Designing tariffs in a competitive energy market using Particle Swarm Optimization techniques

Abstract

The main challenge of the Smart Grid Paradigm is achieving a tight balance between supply and demand of electrical energy. A contemporary approach to address this challenge is the use of autonomous broker agents. These intelligent entities are able to interact with both producers and consumers by offering tariffs, in order to buy or sell energy, respectively, within a new energy market mechanism: the Tariff Market. Agents are incentivized to level supply and demand within their portfolio, in line with maximizing their profit. In this work, we study a profit optimization strategy that was implemented for Mertacor broker-agent, always considering the customized needs of his customers. The agent was developed and tested in the PowerTAC Competition platform, which provides a powerful benchmark for researching Tariff Markets. To fulfill the agent’s objectives, two types of strategies were implemented: (i) a tariff formation strategy and (ii) a tariff update strategy. Both strategies are treated as optimization problems, where the broker’s objective is maximizing its profit as well as maintaining an acceptable customer market share. To this end, Particle Swarm Optimization techniques were adopted. The results look very promising and there is a great future work potential based on them.