Abstract
This paper presents a decision support Tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy ressource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead
scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.
scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.
Original language | English |
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Title of host publication | 7th International Conference on Intelligent System Applications to Power Systems |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2013 |
Publication status | Published - 2013 |
Event | 17th International Conference on Intelligent Systems Application to Power Systems - Tokyo, Japan Duration: 1 Jul 2013 → 4 Jul 2013 http://www.isc.meiji.ac.jp/~hmori/isap2013/ |
Conference
Conference | 17th International Conference on Intelligent Systems Application to Power Systems |
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Country/Territory | Japan |
City | Tokyo |
Period | 01/07/2013 → 04/07/2013 |
Internet address |
Keywords
- Hybrid technique
- Mixed-integer linear programming
- Optimal scheduling
- Particle Swarm Optimization
- Vehicle-to-grid