Building agents for power trading agent competition (PowerTAC)
Current energy models and infrastructures are facing the challenging of restructuring in order to face the changes in energy consumption, production and management. The adoption of renewable power sources combined with the capability of a more autonomous demand-side participation on the grid lead to...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/72841 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | Current energy models and infrastructures are facing the challenging of restructuring in order to face the changes in energy consumption, production and management. The adoption of renewable power sources combined with the capability of a more autonomous demand-side participation on the grid lead to this energy revolution. These changes demand improvements in the way participants act, not only related to the physical infrastructure, but mainly regarding related services, most notably energy markets.
The cost of testing new approaches for smart grid markets in real market is too high, Power TAC provides a safe simulation environment in which participants develop agents to act as brokers. In this context, brokers’ primary goal is to maximize its cash position. In this project, we developed a broker which won first place in our experiment setting which is a simulation involving top 3 agents from 2017 competition. We had separate strategies in three markets, namely wholesale market, tariff market and balancing market to ensure that we can buy electricity at a relatively low price and earn a steady, high revenue in the tariff market through reaching tariff contract with customers. At the same time, we need to maintain a real-time balance as imbalance between supply and demand is highly discouraged. This was done through accurate prediction of customer demand and customer production. |
---|