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• To propose a multistage scenario tree generation method. • To propose a fluctuation boundary determination method for solving energy price uncertainty. • To propose a multistage energy hub and network collaborative stochastic planning model of RIES. Planning a regional energy system based on the advantages of different types of energies and building an economic and efficient regional integrated energy system (RIES) are hot research subjects in the energy planning field. The call for using clean energy and attaining carbon neutrality worldwide has also attracted research attention in the field of low-carbon and clean planning. Because of the growth of the energy system construction cycle together with the utilization of various types of energies, multi-stage planning considering the uncertainty and construction time sequence in a long time scale has gradually become the primary research direction for planning RIES. The uncertainty of multi-energy load growth and the energy price fluctuation in a long time scale considerably affects the construction and economy of the final planning scheme. Aiming to solve the problems of incomplete load data, low accuracy of single load forecasting, and weak regularity of the energy price fluctuation, this study proposes a multi-stage scenario tree generation method based on the conditional generative adversarial network–random forest–Markov chain. And a method of energy price upper and lower boundary determination is based on the multi-index artificial neural network method for analyzing and solving energy price fluctuation. A multi-stage stochastic planning model of RIES is built herein to minimize the comprehensive cost, including the construction investment and regional operation costs, energy hub income, and carbon emission reduction. Finally, the Beichen district of Tianjin, China is considered a case for verifying the effectiveness and applicability of the proposed model and method. Results show that the construction time sequence of the energy hub substantially affects the planning scheme. Energy hub construction in advance benefits the energy hub income and carbon emission reduction; however, the initial stage requires more investment, which affects the planning and construction of renewable energy. Compared with a single-stage deterministic planning scheme, the optimized multi-stage stochastic planning scheme reduces the number of idle facilities and benefits cost recovery, energy hub income, coverage scenarios, and carbon emission reduction. [ABSTRACT FROM AUTHOR]

• To propose a multi-dimensional correlation scenario generation method which considers the time sequence, auto-correlation, and cross-correlation of renewable energy and multi-energy loads. • To propose a risk assessment method of the energy pipeline based on the complex network theory. • To propose a multi-objective bi-level energy network stochastic expansion planning model considering the expansion cost and pipeline risk. Efficiently using multiple energy sources, including renewable energy, is a focus of applied energy research. A regional integrated energy system (RIES) involves coupling use of multiple energy sources, which can improve energy efficiency using multi-energy complementarity. However, increasingly renewable energies and multi-energy sources load access into an energy system increase the multiple uncertainties of RIES, which has considerable impact on both system planning and operation. This study proposes a multi-objective stochastic planning method that is based on the multi-dimensional correlation scenario set generation method for RIES' expansion planning. The scenario generation method considers the characteristics, time sequence, autocorrelation, and cross-correlation of renewable energies and multi-energy loads. The pipeline risk index for energy network expansion planning is defined considering the energy pipeline's importance. A multi-objective stochastic planning model based on chance constraints of the energy network is developed to minimize the investment cost and the energy pipeline risk. Finally, to confirm the effectiveness and the applicability of the proposed model and method, certain numerical cases at Yangzhong City, China, are simulated. Two RIES expansion planning scenarios are then compared. Moreover, the Pareto fronts of the optimized expansion planning schemes are demonstrated, providing reference for balancing the energy network planning scheme economy and the energy pipeline risk. [ABSTRACT FROM AUTHOR]