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 homeu0027s solar energy. We describe a smart solar inverter system with battery which can either operate in an 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 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.
Year
Venue
Field
2018
arXiv: Systems and Control
Inverter,Mathematical optimization,Job shop scheduling,Scheduling (computing),Cost of electricity by source,Solar energy,Solar power,Battery (electricity),Reliability engineering,Mathematics,Solar inverter
DocType
Volume
Citations 
Journal
abs/1810.11178
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Richard Bean136933.05
Hina Khan200.34