Title: Using solar and load predictions in battery scheduling at the residential level,
Abstract: Smart solar inverters can be used to store, monitor and manage a home's solar
energy. We describe a smart solar inverter system with battery which can either
operate in an automatic mode or receive commands over a network to charge and
discharge at a given rate. In order to make battery storage financially viable
and advantageous to the consumers, effective battery scheduling algorithms can
be employed. Particularly, when time-of-use tariffs are in effect in the region
of the inverter, it is possible in some cases to schedule the battery to save
money for the individual customer, compared to the "automatic" mode. Hence,
this paper presents and evaluates the performance of a novel battery scheduling
algorithm for residential consumers of solar energy. The proposed battery
scheduling algorithm optimizes the cost of electricity over next 24 hours for
residential consumers. The cost minimization is realized by controlling the
charging/discharging of battery storage system based on the predictions for
load and solar power generation values. The scheduling problem is formulated as
a linear programming problem. We performed computer simulations over 83
inverters using several months of hourly load and PV data. The simulation
results indicate that key factors affecting the viability of optimization are
the tariffs and the PV to Load ratio at each inverter. Depending on the tariff,
savings of between 1% and 10% can be expected over the automatic approach. The
prediction approach used in this paper is also shown to out-perform basic
"persistence" forecasting approaches. We have also examined the approaches for
improving the prediction accuracy and optimization effectiveness.