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.
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Notes
- 1.
ctb payments are defined as negative numbers, thus their maximum values are represented by the minPosition vector and their minimum values by the maxPosition vector.
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This work was supported in part by the EU funded research project CASSANDRA (FP7-ICT-288429).
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Ntagka, E., Chrysopoulos, A., Mitkas, P.A. (2014). Designing Tariffs in a Competitive Energy Market Using Particle Swarm Optimization Techniques. In: Ceppi, S., et al. Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets. AMEC AMEC TADA TADA 2014 2013 2014 2013. Lecture Notes in Business Information Processing, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-13218-1_10
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