The stochastic and intermittent nature of renewable energy resources (RERs) poses great challenges to the energy scheduling of microgrids. The hybrid energy storage system (HESS), such as a combination of the battery (BA) and ultracapacitor (UC), is considered as an effective way to cope with such challenges. In this paper, a hierarchical energy scheduling framework is proposed for the microgrid with HESSs to optimize the operation under the uncertainty of RERs and loads. The framework consists of two stages of scheduling: hour-ahead scheduling and real-time scheduling. In hour-ahead scheduling, a deterministic optimization model is formulated to minimize the operation cost of microgrids, and to guarantee the operation safety. Subsequently, a decomposition-based method is presented to solve the proposed model. In real-time scheduling, a control strategy of HESSs is developed to accommodate the imbalanced power mainly due to the uncertainty of RERs and loads, while extending the lifetime of BAs. The methodology is tested on a 7-bus microgrid system. Simulation results validate the effectiveness of the formulated model and the developed strategy.

With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time.

To address the challenge of power absorption in grids with high renewable energy integration, electric vehicle battery swapping stations (EVBSSs) serve as critically important flexible resources. Current research on load forecasting for EVBSSs primarily employs Transformer models, which have increasingly shown a lack of adaptability to the rapid growth in scale and complexity. This paper proposes a novel data-driven forecasting model that combines the geographical feature extraction capability of graph convolutional networks (GCNs) with the multitask learning capability of Transformers. The GCN-Transformer model first leverages Spearman’s rank correlation to create a multinode feature set encompassing date, weather, and historical load data. It then employs data-adaptive graph generation for dynamic spatio-temporal graph construction and graph convolutional layers for spatial aggregation tailored to each node. Unique swapping patterns are identified through node-adaptive parameter learning, while the temporal dynamics of multidimensional features are managed by the Transformer’s components. Numerical results demonstrate enhanced accuracy and efficiency in load forecasting for multiple and widely distributed EVBSSs.

In this paper, an integrated modeling and planning method is proposed to solve the optimal problems in a district multi-carrier energy system. Firstly, an energy hub composed of four-tier transfer matrices is modeled to handle the coupling interconnection of multiple energies. Then an integrated planning method which minimizes the total costs of installations, maintenances, primary energy consumptions and network losses is proposed to meet the district energy demands. Three problems of optimization, i.e. optimal installed capacities, economic dispatch and optimal layout are designed for obtaining an integrated solution. The proposed method is finally verified on a 9-buses multiple energy system demanding five types of energies: electricity, hot steam, hot water, hot air and cold air. The proposed method is proved valid and practical.

The peak-load shaving in industrial parks (IPs) presents more complicated than that in residential or commercial districts because of the irregular distribution of peak-valley and two-part tariff (TPT). In this paper, a joint planning method of battery energy storage systems (BESS) and demand response (DR) designed for exploring the feasibility of industrial consumers participating in the peak-load shaving in IPs distribution system is proposed. This bi-level optimization method considering both planning and operation provides a systemic solution which determines the optimal installed capacity of BESS, layout of BESS and joint economic dispatching of BESS and DR, concurrently. The solving process is implemented under the TPT electricity market circumstance, and the annual time-series curve and duration load curve are utilized instead of a typical scenario to deal with the irregular distribution of peak-valley and improve the accuracy of solution. An 11-buses IP distribution system located in Guangdong Province in the south of China is utilized as the study case to verify the validity of proposed method. Finally, the feasibility of industrial consumers participating in the peak-shaving via joint investment of BESS and DR is discussed.