Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

Spyros Giannelos (), Stefan Borozan, Marko Aunedi, Xi Zhang, Hossein Ameli, Danny Pudjianto, Ioannis Konstantelos and Goran Strbac
Additional contact information
Spyros Giannelos: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Stefan Borozan: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Marko Aunedi: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Xi Zhang: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Hossein Ameli: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Danny Pudjianto: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Ioannis Konstantelos: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Goran Strbac: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK

Energies, 2023, vol. 16, issue 13, 1-15

Abstract: The decarbonisation of the electricity grid is expected to create new electricity flows. As a result, it may require that network planners make a significant amount of investments in the electricity grids over the coming decades so as to allow the accommodation of these new flows in a way that both the thermal and voltage network constraints are respected. These investments may include a portfolio of infrastructure assets consisting of traditional technologies and smart grid technologies. One associated key challenge is the presence of uncertainty around the location, the timing, and the amount of new demand or generation connections. This uncertainty unavoidably introduces risk into the investment decision-making process as it may lead to inefficient investments and inevitably give rise to excessive investment costs. Smart grid technologies have properties that enable them to be regarded as investment options, which can allow network planners to hedge against the aforementioned uncertainty. This paper focuses on key smart technologies by providing a critical literature review and presenting the latest mathematical modelling that describes their operation.

Keywords: coordinated voltage control; demand side response; dynamic line rating; electric vehicles; energy storage; grid to vehicle; vehicle to grid; vehicle to building; soft open point (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/13/5088/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/13/5088/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:13:p:5088-:d:1184502

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().