Stochastic Planning of Integrated Energy System via Frank-Copula Function and Scenario Reduction
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,...
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| Vydáno v: | IEEE transactions on smart grid Ročník 13; číslo 1; s. 202 - 212 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1949-3053, 1949-3061 |
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| Abstract | 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 multi-scenario 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 <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-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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| AbstractList | 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 multi-scenario 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 <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-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. 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 multi-scenario 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 [Formula Omitted]-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. |
| Author | Li, Fangxing Fu, Yang Li, Dongdong Shen, Yunwei Lin, Shunfu Liu, Chitao |
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| SubjectTerms | Clustering Cogeneration Costs Electrical loads Emissions control Energy prices Frank-Copula Integrated energy system (IES) Integrated energy systems multi-scenario stochastic programming model multiple uncertainties Normal distribution Optimization Parameter uncertainty Planning Pollutants Power grids Quadratures scenario generation and reduction Solar energy Stochastic models Stochastic processes Stochastic programming Uncertainty |
| Title | Stochastic Planning of Integrated Energy System via Frank-Copula Function and Scenario Reduction |
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