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The increased penetration of distributed and volatile renewable generation requires the demand-side to be actively involved in energy balancing operations. This paper proposes a solution in which big data and machine learning methods are employed to enhance the capabilities of a Virtual Power Plant to participate and intelligently bid into a demand response energy market. The energy market being investigated...
The negative effects of variable renewable generation in the power systems requires the engagement of the demand-side. In fact, consumers-owned energy resources can be engaged to provide the flexibility to the power grid, thus increasing its reliability. This study presents an algorithm for allocating tasks to distributed consumer owned energy resources, to enable consumers to participate in the automated...
This study presents a design of a distributed architecture and an application for automated demand response, in which one electricity aggregator cooperates with a group of consumers. The distributed architecture utilizes an asynchronous message oriented middleware to integrate the defined components. The application adopts a distributed demand response optimization algorithm for the validation of...
This paper presents a cost optimization scheme for an electricity aggregator. The aggregator schedules the charging of the energy storages of its aggregated group of consumers to minimize the hourly spot market costs, while simultaneously maximizing its potential for participation in the reserve market. The optimization is formulated as a distributed iterative algorithm and its performance on the...
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