Open AccessApplication of the method of dynamic programming for solving the optimal unit commitment problem in distribution networks with local distributed generation
Author(s) -
Natasha Dimishkovska,
Atanas Iliev
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/878/1/012012
Subject(s) - distributed generation , renewable energy , energy storage , computer science , distributed power , intermittent energy source , grid , distributed computing , moment (physics) , power (physics) , distributed data store , wind power , power system simulation , mathematical optimization , electric power system , electrical engineering , voltage , engineering , mathematics , physics , geometry , classical mechanics , quantum mechanics
The continuous growth of communities leads to an increase in electrical energy usage. Besides the technological advance of power systems, still, there are outages which lead to an interruption in the power supply. The implementation of distributed generation provides usage of the local renewable energy sources for satisfying local power needs which in good manner complexes the distribution networks. However, because the distributed energy resources depend on the weather conditions, the islanded work is not quite reliable. Grid-connected distributed energy resources have enhanced power supply reliability. In this paper, a dynamic programming model is used for the optimisation of power generation by the local distributed energy resources. Assuming the distributed energy resources have storage systems, the model takes into account the battery charge at the moment and the availability of the distributed energy resources depending on the weather conditions and whether is day or night. It is assumed that the distributed energy resources have equal power capacities. Case study reviews a low voltage distribution network with plenty of distributed energy resources from different renewable energy sources implemented and a cluster of household consumers.