Genetic Algorithm-based Multi-objective Optimization Research Articles

Inerter-based tuned mass dampers for response control of multi-degree-of-freedom structures subjected to wind and earthquake excitations

The present work explores the utilization of the tuned mass damper-inerter (TMDI) in controlling the structural response subjected to wind and earthquake excitations. An example of a 25-storey reinforced concrete building is taken up. The equations of motion of the structure-TMDI combined system are established by assuming the building behaves as a shear-building with multiple degrees of freedom. These equations are solved using Newmark’s time integration method. The objective functions are the wind- and earthquake-induced structural response quantities, such as the maximum inter-storey drift ratio (MIDR) and peak floor acceleration (PFA). The prime contribution of this study is the inclusion of statistical measures such as the mean, 85th, and 95th percentiles for these quantities. A genetic algorithm-based multi-objective optimization is performed to estimate the optimum TMDI parameters. Based on the outcomes of this study, it can be recommended that the TMDI be optimized to perform well under the 95th percentile for MIDR and PFA in effectively reducing the peak structural responses. Frequency domain analysis results demonstrate TMDI’s multiple-mode controlling capability. The energy comparison plots of the uncontrolled and controlled structures prove the effectiveness of the optimum TMDI in providing enough damping and ensuring structural stability.

Compliant liquid dampers-inerter for mitigating wind-, wave-, and earthquake-induced vibrations of monopile offshore wind turbines

This work explores the possibility of installing compliant liquid dampers-inerter (CLDI) on monopile offshore wind turbines (OWTs) for controlling combined vibrations from wind, wave, and earthquakes. For numerical demonstration, the 5-MW NREL monopile OWT is taken up. Seven different wind speeds and a set of 20 earthquakes are considered. Hence, the means of structural response quantities are chosen. The peak and root mean square (RMS) values of displacement and acceleration responses of tower-top are monitored. A genetic algorithm-based multi-objective optimization is performed, and the optimum CLDI parameters are estimated for different ranges of inertance ratio and inerter topology. The optimization comprises four cases: firstly, minimizing the mean of peak responses and second, minimizing the mean of RMS responses in fore-aft (FA) as well as in side-side (SS) directions. Hence, each case has four objective functions, covering two loading scenarios, wind & wave, and earthquake. With the obtained optimum parameters, considering all these cases, the control effectiveness of CLDI is further investigated in time and frequency domains. The time domain results reveal that the CLDI controls the SS vibrations effectively, whereas, in FA direction, the efficiency is marginally less. Additionally, the frequency domain plots indicate the multi-mode control capability of CLDI.

Multi-objective optimization of hollow fiber membrane-based water cooler for enhanced cooling performance and energy efficiency

Water-mediated evaporative cooling systems often suffer from cross-contamination, droplet carryover, and high maintenance costs. This study proposes a novel liquid cooling solution, the hollow fiber membrane-based water cooler (HFMWC), to address these issues. The primary innovation of this study lies in the optimization of key design parameters of the countercurrent HFMWC, aligning them with stringent engineering requirements. Two standard working conditions for wet cooling towers are considered. Numerical modeling and response surface methodology are used to develop regression models correlating six key parameters (water velocity, air/water ratio, fiber length, fiber inner diameter, membrane thickness, and packing fraction) with two performance evaluation indexes (outlet water temperature and consumptive electric power ratio). A genetic algorithm-based multi-objective optimization is employed to obtain Pareto fronts, representing optimal trade-offs between the two indexes. Using the ideal point method, relative optimal solutions are identified, resulting in water temperature drops of 7.56 °C and 13.07 °C, with consumptive electric power ratios of 0.0128 kW h/m3 and 0.0138 kW h/m3 for conditions Ⅰ and Ⅱ, respectively. Most solutions achieve the highest energy efficiency rating while satisfying the design temperature difference. The average deviations between optimized and simulated data for the two indexes are 0.88 % and 3.88 %, demonstrating the reliability of the derived optimal solutions for engineering design. These findings not only offer a promising way to overcome the limitations of traditional evaporative cooling systems, but also contribute to the broader field of sustainable and efficient thermal management technologies.

Modified nondominated sorting genetic algorithm-based multiobjective optimization of a cross-coupled nonlinear PID controller for a Twin Rotor System

This paper presents an innovative nonlinear PID control scheme based on a modified nondominated sorting genetic algorithm validated using a laboratory helicopter model called the Twin Rotor System. The concepts of controlled elitism and dynamic crowding distance are incorporated into the proposed algorithm to progress towards the best solution from the entire population in order to solve the multiobjective optimization problem with good convergence characteristics. The addition of nonlinear functions to the cross-coupled PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement an optimal nonlinear PID control scheme for the angular displacements of the twin-rotor system, with integral square error and control energy taken as the multiobjective problems. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. The results indicate the advantage of the MNGSA-based tuning for the two degrees of freedom MIMO control with standard reference trajectories as per the TRMS330-10 model.

Thermoeconomic optimization for a solar power generating system capable of storing energy by using calcium looping process

Energy storage facilities play a crucial role in mitigating the fluctuations of renewable energy sources such as solar and wind. These facilities can be used in solar-driven power plants to store solar energy during the day for use during periods of low or no sunlight to increase the plant’s capacity factor. This study evaluates the application of calcium looping reaction as a thermochemical energy storage unit in solar power plants to produce renewable electricity 24 h per day. Supercritical and trans-critical CO2 cycles are employed as upper and bottoming cycles, respectively, to produce power from solar energy. The energy storage unit absorbs energy during the day through a chemical reaction and releases it at night via a reverse chemical reaction. The proposed system is evaluated from both thermodynamic and economic points of view. A genetic algorithm-based multi-objective optimization is also carried out considering different scenarios. Energy and exergy efficiencies of the power generation cycle and levelized cost of electricity are found to be 46.4%, 50%, and 132.5 $/MWh, respectively, under the base case operating conditions. The obtained energy efficiency is 2% higher than a similar design operating with subcritical CO2 Brayton and steam cycles. Furthermore, under the optimization conditions, the power cycle exergy efficiency is improved by 1.2%, and the levelized cost of electricity is decreased by 5.1% compared with the base case conditions.

An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging

High penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV charging depends on the incentive pricing mechanism and the load shifting protocols. In this study, a genetic algorithm-based multi-objective optimization model is developed to generate hourly dynamic Time-of-Use electricity tariffs and facilitate the decision making in load scheduling. As an illustrative example, a case study was carried out to examine the effect of applying demand response for EVs in Beijing, China. With the assumptions made, the maximum peak load can be reduced by 9.8% and the maximum customer savings for the EVs owners can reach 11.85%, compared to the business-as-usual case.

Genetic algorithm-based multi-objective optimization model for software bugs prediction

The accuracy and reliability of software are critical factors for consideration in the operation of any electronic or computing device. Although, there exist several conventional methods of software bugs prediction which depend solely on static code metrics without syntactic structures or semantic information of programs which are more appropriate for developing accurate predictive models. In this paper, software bugs are predicted using a Genetic Algorithm (GA)-based multi-objective optimization model implemented in MATLAB on the National Aeronautics and Space Administration (NASA) dataset comprising thirty-eight distinct factors reduced to six (6) major factors via the use of the Principal Component Analysis (PCA) algorithm with SPSS, after which a linear regression equation was derived. The developed GA- based multi-objective optimization model was well-tried and tested. The accuracy and sensitivity level were also analyzed for successful bug detection. The results for optimal values ranging from 95% to 97% were recorded at an average accuracy of 96.4% derived through MATLAB-implemented measures of critical similarities. The research findings reveal that the model hereto proposed will provide an effective solution to the problem of predicting buggy software in general circulation.

Analysis and Performance Optimization of Supercritical CO2 Recompression Brayton Cycle Coupled Organic Rankine Cycle Based on Solar Tower

Abstract Supercritical carbon dioxide (SCO2) Brayton cycle has been proved to be an efficient power cycle to replace the traditional steam Rankine cycle. The thermal efficiency of SCO2 cycle can be further improved by coupling another type of cycle (the bottom cycle) at the waste heat end. A supercritical carbon dioxide recompression Brayton cycle (SCRBC) coupled organic Rankine cycle (ORC) based on solar tower is designed and established. According to the requirements of the waste heat temperature range of the top cycle, R600 is selected as the working medium of ORC. Under the design conditions, the effects of split ratio on the net power, the thermal efficiency, and the exergy loss of the combined cycle are studied. The variation of thermal efficiency of each part of the system with split ratio under different turbine inlet pressures and temperatures is further analyzed, and the influence of turbine inlet pressure and working fluid mass flow ratio ε (mass flow ratio of CO2 to R600) on the system performance is analyzed. Genetic algorithm-based multiobjective optimization is used to obtain the Pareto solution set for the thermal performance and unit investment cost of the system. The results show that the thermal efficiency of the combined cycle can be increased by more than 2% compared with that of a single top cycle. There is an optimal split ratio to maximize the thermal efficiency of the combined cycle, and the positions of the optimal split ratio are different for different turbine inlet pressures. Finally, through the multiobjective optimization method, several groups of Pareto solutions can be found, which can provide some reference for engineering design.

Digital twin models for optimization and global projection of building-integrated solar chimney

Current research on solar chimneys has focused on developing regression-based empirical models, which might not be correct enough compared to the soft computing tools i.e., techniques of artificial intelligence. Current research has also published the search algorithm-based single-objective optimization of solar chimneys, whereas the latest genetic algorithm-based multi-objective optimization can be more useful for decision-making purposes. Current research has also not focused on the macro or global impact of the usage of solar chimneys in context with the energy, economic, environmental, social, and political framework of the world. This research fills these gaps by the development of a digital twin model of the solar chimney using a multivariate regression model based on the least square method and an artificial intelligence (AI) method based on multilayer perceptron artificial neural network (MLP-ANN) and their comparison. A multiobjective optimization study along with the application of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to optimize the geometric dimensions of the solar chimney considering four different climatic zones using an elitist non-dominated sorting genetic algorithm (NSGA-II) to maximize the number of air changes per hour, energy efficiency, and specific environmental influence. The results have shown that the statistical performance indicators like the coefficient of regression, etc., are higher for the MLP-ANN technique as compared to the multivariate regression method, thus, the AI technique is preferred. Comparison of optimization results with the base case condition has shown that the number of air changes can be improved between ∼71% and 87%. The global projection has demonstrated that the integration of solar chimneys in low-income countries with high air-conditioners installation rates must be necessary. It is also recommended to use solar chimneys in high-income countries for the renovation of the existing built environment to decrease the energy intensity of the building sector.

An innovative green multi-generation system centering around concentrating PVTs and biomass heaters, design and multi-objective optimization

This article introduces an innovative off-grid biomass/solar-based building cooling/power system equipped with concentrated photovoltaic thermal (CPVT) panels, a specific biomass heater design, an absorption chiller, and thermal energy storage tanks. The hourly variation of performance, economic, and environmental metrics is evaluated to investigate the proposed system's feasibility for a case study building in India. The effect of significant operational parameters on the system's performance is assessed by observing their influence on levelized cost of energy, biomass effectiveness, solar and biomass heat load ratios, and levelized carbon dioxide emission. The optimum design and operating conditions are ascertained through a genetic algorithm-based multi-objective optimization approach contemplating techno-economic-environmental facets to be satisfied simultaneously. According to the results, the highest monthly heat and electricity produced by the CPVT system are 521.8 kWh and 593.4 kWh in May. The results further reveal that sugarcane baggas is an excellent option compared to the most commonly used biomass in India because of the lowest price sensitivity. The parametric study results show that increasing the generator temperature leads to a favorable techno-environmental condition. Besides, the results reveal that the increase in solar panels is not economically suitable due to a higher levelized cost of energy. The optimization outcomes demonstrate that the optimum values of levelized cost of energy, biomass effectiveness, and levelized carbon dioxide emission are 58.1 USD/MWh, 3342.3 MWh, and 233.6 kg/MWh, respectively. Eventually, it can be observed that the chiller size and mass flow rate entering the CPVT panels are sensitive parameters and the optimum range of the generator temperature is between 91 °C and 95 °C.

Integrating hydrogen liquefaction with steam methane reforming and CO2 liquefaction processes using techno-economic perspectives

The increasing demand for liquid hydrogen as an environmentally friendly energy source necessitates developing an effective hydrogen liquefaction process. In this study, hydrogen production (steam methane reforming), hydrogen liquefaction, and CO2 liquefaction processes were successfully integrated by conducting several parametric studies involving three small-scale models and six scaled-up models. The enhanced liquefaction process included a modified Claude cycle that reduced the specific energy consumption to 6.15 kWh/kgLH2, 47% lower than that of a reference process. Three different refrigerants (nitrogen, liquified natural gas, and hydrogen) were utilized in the integrated process, and an interactive genetic algorithm-based multi-objective optimization model was developed using the MATLAB–Aspen HYSYS interface to minimize the energy consumption and CO2 generation. In addition, a hybrid non-isothermal ortho–para hydrogen cooling heat exchanger was designed by performing an H2 specific heat correction to enhance the robustness of the developed model. Techno-economic analysis of the designed process indicated that the integrated system considerably improved the main economic indicators (Levelized cost of hydrogen) and environmental friendliness of the hydrogen production process by approximately 51% and 95%, respectively. Moreover, CO2 liquefaction was performed most efficiently at a pressure of 120 bar with a reduced overall process cost. In addition, the LOCH (Levelized cost of hydrogen) of all the design integrated processes is less than $3/kg in market accessibility compared to the reference process. The sensitivity analysis results conducted by varying the costs of refrigerants, power (electricity), the selling price of liquid hydrogen, and carbon price revealed that the proposed integrated process had a higher application potential than other commonly used processes. In particular, under conditions of 45 tonne/day or more of carbon prices, the integrated process is environmentally and economically advantageous over the CO2 emission process with a reduced PBP of at least 10%.

Solar still desalination system equipped with paraffin as phase change material: exergoeconomic analysis and multi-objective optimization.

The current work is about analysis and multi-objective optimization (MOO) of weir-type solar still systems equipped with phase change material (PCM) regarding the exergetic and economic performance. To do so, the energetic and exergetic modeling of the suggested system is conducted then the substantial economic factors is applied to obtain the total cost rate of the considered SSDS. The total exergetic efficiency and total annual cost (TAC) is considered objective functions. Four parameters include mass of the PCM (mPCM), inlet brine water flow rate ([Formula: see text]), gap distance (d), and insulation width (xins) is chosen as decision variables. Moreover, a genetic algorithm-based MOO was applied to find the optimum states of evaluated solar still unit. The outputs represented that increasing the brine feed water mass flow rate does not affect the TAC while decreasing distilled water production rate. The scattered distribution of optimum states infers that the optimum value of PCM mass is about 1 kg. In addition, applied MOO reveals that with optimization of the studied system, the exergy efficiency increases about 1.47% and the annual distilled water increases 4.35% compared with the non-optimized system. The suggested system is capable to produce fresh water in remote areas without any pollution as well as in a low cost rate.

Optimal Design of Combined Two-Tank Latent and Metal Hydrides-Based Thermochemical Heat Storage Systems for High-Temperature Waste Heat Recovery

The integration of thermal energy storage systems (TES) in waste-heat recovery applications shows great potential for energy efficiency improvement. In this study, a 2D mathematical model is formulated to analyze the performance of a two-tank thermochemical heat storage system using metal hydrides pair (Mg2Ni/LaNi5), for high-temperature waste heat recovery. Moreover, the system integrates a phase change material (PCM) to store and restore the heat of reaction of LaNi5. The effects of key properties of the PCM on the dynamics of the heat storage system were analyzed. Then, the TES was optimized using a genetic algorithm-based multi-objective optimization tool (NSGA-II), to maximize the power density, the energy density and storage efficiency simultaneously. The results indicate that the melting point Tm and the effective thermal conductivity of the PCM greatly affect the energy storage density and power output. For the range of melting point Tm = 30–50 °C used in this study, it was shown that a PCM with Tm = 47–49 °C leads to a maximum heat storage performance. Indeed, at that melting point narrow range, the thermodynamic driving force of reaction between metal hydrides during the heat charging and discharging processes is almost equal. The increase in the effective thermal conductivity by the addition of graphite brings about a tradeoff between increasing power output and decreasing the energy storage density. Finally, the hysteresis behavior (the difference between the melting and freezing point) only negatively impacts energy storage and power density during the heat discharging process by up to 9%. This study paves the way for the selection of PCMs for such combined thermochemical-latent heat storage systems.

Multi-objective-based seismic fragility relocation for a Korean nuclear power plant

An optimal risk-informed seismic fragility relocation is proposed for structures, systems, and components (SSCs) of nuclear power plants (NPPs) that mitigates the core damage risk of the NPP and raises the cost performance. The proposed approach employs a seismic probabilistic safety assessment technique to effectively distribute seismic fragilities of SSCs from the perspective of overall plant risk. The main objectives of the optimal seismic fragility relocation are to mitigate the core damage risk and minimize the total cost for the SSCs of the NPP. To deal with such conflicting purposes, a genetic algorithm-based multi-objective optimization scheme is adopted. This approach systematically obtains a set of Pareto optimal solutions that are neither inferior nor superior to each other. The process for choosing the most appropriate seismic fragility distribution plan from the optimal front of Pareto solutions is also discussed with an emphasis on the study of the uncertainty quantification on such optimal Pareto solutions. As an example for such an optimal fragility relocation study, this study considers a typical type of Korean NPP. The numerical results confirm that the proposed approach provides diverse optimal seismic fragility relocation plans which can effectively reduce the core damage risk as well as the cost compared to the current fragility distribution of this example NPP. Finally, the best optimal alternatives for the fragility relocation are selected based on the Pareto optimal solution’s robustness of performances against their perturbations.

Development, evaluation, and multi-objective optimization of a multi-effect desalination unit integrated with a gas turbine plant

In this research study, a 40 MW gas turbine power plant, which is coupled with a multi-effect desalination system with thermal vapor compression, is investigated. The energy, exergy and exergoeconomic analyses of the integrated plant are presented. As the number of effects in a desalination system is a key parameter, the effect of number of effects on the system performance is investigated. The genetic algorithm-based multi-objective optimization is applied to determine the best decision variables.To achieve the best optimization states, different scenarios of multi objective optimization based on the total exergy destruction rate, unit electricity price, total cost rate, gain output ratio, distilled water cost, and total exergy efficiency are examined. Additionally, seven decision variables are compressor pressure ratio (rp), combustion temperature (T3), compressor isentropic efficiency (ηc), gas turbine isentropic efficiency (ηGT), top brain temperature (TBT), last effect temperature (LET), and ejector compression ratio (Cr). The parametric analysis results indicated that although increasing the number of effect enhance the distilled water production rate, it increases the total cost rate of the system. With motive steam flow rate of 14 kg/s, results showed that distilled water production rate is 12294m3/day. Additionally, with three different scenarios the optimal states of integrated system are introduced. The results of optimization scenarios suggested different optimal states that are best guide for designer to select the best system configuration.

Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms

River sediment produced through weathering is one of the principal landscape modification processes on earth. Rivers are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the oceans. Estimation of suspended sediment yield is always a key parameter for planning and management of any river system. It is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and non-stationarity. The suspended sediment modeling of the river depends on the number of factors such as rock type, relief, rainfall, temperature, water discharge and catchment area. In this study, we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network (GA-MOO-ANN) with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire Mahanadi River basin. The model was validated by comparing statistically with other models, and it appeared that the GA-MOO-ANN model has the lowest root mean squared error (0.009) and highest coefficient of correlation (0.885) values among all comparative models (traditional neural network, multiple linear regression, and sediment rating curve) for all stations. It was also observed that the proposed model is the least biased (0.001) model. Thus, the proposed GA-MOO-ANN is the most capable model, compared to other studied models, for estimating the suspended sediment yield in the entire Mahanadi river basin, India. The results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment area. The models provide the best result at Tikarapara, the gauge station location in the extreme downstream, having the largest catchment area.

Electric discharge machining using rapid manufactured complex shape copper electrode: Parametric analysis and process optimization for material removal rate, electrode wear rate and cavity dimensions

The present investigation addresses the machining outcome of electric discharge machining using a rapid manufactured complex shape copper electrode. Developed rapid manufacturing technique using an amalgamation of polymer 3D printing and pressureless sintering of loose powder as rapid tooling has been used to fabricate copper electrode from the computer-aided design model of the desired shape. The fabricated electrode was used for the electric discharge machining of the D-2 steel workpiece. Central composite design was employed to study the electric discharge machining parameters (pulse duration, duty cycle and peak current) effect on the electric discharge machining characteristics such as material removal rate, electrode wear rate and cavity dimensional deviation as overcut from electrode computer-aided design model. Analysis of variance was executed to attain significant parameters along with interactions. Peak current was found to be the utmost dominating parameter for three responses. The high percentage of carbon was observed on the electrode surface after electric discharge machining at the high level of pulse duration and resulted in low electrode wear rate. The high percentage of dimensional deviation was noticed at the maximum duty cycle and maximum peak current by the substantial interactions. Genetic algorithm-based multi-objective optimization was employed for the electric discharge machining parameters optimization to maximize material removal rate, minimize electrode wear rate and dimensional deviation. The multi-feature complex copper electrode was fabricated and used for electric discharge machining as the case study to check the efficacy of the optimized process. It was witnessed that the process was competent to fabricate complex shape cavity as per the desired computer-aided design model shape with efficient material removal rate and electrode wear rate.

Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks

In this era of advanced distribution automation technologies, demand response is becoming an important tool for electricity network management. The available flexible loads can efficiently help in alleviating the network constraints and achieving demand-supply balance. Therefore, this forms the rationale behind this paper, which aims to implement a multi-agent system framework in order to achieve flexible price-based demand response. A genetic algorithm-based multi-objective optimization technique is applied to determine the optimal locations and the amount of required demand reduction in order to keep the network within statutory limits. The methodology is based on probabilistic estimation of the granularity of total available flexible demand from shiftable home appliances in each low-voltage feeder. Moreover, an optimal decision making for the start time of appliances upon receiving a real-time price signal is proposed. This is accomplished by considering the willingness to participate as well as price demand elasticity of the different clusters of customers. To fully demonstrate the feasibility and effectiveness of the proposed framework, a modified IEEE 69 bus distribution network comprising 1824 low voltage residential customers has been implemented and analyzed.

Exploring the boundaries of Solar Home Systems (SHS) for off-grid electrification: Optimal SHS sizing for the multi-tier framework for household electricity access

With almost 1.1 billion people lacking access to electricity, solar-based off-grid products like Solar Home Systems (SHS) have become a promising solution to provide basic electricity needs in un(der)-electrified regions. Therefore, optimal system sizing is a vital task as both oversizing and undersizing a system can be detrimental to system cost and power availability, respectively. This paper presents an optimal SHS sizing methodology that minimizes the loss of load probability (LLP), excess energy dump, and battery size while maximizing the battery lifetime. A genetic algorithm-based multi-objective optimization approach is utilized to evaluate the optimal SHS sizes. The potential for SHS to cater to every tier of the Multi-tier framework (MTF) for measuring household electricity access is examined. The optimal system sizes for standalone SHS are found for different LLP thresholds. Results show that beyond tier 2, the present day SHS sizing needs to be expanded significantly to meet the load demand. Additionally, it is deemed untenable to meet the electricity needs of the higher tiers of MTF purely through standalone SHS without compromising one or more of the system metrics. A way forward is proposed to take the SHS concept all the way up the energy ladder such that load demand can also be satisfied at tier 4 and 5 levels.

Scenario-Based Multi-Objective Optimization of an Air-Based Building-Integrated Photovoltaic/Thermal System

In this paper, a genetic algorithm-based multi-objective optimization of a building-integrated photovoltaic/thermal (BIPV/T) system is carried out to find the best system configurations which lead to maximum energetic and exergetic performances for Kermanshah, Iran climatic condition. In the proposed BIPV/T system, the cooling potential of ventilation and exhaust airs are used in buildings for cooling the PV panels and also heating the ventilation air by heat rejection of PV panels. Four scenarios with various criteria in the form of system efficiencies and useful outputs are considered to reflect all possible useful outputs in the optimization procedure. This study models a glazed BIPV/T system with various collector areas (Apv=10,15,25,and30m2) and different length to width ratio (L/W=0.5,1,1.5,and2) to determine the optimum air mass flow rate, bottom heat loss coefficient, depth of the channel as well as the optimum depth of the air gap between PV panel and glass cover that maximize two defined objective functions in different scenarios. Results showed that using fourth scenario (with the annual total useful thermal and electrical outputs as objective functions) and first scenario (with the annual average first- and second-law efficiencies as objective functions) for optimizing the proposed BIPV/T system leads to the highest amount of useful thermal and overall outputs, respectively. Moreover, it was concluded that, if the electrical output of the system is more important than the thermal output, the first scenario gives better results.