Name
Abstract | ||
|---|---|---|
Distributed generation (DG) uses many small on-site energy harvesting deployments at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to a large fraction of buildings is challenging. In this paper, we explore an alternative approach that combines market-based electricity pricing models with on-site renewables and modest energy storage (in the form of batteries) to incentivize DG. We propose a system architecture and optimization algorithm, called GreenCharge, to efficiently manage the renewable energy and storage to reduce a building's electric bill. To determine when to charge and discharge the battery each day, the algorithm leverages prediction models for forecasting both future energy demand and future energy harvesting. We evaluate GreenCharge in simulation using a collection of real-world data sets, and compare with an oracle that has perfect knowledge of future energy demand/harvesting and a system that only leverages a battery to lower costs (without any renewables). We show that GreenCharge's savings for a typical home today are near 20%, which are greater than the savings from using only net metering. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/JSAC.2013.130711 | IEEE Journal on Selected Areas in Communications |
Keywords | Field | DocType |
building management systems,energy harvesting,energy management systems,energy storage,load forecasting,metering,power markets,pricing,renewable energy sources,smart power grids,supply and demand,GreenCharge,carbon emissions,demand peaks,distributed generation,distribution losses,electric bill,electric grid,energy demand forecasting,energy storage,intermittent renewables,local supply and demand,market-based electricity pricing models,net metering,on-site energy harvesting deployments,on-site renewables,optimization algorithm,prediction models,real-world data sets,renewable energy,smart buildings,system architecture,transmission losses,Energy storage,Peak Shaving,Renewable Energy,Smart Grid | Smart grid,Efficient energy use,Computer science,Real-time computing,Peak demand,Zero-energy building,Distributed generation,Pumped-storage hydroelectricity,Intermittent energy source,Environmental economics,Net metering | Journal |
Volume | Issue | ISSN |
31 | 7 | 0733-8716 |
Citations | PageRank | References |
15 | 0.79 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mishra, A. | 1 | 15 | 0.79 |
| David E. Irwin | 2 | 899 | 98.12 |
| Prashant J. Shenoy | 3 | 6386 | 521.30 |
| Jim Kurose | 4 | 5307 | 610.06 |