Multi-objective optimization of hybrid renewable energy system by using novel autonomic soft computing techniques

2021 ◽  
Vol 94 ◽  
pp. 107350
Author(s):  
Gourab Das ◽  
M. De ◽  
K.K. Mandal
Keyword(s):  
Renewable Energy ◽  
Soft Computing ◽  
Energy System ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Soft Computing Techniques ◽  
Hybrid Renewable Energy

scholarly journals Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics

2021 ◽  
Author(s):  
Yingfeng Chen ◽  
Rui Wang ◽  
Mengjun Ming ◽  
Shi Cheng ◽  
Yiping Bao ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Supply ◽  
Energy System ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Load Demand ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Load Characteristics ◽  
Hybrid Renewable Energy

AbstractFinding the optimal size of a hybrid renewable energy system is certainly important. The problem is often modelled as an multi-objective optimization problem (MOP) in which objectives such as annualized system cost, loss of power supply probability etc. are minimized. However, the MOP model rarely takes the load characteristics into account. We argue that ignoring load characteristics may be inappropriate when designing HRES for a place with intermittent high load demand. For example, in a training base the load demand is high when there are training tasks while the demand decreases to a low level when there is no training task. This results in an interesting issue, that is, when the loss of power supply probability is determined at a specific value, say 15%, then it is very likely that most of loss of power supply would occur right in the training period which is unexpected. Therefore, this study proposes a constraint multi-objective model to deal with this issue—in addition to the general multi-objective optimization model, the loss of power supply probability over a critical period is set as a constraint. Correspondingly, the non-dominated sorting genetic algorithm II with a relaxed $$\epsilon $$ ϵ constraint handling strategy is proposed to address the constraint MOP. Experimental results on a real world application demonstrate that the proposed model and algorithm are both effective and efficient.

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