Multi-Objective Optimization for Peak Shaving with Demand Response under Renewable Generation Uncertainty

Sane Lei Lei Wynn, Watcharakorn Pinthurat () and Boonruang Marungsri ()
Additional contact information
Sane Lei Lei Wynn: School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Watcharakorn Pinthurat: School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
Boonruang Marungsri: School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Energies, 2022, vol. 15, issue 23, 1-19

Abstract: With high penetration of renewable energy sources (RESs), advanced microgrid distribution networks are considered to be promising for covering uncertainties from the generation side with demand response (DR). This paper analyzes the effectiveness of multi-objective optimization in the optimal resource scheduling with consumer fairness under renewable generation uncertainty. The concept of consumer fairness is considered to provide optimal conditions for power gaps and time gaps. At the same time, it is used to mitigate system peak conditions and prevent creating new peaks with the optimal solution. Multi-objective gray wolf optimization (MOGWO) is applied to solve the complexity of three objective functions. Moreover, the best compromise solution (BCS) approach is used to determine the best solution from the Pareto-optimal front. The simulation results show the effectiveness of renewable power uncertainty on the aggregate load profile and operation cost minimization. The results also provide the performance of the proposed optimal scheduling with a DR program in reducing the uncertainty effect of renewable generation and preventing new peaks due to over-demand response. The proposed DR is meant to adjust the peak-to-average ratio (PAR) and generation costs without compromising the end-user’s comfort.

Keywords: multi-objective gray wolf optimization; demand response; generation scheduling; microgrid; renewable energy uncertainties (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/23/8989/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/23/8989/ (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:15:y:2022:i:23:p:8989-:d:986410

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 ().