This paper formalizes a multi-level optimization problem for a distribution power grid. The grid is set up with Electrical Vehicles (EVs) to minimize the total energy cost and satisfy different loads distributed in the grid. At the higher level, the Distribution System Operator (DSO) must deal with a Balancing Market to minimize costs. Instead, at a lower level, Electrical Vehicle Aggregators (EVAs) aim at controlling the charging and discharging of Electrical Vehicles (EVs) maximizing their profit. The optimization problem of EVAs is solved analytically through KKT (Karush-Khun-Tucker) conditions and inserted into the DSO optimization problem. The complete optimization model has been implemented and tested in the IEEE 13-bus system. The results show that the proposed bi-level model significantly reduced the variation of peak consumption by 64.56% and the power cost by 3.07%.

A Multilevel Optimization Model for a Distribution Power Grid with the Active Participation of Electric Vehicles Via Aggregators

Valderrama, Daniel Fernández;Ferro, Giulio;Parodi, Luca;Robba, Michela
2023-01-01

Abstract

This paper formalizes a multi-level optimization problem for a distribution power grid. The grid is set up with Electrical Vehicles (EVs) to minimize the total energy cost and satisfy different loads distributed in the grid. At the higher level, the Distribution System Operator (DSO) must deal with a Balancing Market to minimize costs. Instead, at a lower level, Electrical Vehicle Aggregators (EVAs) aim at controlling the charging and discharging of Electrical Vehicles (EVs) maximizing their profit. The optimization problem of EVAs is solved analytically through KKT (Karush-Khun-Tucker) conditions and inserted into the DSO optimization problem. The complete optimization model has been implemented and tested in the IEEE 13-bus system. The results show that the proposed bi-level model significantly reduced the variation of peak consumption by 64.56% and the power cost by 3.07%.
2023
979-8-3503-1140-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1155320
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