A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

Saeid Esmaeili, Amjad Anvari-Moghaddam, Shahram Jadid and Josep M. Guerrero
Additional contact information
Saeid Esmaeili: Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
Amjad Anvari-Moghaddam: Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark
Shahram Jadid: Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
Josep M. Guerrero: Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark

Energies, 2018, vol. 11, issue 7, 1-19

Abstract: Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

Keywords: hourly reconfiguration; MILP; MPC; operational scheduling; stochastic optimization (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/7/1884/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/7/1884/ (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:11:y:2018:i:7:p:1884-:d:158851

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 ().