Aggregation of Consumers Participation in the Ramping of a Demand Response Event
Omid Abrishambaf, Pedro Faria, Zita Vale
Abstract
Omid Abrishambaf, Pedro Faria, Zita Vale
Abstract
As the global population is daily soaring, the need for electrical energy is also increasing. This makes the role of the power distribution network more tangible, as the efficiency of all sectors should be increased. The need for smart management and strategic planning, such as demand response programs are obvious in the context. This paper proposes an aggregator model that employs DR programs for managing the network balance. In this model, a specific analysis has been provided for the ramp period and demand response timeline to show the financial behaviors of the aggregator. In the case study of the paper, two demand response events are proposed using actual consumption profiles and a cost comparison has been presented using various pricing schemes. The results remark the costs related to the ramp period before the event and show how such costs are important in daily electricity expenses of the aggregator model.
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As the global population is daily soaring, the need for electrical energy is also increasing. This makes the role of the power distribution network more tangible, as the efficiency of all sectors should be increased. The need for smart management and strategic planning, such as demand response programs are obvious in the context. This paper proposes an aggregator model that employs DR programs for managing the network balance. In this model, a specific analysis has been provided for the ramp period and demand response timeline to show the financial behaviors of the aggregator. In the case study of the paper, two demand response events are proposed using actual consumption profiles and a cost comparison has been presented using various pricing schemes. The results remark the costs related to the ramp period before the event and show how such costs are important in daily electricity expenses of the aggregator model.
Key concepts: News aggregator, Demand response, Timeline, Load management, Context (archaeology), Smart grid, Electricity, Demand patterns