Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy

Rajkumar, Rajparthiban; Ramachandaramurthy, Vigna K.; Yong, B.L.; Chia, D.B.

doi:10.1016/j.energy.2011.06.017

Cited by 158 publications

(59 citation statements)

References 13 publications

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“…Although hybrid techniques enhance the overall performance of the optimization, they may suffer from some limitations. Examples of such limitations are the partial optimism of the hybrid MCS-PSO method in [40], suboptimal solutions of the hybrid iterative/GA in [41], cost-sizing compromise of the hybrid methods in [42,43], design complexity of the hybrid ANN/GA/MCS method in [44], random adjusting of the inertia weight of the evolutionary algorithm in [46] and coding complexity of the optimization-MCS in [47].…”

Section: Hybrid Techniquesmentioning

confidence: 99%

“…This combination is referred to as hybrid techniques. Examples of such techniques are SA-Tabu search; Monte Carlo simulation (MCS)-PSO; hybrid iterative/GA; MODO (multiobjective design optimization)/GA; artificial neural fuzzy interface system (ANFIS); artificial neural network/GA/MCS; PSO/DE (differential evolution); evolutionary algorithms and simulation optimization-MCS which have been used in several studies for optimizing HRESs [38][39][40][41][42][43][44][45][46][47]. Although hybrid techniques enhance the overall performance of the optimization, they may suffer from some limitations.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

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Optimizing Hybrid Renewable Energy Systems: A Review

Ghofrani¹,

Hosseini²

2016

Sustainable Energy - Technological Issues, Applications and Case Studies

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With the fast progression of renewable energy markets, the importance of combining different sources of power into a hybrid renewable energy system (HRES) has gained more attraction. These hybrid systems can overcome limitations of the individual generating technologies in terms of their fuel efficiency, economics, reliability and flexibility. One of the main concerns is the stochastic nature of photovoltaic (PV) and wind energy resources. Wind is often not correlated with load patterns and may be discarded sometimes when abundantly available. Also, solar energy is only available during the day time. A hybrid energy system consisting of energy storage, renewable and nonrenewable generation can alleviate the issues associated with renewable uncertainties and fluctuations. Large number of random variables and parameters in a hybrid energy system requires an optimization that most efficiently sizes the hybrid system components to realize the economic, technical and designing objectives. This chapter provides an overview of optimal sizing and optimization algorithms for hybrid renewable energy systems as well as different objective functions considered for designing such systems.

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Section: Hybrid Techniquesmentioning

confidence: 99%

Section: Hybrid Techniquesmentioning

confidence: 99%

Optimizing Hybrid Renewable Energy Systems: A Review

Ghofrani¹,

Hosseini²

2016

Sustainable Energy - Technological Issues, Applications and Case Studies

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“…This is when the AI generates results based on the prediction or classification of input data (with an appropriate degree of error) and presents it as new sets of data patterns of previous examples and models [107]. The examples of the AI techniques that are commonly used for the design and sizing of the power supply systems are the Artificial Neural Network (ANN) [110,111], Fuzzy Logic [108,112], Genetic Algorithms (GA) [43,66,101,105,113], wavelet transforms [114], and hybrid methods with multi-objectives (combinations of two or more AI techniques) [115,116]. An example of PV system sizing using an AI technique of the ANN is where the longitude and latitude of the BS site location are considered as the inputs to the ANN.…”

Section: System Sizing and Optimizationmentioning

confidence: 99%

“…Additionally, the daily average load demand together with the daily average available energy from the sun are balanced in order to obtained the number of PV arrays required by the system [107]. Being the simplest technique in sizing the power supply components, the energy balance takes into consideration the path losses and efficiencies of the energy sources (i.e., renewable energy), converters, and controllers, while the reliability of the supply approach estimates the loss of load probability by calculating the ratio of all energy deficits to the load demand at a certain period of time [34,108,109]. Likewise, other similar concepts for the reliability of the supply approach are loss of power probability (LOPP), loss of power supply probability (LPSP), and load coverage rate (LCR) [107].…”

Section: System Sizing and Optimizationmentioning

confidence: 99%

Sustainable Power Supply Solutions for Off-Grid Base Stations

2015

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Abstract:The telecommunication sector plays a significant role in shaping the global economy and the way people share information and knowledge. At present, the telecommunication sector is liable for its energy consumption and the amount of emissions it emits in the environment. In the context of off-grid telecommunication applications, offgrid base stations (BSs) are commonly used due to their ability to provide radio coverage over a wide geographic area. However, in the past, the off-grid BSs usually relied on emission-intensive power supply solutions such as diesel generators. In this review paper, various types of solutions (including, in particular, the sustainable solutions) for powering BSs are discussed. The key aspects in designing an ideal power supply solution are reviewed, and these mainly include the pre-feasibility study and the thermal management of BSs, which comprise heating and cooling of the BS shelter/cabinets and BS electronic equipment and power supply components. The sizing and optimization approaches used to design the BSs' power supply systems as well as the operational and control strategies adopted to manage the power supply systems are also reviewed in this paper.

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“…Some researchers discussed an optimal design model that used the form of PV/wind/battery. Then, they used genetic algorithm to solve the problem of configuration with the power sources [1][2][3][4]. Considering controllable load, the study provided an optimization method of genetic algorithm and tabu search in an isolated island in Japan [5].…”

mentioning

confidence: 99%

Optimal Configuration for Distributed Generations in Micro-grid System Considering Diesel as the Main Control Source

Chen¹,

Wei²

2015

JEPE

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This paper proposes an optimal configuration of the distributed hybrid renewable generations based on the stand-alone micro-grid system, considering the diesel as the main control source. Due to the natural sources and load of user changes randomly and the non-linearity of the power output by renewable generations, an intelligent optimization method based on the improvement of the genetic algorithm and the control strategy are discussed. The instance analysis is compared with the optimization result of the hybrid system based on HOMER (hybrid optimization of multiple energy resources) and GA (genetic algorithm) method on Matlab software. The simulation result of the optimal configuration showed the new hybrid renewable system and would improve the power supply situation which decreased the cost of energy greatly compared with the conventional form of power supply system which was operated only by diesel. The conclusion of the comparing result between HOMER and GA method shows the advantages of the strategy for the diesel as main control sources.

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Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy

Cited by 158 publications

References 13 publications

Optimizing Hybrid Renewable Energy Systems: A Review

Optimizing Hybrid Renewable Energy Systems: A Review

Sustainable Power Supply Solutions for Off-Grid Base Stations

Optimal Configuration for Distributed Generations in Micro-grid System Considering Diesel as the Main Control Source

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