Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints

Julian Garcia-Guarin, David Alvarez, Arturo Bretas and Sergio Rivera
Additional contact information
Julian Garcia-Guarin: Electrical and Electronics Engineering Department, Engineering Faculty, Universidad Nacional de Colombia, Bogotá 111321, Colombia
David Alvarez: Electrical and Electronics Engineering Department, Engineering Faculty, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Arturo Bretas: Engineering Faculty, University of Florida, Gainesville, FL 32611, USA
Sergio Rivera: Electrical and Electronics Engineering Department, Engineering Faculty, Universidad Nacional de Colombia, Bogotá 111321, Colombia

Energies, 2020, vol. 13, issue 17, 1-19

Abstract: Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

Keywords: load shedding; optimization of energy demand supply; smart microgrid scheduling; 0-1 knapsack problem (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/17/4567/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/17/4567/ (text/html)

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:gam:jeners:v:13:y:2020:i:17:p:4567-:d:408303

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().