Flexibility exchange strategy to facilitate congestion and voltage profile management in power networks

LIAO, Huilian and MILANOVIĆ, Jovica V. (2018). Flexibility exchange strategy to facilitate congestion and voltage profile management in power networks. IEEE Transactions on Smart Grid.

Liao-FleibilityExchangeStrategy(AM).pdf - Accepted Version
All rights reserved.

Download (1MB) | Preview
Official URL: https://ieeexplore.ieee.org/document/8453879/
Link to published version:: https://doi.org/10.1109/TSG.2018.2868461


This paper proposes a novel flexibility exchange strategy to facilitate the management of congestion issues and voltage profiles (e.g. avoiding voltage violation and reducing voltage fluctuation) via minimum participation from customers or aggregators. In the proposed approach, the expectation of voltage profiles and power flow is determined by network constraints and customers' requirement, and it is used to guide the estimation of network state towards the expected state so that the pre-defined expectation (regarding voltage profile and power flow) is fulfilled. Availability of flexibility exchange from customers is integrated in estimation process. Flexibility factors are proposed to constrain the variation of network variables including voltage, power consumption/generation and power flow. A genetic algorithm based optimisation procedure is applied to obtain the minimum power variation from customers (i.e., minimum power variation from customers) while the defined expectation and constraints of flexibility availability are met. The approach is tested out on two representative distribution networks and the results have demonstrated the feasibility of the proposed approach in obtaining optimal flexibility exchange strategy that meets the pre-defined requirement/expectation whilst involving the least power variation from customers

Item Type: Article
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Engineering Research
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number: https://doi.org/10.1109/TSG.2018.2868461
Depositing User: Huilian Liao
Date Deposited: 20 Sep 2018 13:12
Last Modified: 18 Mar 2021 07:49
URI: https://shura.shu.ac.uk/id/eprint/22415

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics