Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Demand Response Shifting Management Applied to Distributed Generation and Pumping
Recent energy policies in countries around the world, including in Europe, point to the need to integrate growing amounts of distributed generation in electric power systems. This situation led to several changes in the operation and planning of power systems. This paper presents a methodology focusing on demand response programs, distributed generation and pumping, which is aimed to be used by a Virtual Power Player, who is able to manage the available resources minimizing the operation costs. The influence of demand response shifting management, in which was possible to shift load from a critical period to other more benefic, was also taken into account. In this paper it was used Artificial Intelligence, Artificial Neural Networks (ANN), to predict the power the VPP would have to pump to reservoirs to fulfill the reservoir operator demands along the day. The case study includes 2223 consumers and 47 distributed generators units. The implemented scenario corresponds to a real day in Portuguese power system, 9th March 2014. ; The present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; 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.
Demand Response Shifting Management Applied to Distributed Generation and Pumping
Recent energy policies in countries around the world, including in Europe, point to the need to integrate growing amounts of distributed generation in electric power systems. This situation led to several changes in the operation and planning of power systems. This paper presents a methodology focusing on demand response programs, distributed generation and pumping, which is aimed to be used by a Virtual Power Player, who is able to manage the available resources minimizing the operation costs. The influence of demand response shifting management, in which was possible to shift load from a critical period to other more benefic, was also taken into account. In this paper it was used Artificial Intelligence, Artificial Neural Networks (ANN), to predict the power the VPP would have to pump to reservoirs to fulfill the reservoir operator demands along the day. The case study includes 2223 consumers and 47 distributed generators units. The implemented scenario corresponds to a real day in Portuguese power system, 9th March 2014. ; The present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; 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.
Demand Response Shifting Management Applied to Distributed Generation and Pumping
Diogo Boldt (Autor:in) / Pedro Faria (Autor:in) / Zita Vale (Autor:in)
07.12.2015
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC:
690
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