Hierarchical predictive control scheme for distributed energy storage integrated with residential demand and photovoltaic generation. Issue 15 (1st November 2015)

• Record Type: Journal Article
• Authors: Lampropoulos, Ioannis; Garoufalis, Panagiotis; van den Bosch, Paul P.J.; Kling, Wil L.
• Is Part Of: IET generation, transmission & distribution. Volume 9:Issue 15(2015)
• Page Start: 2319
• Page End: 2327
• Publication Date: 2015-11-01
• Subjects: distributed power generation -- power generation control -- power generation economics -- predictive control -- hierarchical systems -- demand side management -- battery storage plants -- energy management systems -- building management systems -- building integrated photovoltaics -- power grids -- optimisation -- time‐domain analysis -- power generation scheduling -- power markets; imbalance settlement system -- day‐ahead auction -- electricity markets -- intra‐hour problems -- day‐ahead problems -- coordination parameters -- scheduling -- time domain analysis -- hierarchical decomposition -- integrated system -- economic optimisation -- photovoltaic installations -- residential customers -- low‐voltage distribution grid -- battery energy storage system -- energy management -- photovoltaic generation -- residential demand -- distributed energy storage -- hierarchical predictive control scheme; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2014.0908
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : A hierarchical control scheme is defined for the energy management of a battery energy storage system which is integrated in a low‐voltage distribution grid with residential customers and photovoltaic installations. The scope is the economic optimisation of the integrated system by employing predictive control techniques. The approach is based on hierarchical decomposition of the optimisation problem in the time domain by composing a three‐level scheduling and control scheme, that is, day‐ahead, intra‐hour, and real‐time, where the initial and final states of each sub‐problem are chosen as coordination parameters. The day‐ahead and the intra‐hour problems address the interactions with electricity markets during the scheduling phase. The real‐time algorithm is able to adapt the operation of the battery system according to updated information about market conditions, residential demand, and local generation, and subject to the network capacity and other technical constraints. The simulation scenarios address the interactions with the day‐ahead auction and the imbalance settlement system in the Netherlands.