In this paper, we assess the effectiveness of forecasting and optimization algorithms on a laboratory demonstration platform that mimics a domestic distribution grid with a high penetration of photovoltaic (PV) systems. Despite the uncertainties, the considered algorithms ensure efficient and secure real-time (RT) operation of the distribution grid, as well as the provision of flexibility services from the low-voltage (LV) distribution grid to the upstream medium-voltage (MV) grid. Uncertainties arise from the variations in PV systems power production and end-users' power consumption, as well as RT deployment of flexibility services. As a result of the considered algorithms, the distribution grid becomes active in the provision of flexibility services. The forecasting and optimization algorithms are based on Bayesian bootstrap quantile regression (BBQR) and distributionally robust chance-constrained (DRCC) programming, respectively. This paper also evaluates the framework of the laboratory demonstration platform for the deployment of the considered algorithms.

Real-Time Distribution Grid Control and Flexibility Provision under Uncertainties: Laboratory Demonstration

Proto D.;Mottola F.
2022

Abstract

In this paper, we assess the effectiveness of forecasting and optimization algorithms on a laboratory demonstration platform that mimics a domestic distribution grid with a high penetration of photovoltaic (PV) systems. Despite the uncertainties, the considered algorithms ensure efficient and secure real-time (RT) operation of the distribution grid, as well as the provision of flexibility services from the low-voltage (LV) distribution grid to the upstream medium-voltage (MV) grid. Uncertainties arise from the variations in PV systems power production and end-users' power consumption, as well as RT deployment of flexibility services. As a result of the considered algorithms, the distribution grid becomes active in the provision of flexibility services. The forecasting and optimization algorithms are based on Bayesian bootstrap quantile regression (BBQR) and distributionally robust chance-constrained (DRCC) programming, respectively. This paper also evaluates the framework of the laboratory demonstration platform for the deployment of the considered algorithms.
978-1-6654-4280-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/895104
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