Xingwu Guo’s research while affiliated with State Grid Energy Research Institute and other places

Accurately predicting electricity demand is of crucial importance for optimizing power resource allocation, improving grid operation safety and providing significant economic benefits. In recent years, deep learning models have been widely employed in time series analysis tasks, such as recurrent neural networks (RNNs), gradient boosting decision trees (GBDT), and transformer networks. However, these methods only focus on a single time dimension. For a specific periodic process, the temporal variations at each time point within the process are not only related to neighboring moments but also highly correlated with neighboring periods, exhibiting both intraperiod and interperiod temporal changes. In this study, we propose the TimesNet temporal forecasting model and conduct experiments using electricity consumption data from the A city, combined with a series of covariates such as temperature and holidays, to train the model and predict electricity consumption. We compare the performance of TimesNet with other methods, and the results demonstrate that the TimesNet model outperforms the other models. Overall, the hybrid model we propose provides a valuable framework for accurately predicting electricity demand and holds practical significance for managing and operating power grids in urban areas.

The accuracy of electricity demand forecasting is closely related to the correctness of decision-making in the power system, ensuring stable energy supply. Stable energy supply is a necessary guarantee for socioeconomic development and normal human life. Accurate electricity demand forecasting can provide reliable guidance for electricity production and supply dispatch, improve the power system's supply quality, and ultimately enhance the security and cost-effectiveness of power grid operation, which is crucial for boosting economic and social benefits. Currently, research on electricity demand forecasting mainly focuses on the single-factor relationship between power consumption and economic growth, industrial development, etc., while neglecting the study of multiple influencing factors and considering different time dependencies. To address this challenge, we propose a transformer-based forecasting model that utilizes transformer networks and fully connected neural networks (FC) for electricity demand forecasting in different industries within a city. The model employs the encoder part of the transformer to capture the dependencies between different influencing factors and uses FC to capture time dependencies. We evaluate our approach on electricity demand forecasting datasets from multiple cities and industries using various metrics. The experimental results demonstrate that our proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. Overall, we provide a valuable framework in the field of electricity demand forecasting, which holds practical significance for stable power system operations.

Since 2021, the state has successively issued a series of energy storage development policies. Among them, the guiding opinions on accelerating the development of new energy storage, and the notice on further promoting the participation of new energy storage in the power market and dispatching and utilization have defined the boundaries of the energy storage industry in terms of planning and construction, allocation scale, and income mode. The Henan provincial government issued relevant policies in combination with the actual situation, clarifying the direction for the development of energy storage in the province. In order to analyze the economy of electrochemical energy storage, we use units-of-production method to calculate energy storage cost and benefit.

Under the dual carbon target, with the gradual increase of the scale of new energy grid connection, the randomness and volatility of new energy output will further affect the safe and stable operation of the system. In view of the frequent fluctuation of the section load rate of the county external liaison line, this paper proposes a self balancing transaction scheduling strategy of new energy power within the county considering the section load rate, and establishes a day ahead real-time two-tier transaction joint optimization model considering the section load rate. The simulation calculation shows that this strategy can significantly improve the local consumption rate of new energy compared with the simple physical method using only energy storage technology, Reduce the power flow fluctuation of tie line, optimize the section load rate and maintain the stability of power grid.

Under the “double carbon” goal and background, new energy power generation will usher in great development. The investment and construction of new energy projects and the scale of grid connected transmission are increasing, which puts forward higher requirements for the investment decision-making management and income of county new energy subjects. Based on the power curve trading and non cooperative game theory and the day ahead real-time two-stage adjustment and correction mechanism of county subjects, this paper briefly analyzes the new energy consumption and income of county subjects based on internal non cooperative game under curve trading.