Tri-level hierarchical coordinated control of large-scale EVs charging based on multi-layer optimization framework

•Proposing 2 layer optimization stackelberg incorporating timely decision for control scheme.•Developing MIQP considering variable for realistic modeling of energy market operation.•To the best of knowledge, previous literature considered either assumed or Time-of-Use (ToU) rates.•Such assumptions m...

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Bibliographic Details
Published in:Electric power systems research Vol. 226; p. 109923
Main Authors: Aljohani, Tawfiq, Mohamed, Mohamed A., Mohammed, Osama
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2024
Subjects:
Electric vehicles
Energy markets
EVs dynamic charging
Hierarchical energy management
Mixed-integer quadratic programming (MIQP)
Stackelberg model
Hierarchical energy management
EVs dynamic charging
Electric vehicles
Stackelberg model
Energy markets
Mixed-integer quadratic programming (MIQP)
ISSN:0378-7796, 1873-2046
Online Access:Get full text
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Summary:•Proposing 2 layer optimization stackelberg incorporating timely decision for control scheme.•Developing MIQP considering variable for realistic modeling of energy market operation.•To the best of knowledge, previous literature considered either assumed or Time-of-Use (ToU) rates.•Such assumptions may constitute a serious flaw as impact of sudden large EVs population.•Integrating practical variables to model the dynamic and realistic operation of the energy grid. The stochastic nature of electric vehicles (EVs) has made predicting and ultimately controlling their integration on a large scale a very challenging task. This work proposes a two-layer optimization framework based on the Stackelberg leader and follower to manage a tri-level energy management strategy to coordinate EVs charging. The first layer incorporates an aggregator that collects the energy requirements of all EVs in a decentralized manner, and sends them to an attached microgrid, which constitutes the second layer of the proposed scheme, for further processing. Then, the microgrid runs a lower-level energy optimization problem in a centralized manner based on the inputs from its aggregators downstream and a system operator upstream, which operates in the third level. Simultaneously, the coordination between the system operator and its attached microgrids is formulated as an upper-level energy optimization problem. The work incorporates the dynamics of the energy system by modeling practical economic, technical, and operational variables. The formulated problem is solved via mixed-integer quadratic programming (MIQP). The results show that the proposed strategy has successfully influenced the charging requirements of EVs due to the dynamic energy price signals issued following the system's timely operation. In addition, the results demonstrated an optimal energy exchange to support optimal operation and reduce overall costs.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2023.109923