Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy

Yang Lei, Dan Wang, Hongjie Jia, Jingcheng Chen, Jingru Li, Yi Song and Jiaxi Li

Applied Energy, 2020, vol. 276, issue C, No S0306261920309077

Abstract: 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.

Keywords: RIES expansion stochastic planning; Scenario generation; Multiple uncertainties; Energy pipeline risk; Multi-objective chance constraints planning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (38)

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DOI: 10.1016/j.apenergy.2020.115395

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