Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm

Al-Attar Ali Mohamed, Shimaa Ali, Salem Alkhalaf, Tomonobu Senjyu and Ashraf M. Hemeida
Additional contact information
Al-Attar Ali Mohamed: Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Shimaa Ali: Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt
Salem Alkhalaf: Department of Computer Science, Alrass College of Science and Arts, Qassim University, Qassim, Arrass 51921, Saudi Arabia
Tomonobu Senjyu: Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Senbaru 9030213, Japan
Ashraf M. Hemeida: Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt

Sustainability, 2019, vol. 11, issue 23, 1-20

Abstract: This article offers a multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models. Many renewable and non-renewable energy resources like photovoltaic system (PV), micro-turbine (MT), fuel cell (FC), and wind turbine system (WT) are incorporated in a grid-connected hybrid power system to supply energy demand. The main aim of this article is to maximize environmental, technical, and economic benefits by minimizing various objective functions such as the annual cost, power loss and greenhouse gas emission subject to different power system constraints and uncertainty of renewable energy sources. For each load model, optimum DER size and its corresponding location are calculated. To test the feasibility and validation of the multi-objective water cycle algorithm (MOWCA) is conducted on the IEEE-33 bus and IEEE-69 bus network. The concept of Pareto-optimality is applied to generate trilateral surface of non-dominant Pareto-optimal set followed by a fuzzy decision-making mechanism to obtain the final compromise solution. Multi-objective non-dominated sorting genetic (NSGA-III) algorithm is also implemented and the simulation results between two algorithms are compared with each other. The achieved simulation results evidence the better performance of MOWCA comparing with the NSGA-III algorithm and at different load models, the determined DER locations and size are always righteous for enhancement of the distribution power system performance parameters.

Keywords: Multi-objective Water Cycle Algorithm; different load models; hybrid power system; greenhouse gas emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/23/6550/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/23/6550/ (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:jsusta:v:11:y:2019:i:23:p:6550-:d:289081

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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