Journal ArticleDOI
Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid
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This paper presents an optimization technique base on a Multi-Objective Genetic Algorithm (MOGA) which uses high temporal resolution insolation data taken at 10 seconds data rate instead of more commonly used hourly data rate to determine the baseline system cost necessary to meet the load requirements.Abstract:
Renewable energy sources continues to gain popularity. However, two major limitations exist that prevent widespread adoption: availability of the electricity generated and the cost of the equipment. Distributed generation, (DG) grid-tied photovoltaic-wind hybrid systems with centralized battery back-up, can help mitigate the variability of the renewable energy resource. The downside, however, is the cost of the equipment needed to create such a system. Thus, optimization of generation and storage in light of capital cost and variability mitigation is imperative to the financial feasibility of DC microgrid systems. PV and wind generation are both time dependent and variable but are highly correlated, which make them ideal for a dual-sourced hybrid system. This paper presents an optimization technique base on a Multi-Objective Genetic Algorithm (MOGA) which uses high temporal resolution insolation data taken at 10 seconds data rate instead of more commonly used hourly data rate. The proposed methodology employs a techno-economic approach to determine the system design optimized by considering multiple criteria including size, cost, and availability. The result is the baseline system cost necessary to meet the load requirements and which can also be used to monetize ancillary services that the smart DC microgrid can provide to the utility at the point of common coupling (PCC) such as voltage regulation. The hybrid smart DC microgrid community system optimized using high-temporal resolution data is compared to a system optimized using lower-rate temporal data to examine the effect of the temporal sampling of the renewable energy resource.read more
Citations
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Journal ArticleDOI
Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems
TL;DR: An update literature review on trends in optimization techniques used for the design and development of solar photovoltaic–wind based hybrid energy systems is presented and suggests using hybridization of two or more algorithms to overcome the limitations of a single algorithm.
Journal ArticleDOI
Energy Management and Control System for Laboratory Scale Microgrid Based Wind-PV-Battery
TL;DR: In this article, an energy management and control system for laboratory scale microgrid based on hybrid energy resources such as wind, solar, and battery is proposed, which operates in autonomous mode and has an open architecture platform for testing multiple different control configurations.
Journal ArticleDOI
A review on hybrid renewable energy systems
TL;DR: In this paper, a comprehensive review of optimal sizing, energy management, operating and control strategies and integration of different renewable energy sources to constitute a hybrid system is presented, where the feasibility of different controllers such as microcontroller, proportional integral controller, hysteresis controller and fuzzy controller are presented.
Journal ArticleDOI
Review of hybrid renewable energy systems with comparative analysis of off-grid hybrid system
TL;DR: In this paper, the authors present a case study of remote area Barwani, India and results are compared using Homer and PSO as compared to HOMER, and the resulting analysis reveals that configurations of hybrid system are the most techno-economical feasible solution concerning COE, renewable fraction, maximum renewable penetration, levelized cost, operating cost, mean electrical efficiency, and emission amongst various hybrid system configurations.
Journal ArticleDOI
A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids
TL;DR: In this paper, a review of existing optimization objectives, constraints, solution approaches and tools used in microgrid energy management is presented, which can provide a foundation to embark on an in depth study in the area of energy management for smart microgrid network.
References
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