This paper presents an energy management platform based on a receding-horizon scheme for the optimal control of active and reactive power flows in microgrids, including small-size photovoltaic, combined heat and power, wind generation, mini-hydro, and energy storage. The objective function to be minimized can be set both on the daily operational costs (economic indicator) and on the global CO2 emissions of the system (environmental indicator), whereas decision variables are the production schedules of the generators and the power flows across the grid. The tool includes the technical constraints characterizing low voltage/medium voltage (LV/MV) microgrids and gives the user the possibility to select different models for the electrical network (nonlinear power flow equations, linear approximation, and single bus-bar) and different optimization ranges. The energy management system has been validated through an experimental campaign on the smart polygeneration microgrid of the University of Genoa, which provides electricity and thermal energy to the Savona Campus, an 'open-air' demo-site of an environmentally sustainable urban district with a population of about 2200 people. The results of the tests on the field highlight the robustness of the developed platform and the capability of the receding-horizon algorithm at the core of it of successfully treating data uncertainties.
An Energy Management Platform for the Optimal Control of Active and Reactive Powers in Sustainable Microgrids
Delfino F.;Ferro G.;Robba M.;Rossi M.
2019-01-01
Abstract
This paper presents an energy management platform based on a receding-horizon scheme for the optimal control of active and reactive power flows in microgrids, including small-size photovoltaic, combined heat and power, wind generation, mini-hydro, and energy storage. The objective function to be minimized can be set both on the daily operational costs (economic indicator) and on the global CO2 emissions of the system (environmental indicator), whereas decision variables are the production schedules of the generators and the power flows across the grid. The tool includes the technical constraints characterizing low voltage/medium voltage (LV/MV) microgrids and gives the user the possibility to select different models for the electrical network (nonlinear power flow equations, linear approximation, and single bus-bar) and different optimization ranges. The energy management system has been validated through an experimental campaign on the smart polygeneration microgrid of the University of Genoa, which provides electricity and thermal energy to the Savona Campus, an 'open-air' demo-site of an environmentally sustainable urban district with a population of about 2200 people. The results of the tests on the field highlight the robustness of the developed platform and the capability of the receding-horizon algorithm at the core of it of successfully treating data uncertainties.File | Dimensione | Formato | |
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