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Interval optimization based operational strategy of integrated energy system under renewable energy resources and loads uncertainties
Author(s) -
Li Yuanmei,
Wang Kaike,
Gao Bingtuan,
Zhang Bin,
Liu Xiaofeng,
Chen Chen
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6009
Subject(s) - interval (graph theory) , mathematical optimization , renewable energy , energy (signal processing) , robust optimization , interval arithmetic , degree (music) , optimization problem , computer science , mathematics , engineering , statistics , mathematical analysis , physics , electrical engineering , combinatorics , acoustics , bounded function
Summary Uncertainties from renewable energy resources (RESs) and energy demands have brought enormous challenges to the optimal operation of integrated energy system (IES). An interval optimization based operational strategy for IES is proposed to overcome uncertainties. Firstly, embarking from a deterministic IES operation model, an interval method is presented to quantify the uncertainties instead of possibility distribution so as to better characterize the impact of RESs and loads on the operation of the IES. Secondly, the interval optimization model under multiple uncertainties is presented. In the proposed model, the total daily cost is optimized and system operation constraints are fully considered. Thirdly, the order interval relation and possibility degree are adopted to transform the interval model to deterministic model, which is solved by CPLEX optimizer. Finally, case studies considering influence of different uncertainty objects and uncertainty possibility degree levels are performed and analyzed extensively. The simulation results show that the optimized interval numbers will be increased gradually as uncertainty fluctuation degree increased from ±5% to ±25%. Comparing with automatic robust convex optimization method, the robust optimized values are in accordance with the upper values of optimized interval number optimization method, and the midpoints of interval results optimized by interval method are 4.1%, 8.7%, 11.7%, 16.5%, and 8.0% less than robust optimization results, respectively.