scholarly journals Multi-Objective Optimization of Hybrid Renewable Tri-Generation System Performance for Buildings

2022 ◽  
Vol 12 (2) ◽  
pp. 888
Author(s):  
Mohamed Ghorab ◽  
Libing Yang ◽  
Evgueniy Entchev ◽  
Euy-Joon Lee ◽  
Eun-Chul Kang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Heat Exchanger ◽  
Simulation Analysis ◽  
Ghg Emissions ◽  
Energy System ◽  
Energy Performance ◽  
Performance Parameters ◽  
Multi Objective ◽  
Hybrid Renewable Energy System ◽  
Hybrid Renewable Energy

Hybrid renewable energy systems are subject to extensive research around the world and different designs have found their way to the market and have been commercialized. These systems usually employ multiple components, both renewable and conventional, combined in a way to increase the system’s overall efficiency and resilience and to lower GHG emissions. In this paper, a hybrid renewable energy system was designed for residential use and its annual energy performance was investigated and optimized. The multi-module hybrid system consists of a Ground-Air Heat Exchanger (GAHX), Photovoltaic Thermal (PVT) panels and Air to Water Heat Pump (AWHP). The developed system’s annual performance was simulated in the TRaNsient SYStem (TRNSYS) environment and optimized using the General Algebraic Modelling System (GAMS) platform. Multi-objective non-linear optimization algorithms were developed and applied to define optimal system design and performance parameters while reducing cost and GHG emissions. The results revealed that the designed system was able to satisfy building thermal heating/cooling loads throughout the year. The ground source heat exchanger contributed 21.3% and 26.3% of the energy during heating and cooling seasons, respectively. The initial design was optimized in terms of key performance parameters and module sizes. The annual simulation analysis showed that the system was able to self-generate and meet nearly 29.4% of the total HVAC electricity needs, with the rest being supplied by the grid. The annual system module performance efficiencies were 13.4% for the PVT electric and 5.5% for the PVT thermal, with an AWHP COP of 4.0.

scholarly journals Simulation-Based Optimization of Hybrid Renewable Energy System for Off-grid Rural Electrification

2021 ◽  
Vol 10 (4) ◽  
pp. 667-686
Author(s):  
Akinola Sunday Oladeji ◽  
Mudathir Funsho Akorede ◽  
Salihu Aliyu ◽  
Abdulrasaq Apalando Mohammed ◽  
Adebayo Wahab Salami
Keyword(s):  
Renewable Energy ◽  
Rural Areas ◽  
Energy System ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Hybrid Renewable Energy

There is a need to develop an optimization tool that can be applied in the feasibility study of a hybrid renewable energy system to find the optimal capacity of different renewable energy resources and support the decision makers in their performance investigation. A multi-objective function which minimizes the Levelized Cost of Energy (LCOE) and Loss of Load Probability Index (LLPI) but maximizes the novel Energy Match Ratio (EMR) was formulated. Simulation-based optimization method combined with ε-constraint technique was developed to solve the multi-objective optimization problem. In the study, ten-year hourly electrical load demand, using the end-use model, is estimated for the communities. The performance of the developed algorithm was evaluated and validated using Hybrid Optimization Model for Electric Renewables (HOMER®) optimization software. The developed algorithm minimized the LCOE by 6.27% and LLPI by 167% when compared with the values of LCOE ($0.444/kWh) and LLPI (0.000880) obtained from the HOMER® optimization tool. Also, the LCOE with the proposed approach was calculated at $0.417/kWh, which is lower than the $0.444/kWh obtained from HOMER®. From environmental perspective, it is found that while 141,370.66 kg of CO2 is saved in the base year, 183,206.51 kg of CO2 is saved in the ninth year.The study concluded that the approach is computationally efficient and performed better than HOMER® for this particular problem.The proposed approach could be adopted for carrying out feasibility studies and design of HRES for Off-Grid electrification, especially in the rural areas where access to the grid electricity is limited

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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