Loading...
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 424.77 KB | Adobe PDF |
Advisor(s)
Abstract(s)
Energy systems worldwide are complex and challenging
environments. Multi-agent based simulation platforms are increasing at a high
rate, as they show to be a good option to study many issues related to these
systems, as well as the involved players at act in this domain. In this scope the
authors’ research group has developed a multi-agent system: MASCEM (Multi-
Agent System for Competitive Electricity Markets), which performs realistic
simulations of the electricity markets. MASCEM is integrated with ALBidS
(Adaptive Learning Strategic Bidding System) that works as a decision support
system for market players. The ALBidS system allows MASCEM market
negotiating players to take the best possible advantages from each market
context. However, it is still necessary to adequately optimize the players’
portfolio investment. For this purpose, this paper proposes a market portfolio
optimization method, based on particle swarm optimization, which provides the
best investment profile for a market player, considering different market
opportunities (bilateral negotiation, market sessions, and operation in different
markets) and the negotiation context such as the peak and off-peak periods of
the day, the type of day (business day, weekend, holiday, etc.) and most
important, the renewable based distributed generation forecast. The proposed
approach is tested and validated using real electricity markets data from the
Iberian operator – MIBEL.
Description
Keywords
Multi-agent based simulation MASCEM ALBidS
Citation
Publisher
Springer