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

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)...

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Bibliographic Details
Published in:Sustainability 2019-12, Vol.11 (23), p.6550
Main Authors: Mohamed, Al-Attar Ali, Ali, Shimaa, Alkhalaf, Salem, Senjyu, Tomonobu, Hemeida, Ashraf M.
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Decision making
Distributed generation
Electric power distribution
Energy demand
Energy resources
Energy sources
Fuel technology
Genetic algorithms
Greenhouse effect
Greenhouse gases
Hydrologic cycle
Mathematical problems
Multiple objective analysis
Optimization
Photovoltaics
Random variables
Renewable energy sources
Renewable resources
Simulation
Sorting algorithms
Stress concentration
Turbines
Wind power
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su11236550