Search

Urban cities are the main force of energy consumption and carbon emission. In the context of “dual carbon”, promoting low-carbon transformation of urban energy systems has become the top priority of urban planning. However, while the share of renewable energy output increases, the requirement for system flexibility also increases. To this end, an urban energy system expansion planning model that accounts for both long-term and short-term uncertainties is proposed. Multiple forms of energy, including electricity, gas, and heat are encompassed in this model. At the planning level, uncertainty and operational flexibility during the real-time operation stage are estimated, and a stochastic optimization approach is employed for solving. The capacity expansion of renewable energy generators and energy hubs (EHs) is considered by the model, with the imposition of carbon emission quota constraints to ensure the attainment of carbon emission reduction targets. The results show that the model can effectively improve the economy of urban energy system and the rate of consumption of renewable energy, and can meet different carbon-emission reduction requirements.

Power battery performance plays a pivotal role in the comprehensive performance of electric vehicles, and thus accurate identification of the parameters of the lithium-ion battery model is crucial for subsequent state-of-charge estimation and state-of-health prediction of the battery system. In order to improve the accuracy of parameter identification algorithm of lithium-ion battery model, a second-order RC equivalent circuit model of the battery is established, and a least-squares algorithm based on variable forgetting factor is used to identify the parameters of the lithium-ion battery model online. By building a test platform for charge and discharge experiments, based on the experimental data of two different operating conditions, the proposed algorithm, recursive least squares algorithm and traditional forgetting factor least squares algorithm are used to identify the parameters, and the accuracy of the proposed algorithm is described based on the comparison of the error between the estimated port voltage and the actual value obtained from the experimental test. The experimental results show that the recursive least squares algorithm based on the variable forgetting factor shows fast convergence and high estimation accuracy in the identification of lithium-ion battery parameters.