Load Management Through Agent Based Coordination of Flexible Electricity Consumers
- 920 Downloads
Demand Response (DR) offers a cost-effective and carbon-friendly way of performing load balancing. DR describes a change in the electricity consumption of flexible consumers in response to the supply situation. In DR, flexible consumers may perform their own load balancing through load management (LM) mechanisms. However, the individual amount of load balancing capacity exhibited by the majority of flexible consumers is limited and as a result, coordinated LM of several flexible electricity consumers is needed in order to replace existing conventional fossil based load balancing services. In this paper, we propose an approach to perform such coordination through a Virtual Power Plant (VPP). We represent flexible electricity consumers as software agents and we solve the coordination problem through multi-objective multi-issue optimization using a mediator-based negotiation mechanism. We illustrate how we can coordinate flexible consumers through a VPP in response to external events simulating the need for load balancing services.
KeywordsDemand response Load balancing Load management Multi-agent systems Distributed coordination Virtual power plant
Unable to display preview. Download preview PDF.
- 1.Pudjianto, D., Ramsay, C., Strbac, G.: Microgrids and virtual power plants: concepts to support the integration of distributed energy resources. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 222, 731–741 (2008)Google Scholar
- 5.Watson, D.S.: Fast automated demand response to enable the integration of renewable resources (2013)Google Scholar
- 6.Clausen, A., Ghatikar, G., Jørgensen, B.N.: Load management of data centers as regulation capacity in denmark (2014)Google Scholar
- 8.Mathieu, S., Ernst, D., Louveaux, Q.: An efficient algorithm for the provision of a day-ahead modulation service by a load aggregator. In: 4th European Innovative Smart Grid Technologies (ISGT 2013) (2013)Google Scholar
- 9.Hinrichs, C., Sonnenschein, M., Lehnhoff, S.: Evaluation of a self-organizing heuristic for interdependent distributed search spaces. In: ICAART (1), pp. 25–34 (2013)Google Scholar
- 11.Hinrichs, C., Bremer, J., Sonnenschein, M.: Distributed hybrid constraint handling in large scale virtual power plants. In: 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), pp. 1–5. IEEE (2013)Google Scholar
- 14.Rytter, M., Sørensen, J., Jørgensen, B., Körner, O.: Advanced model-based greenhouse climate control using multi-objective optimization. In: IV International Symposium on Models for Plant Growth, Environmental Control and Farm Management in Protected Cultivation, vol. 957, pp. 29–35 (2012)Google Scholar