Optimal placement of data concentrators for expansion of the smart grid communications network. Issue 4 (12th August 2019)

• Record Type: Journal Article
• Authors: Zhen, Todd; Elgindy, Tarek; Alam, S.M. Shafiul; Hodge, Bri‐Mathias; Laird, Carl D.
• Is Part Of: IET smart grid. Volume 2:Issue 4(2019)
• Page Start: 537
• Page End: 548
• Publication Date: 2019-08-12
• Subjects: linear programming -- optimisation -- integer programming -- distributed power generation -- telecommunication network reliability -- smart power grids; improved communication networks -- distributed generation sources -- optimal placement -- communication resources -- advanced metering infrastructure -- resilient power system -- network design formulations -- mixed‐integer linear -- nonlinear programming approaches -- network congestion -- data concentrators -- network routeing -- network connectivity -- robustness -- average residual buffer capacity -- maximising average residual capacity -- minimum residual capacity -- total reciprocal residual capacity -- much‐improved network load allocation -- network reliability -- multiperiod network expansion shows -- smart grid expansion -- smart grid communications network -- evolving power systems -- renewables penetration; B0170N Reliability -- B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- C1180 Optimisation techniques -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0006
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high‐performing, reliable, and resilient power system. Three network design formulations based on mixed‐integer linear and non‐linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much‐improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality‐of‐service in the event of an outage. Analysis of multi‐period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.