LAPSE:2018.0572v1
Published Article
LAPSE:2018.0572v1
Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles
Ruifeng Shi, Shaopeng Li, Changhao Sun, Kwang Y. Lee
September 21, 2018
A prospect of increasing penetration of uncoordinated electric vehicles (EVs) together with intermittent renewable energy generation in microgrid systems has motivated us to explore an effective strategy for safe and economic operation of such distributed generation systems. This paper presents a robust economic dispatch strategy for grid-connected microgrids. Uncertainty from wind power and EV charging loads is modeled as an uncertain set of interval predictions. Considering the worst case scenario, the proposed strategy can help to regulate the EV charging behaviors, and distributed generation in order to reduce operation cost under practical constraints. To address the issue of over-conservatism of robust optimization, a dispatch interval coefficient is introduced to adjust the level of robustness with probabilistic bounds on constraints, which gradually improves the system's economic efficiency. In addition, in order to facilitate the decision-making strategies from an economic perspective, this paper explores the relationship between the volatility of uncertain parameters and the economy based on the theory of interval forecast. Numerical case studies demonstrate the feasibility and robustness of the proposed dispatch strategy.
Keywords
adjustable robust optimization, economic analysis, electric vehicles, grouping dispatch, microgrids, multi-dispatch, wind power
Suggested Citation
Shi R, Li S, Sun C, Lee KY. Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles. (2018). LAPSE:2018.0572v1
Author Affiliations
Shi R: School of Control & Computer Engineering, North China Electric Power University, Beijing 102206, China; China Institute of Energy and Transportation Integrated Development, Beijing 102206, China
Li S: School of Control & Computer Engineering, North China Electric Power University, Beijing 102206, China
Sun C: Smart Energy Institute, Nanjing SAC Power Grid Automation Co., Ltd., Nanjing 211100, China
Lee KY: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2050
Year
2018
Publication Date
2018-08-07
Published Version
ISSN
1996-1073
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PII: en11082050, Publication Type: Journal Article
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doi:10.3390/en11082050
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Sep 21, 2018
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Sep 21, 2018
 
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Calvin Tsay
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