Abstract
In this work, we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load-controllable devices and small-scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multiagent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, ECOOP, where agents learn a prediction model regarding potential coalition partners and so can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterization for our solution in terms of complexity, as well as an empirical analysis against real consumption data sets, based on the macro-model of the Australian energy market, showing a performance improvement of about 17%.
| Original language | English |
|---|---|
| Pages (from-to) | 401-427 |
| Number of pages | 27 |
| Journal | Computational Intelligence |
| Volume | 33 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Aug 2017 |
Keywords
- multiagent systems, smart electricity grids, power regulation, dynamic coalition formation
ASJC Scopus subject areas
- Computational Mathematics
- Artificial Intelligence
