Simultaneous power and heat scheduling of microgrids considering operational uncertainties: A new stochastic p-robust optimization approach

Mohammadreza Mazidi, Navid Rezaei and Abdolsalam Ghaderi

Energy, 2019, vol. 185, issue C, 239-253

Abstract: Uncertainties of renewable power generation, load, and electricity price imposes serious challenges in optimal scheduling of microgrids (MGs). To cope with this issue, advanced models should be developed to optimize scheduling problem of MGs under uncertainties. However, the optimality and robustness of solutions as well as the computational burden are still a matter of concern when applied to scheduling of a practical MG. To provide a model which is adjustably robust and computationally tractable and also financial effective at the same time, this paper proposes a novel uncertainty handling framework based on stochastic p-robust optimization method for day-ahead scheduling of MGs including renewable and non-renewable distributed generations, combined heat and power systems, energy-storage systems, as well as demand response providers. The objective is to find optimal scheduling of MG in a way that the expected profit is maximized while the regret in each scenario is bounded. The effectiveness of the proposed model is tested on a MG test system thorough different case studies. Simulation results demonstrate that the robustness of the scheduling results is enhanced significantly without substantial decrease in the expected profit of MG.

Keywords: Combined heat and power (CHP) system; Day-ahead scheduling; Microgrid; Stochastic p-robust optimization method (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219313738
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:185:y:2019:i:c:p:239-253

DOI: 10.1016/j.energy.2019.07.046

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().