SMACS MODEL, a stochastic multihorizon approach for charging sites management, operations, design, and expansion under limited capacity conditions

被引:22
作者
Bordin, Chiara [1 ]
Tomasgard, Asgeir [2 ]
机构
[1] SINTEF Energy Res, Trondheim, Norway
[2] Norwegian Univ Sci & Technol, Trondheim, Norway
关键词
Electric vehicles; Stochastic optimisation; Multihorizon; Charging sites; Battery degradation; Design; Expansion; ENERGY-STORAGE SYSTEM; OPTIMIZATION MODEL; DISTRIBUTION TRANSFORMERS; OPTIMAL PLACEMENT; BATTERY; STATIONS; GENERATION; WIND; COST; SIMULATION;
D O I
10.1016/j.est.2019.100824
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The increasing demand of electric vehicles creates challenges for the electric grid both on the transmission level and distribution level. Charging sites in particular will have to face strong challenges especially in those countries where a massive penetration of electric vehicles happened in the last years and even more is expected in the forthcoming future. Such an increased forecast demand will lead to a capacity lack within the existing charging sites, therefore new investments in design and expansion have to be planned. We propose the so called SMACS MODEL that stands for Stochastic Multihorizon Approach for Charging Sites Management, Operations, Design and Expansion under Limited capacity conditions. The model is built to analyse critical decisions in terms of transformer expansion, grid reinforcements, renewable installation and storage integration, over a time horizon of 10 years, with a particular focus on the long term uncertainty in the price variations of the available resources. Long term investment decisions and short term operational decisions are addressed simultaneously in a holistic approach that includes also battery degradation issues and is able to tackle the optimal trade off between battery replacements, grid reinforcements and renewable installations throughout the chosen time horizon. Compared to traditional decision approaches the model is able to take more precise decisions due to its higher insight on the long term costs projections, the inclusion of battery degradation issues and the inclusion of grid rules and regulations limits that affect the final decisions.
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页数:19
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