Conference paper
Dispatch of distributed energy resources to provide energy and reserve in smart grids using a particle swarm optimization approach
The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized.
The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation.
Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network.
The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2013 |
| Pages: | 51-58 |
| Proceedings: | 2013 IEEE Symposium on Computational Intelligence Applications in Smart Grid |
| ISBN: | 1467360015 , 1467360023 , 9781467360012 and 9781467360029 |
| ISSN: | 23267690 and 23267682 |
| Types: | Conference paper |
| DOI: | 10.1109/CIASG.2013.6611498 |
| ORCIDs: | Morais, Hugo |
Bioengineering Power, Energy and Industry Applications SDG 7 - Affordable and Clean Energy particle swarm optimisation power distribution planning power generation dispatch power generation scheduling power markets smart power grids
33 bus distribution network Business Demand response Electricity Energy resources Equations Generators Mathematical model Network simulation Smart grids demand response distributed energy resources distributed generation resources distributed generation units distribution network electricity markets joint dispatch medium voltage consumers optimal schedule particle swarm optimization particle swarm optimization approach power distribution operation resources scheduling problem smart grids virtual power player virtual power players
