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Economic Impact of Demand Response in the Scheduling of Distributed Energy Resources
Demand Response (DR) allows consumers to participate in energy markets, thus assuming an active role. However, the need of an aggregator capable of managing these resources and making decisions accordingly with the objectives of such resources has not been fully addressed. The aggregator activities are complex, and therefore, in the need of intelligent support to accomplish reasonable solutions. This paper proposes a methodology to evaluate the advantages of using DR programs in the resource rescheduling while classification and regression trees are introduced to support the aggregator in terms of scheduling and tariffs definition. Often these techniques are used to help the aggregator decide, as they also learn through training. Focus is given to the use of trees to predict and decide, the consumers' prices and reduction levels to apply, respectively. The case study has 548 distributed generators, 10 external suppliers and 20310 consumers ; The present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; ELECON Project, REA grant agreement No 318912 (FP7 PIRSES-GA-2012- 318912); H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.
Economic Impact of Demand Response in the Scheduling of Distributed Energy Resources
Demand Response (DR) allows consumers to participate in energy markets, thus assuming an active role. However, the need of an aggregator capable of managing these resources and making decisions accordingly with the objectives of such resources has not been fully addressed. The aggregator activities are complex, and therefore, in the need of intelligent support to accomplish reasonable solutions. This paper proposes a methodology to evaluate the advantages of using DR programs in the resource rescheduling while classification and regression trees are introduced to support the aggregator in terms of scheduling and tariffs definition. Often these techniques are used to help the aggregator decide, as they also learn through training. Focus is given to the use of trees to predict and decide, the consumers' prices and reduction levels to apply, respectively. The case study has 548 distributed generators, 10 external suppliers and 20310 consumers ; The present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; ELECON Project, REA grant agreement No 318912 (FP7 PIRSES-GA-2012- 318912); H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.
Economic Impact of Demand Response in the Scheduling of Distributed Energy Resources
João Spínola (author) / Pedro Faria (author) / Zita Vale (author)
2016-01-11
Conference paper
Electronic Resource
English
DDC:
690
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