Open AccessStochastic optimization model for integrated energy system under uncertainty based on chance-constrained programming
Author(s) -
Baoju Li,
Yang Sun,
Xu Li,
Ruosi Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012153
Subject(s) - stochastic programming , mathematical optimization , electric power system , computer science , integer programming , programming paradigm , linear programming , renewable energy , energy (signal processing) , stability (learning theory) , power (physics) , engineering , mathematics , statistics , physics , quantum mechanics , machine learning , electrical engineering , programming language
To solve the day-ahead optimal dispatching of the integrated energy system (IES), the influence of uncertainties in the operation is taken into consideration, and the application of combined heat and power (CHP) and Power to Gas (P2G) equipment can improve the system’s ability to accommodate renewable energy and reduce system operating costs. In order to minimize the operating cost of IES, a mixed-integer linear programming (MILP) optimization model based on chance-constrained is proposed in this paper. A scenario-based simulation method is proposed to convert the chance-constrained programming (CCP) model into a deterministic one. The model in this paper can effectively reduce the risk of system operation in uncertain environments and improve the stability of system operation.