Design and mixed integer linear programming optimization of a solar/battery based Conex for remote areas and various climate zones

Summary A system of parallelly placed Li‐ion batteries placed at different spacings and cooled by different coolants is analyzed. The prime focus is on the effect of spacings between the batteries on battery thermal performance. The temperature and heat flux at the coolant and battery interface is taken as continuous. Coolants used belong to thermal oils, gases, conventional oils, liquid metals, and nanofluids. Finite volume method‐based numerical analysis is performed upon validation with experimental work reported in the literature, by developing a code in the C language. The simultaneous effect of battery aspect ratio, thermal conductivity, Reynolds number (Ref), and heat generation at different battery spacings for each coolant on average Nusselt number (NuH) is explored. An analytical model is developed and artificial neuro‐fuzzy interference system (ANFIS) prediction of NuH is carried out later. Battery aspect ratio, thermal conductivity, and heat generation for any given coolant do not affect the NuH. However, the coolant Prandtl number at different battery spacings has a prominent effect on NuH. Thermal oils and liquid metal coolants show a significant and non‐linear variation of NuH with increased battery spacings. The effect of Ref on NuH was linear as expected for all the coolants at different battery spacings. However, NuH variations for liquid metal at different Ref were highly non‐linear. The mathematical model developed and ANFIS regression analysis indicated a very comfortable prediction of NuH. The R‐squared value of the mathematical model is up to 0.98 while from ANFIS it is up to 0.985 showing a very good fitness of these models.

Section: Introductionmentioning

confidence: 99%

Numerical analysis and machine learning for battery thermal performance cooled with different fluids

Afzal

Kumar

Jilte

et al. 2022

“…One of the main challenges of using these shelters is their power supply for heating and cooling in severe conditions. Therefore, using these solar panels along with a storage system is very beneficial in these cases 9 …”

Section: Introductionmentioning

confidence: 99%

“…Research on photovoltaic systems has mainly been carried out in countries with considerable solar radiation. These studies usually consider different variables and functions 9 . As an example, in Malaysia, the size optimization of solar panels and batteries is done to reach the minimum levelized cost of energy 15 .…”

Section: Introductionmentioning

confidence: 99%

“…10 Although batteries are the common systems for energy saving, they have a limited life cycle, and therefore, fuel cells are used as an alternative for energy saving. 8,9 In addition to technological issues, fuel cells have attracted great attention due to the high thermal capacity of hydrogen, extensive applications, cleanness, and minimum side effects for the environment. Malik et al 11 studied a solar-based energy system to generate hydrogen as an energy storage media.…”

Section: Introductionmentioning

confidence: 99%

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Using deep learning artificial intelligence and multiobjective optimization in obtaining the optimum ratio of a fuel cell to electrolyzer power in a hydrogen storage system

Mansir

Musharavati

Abubakar

2022

Summary Due to the depletion of fossil fuel sources and their pollution, renewable energy sources have attracted researchers' attention for energy supply; power generation using solar energy is one of the most important examples of these sources. This energy needs to be saved because of climate changes, to be used with high confidence. In this investigation, the heating and cooling of 100 Conex in Riyadh, Saudi Arabia, was studied, which used hydrogen storage as the central storage system. The main purpose of this project is to determine the ratio of fuel cell power to the electrolyzer power in this storage system, to reach the minimum dependency of the system on the urban power grid and at the same time, the minimum cost of electrolyzer and fuel cell. Because the shelters were simulated by Energy Plus and Open Studio Software and the energy supply system by TRNSYS Software, the optimization was not possible. Therefore, this system was changed into a network in MATLAB Software after simulation with deep learning artificial intelligence; then, it was optimized by a genetic algorithm. In order to accurately form a network, different parameters of artificial intelligence were studied precisely. The best result was obtained from a neural network with two hidden layers, each one containing 10 neurons, sigmoid activation function, and without dropout, with a correlation coefficient of 0.9998, which shows an excellent accuracy. The results of optimization also indicated that at all points, the Pareto front for fuel cell power is less than or equal to the power of the electrolyzer, and this ratio is 0.47 at the optimum point in terms of cost and electric consumption.

“…Researchers rarely prefer traditional techniques due to their limited space optimization 3 . Taslimi et al 4 used a LP technique to optimize an off‐grid PV/battery system in different climatic conditions in Iran. Their proposed model shows that PV/battery systems are more economically useful and efficient in desert or semiarid regions.…”

Section: Introductionmentioning

confidence: 99%

Size optimization of a stand‐alone hybrid photovoltaic/wind/battery renewable energy system using a heuristic optimization algorithm

Ayan

Toylan

2022

Today, it has become imperative to design these systems at optimum size to make reliable and minimize the total costs of renewable energy systems. There are many studies about size optimization in the literature. However, few studies have approached the problem with a heuristic method at different levels of reliability and compared the results with commercial software. This paper aims to fill this gap by proposing an artificial bee colony (ABC) algorithm with different reliability ratios for renewable energy systems size optimization and comparing the results of the proposed method with the results of the HOMER Pro software.The current methodology is validated with a real case study on the load demand of a school building (latitude 41.37, longitude 27.3) in Turkey. The peak and average load per day for the school building are 7.4 kW and 51.8 kW, respectively.The proposed method has presented 14.71%, 14.36%, and 6.23%, respectively, more economical solutions than HOMER Pro software for hybrid photovoltaic (PV)/wind/battery, PV/battery, and wind/battery renewable energy system scenarios. In addition, the ABC algorithms with different reliability ratios (%0, %0.2, %1, and %3) are used to analyze the effect on the system cost of the loss of power supply probability. According to the results, the hybrid renewable energy system consisting of 230 PV panels, 6 wind turbines, and 34 batteries is the most economical option with a total annual cost of $42 717.58 compared to stand-alone systems with a reliability %0 ratio. It has been seen that increasing the loss of power supply probability reduces system costs. Consequently, this study shows that using the ABC algorithm in size optimization of renewable energy systems yields accelerates the decision-making process and more optimized solutions.