Records with Keyword: Genetic Algorithm
Showing records 1 to 25 of 175. [First] Page: 1 2 3 4 5 Last
Optimization of Apartment-Complex Layout Planning for Daylight Accessibility in a High-Density City with a Temperate Climate
Sewon Lee, Kyung Sun Lee
March 29, 2023 (v1)
Keywords: building layout, daylight accessibility, Genetic Algorithm, Optimization, urban plan, useful daylight illumination (UDI)
As interest in sustainable design increases, many methods have been suggested to develop an integrated sustainable design process. However, due to the lack of a scientific procedure using parametric tools for an objective evaluation, it is difficult to move forward with integrated sustainable design. In addition, the design priority of the indoor environment is still relatively low because of the score composition of the green-building certification system. Therefore, this study aimed to develop a simulation tool and method to help apartment-complex layout planning in urban contexts by focusing on the indoor daylight environment. In particular, Korean cities are densely formed with high-rise buildings in a small area, so the Korean Building Act has complicated provisions to reduce overshadowing between buildings. To reduce unnecessary wasted time while checking these complicated regulations, a simulation was used to automatically check building offsets. Galapagos, a component of Rhino-... [more]
Network and Reserve Constrained Economic Analysis of Conventional, Adjustable-Speed and Ternary Pumped-Storage Hydropower
Soumyadeep Nag, Kwang Y. Lee
March 29, 2023 (v1)
Keywords: adjustable-speed pumped hydro, arbitrage, Genetic Algorithm, MATPOWER, regulation, ternary pumped hydro
With increasing renewable penetration and projected increase in natural disasters, the reliability and resiliency of a power system become crucial issues. As network inertia drops with increasing penetration of renewables, operators search for flexible resources that can help cope with a disruptive event or manage renewable intermittency. Energy storage is a solution, but the type of storage solution needs to be profitable to exist in the current and upcoming power markets. Advanced pumped-storage hydropower (PSH) is one solution that can help cope with such requirements, which will in turn help to increase the renewable penetration in the system. This paper qualitatively compares the revenue earning potential of PSH configurations, including, adjustable-speed PSH (AS-PSH) and ternary PSH (T-PSH) in comparison to conventional PSH (C-PSH) from the arbitrage and regulation markets, with and without the presence of wind penetration. In addition, a framework for quantitative analysis of an... [more]
A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles
Bo Huang, Yuting Ma, Chun Wang, Yongzhi Chen, Quanqing Yu
March 29, 2023 (v1)
Keywords: fusion model, Genetic Algorithm, parameter identification, supercapacitor
The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE ca... [more]
Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration
Maël Riou, Florian Dupriez-Robin, Dominique Grondin, Christophe Le Loup, Michel Benne, Quoc T. Tran
March 28, 2023 (v1)
Keywords: Genetic Algorithm, microgrid, off-grid, reliability, sizing
Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case stud... [more]
Review of Intelligent Control Systems for Natural Ventilation as Passive Cooling Strategy for UK Buildings and Similar Climatic Conditions
Esmail Mahmoudi Saber, Issa Chaer, Aaron Gillich, Bukola Grace Ekpeti
March 28, 2023 (v1)
Keywords: buildings, fuzzy logic control, Genetic Algorithm, intelligent control system, natural ventilation, neural network, ventilative cooling
Natural ventilation is gaining more attention from architects and engineers as an alternative way of cooling and ventilating indoor spaces. Based on building types, it could save between 13 and 40% of the building cooling energy use. However, this needs to be implemented and operated with a well-designed and integrated control system to avoid triggering discomfort for occupants. This paper seeks to review, discuss, and contribute to existing knowledge on the application of control systems and optimisation theories of naturally ventilated buildings to produce the best performance. The study finally presents an outstanding theoretical context and practical implementation for researchers seeking to explore the use of intelligent controls for optimal output in the pursuit to help solve intricate control problems in the building industry and suggests advanced control systems such as fuzzy logic control as an effective control strategy for an integrated control of ventilation, heating and co... [more]
Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance
Chiweta Emmanuel Abunike, Ogbonnaya Inya Okoro, Sumeet S. Aphale
March 28, 2023 (v1)
Subject: Optimization
Keywords: efficiency, genetic aggregation response surface, Genetic Algorithm, pole embrace coefficients, switched reluctance motor, torque ripple
In this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of determining optimal pole embrace is formulated as a multi-objective optimization problem with the objective of optimizing average torque, efficiency and torque ripple, and response surface models were obtained based on the genetic aggregation method. The results obtained by genetic aggregation response surface (GARS) and the non-dominated genetic algorithm (NSGA-II) were validated with the finite element method (FEM) model of the initial SRM. The optimized model displayed better efficiency profile over a wide speed range. The initial and optimized models recorded maximum efficiencies of 85% and 94.05%, respectively, at 2000 rpm. The efficiency values of 93.97−94.05% were achieved for the th... [more]
A New Method for Intelligent Prediction of Drilling Overflow and Leakage Based on Multi-Parameter Fusion
Mu Li, Hengrui Zhang, Qing Zhao, Wei Liu, Xianzhi Song, Yangyang Ji, Jiangshuai Wang
March 28, 2023 (v1)
Keywords: Genetic Algorithm, mud overflow and leakage, multi-parameter fusion, neural network
The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 p... [more]
Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm
Borche Postolov, Nikolay Hinov, Atanas Iliev, Dimitar Dimitrov
March 28, 2023 (v1)
Keywords: Genetic Algorithm, hydrothermal scheduling, Laplace crossover, MPTM mutation, self-adaptive penalty
This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem.
Model Parameterization with Quantitative Proteomics: Case Study with Trehalose Metabolism in Saccharomyces cerevisiae
Chuan Fu Yap, Manuel Garcia-Albornoz, Andrew F. Jarnuczak, Simon J. Hubbard, Jean-Marc Schwartz
March 28, 2023 (v1)
Keywords: Genetic Algorithm, heat stress, kinetic model, metabolic engineering, metabolic modelling, trehalose metabolism
When Saccharomyces cerevisiae undergoes heat stress it stimulates several changes that are necessary for its survival, notably in carbon metabolism. Notable changes include increase in trehalose production and glycolytic flux. The increase in glycolytic flux has been postulated to be due to the regulatory effects in upper glycolysis, but this has not been confirmed. Additionally, trehalose is a useful industrial compound for its protective properties. A model of trehalose metabolism in S. cerevisiae was constructed using Convenient Modeller, a software that uses a combination of convenience kinetics and a genetic algorithm. The model was parameterized with quantitative omics under standard conditions and validated using data collected under heat stress conditions. The completed model was used to show that feedforward activation of pyruvate kinase by fructose 1,6-bisphosphate during heat stress contributes to the increase in metabolic flux. We were also able to demonstrate in silico tha... [more]
A Modified 2-DOF Control Framework and GA Based Intelligent Tuning of PID Controllers
Gun-Baek So
March 28, 2023 (v1)
Keywords: disturbance rejection, first-order processes with time delay model, Genetic Algorithm, PID controller, set-point weighting
Although a controller is well-tuned for set-point tracking, it shows poor control results for load disturbance rejection and vice versa. In this paper, a modified two-degree-of-freedom (2-DOF) control framework to solve this problem is proposed, and an optimal tuning method for the pa-rameters of each proportional integral derivative (PID) controller is discussed. The unique feature of the proposed scheme is that a feedforward controller is embedded in the parallel control structure to improve set-point tracking performance. This feedforward controller and the standard PID con-troller are combined to create a new set-point weighted PID controller with a set-point weighting function. Therefore, in this study, two controllers are used: a set-point weighted PID controller for set-point tracking and a conventional PID controller for load disturbance rejection. The parameters included in the two controllers are tuned separately to improve set-point tracking and load dis-turbance rejection p... [more]
Numerical Methods for Optimization of the Horizontal Directional Drilling (HDD) Well Path Trajectory
Rafał Wiśniowski, Paweł Łopata, Grzegorz Orłowicz
March 28, 2023 (v1)
Subject: Optimization
Keywords: Genetic Algorithm, horizontal directional drilling, Numerical Methods, optimization methods, trenchless technologies, well path trajectory design
Advances in the field of material engineering, computerization, automation, and equipment miniaturization enable modernization of the existing technologies and development of new solutions for construction, inspection, and renovation of underground pipelines. Underground pipe installations are used in the energy sector, gas industry, telecommunications, water and sewage transport, heating, chemical industry, and environmental engineering. In order to build new pipeline networks, dig and no-dig techniques are used. Horizontal Directional Drilling (HDD) is one of the most popular trenchless technologies. The effectiveness of HDD technology application is mostly determined by its properly designed trajectory. Drilling failures and complications, which often accompany the application of the HDD technology, result from poor design of the well path in relation to the existing geological and drilling conditions. The article presented two concepts of Horizontal Directional Drilling well path t... [more]
A Hybrid Optimization Approach for Autonomy Enhancement of Nearly-Zero-Energy Buildings Based on Battery Performance and Artificial Neural Networks
Giorgos S. Georgiou, Pavlos Nikolaidis, Soteris A. Kalogirou, Paul Christodoulides
March 28, 2023 (v1)
Subject: Optimization
Keywords: artificial neural networks, building energy optimization, building integrated photovoltaics, electrical energy storage, Genetic Algorithm, linear programming, nearly zero energy buildings
Reducing the primary energy consumption in buildings and simultaneously increasing self-consumption from renewable energy sources in nearly-zero-energy buildings, as per the 2010/31/EU directive, is crucial nowadays. This work solved the problem of nearly zeroing the net grid electrical energy in buildings in real time. This target was achieved using linear programming (LP)—a convex optimization technique leading to global solutions—to optimally decide the daily charging or discharging (dispatch) of the energy storage in an adaptive manner, in real time, and hence control and minimize both the import and export grid energies. LP was assisted by equally powerful methods, such as artificial neural networks (ANN) for forecasting the building’s load demand and photovoltaic (PV) on a 24 hour basis, and genetic algorithm (GA)—a heuristic optimization technique—for driving the optimum dispatch. Moreover, to address the non-linear nature of the battery and model the energy dispatch in a more r... [more]
Data-Driven Optimization for Capacity Control of Multiple Ground Source Heat Pump System in Heating Mode
Guiqiang Wang, Haiman Wang, Zhiqiang Kang, Guohui Feng
March 27, 2023 (v1)
Keywords: data-driven model, Genetic Algorithm, ground source heat pump, model-based optimization
With the rapid development of ground source heat pump (GSHP) system, energy saving measures are of special interest for practice. In order to meet heating demand, capacity control of GSHP system can be carried out by regulating either part load ratio (PLR) or supply water temperature. A data-driven optimization approach was developed and applied on a school building in heating mode, which aims at minimizing energy consumption without compromising thermal comfort. An artificial neural network (ANN) model of the GSHP system was proposed and trained with experimental data as well as simulated data of a validated physics-based model, which was employed for data supplement to cover more data variations. The multi-objective optimization problem was then solved using genetic algorithm. The results suggest the optimal operation strategy for either continuous or staged capacity control regarding heating demand variation. With the proposed optimal control strategy, energy savings as compared to... [more]
A Hot Water Split-Flow Dual-Pressure Strategy to Improve System Performance for Organic Rankine Cycle
Shiqi Wang, Zhongyuan Yuan
March 27, 2023 (v1)
Keywords: Genetic Algorithm, net output power, organic Rankine cycle, split-flow hot water
The organic Rankine cycle (ORC) is widely used to recover industrial waste heat. For an ORC system using industrial waste hot water as a heat source, a novel hot water split-flow dual-pressure organic Rankine cycle (SFD-ORC) system is developed to improve the performance of the ORC. The maximum net power output was selected to compare three ORC systems, including basic ORC (B-ORC), conventional dual-pressure ORC (CD-ORC) and SFD-ORC. A genetic algorithm (GA) was used to optimize the parameters to search the maximum net power output of ORCs. The maximum net output power was taken as the standard of performance evaluation. The results show that, under the same hot water inlet temperature condition, the optimal hot water outlet temperature of B-ORC is much higher than that of CD-ORC and SFD-ORC, which indicates that less thermal energy could be utilized to convert to power in B-ORC. The optimal hot water temperature at the outlet of evaporator 1 in SFD-ORC is higher than that in CD-ORC, w... [more]
Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine
Waqas Ahmad, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, Adam Glowacz
March 27, 2023 (v1)
Keywords: electricity load forecasting, Extreme Learning Machine, feature selection, Genetic Algorithm, Grid Search, smart grid, Support Vector Machine
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e.... [more]
Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms
Dinh Thanh Viet, Vo Van Phuong, Minh Quan Duong, Quoc Tuan Tran
March 27, 2023 (v1)
Keywords: Genetic Algorithm, neural network, Particle Swarm Optimization, Renewable and Sustainable Energy, wind power forecasting
As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to... [more]
A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems
Ali Thaeer Hammid, Omar I. Awad, Mohd Herwan Sulaiman, Saraswathy Shamini Gunasekaran, Salama A. Mostafa, Nallapaneni Manoj Kumar, Bashar Ahmad Khalaf, Yasir Amer Al-Jawhar, Raed Abdulkareem Abdulhasan
March 27, 2023 (v1)
Keywords: dynamic programming, Genetic Algorithm, heuristic method, hydropower generation, optimal generation scheduling, Renewable and Sustainable Energy
The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various... [more]
Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study
Juan Carlos Bravo-Rodríguez, Francisco J. Torres, María D. Borrás
March 27, 2023 (v1)
Subject: Optimization
Keywords: classification, decision tree, feature selection, Genetic Algorithm, K-NN algorithm, power quality disturbances, S-transform, support vector machine, swarm optimization
The economic impact associated with power quality (PQ) problems in electrical systems is increasing, so PQ improvement research becomes a key task. In this paper, a Stockwell transform (ST)-based hybrid machine learning approach was used for the recognition and classification of power quality disturbances (PQDs). The ST of the PQDs was used to extract significant waveform features which constitute the input vectors for different machine learning approaches, including the K-nearest neighbors’ algorithm (K-NN), decision tree (DT), and support vector machine (SVM) used for classifying the PQDs. The procedure was optimized by using the genetic algorithm (GA) and the competitive swarm optimization algorithm (CSO). To test the proposed methodology, synthetic PQD waveforms were generated. Typical single disturbances for the voltage signal, as well as complex disturbances resulting from possible combinations of them, were considered. Furthermore, different levels of white Gaussian noise were a... [more]
Dynamic Residential Energy Management for Real-Time Pricing
Leehter Yao, Fazida Hanim Hashim, Chien-Chi Lai
March 27, 2023 (v1)
Keywords: fuzzy controller, Genetic Algorithm, home energy management system, photovoltaic panel, real-time price
A home energy management system (HEMS) was designed in this paper for a smart home that uses integrated energy resources such as power from the grid, solar power generated from photovoltaic (PV) panels, and power from an energy storage system (ESS). A fuzzy controller is proposed for the HEMS to optimally manage the integrated power of the smart home. The fuzzy controller is designed to control the power rectifier for regulating the AC power in response to the variations in the residential electric load, solar power from PV panels, power of the ESS, and the real-time electricity prices. A self-learning scheme is designed for the proposed fuzzy controller to adapt with short-term and seasonal climatic changes and residential load variations. A parsimonious parameterization scheme for both the antecedent and consequent parts of the fuzzy rule base is utilized so that the self-learning scheme of the fuzzy controller is computationally efficient.
Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries
Zeyu Chen, Jiahuan Lu, Bo Liu, Nan Zhou, Shijie Li
March 27, 2023 (v1)
Subject: Optimization
Keywords: battery aging, energy management, Genetic Algorithm, global optimization, particle swarm algorithm, plug-in electric vehicles, state of health
The performance of lithium-ion batteries will inevitably degrade during the high frequently charging/discharging load applied in electric vehicles. For hybrid electric vehicles, battery aging not only declines the performance and reliability of the battery itself, but it also affects the whole energy efficiency of the vehicle since the engine has to participate more. Therefore, the energy management strategy is required to be adjusted during the entire lifespan of lithium-ion batteries to maintain the optimality of energy economy. In this study, tests of the battery performances under thirteen different aging stages are involved and a parameters-varying battery model that represents the battery degradation is established. The influences of battery aging on energy consumption of a given plug-in hybrid electric vehicle (PHEV) are analyzed quantitatively. The results indicate that the variations of capacity and internal resistance are the main factors while the polarization and open circu... [more]
Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators
Taimur Al Shidhani, Anastasia Ioannou, Gioia Falcone
March 24, 2023 (v1)
Keywords: electricity, environmental, financial, Genetic Algorithm, multi-objective optimisation, power system expansion planning, Renewable and Sustainable Energy, social
The increase in global electricity demand, along with its impact on climate change, call for integrating sustainability aspects in the power system expansion planning. Sustainable power generation planning needs to fulfill different, often contradictory, objectives. This paper proposes a multi-objective optimisation model integrating four objective functions, including minimisation of total discounted costs, carbon emissions, land use, and social opposition. Other factors addressed in the model include renewable energy share, jobs created, mortality rates, and energy diversity, among others. Single-objective linear optimisations are initially performed to investigate the impact of each objective function on the resulting power generation mix. Minimising land use and discounted total costs favoured fossil fuels technologies, as opposed to minimising carbon emissions, which resulted in increased renewable energy shares. Minimising social opposition also favoured renewable energy shares,... [more]
An Evolutionary EMI Filter Design Approach Based on In-Circuit Insertion Loss and Optimization of Power Density
Massimiliano Luna, Giuseppe La Tona, Angelo Accetta, Marcello Pucci, Maria Carmela Di Piazza
March 24, 2023 (v1)
Subject: Optimization
Keywords: electrical drives, EMI filter, Genetic Algorithm, optimal design, power converter, power density
Power density is one of the most significant issues in designing electromagnetic interference (EMI) filters for power electronic-based applications. Therefore, an effective EMI filter design should consider both its capability to ensure the compliance with the related EMI standard limits and the possibility to build it by suitable components leading to the most compact configuration as well. To fulfill the above requirements, in this paper, an automatic procedure to get an improved design of EMI filters is proposed. Specifically, according to the proposed method, the values of filter parameters for both common mode (CM) and differential mode (DM) sections are selected by a genetic algorithm (GA) exploiting the in-circuit insertion loss, thus obtaining a more effective design. Besides, the components that set up the filter are selected by a rule-based procedure searching through a suitable database of commercial components to identify those allowing for the maximum power density. Experi... [more]
Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells
Premkumar Vincent, Gwenaelle Cunha Sergio, Jaewon Jang, In Man Kang, Jaehoon Park, Hyeok Kim, Minho Lee, Jin-Hyuk Bae
March 24, 2023 (v1)
Keywords: finite difference time domain, Genetic Algorithm, optical modelling, solar cell optimization
Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.
A Dual-Stage Modeling and Optimization Framework for Wayside Energy Storage in Electric Rail Transit Systems
Oindrilla Dutta, Mahmoud Saleh, Mahdiyeh Khodaparastan, Ahmed Mohamed
March 24, 2023 (v1)
Keywords: battery, DC rail transit system, energy management, flywheel, Genetic Algorithm, Optimization, peak-demand reduction, supercapacitor, train
In this paper, a dual-stage modeling and optimization framework has been developed to obtain an optimal combination and size of wayside energy storage systems (WESSs) for application in DC rail transportation. Energy storage technologies may consist of a standalone battery, a standalone supercapacitor, a standalone flywheel, or a combination of these. Results from the dual-stage modeling and optimization process have been utilized for deducing an application-specific composition of type and size of the WESSs. These applications consist of different percentages of energy saving due to regenerative braking, voltage regulation, peak demand reduction, estimated payback period, and system resiliency. In the first stage, sizes of the ESSs have been estimated using developed detailed mathematical models, and optimized using the Genetic Algorithm (GA). In the second stage, the respective sizes of ESSs are simulated by developing an all-inclusive model of the transit system, ESS and ESS managem... [more]
A Novel Exergy Indicator for Maximizing Energy Utilization in Low-Temperature ORC
Marcin Jankowski, Aleksandra Borsukiewicz
March 24, 2023 (v1)
Subject: Optimization
Keywords: energy utilization, Exergy, Genetic Algorithm, multi-objective optimization, ORC
In the last decade, particular attention has been paid to the organic Rankine cycle (ORC) power plant, a technology that implements a classical steam Rankine cycle using low-boiling fluid of organic origin. Depending on the specific application and the choice of the designer, the ORC can be optimized using one or several criteria. The selected objectives reflect various system performance aspects, such as: thermodynamic, economic, environmental or other. In this study, a novel criterion called exergy utilization index (XUI) is defined and used to maximize the utilization of an energy source in the ORC system. The maximization of the proposed indicator is equivalent to bring the heat carrier outlet temperature to the ambient temperature as close as possible. In the studied case, the XUI is applied along with the total heat transfer area of the system, and the multi-objective optimization is performed in order to determine the optimal operating conditions of the ORC. Moreover, to reveal... [more]
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