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A hybrid modified FA-ANFIS-P&O approach for MPPT in photovoltaic systems under PSCs

INTERNATIONAL JOURNAL OF ELECTRONICS 2020年第5期107卷 703-718页

作者： Farzaneh, Javad Semnan Univ Dept Elect Engn Semnan Iran

A modified firefly algorithm (MFO)-based adaptive neuro-fuzzy inference system (ANFIS) combined with the perturbation and observation (P&O) is used in this paper to track the maximum power point (MPP) in photovoltaic systems (PVs). The proposed method identifies and tracks the MPP in two stages. First, according to the irradiance on the solar panels, the ANFIS approximately identifies the MPP. In the second stage, the P&O method starts to act in the tracking cycle and initiates an accurate searching process from that point. The suggested hybrid method covers the problems of commonly-used methods, such as inability in detecting the global MPP under partial shading conditions (PSCs) and trapping in the local optima. Furthermore, the method provides significantly higher speed for the MPP tracking under various irradiance patterns. To prove the above-mentioned claims, the given approach is compared with the P&O method as a common method in the MPPT and particle swarm optimisation (PSO) which operates based on swarm intelligence. Simulation results obtained from MATLAB/Simulink environment show that the proposed method identifies and tracks the MPP under uniform irradiance and PSCs in a very short time of roughly 0.2?s.

关键词： PV system modified firefly algorithm adaptive neuro fuzzy inference system perturbation and observation solar energy

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Enhancing building energy efficiency by adaptive facade: A computational optimization approach

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APPLIED ENERGY 2020年 265卷 114797-000页

作者： Dac-Khuong Bui Tuan Ngoc Nguyen Ghazlan, Abdallah Ngoc-Tri Ngo Tuan Duc Ngo Univ Melbourne Dept Infrastruct Engn Parkville Vic 3010 Australia Univ Danang Univ Sci & Technol Fac Project Management Danang Vietnam

The energy consumption in buildings, which accounts for approximately one-third of the total energy used in the world, can be reduced significantly by employing adaptive facades. In this study, a computational optimization approach is proposed to enhance the energy efficiency of buildings based on the design of an adaptive facade system, which can adapt its thermal and visible transmittance for dynamically varying climatic conditions. The engine of the adaptive facade design approach is an automated optimization process, which combines the building energy simulation program (EnergyPlus) with an optimization technique through Eppy, a powerful Python toolkit. The modified firefly algorithm, an in-house optimization tool, is employed to design the adaptive facade system in this study. However, our proposed method is not tied to any particular optimization tool and does not impose any restrictions on a type of building. To this end, the capability of the proposed method for enhancing building energy efficiency is validated by two case studies, namely a typical single office room and a medium office building. We found that the proposed adaptive facade system can reduce the energy consumption by 14.9-29.0% and 14.2-22.3% for the first and second case study, respectively, compared to the static facades. These significant findings demonstrate the potential of adaptive facades to enhance the energy efficiency of buildings.

关键词： Adaptive facade Smart windows Facade optimization modified firefly algorithm Building performance simulation Building energy efficiency

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Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods

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ENERGIES 2021年第16期14卷 4935页

作者： Reddy, B. Koti Singh, Amit Kumar Lovely Profess Univ Sch Elect & Elect Engn Jalandhar 144402 India

The World Energy Council, in its 2019 World Energy Scenarios Report, advised policymakers to identify innovative opportunities for the integration of renewable energy resources into existing electrical power systems to achieve a fast and affordable solution. However, large-scale industries with cogeneration units are facing problems in handling the higher penetration levels of intermittent renewable energies. This paper addresses large-size photovoltaic power integration problems and their optimal operation. This work considers the case of a chemical industry having both cogeneration power and solar photovoltaics. Here, a modified firefly algorithm and a hybrid power resource optimization solver are proposed. The results of the proposed method are compared with other benchmark techniques, to confirm its advantages. The proposed techniques can be used in industries having cogeneration power plants with photovoltaics for better optimization and to meet the guidelines specified in IEEE 1547. The voltage ramp index is proposed to determine the voltage ramp up and down with intermittent solar irradiance. Additionally, a machine learning technique is used to predict the cogeneration plant efficiency at different loads and the solar irradiance under varying weather conditions. Finally, this paper proposes the effectiveness of the modified heuristic technique and certain guidelines, including solvers for industrial use.

关键词： cogeneration hybrid power resource optimization solver integration guidelines machine learning modified firefly algorithm photovoltaics voltage ramp index

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Double-layer firefly algorithm for Simultaneous Optimal Sizing and Operation of Energy Hubs

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第16期45卷 1846-1857页

作者： El-Zonkoly, Amany M. Abdelaziz, Almoataz Y. Eladl, Alaa M. AAST Coll Engn & Technol Dept Elect & Control Engn Alexandria Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt

For economical and efficient use of local multi-carrier energy systems, energy hubs (EHs) were introduced as the best solution. EHs as one type of distributed generation have made the use of multi-carrier energy systems attractive with better energy conservation. To achieve the economic and environmental objectives of the EH, the sizes of its components need to be optimally selected. In addition, to check the incentive of the EH, it must be studied under operation. For this reason, the economic dispatch of the EH is to be carried out. However, the two objectives of optimal design and optimal dispatch have been previously addressed as two separate problems one step after another. In this paper, a double-layer firefly based optimization algorithm is proposed to both optimally size and dispatch the EH components, simultaneously. The two problems are combined in one multi-objective optimization problem. The objectives are to minimize the total capital and operational cost of the EH, minimize the system emission, and maximize the cost of exported energy to the network. The proposed algorithm is effectively applied to a commercial facility (hospital) as a case study.

关键词： energy hubs optimal sizing optimal dispatch modified firefly algorithm

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Energy-efficient virtual machine placement using enhanced firefly algorithm

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MULTIAGENT AND GRID SYSTEMS 2016年第3期12卷 167-198页

作者： Barlaskar, Esha Singh, Yumnam Jayanta Issac, Biju Assam Don Bosco Univ Sch Technol Dept Comp Sci & Engn & Informat Technol Gauhati Assam India Univ Teesside Sch Comp Middlesbrough Cleveland England

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing.

关键词： Energy efficiency virtual machine placement hierarchical clustering modified firefly algorithm

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An Innovative Hybrid Model Based on Data Pre-Processing and modified Optimization algorithm and Its Application in Wind Speed Forecasting

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ENERGIES 2017年第7期10卷 954-954页

作者： Jiang, Ping Wang, Zeng Zhang, Kequan Yang, Wendong Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Lanzhou Univ Coll Atmospher Sci Key Lab Arid Climat Change & Reducing Disaster Ga Lanzhou 730000 Gansu Peoples R China

Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA) and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics.

关键词： back propagation (BP) forecasting accuracy modified firefly algorithm wind speed singular spectrum analysis

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EVALUATION AND ENHANCEMENT OF NATURE-INSPIRED OPTIMIZATION FOR ANTENNA IMPEDANCE MATCHING

EVALUATION AND ENHANCEMENT OF NATURE-INSPIRED OPTIMIZATION F...

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作者： Homsup, Nathawut PennState University Libraries

学位级别：Doctor of Philosophy

Advancements in the technology of communication systems have led to increased requirements and constraints on antennas in terms of cost, bandwidth, and many other performance parameters. In the design of any antenna, a significant amount of time and resources is spent on impedance matching. There are two widely used approaches to impedance matching. The first is to modify the antenna geometry by identifying the appropriate degree of freedom within the structure. The second is the lumped element approach, which modifies the antenna with a passive network consisting of inductors and capacitors to the impedance mismatch between the source and the antenna load. In the second approach, different types of circuits can be used in order to obtain the best results, such as a two lumped elements L network, a T network, a three lumped elements Pi network, or a network with more lumped elements. In order to achieve the desired results from these circuits, we must choose the optimal values of inductor, capacitor, and transducer elements. To optimize parameters in impedance matching network design, two of the most promising approaches are those of the nature-inspired genetic algorithm (GA) and swarm intelligence algorithms. Examples of these swarm algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), and firefly algorithm (FA). Nature-inspired optimization methods have been used in antenna design for decades in the form of GA and PSO. However, many other algorithms are relatively new to the antenna design problems, even though, these algorithms have already been successfully applied to many other problems and have gained interest in numerous engineering and scientific research *** dissertation compares the described optimization methods by using them to minimize the maximum VSWR over the frequencies 3.5-3.85MHz of the impedance matching network that connects to a high-frequency dipole antenna. In order to satisfy the match over a finite bandwidt

关键词： Antenna Design Impedance Matching firefly algorithm Nature-Inspired Optimization Swarm Intelligence algorithm modified firefly algorithm

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An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants

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ENERGY 2015年 83卷 734-748页

作者： Bornapour, Mosayeb Hooshmand, Rahmat-Allah Univ Isfahan Dept Elect Engn Esfahan Iran

In this paper, a stochastic model is proposed for planning the location and operation of Molten Carbonate Fuel Cell Power Plants (MCFCPPs) in distribution networks when used for Combined Heat, Power, and Hydrogen (CHPH) simultaneously. Uncertainties of electrical and thermal loads forecasting;the pressures of hydrogen, oxygen, and carbon dioxide imported to MCFCPPs;and the nominal temperature of MCFCPPs are considered using a scenario-based method. In the method, scenarios are generated using Roulette Wheel Mechanism (RWM) based on Probability Distribution Functions (PDF) of input random variables. Using this method, probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective functions, placement, and operation of MCFCPPs as CHPH change this problem to a mixed integer nonlinear one. So, multi-objective modified firefly algorithm (MFA) and Pareto optimal method are employed for solving the multi-objective problem and for compromising between the objective functions. During the simulation process, a set of non-dominated solutions are stored in a repository. The 69-bus distribution system is used for evaluating the proper function of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Molten carbonate fuel cell power plants Combined heat Power and hydrogen modified firefly algorithm Pareto optimal set Scenario-based stochastic programming

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Probabilistic hydrogen, thermal and electrical management of PEM-fuel cell power plants in distribution networks

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2012年第23期37卷 18243-18260页

作者： Niknam, Taher Zare, Mohsen Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

This paper presents a Stochastic Multi-objective Optimal Operation Management (SMOOM) framework of distribution networks in presence of PEM-Fuel Cell Power Plants (FCPPs) and boilers. Operational costs, thermal recovery, power trade with grid and hydrogen management strategies are considered in this model. Furthermore, four objective functions has been considered as criteria for SMOOM, i.e. electrical energy losses, voltages deviations from their nominal values, total emissions emitted by CHP systems and grids, and total operational costs of CHP systems, as well as electrical energy cost of grids. A 2m + 1 Point Estimated Method is used to cope with the uncertain variables i.e. electrical and thermal loads, gas price of FCPPs consumption, fuel cost of residential loads, purchasing and selling tariff of electricity, hydrogen price, operation temperature of fuel cell stack, and the pressures of hydrogen and oxygen of anode and cathode, respectively. A new multi-objective modified firefly algorithm (MFA) is implemented for minimizing the objective functions while the operational constraints are satisfied. Finally, a 69-bus distribution network is utilized to examine the performance of the proposed strategy regarding the rest. Copyright (c) 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Combined Heat and Power (CHP) system Fuel cell modified firefly algorithm Multi-objective optimization Point estimated method Optimal operation management

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