**5. Conclusions**

In the present work, smart BMS for residential users with a grid-connected PV-storage system is proposed. The BMS is Internet-connected and it downloads 1-day ahead weather forecasts, which are used to obtain a provisional energy production for the PV generator. These data are compared with load estimations, based on historical data. The result is a provisional energy balance, which is used by the BMS to select the best strategy to discharge batteries. In particular, the BMS preserves battery charge, when high load and low production is expected, and performs peak shaving, when loads exceed a user-defined limit. The combination of these methods results in a reduction in absorption peaks from the grid, with negligible variations in terms of self-sufficiency. The proposed BMS is efficient in case of undersized batteries, where the energy available in the storage is often not sufficient to supply all the loads. For example, in case of a family composed of two persons with a PV plant with rated power 4 kW and a storage of 2 kWh, the reduction in absorption peak from the grid during winter days varies from 39 to 50%. Other combinations of PV and storage sizes are investigated and improvements in terms of peaks reduction are generally around 10%.

**89**

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw…*

PVGIS Photovoltaic Geographical Information System

*γth* temperature factor of power of PV generator (%/°C)

*DOI: http://dx.doi.org/10.5772/intechopen.80562*

BS battery system

PV photovoltaic

*ηcabl* Joule losses

*ηdirt* losses due to dirt *ηmis* losses due to mismatch *ηmix* global losses of PV generator *ηMPPT* DC/DC conversion losses *ηrefl* losses due to reflection *ηshad* losses due to shadings

ANN artificial neural networks

RES renewable energy sources

STC standard test conditions

SBMS smart battery management system

*ηcharge* charge efficiency of the battery *ηDC/AC* DC/AC conversion losses

*ηtherm* thermal losses of PV generator *Cbat* rated capacity of the battery (kWh)

*Eload* energy consumptions (kWh)

*EPV* PV production (kWh)

*G* solar irradiance (W/m2

(kWh)

*Ebatt,disch* battery energy provided to the loads (kWh)

*Eload, slot\_x* provisional loads in the time slot x (kWh) *Eload,TDT* estimated loads during the *TDT* (kWh)

*EPV\_1day-ahead* 1-day ahead expected PV production (kWh)

*GNOCT* solar Irradiance at NOCT conditions (W/m2

shaving strategy (kW) *Pmax,absorbed* maximum power absorbed from the grid (kW)

*NOCT* nominal operating cell temperature (°C) *PAC* AC power production of PV generator (kW) *Pbat* power exchanged by the battery (kW)

*PPV,r* rated power of PV generator (kW) *Rsuff* provisional self-sufficiency parameter *Rthre* threshold for the parameter *Rsuff SOC* state of charge of the battery *SOCmax,safety* maximum safety limit of SOC *SOCmin,a* minimum SOC in the time slot a *SOCmin,b* minimum SOC in the time slot b

*Eloads\_1day-ahead, 6 a.m.–6 p.m.* 1-day ahead expected loads in the time slot 6 a.m.–6 p.m.

*Pload,max* maximum load power satisfied by the grid in case of peak

)

)

BMS battery management system BESS battery energy storage system MPPT maximum power point tracker

**Nomenclature**

**Acronyms**

**Symbols**

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw… DOI: http://dx.doi.org/10.5772/intechopen.80562*
