Publication
Methodology for real impact assessment of the best location of distributed electric energy storage
| Summary: | This paper presents a methodology to provide a decision maker, e.g. the distribution system operator,information on the associated impacts of the operation of distributed electric energy storage systems(ESS1) in an urban environment, in order to support the choice of the best locations of storage units. Thedeveloped methodology uses three types of profile prototypes based on actual data, obtained throughclustering techniques. These profiles, which include electricity demand, electricity prices and renewableelectricity production, are used to optimize the placement of electric energy storage units. The paper con-siders expected attitudes of the main stakeholders towards distributed electric ESS implementation, anddiscusses possible regulatory framework options to define the distributed electric ESS business model.The model was applied to a case study using the nanophosphate lithium-ion battery technology as anexample. Results show a significant influence of the charge/discharge profile of batteries on the choiceof their best locations. |
|---|---|
| Main Authors: | Gonçalves, José |
| Other Authors: | Martins, António; Pires Neves, Luís |
| Subject: | Distributed electric energy storage Genetic algorithms Electric networks Energy profiles Energy service Methodologya |
| Year: | 2016 |
| Country: | Portugal |
| Document type: | article |
| Access type: | restricted access |
| Associated institution: | Universidade de Leiria e Oeste |
| Language: | English |
| Origin: | IC-online |
| Summary: | This paper presents a methodology to provide a decision maker, e.g. the distribution system operator,information on the associated impacts of the operation of distributed electric energy storage systems(ESS1) in an urban environment, in order to support the choice of the best locations of storage units. Thedeveloped methodology uses three types of profile prototypes based on actual data, obtained throughclustering techniques. These profiles, which include electricity demand, electricity prices and renewableelectricity production, are used to optimize the placement of electric energy storage units. The paper con-siders expected attitudes of the main stakeholders towards distributed electric ESS implementation, anddiscusses possible regulatory framework options to define the distributed electric ESS business model.The model was applied to a case study using the nanophosphate lithium-ion battery technology as anexample. Results show a significant influence of the charge/discharge profile of batteries on the choiceof their best locations. |
|---|
Funded activities
No funded projects found.