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Smart solar inverters can be used to store, monitor and manage a home's solarenergy. We describe a smart solar inverter system with battery which can eitheroperate in an automatic mode or receive commands over a network to charge anddischarge at a given rate. In order to make battery storage financially viableand advantageous to the consumers, effective battery scheduling algorithms canbe employed. Particularly, when time-of-use tariffs are in effect in the regionof the inverter, it is possible in some cases to schedule the battery to savemoney for the individual customer, compared to the "automatic" mode. Hence,this paper presents and evaluates the performance of a novel battery schedulingalgorithm for residential consumers of solar energy. The proposed batteryscheduling algorithm optimizes the cost of electricity over next 24 hours forresidential consumers. The cost minimization is realized by controlling thecharging/discharging of battery storage system based on the predictions forload and solar power generation values. The scheduling problem is formulated asa linear programming problem. We performed computer simulations over 83inverters using several months of hourly load and PV data. The simulationresults indicate that key factors affecting the viability of optimization arethe 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. Theprediction approach used in this paper is also shown to out-perform basic"persistence" forecasting approaches. We have also examined the approaches forimproving the prediction accuracy and optimization effectiveness.

Using solar and load predictions in battery scheduling at the residential level