Name
Abstract | ||
|---|---|---|
To meet the demand for locally-produced and sustainable power, community microgrids distribute power generated by roof-mounted solar PV systems to 'green' consumers. In this context, we consider the problem of matching one or more inherently intermittent solar energy producers with each green consumer so that, with a high probability, a certain component of their load is met from solar generation. We formulate this optimal matching as a stochastic optimization problem which incorporates the uncertainty of both solar and loads. To solve the problem, we propose two approaches which make different assumptions on the distributions of solar generation and loads. We compare the performance of these algorithms using real data, and find that, for our dataset, the approach that assumes Gaussian mixture models for solar and loads best fits our design requirements. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3208903.3208930 | E-ENERGY'18: PROCEEDINGS OF THE 9TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS |
Keywords | Field | DocType |
Virtual power plant,matching,solar,loads,stochastic optimization | Stochastic optimization,Mathematical optimization,Optimal matching,Computer science,Solar energy,Virtual power plant,Photovoltaic system,Mixture model | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sun Sun | 1 | 53 | 5.66 |
| Srinivasan Keshav | 2 | 3778 | 761.32 |
| Catherine Rosenberg | 3 | 1877 | 137.46 |
| Matthew Peloso | 4 | 0 | 0.34 |