With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be the primary premise for the optimal operation of integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network forecasting model. First, Copula correlation analysis is used to conduct correlation analysis on multiple loads and various influencing factors. The influencing factors that have a great correlation with multiple loads were screened out as the input feature set of the model to eliminate the influence of interfering factors. Then, a deep bidirectional long and short-term memory neural network was constructed. Combined with the input feature set screened by the Copula correlation analysis method, the useful information contained in the historical data was more comprehensively learned from the forward and backward directions for training and forecasting. Through the actual calculation example analysis and comparison with other models, the forecasting accuracy of the method presented in this paper was improved to a certain extent.
Interconnectivity is an important development trend of future energy revolution. A more precise planning model is needed to enhance the interaction of multiple energy sources. In this paper, a joint planning model of active distribution network and transportation network including electricity, gas, heat, and traffic loads is proposed. The main highlights of this model are summarized below. Firstly, this issue puts forward a novel mixed user equilibrium mathematical model nesting fast charging station user equilibrium considering the charging fee, charging time, and queuing time of various charging facilities. Secondly, this work constructs a new generation of suburban integrated energy system (SIES) model considering electricity, biogas, gas, and heat energy, and fully explores the significant role of biogas digestors in a SIES. Thirdly, because the proposed model is a mixed integer nonlinear problem, the piecewise linear method, big-M method, and second-order cone relaxation are used to deal with the nonlinear constraints. Finally, a SIES based on the IEEE 33-node distribution network and a 12-node traffic network is designed for case studies. The results revealed that the new equilibrium will decrease vehicle charging cost strongly up to 19.58%. Moreover, biogas digesters are conducive to new energy consumption, resulting in the proportion of power supply from the upper-level grid falling to 30.87%.
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