We address the problem of assessing the power flexibility that a pool of prosumers equipped with a generalized storage device can offer to the electrical grid as an ancillary service for balancing power demand and generation. A key feature of the proposed approach is that the disaggregation policy is computed jointly with the aggregate flexibility set, and it is hence readily available for the pool to supply any (feasible) power profile request from the grid. Each prosumer is assumed to provide a contribution which is an affine function of the aggregated power profile. The coefficients of the affine policies are designed by solving a distributed optimization program where the volume of the aggregate flexibility set is maximized while satisfying the power and energy constraints of each storage device and additional constraints involving multiple (possibly all) devices. Simulation results show the superiority of the proposed approach with respect to a state-of-the-art method that inspired our work.

Ancillary services provision via aggregation: Joint power flexibility assessment and disaggregation policy design

Zamudio, Daniel;Falsone, Alessandro;Prandini, Maria
2023-01-01

Abstract

We address the problem of assessing the power flexibility that a pool of prosumers equipped with a generalized storage device can offer to the electrical grid as an ancillary service for balancing power demand and generation. A key feature of the proposed approach is that the disaggregation policy is computed jointly with the aggregate flexibility set, and it is hence readily available for the pool to supply any (feasible) power profile request from the grid. Each prosumer is assumed to provide a contribution which is an affine function of the aggregated power profile. The coefficients of the affine policies are designed by solving a distributed optimization program where the volume of the aggregate flexibility set is maximized while satisfying the power and energy constraints of each storage device and additional constraints involving multiple (possibly all) devices. Simulation results show the superiority of the proposed approach with respect to a state-of-the-art method that inspired our work.
2023
Aggregates; Approximation error; Batteries; Mathematical models; Task analysis; Trajectory; Wind
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1256266
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