Wei Liu

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 10⁵ kW²) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

The large-scale integration of electric vehicles into the power grid, coupled with the variability of renewable energy output, presents significant challenges to the stable operation of the grid. Therefore, this study proposes a bi-objective optimal scheduling strategy that considers the charging and discharging of electric vehicles as well as the output of renewable energy. First, a bi-objective optimization model is established, focusing on user charging costs and grid load fluctuations. Second, the Monte Carlo method is employed to simulate the daily load of electric vehicles alongside the output from renewable energy sources. Finally, the enhanced multi-objective backbone particle swarm optimization algorithm is utilized to solve the model. The experimental results demonstrate that the proposed scheduling strategy effectively reduces the charging costs for electric vehicle owners, minimizes load fluctuations, and enables the economic and stable operation of microgrids, thereby confirming the feasibility of the scheduling scheme.

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%.

The rigid constraint of using heat in determining electricity for thermal power units is eliminated to improve the absorption capacity of wind power. In this study, heat storage devices and electric boilers are added to the cogeneration system to alleviate the wind curtailment phenomenon. First, the main reasons for wind curtailment are analyzed according to the structural characteristics of the power supply in the northern part of China. Second, a scheduling model of a cogeneration system, including a heat storage device and an electric boiler, is constructed. An improved artificial bee colony algorithm program is also designed and compiled based on MATLAB. Finally, the feasibility of the proposed scheme is verified by simulation examples, and an economic analysis of wind power consumption is performed. Results show that adding electric boilers lessens coal consumption costs and improves economic benefits.

The steel-making and continuous casting process is the connection between the iron process and the rolling process; it is the bottleneck in the iron and steel production. A better scheduling of this process is the key factor to ensure the whole production in a well-organized rhythm for iron and steel production manufacturing productivity. The branch and cut method in the standard commercial solver CPLEX is introduced to solve the scheduling problem. The linearization methods for the linear conversion are highlighted. Due to the limited calculation time requirement about the scheduling results and the scale of the problem, special methods for the efficiency tuning are applied according to the characters of the problem. Numerical testing supported by Shanghai Bashan steel plant demonstrated that the computational results show significant improvement compared to the traditional manual scheduling results.