Pareto optimality for FACTS devices placement considering demand variations

• Published in: Electric power systems research 2022-10, Vol.211, p.108177, Article 108177
• Main Authors: Marrero-Vera, Samuel, Hernandez-Tejera, Mario, Nuez-Pestana, Ignacio
• Format: Article
• Language: English
• Subjects: FACTS placement; Pareto optimisation; Reactive power compensation; Transmission expansion planning
• ISSN: 0378-7796
• DOI: 10.1016/j.epsr.2022.108177

The placement of flexible AC transmission system (FACTS) devices in modern power systems requires analytical tools that enable the detailed modelling of demand. In this regard, the different methods found in the literature that have been proposed for demand modelling do not consider demand patterns. In this paper, a methodology for FACTS device placement considering demand variations is proposed. The mean relative improvement of different performance indices is used to evaluate the candidate solutions considering several demand scenarios. Pareto optimality is used for decision making. A one-year test, comprising 8760 demand scenarios, was carried out. The results concur with those in the published literature, and the methodology is shown to be successful in accounting for demand variations. Additionally, two tests were used to evaluate the worst case scenario approach by analysing the peak demand and least loading margin scenarios. It was found that, when demand scenarios with variable load share are used, the peak demand scenario may not coincide with the least loading margin scenario. The results show significant discrepancies between the mean annual and the worst case scenario approach, whether this is the peak demand or the least loading margin scenario. [Display omitted] •The increase of uncertainty in power systems requires more detailed analysis tools.•The majority of FACTS devices placement methods do not consider load variations.•Load variations influence FACTS devices placement results.•Discrepancies have been found between the single-scenario and the mean annual approaches.•Using disaggregated demand data, the peak scenario may not be the worst-case scenario.