A dynamic data-driven optimization framework for demand side management in microgrids

Haluk Damgacioglu, Mehrad Bastani, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The efficient utilization of distributed generation resources (DGs) and demand side management (DSM) in large-scale power systems play a crucial role in satisfying and controlling electricity demand through an economically viable and environmentally friendly way. However, uncertainties in power generation from DGs, variations in load demand, and conflicts in objectives (emission, cost, etc.) pose major challenges to determine the optimal operation planning of microgrids. In this chapter, we propose a dynamic data-driven multi-objective optimization model for a day-ahead operation planning for microgrids, integrating interruption load management (ILM) as a DSM program, while collectively considering the total cost and emissions as objective functions. The proposed model includes three modules that interact with each other: (1) a simulation module that captures the behavior of operating components such as solar panels, wind turbines, etc. and provides the data for the optimization model; (2) an optimization module that determines the optimal operational plan, which includes utilization of diesel generators, purchased electricity from utility and interrupted load, considering cost and emissions objective functions using-constraint method; and (3) a rulebased real-time decision making module that adapts the operation plan from the optimization model based on dynamic data from the microgrid and sends the revised plan back to the microgrid. The capabilities and performance of the proposed dynamic data-driven optimization framework are demonstrated through a case study of a typical electrical power system. The resultant operation plan is quite promising regarding total cost and CO2 emission.

Original languageEnglish (US)
Title of host publicationHandbook of Dynamic Data Driven Applications Systems
PublisherSpringer International Publishing
Pages489-504
Number of pages16
ISBN (Electronic)9783319955049
ISBN (Print)9783319955032
DOIs
StatePublished - Nov 13 2018

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint Dive into the research topics of 'A dynamic data-driven optimization framework for demand side management in microgrids'. Together they form a unique fingerprint.

  • Cite this

    Damgacioglu, H., Bastani, M., & Celik, N. (2018). A dynamic data-driven optimization framework for demand side management in microgrids. In Handbook of Dynamic Data Driven Applications Systems (pp. 489-504). Springer International Publishing. https://doi.org/10.1007/978-3-319-95504-9_21