Multi-mode monitoring and energy management for photovoltaic-storage systems

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution
network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response,
photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations
and improve installation performance. However, during this procedure other functionalities that energy storage
could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the
optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that
interacts with parameters of the electrical grid at the common connection point and also allows the compensation
of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural
network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The
results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in
the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand
forecasting model that improves the supervision, automation and analysis of daily energy production. A series of
results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of
multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the
total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively.