Techno-Economic Analysis of Solar Absorption Cooling for Commercial buildings in India

Narayanan, Muthalagappan

doi:10.14710/ijred.6.3.253-262

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, for better optimization, the NREL firstly recommend simulating and performing the integrated hybrid system with HOMER and then proceed to other software like HYBRID2 (Ayop et al 2018). Moreover, despite, there are various software tools that are available for modelling and optimizing renewable energy systems in the market (Bartolucci et al 2023;Zhao et al 2023;Güven and Samy 2022;Samy et al 2018;, Narayanan 2017, Zwalnan et al 2021Missoum and Loukarfi, 2021), the HOMER remains one of the most popular tools for sizing of the integrated system.…”

Section: Technical and Economic Inputsmentioning

confidence: 99%

HOMER optimization of standalone PV/Wind/Battery powered hydrogen refueling stations located at twenty selected French cities

Oueslati

2023

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

The current study proposes a model of autonomous Hydrogen Refuelling Stations (HRFS) installed on different sites in twenty French cities powered by renewable clean energy sources. The station is fully powered by photovoltaic (PV) panels, wind turbines with battery storage and involving an electrolyzer and hydrogen tank for producing and storing hydrogen. Using Homer simulation, three scenarios are investigated to propose an optimized model, namely Scenario 1 containing (PV-Wind-Battery) system, Scenario 2 with (Wind-Battery) technologies and Scenario 3 with (PV-Battery) components. The otimization process executed demonstrates very competitive levelized cost of energy (LCOE) and levelized cost of hydrogen (LCOH) especially for the third scenario solely based on PV power with LCOE in range $0.354-0.435/kWh and a LCOH varying within $13.5-16.5/kg, for all 20 cities. An average net present cost (NPC) value of $ 1,561,429 and $ 2,522,727 are predicted for the first and second architectures while least net present cost of $1,038,117 is estimated for the third combination solely based on solar power according to all sites considered. For instance, minimum values are obtained for Marseille city with LCOE=$ 0.354/kWh and a LCOH=$ 13.5 /kg in conformity with the minimum obtained value of NPC value of $886,464 with respect to the winner third scenario. In addition, more costly hydrogen production is expected for Grenoble city especially for scenario 1 and 2 where wind turbine technology is introduced. On another hand, thorough analysis of PV/wind hydrogen techno-economic operation is provided including improvements recommendations, scenarios comparison and environmental impact discussion.

show abstract

Section: Technical and Economic Inputsmentioning

confidence: 99%

HOMER optimization of standalone PV/Wind/Battery powered hydrogen refueling stations located at twenty selected French cities

Oueslati

2023

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

show abstract

“…As the society and economy develop rapidly, the building energy consumption has been accounting for an increasing share within the total energy consumption of the society (Narayanan et al 2017). Buildings hold significant potential both in terms of demand response (DR) and energy efficiency improvement (EEI) (Esmaeili Shayan et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

Su,

Duan,

Zhuang

et al. 2023

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption. The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.

show abstract

“…Narayanan [36] used TRNSYS to simulate and perform a techno-economic analysis of a single stage Type 107 absorption chiller. They determined that with a capital cost of $82,000 for the solar adsorption system compared to $13,000 for an air conditioner system (16 unit) provided a simple payback period of 13.6 years, where it takes 15.5 years for the solar absorption system to pay off its initial investment.…”

Section: Economics Of Solar Combination Systemsmentioning

confidence: 99%

Techno-Economic Analysis of a Solar Adsorption Cooling System for Residential Applications in Canada

McNally¹

View full text Add to dashboard Cite

Techno-Economic Analysis of Solar Absorption Cooling for Commercial buildings in India

Cited by 3 publications

References 8 publications

HOMER optimization of standalone PV/Wind/Battery powered hydrogen refueling stations located at twenty selected French cities

HOMER optimization of standalone PV/Wind/Battery powered hydrogen refueling stations located at twenty selected French cities

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

Techno-Economic Analysis of a Solar Adsorption Cooling System for Residential Applications in Canada

Contact Info

Product

Resources

About