With the rapid development of social economy, energy consumption has continued to grow, and the problem of pollutant emissions in various energy sources has gradually become the focus of social attention. Cities account for two-thirds of global primary energy demand that make urban energy systems a center of sustainable transitions. This paper builds a bi-level planning model for steady-state optimal configuration to realize the reasonable planning of the urban energy structure. The first level mainly analyzes the steady-state relationship between energy systems, the second level is based on the steady-state relationship of multiple energy sources to minimize the construction and operating costs of urban energy systems and pollutant emissions. Nonlinear system dynamics and the Improved Moth Flame Optimization Algorithm (IMFO) algorithm are implemented to solve the model. In addition, this paper uses instances to verify the application of a planning model in a certain city energy system in China. Under the premise of ensuring the stability of the urban energy system, two energy planning programs are proposed: mainly coal or mainly high-quality energy. The coal planning volumes are used as the basis for sub-scenario planning and discussion. Lastly, this paper proposes a series of development suggestions for different planning schemes.

In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.

In order to solve the operation optimization problem of integrated energy system considering the demand side, this paper takes the integrated energy system of industrial park as the research object and proposes an optimal scheduling model considering the comprehensive demand response. Algorithm analysis shows that RIES containing multiple energy storage devices fully consider the comprehensive demand side response.