Open AccessLow carbon multi‐objective scheduling of integrated energy system based on ladder light robust optimization
Author(s) -
Zhang Xiaohui,
Zhao Xiaoxiao,
Zhong Jiaqing,
Ma Ning
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12498
Subject(s) - robustness (evolution) , robust optimization , mathematical optimization , optimization problem , scheduling (production processes) , computer science , wind power , electric power system , engineering , power (physics) , mathematics , electrical engineering , biochemistry , chemistry , physics , quantum mechanics , gene
Under the low carbon background, a multi‐objective scheduling model of electrical‐thermal integrated energy system which aims at minimizing the integrated operating costs and minimizing the carbon trading costs is established. The uncertainty of wind power brings new challenge to integrated energy systems. In recent years, light robust optimization has been effectively applied to solve the uncertainty of wind power. However, traditional light robust optimization has the shortcomings of over‐conservative decision or excessive violation of constraints. Therefore, light robustness coefficient is introduced to adjust the deterioration tolerance of the objective function. To constrain the relaxation, the relaxation threshold and the ladder penalty coefficient are defined to generate the ladder penalty cost. Then, the ladder light robust optimization is used to deal with the uncertainty of the source‐load sides and a multi‐objective scheduling model based on the ladder light robust optimization is proposed. Finally, the Multi‐objective Optimization Bacterial Colony Chemotaxis (MOBCC) algorithm is used to solve the model. Simulations results show that the proposed model can reduce the total costs by 5.56% and the wind power curtailment rate to 0.45%. Furthermore, different schemes are compared to verify that the ladder light robust optimization can provide a trade‐off between economy and conservation of the system.