Name
Abstract | ||
|---|---|---|
Market-based electricity pricing provides consumers an opportunity to lower their electric bill by shifting consumption to low price periods. In this paper, we explore how to lower electric bills without requiring consumer involvement using an intelligent charging system, called SmartCharge, and an on-site battery array to store low-cost energy for use during high-cost periods. SmartCharge's algorithm reduces electricity costs by determining when to switch the home's power supply between the grid and the battery array. The algorithm leverages a prediction model we develop, which forecasts future demand using statistical machine learning techniques. We evaluate SmartCharge in simulation using data from real homes to quantify its potential to lower bills in a range of scenarios. We show that typical savings today are 10-15%, but increase linearly with rising electricity prices. We also find that SmartCharge deployed at only 22% of 435 homes reduces the aggregate demand peak by 20%. Finally, we analyze SmartCharge's installation and maintenance costs. Our analysis shows that battery advancements, combined with an expected rise in electricity prices, have the potential to make the return on investment positive for the average home within the next few years. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2208828.2208857 | e-Energy |
Keywords | Field | DocType |
aggregate demand peak,smart home,electric bill,electricity cost,battery advancement,market-based electricity pricing,energy storage,lower bill,electricity price,on-site battery array,electricity bill,battery array,average home,switches,battery,demand forecasting,electricity,energy conservation,pricing,learning artificial intelligence,energy,real time systems,prediction model,grid,return on investment,maintenance engineering,statistical analysis,aggregate demand | Electricity market,Energy storage,Stand-alone power system,Energy conservation,Demand forecasting,Electricity,Engineering,Electricity retailing,Operations management,Environmental economics,Electricity pricing | Conference |
Citations | PageRank | References |
43 | 2.61 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aditya Mishra | 1 | 128 | 8.50 |
| David Irwin | 2 | 563 | 30.93 |
| Prashant J. Shenoy | 3 | 6386 | 521.30 |
| Jim Kurose | 4 | 5307 | 610.06 |
| Ting Zhu | 5 | 214 | 12.32 |