Day-Ahead Rolling Window Optimization of Islanded Microgrid with Uncertainty

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units


The integral variability of electricity demand and intermittency of renewable energy resources (RERs) pose special challenges in the operation of islanded Microgrid (MG). The uncertainty associated with load and generation data further magnifies the problem resulting in huge energy curtailments in real time thus making MG operation expensive. Hence, this paper proposes a stochastic 24-hour ahead rolling window optimal energy scheduling framework to minimize the amount of lost load and lost generation in islanded MG while considering the demand response (DR) potential of heating, ventilation air-conditioning (HVAC) loads, end user thermal preferences and storage systems. These thermal characteristics are modeled via two-capacity building model. The proposed methodology is formulated as a stochastic linear programming problem. The effectiveness of the framework is validated by simulation for the entire winter season using realistic data. Ultimately, the results are used to calculate and control loss of load probability (LOLP) and loss of load expectation (LOLE) in islanded MG at minimum cost.


Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
Publication statusPublished - 10 Dec 2018
MoE publication typeA4 Article in a conference publication
EventIEEE PES Europe Conference on Innovative Smart Grid Technologies - Sarajevo, Bosnia and Herzegovina
Duration: 21 Oct 201825 Oct 2018
Conference number: 8

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe
ISSN (Print)2165-4816
ISSN (Electronic)2165-4824


ConferenceIEEE PES Europe Conference on Innovative Smart Grid Technologies
Abbreviated titleISGT Europe
CountryBosnia and Herzegovina

    Research areas

  • Islanded MG, LOLE, LOLP, stochastic optimization, two-capacity building model

ID: 31892945