This paper addresses the problem of optimal management of consumer flexibility in an electric distribution system. Aggregation of a number of consumers clustered according to appropriate criteria, is one of the most promising approaches for modifying the daily load profile at nodes of an electric distribution network. Modifying the daily load profile is recognized as one of the strongest needs both for safe and efficient operation of the network. The paper proposes an optimization approach allowing the aggregator, i.e., the operator which manages the aggregated consumers, to gather flexibility and generate bids for the energy market, with the aim of maximizing its revenue. It is shown that this problem can be solved through mixed integer linear programming. Numerical simulation results are provided for validating the proposed approach.
Optimization models for consumer flexibility aggregation in smart grids: The ADDRESS approach / Alessandro, Agnetis; Gabriella, Dellino; Gianluca De Pascale, ; Innocenti, Giacomo; Marco, Pranzo; Antonio, Vicino. - ELETTRONICO. - (2011), pp. 96-101. (Intervento presentato al convegno 2011 IEEE 1st International Workshop on Smart Grid Modeling and Simulation, SGMS 2011 tenutosi a Brussels, Belgium nel 17 Oct. 2011) [10.1109/SGMS.2011.6089206].
Optimization models for consumer flexibility aggregation in smart grids: The ADDRESS approach
INNOCENTI, GIACOMO;
2011
Abstract
This paper addresses the problem of optimal management of consumer flexibility in an electric distribution system. Aggregation of a number of consumers clustered according to appropriate criteria, is one of the most promising approaches for modifying the daily load profile at nodes of an electric distribution network. Modifying the daily load profile is recognized as one of the strongest needs both for safe and efficient operation of the network. The paper proposes an optimization approach allowing the aggregator, i.e., the operator which manages the aggregated consumers, to gather flexibility and generate bids for the energy market, with the aim of maximizing its revenue. It is shown that this problem can be solved through mixed integer linear programming. Numerical simulation results are provided for validating the proposed approach.File | Dimensione | Formato | |
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