期刊
IEEE TRANSACTIONS ON SMART GRID
卷 13, 期 1, 页码 202-212出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3119939
关键词
Integrated energy system (IES); multiple uncertainties; multi-scenario stochastic programming model; FrankCopula; scenario generation and reduction
资金
- National Natural Science Foundation of China [51977127]
- Science and Technology Commission of Shanghai Municipality [19020500800]
- Shuguang Program - Shanghai Education Development Foundation [20SG52]
- Shanghai Municipal Education Commission
This paper proposes a multiscenario stochastic programming model to handle uncertainties in power system planning, including wind and solar power output, load demands, energy prices, and pollutant emission factors. By generating different scenarios and reducing them through clustering and discrete approximation, the paper analyzes the impacts of uncertain parameters on the optimal configuration and economy of the integrated energy system.
Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multiscenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by k-means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influences of each uncertain parameter on the optimal configuration and economy of the IES.
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