strategy can provide different sales strategies according to the expected payback period of customers. This smart sales strategy can output more accurate declared maximum demand values than other traditional sales strategies, providing a more economical solution for customers.