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,...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on smart grid Ročník 13; číslo 1; s. 202 - 212
Hlavní autoři: Lin, Shunfu, Liu, Chitao, Shen, Yunwei, Li, Fangxing, Li, Dongdong, Fu, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
ISSN:1949-3053, 1949-3061
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2021.3119939