A method for evaluating the charging and discharging scheduling potential of electric vehicles in case of the occasional operation of a power grid is proposed, which describes the variations in the users’ responses. Considering the social relationships and mutual learning among users, an evolutionary game model for the optimal response in a complex network is established to study the response rule of the users for the scheduling measures. Wardrop's first traffic equilibrium model is applied to calculate the shortest time for the vehicles to arrive at the charging station from their response sites. Finally, the scheduling potential at a charging station is effectively evaluated considering the influence of the type of activities that the users participate in and their credits. Taking a test traffic network and the traffic network of Beijing third ring road as examples, the responses of the electric vehicle users are studied, and the scheduling potential of electric vehicles in different scenarios is verified. Although an increased incentive can improve the response level of the electric vehicles, it may lead to a reduction in the scheduling capacity, delays in the charging and discharging starting time, and a shortened scheduling duration.

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting.

With the large-scale development of distributed energy on the demand side, the trend of “supply exceeding demand” has gradually become prominent, and regional peer-to-peer (P2P) energy trading has become an important measure to improve the local consumption of distributed energy. However, most existing studies usually assume that prosumers behave entirely rationally with the goal of maximum benefit, which has been proved to deviate from the observed actual behaviors. Aiming at the optimal energy of prosumers participating in the P2P market, a prospect theory-based two-stage stochastic optimization model considering the bounded rationality was proposed to accurately simulate the decision-making behavior. Then, a benefit maximization model for the energy trading service provider (ETSP) was constructed considering the power flow constraint to ensure the safe operation of the system. Finally, an improved R-ADMM algorithm considering timeout was proposed to solve the above model and improve the convergence speed. The effectiveness of the proposed model and algorithm was verified via simulation.

The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is transformed into a convex optimization problem by second-order conic relaxation and Karush–Kuhn–Tucker optimality conditions. Secondly, a polyhedral approximation method is applied to construct a mixed integer linear programming, which can be solved quickly by branch and bound method. Finally, the model is solved many times to obtain the Pareto front according to the scalarization basic theorem. Based on an IEEE 33-bus distribution network model, simulation results show that the proposed algorithm can obtain an exact global optimal solution quickly compared with the heuristic method.

Given the inherent volatility, stochasticity, and uncertainty associated with short-term power load data, this study proposes a forecasting model that employs a hybrid data-processing strategy in conjunction with integrated deep learning techniques. Initially, historical load data were analyzed using complementary ensemble empirical mode decomposition, which decomposes the data into subsequences characterized by distinct frequencies and eigenmode functions. Subsequently, the entropy values of the subsequences were computed using sample entropy. Subsequences that exhibit similar entropy values were reconstructed, yielding four distinct subsequences that capture key characteristics of the historical load sequence: random, detailed, low-frequency, and trend components. Random forests (RFs) were employed to evaluate the relative importance of various influencing factors on these four subsequences by enabling data dimensionality reduction and minimizing data redundancy. Subsequently, the RF was used to evaluate the importance of different influencing factors in the four subsequences. A bidirectional long short-term memory (BiLSTM) network was employed to anticipate different subsequences. Furthermore, an attention mechanism was integrated to dynamically allocate weights to the influencing factors, improving the predictive performance of the BiLSTM model. Finally, the component residuals were fitted employing an extreme gradient boosting model. Actual electric load and meteorological data from a developed province along the Chinese coast were employed for model comparison and validation. The results demonstrated that the proposed model significantly improved prediction accuracy on the real dataset, with notable percentage-point gains, providing a new solution for short-term electric load forecasting.

The network reconstruction of ship power system is one of the pivotal ways to recover from system faults. Existing fault reconfiguration strategies are usually based on the DC power flow model without considering voltage and power transmission constraints. However, some important loads on ships can only work normally within a certain voltage range. Therefore, it is necessary to research fault reconfiguration strategy considering voltage security constraints. This paper proposes a shipboard grid fault reconfiguration strategy based on the distflow model. And a second order conic programming (SOCP) method is proposed to convert the fault reconfiguration model to convex optimization model in order to obtain the global optimal solution. Based on an improved IEEE 33-bus distribution network model, simulation results show that the proposed fault reconfiguration strategy can minimize the power loss and improve the voltage quality of important loads after recovery. And the SOCP method shows good performance on global optimal solution.