Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming

Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated ene...

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Bibliographic Details
Published inEnergies (Basel) Vol. 15; no. 18; p. 6763
Main Authors Sun, Xinyu, Wu, Hao, Guo, Siqi, Zheng, Lingwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Alternative energy sources
Clean energy
Economic forecasting
Electricity
Energy
Energy resources
Energy sources
Fuzzy sets
Green technology
hybrid algorithm
integrated energy system
Linear programming
Methods
Monte Carlo simulation
Optimization
Optimization models
Random variables
Renewable energy sources
Renewable resources
Scheduling
Simulation methods
Thermal analysis
type-II fuzzy interval chance-constrained programming
type-II fuzzy sets
Uncertainty
Volatility
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Summary:Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated energy system (IES). Therefore, for the different characteristics of the multiple uncertainties of IES, this paper proposes a type-II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model to solve the above problem. In this model, type-II fuzzy sets are used to describe the uncertainty of RES in an IES, and interval numbers are used to describe the load uncertainty, thus constructing a T2FICCP-based IES day-ahead economic scheduling model. The model was resolved with a hybrid algorithm based on interval linear programming and T2FICCP. The simulations are conducted for a total of 20 randomly selected days to obtain the advance operation plan of each unit and the operation cost of the system. The research results show that the T2FICCP optimization model has less dependence on RES output power and load forecasting error, so can effectively improve the economy of IES, while ensuring the safe and stable operation of the system.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15186763