Low Cost and Reliable Energy Management in Smart Residential Homes Using the GA Based Constrained Optimization

Recently smart grids have given chance to residential customers to schedule operation times of smart home appliances to reduce electricity bills and the peak-to-average ratio through the demand side management. This is apparently a multi-objective combinatorial optimization problem including the constraints and consumer preferences that can be solved for optimized operation times under reasonable conditions. Although there are a limited number of techniques used to achieve this goal, it seems that the binary-coded genetic algorithm (BCGA) is the most suitable approach to do so due to on/off controls of smart home appliances. This paper proposes a BCGA method to solve the above-mentioned problem by developing a new crossover algorithm and the simulation results show that daily energy cost and peak to average ratio can be managed to reduce to acceptable levels by contributing significantly to residential customers and utility companies

Renewable Energy Inclusion on Economic Power Optimization using Thunderstorm Algorithm

This paper presents an economic operation considered renewable energy which is optimized using thunderstorm algorithm. The problem is constrained by an emission standard and various technical limits implemented on the 62-bus system model. Simulations showed that the renewable energy inclusion penetrates to the unit commitment of generating units with strongly approach for the computational solution. This inclusion also affects to the individual power production in accordance to the fuel cost and pollutant discharge

Minimization of operational cost for an off-grid renewable hybrid system to generate electricity in residential buildings through the SVM and the BCGA methods

WOS: 000336779000047Recently Turkey's electricity demand has shown a considerable increase due to its population and economic growths. It is understandable that this may be very influential on electricity price on market as well as other factors such as an increase in natural gas price, etc. It should be noted that the half of Turkey's electricity generation is supplied from natural gas and 95% of this source is imported from other countries. However, Turkey is rich in wind and solar energy potentials to generate electricity and it is believed that this makes a considerable impact on reducing high electricity unit cost to competitive one. In this regard, these potentials can be utilized for electricity generation in order to meet a significant portion of the power demanded by residential houses through a PV/wind system. The electricity cost of the renewable system can be minimized by optimally scheduling generated and consumed powers. In this paper, optimal power scheduling in such systems is carried out by using the BCGA and the SVM methods. The results indicated that the proposed approach was able to minimize the operation cost in the hybrid system through the optimal power scheduling. (c) 2014 Elsevier B.V. All rights reserved

Determination of capacitance range in the self-excited induction generator through the hybrid genetic algorithms

2010 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2010 -- 14 June 2010 through 16 June 2010 -- Pisa -- 81684The self-excited induction generators (SEIGs) are mainly demanded for generation of electricity in remote areas due to several advantages such as less maintenance, rugged construction etc. The SEIG's excitation is usually supplied from a capacitor bank whose capacitance plays major role in transferring power into the load. Previous investigation has shown that excitation is only possible between minimum and maximum capacitances and the resonant capacitance lies down in this range. Determination of these capacitances involves in a numerical solution of transcendental equations extracted from the steady state equivalent circuit of the SEIG. Although the Newton-Raphson method is extensively used to solve these equations however it is inefficient in most cases. In this paper, the hybrid genetic algorithms (HGA) technique is proposed to solve these equations to obtain necessary capacitance range for excitation under ohmic, inductive and capacitive load conditions. The proposed approach produces meaningful and encouraging outcomes for understanding the SEIG's performance from various aspects. © 2010 IEEE

An Improved Approach to Minimise Energy Cost in a Small Wind-Photovoltaic Hybrid System

4th International Renewable and Sustainable Energy Conference (IRSEC) -- NOV 14-17, 2016 -- Marrakech, MOROCCOWOS: 000466883000161Today recent developments in renewable technology such as wind and photovoltaic systems encourage people to generate electricity with low investment costs. In this manner, this may result in partial reduction in power demand hence import of primary sources may gradually decline as well as foreign dependence on energy. Furthermore this lets decrease power losses in transmission lines by reducing power generation capacity in power stations. The widely use of wind turbines and photovoltaic systems in residential and official buildings depends on being economic investment and operation costs. The unit investment cost shows no big difference in all the renewable systems but, to make operation cost lower the balance between production and consumption at any time interval must be maintained without shedding loads. For instance, in case of an hour time interval this balance should be maintained for 24 times in a day however this may not always be possible since sources of renewable energy are irregular. At some time intervals, a power boost is needed either by adding a power source to renewable system or by shedding loads according to their priorities but this is not desired case. In this study, the problem with 7 constraints was solved by a combinatorial optimization based the real-coded genetic algorithms to reduce operation cost to acceptable level and balance between generation and consumption at all the time intervals. The results are encouraging and meaningful for similar applications

Optimal load management in a low power off-grid wind-photovoltaic microhybrid system

IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) -- JUN 06-10, 2016 -- Florence, ITALYWOS: 000387085800160The most significant problem is to balance between generation and consumption in power systems ranging from small to large scales. This may be done by daily load scheduling in small-scale systems using metaheuristic optimization techniques such as the GA, the PSO and the SA etc. In the case of an off-grid low power wind-photovoltaic hybrid system, it is expected to power the loads without shedding and this may require scheduling loads on the basis of power balance, continuously energizing loads and low operation cost as much as possible at each interval of in a day. The fact is that it is a complex optimization problem with several constraints and this problem should be solved 24 times for an hour time interval. Due to incapability of deterministic techniques the GA or similar techniques may be the best approach to solve this problem for optimal power management. The construction of objective function is considered to reduce operation cost and use stored energy in the micropower system consisting of a 1 kW wind turbine, 8 photovoltaic panels, a diesel generator and 4 gel batteries. The problem under consideration is solved by the GA for an hour time interval during a day. The results indicated that the balance between generation and consumption can mostly be maintained and operation cost and energy efficiency are improved with respect to an unscheduled case.IEEE, IEEE Advancing Technol Human, Electromagnet Soc, IEEE Ind Applicat Soc, IEEE Power & Energy Soc, IEEE Italy Sect, Sapienza Univ Rome, Univ Florenc

Improved Approach To Extract Maximum Power From A Small-Scale Fixed PV System

IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) -- JUN 06-10, 2016 -- Florence, ITALYWOS: 000387085800091It is obvious that irradiation level and cell temperature are mainly influential on I-V characteristic of a PV module. A change in optimal operation point of voltage affects maximum electricity generation transferred to a load. Therefore, it is necessary to use a dc-dc converter to adjust PV output voltage to optimal point of it under constant load conditions. To do that I-V curve of the PV module should first be obtained at given insolation rate and temperature and the dc-dc converter is used to set the voltage to optimal point. In this study, at certain ranges of irradiation level and temperature the I-V curve was obtained through parameter extraction from the single diode model using the genetic algorithm. The duty ratio of the dc-dc converter is optimized until the input resistance of the converter equals the optimal resistance under varying irradiation level and temperature. The results are meaningful and encouraging compared to those of similar works.IEEE, IEEE Advancing Technol Human, Electromagnet Soc, IEEE Ind Applicat Soc, IEEE Power & Energy Soc, IEEE Italy Sect, Sapienza Univ Rome, Univ Florenc

Improving CdTe QDSSC's performance by Cannula synthesis method of CdTe QD

WOS: 000457727300039In this study, CdTe Quantum Dots (QDs) used in QD Sensitized Solar Cell (QDSSC) design were synthesized by Cannula method. Cannula method is a method used to minimize problems occurring in hot injection method, while reducing the Full Width Half Maximum (FWHM) value to 27.20 nm, QD's Photoluminescence Quantum Yield (PLQY) increased to 25.66 +/- 2.1%. This study was carried out to determine the effects of CdTe QDs synthesized by Cannula method on CdTe QDSSC application. Firstly, CdTe QDSSCs with the same parameter values (TiO2 thickness, redox couple, collecting electrode, coating technique and duration) were designed with CdTe QDs synthesized by using classical method and Cannula method. The Photo Conversion Efficiency (PCE) of QDSSCs using CdTe QDs synthesized by Cannula method was 0.234%, 2.68 times bigger than the PCE of the CdTe QDSSC synthesized by the classical method. Then, to determine how the size of CdTe QD affects the PCE of CdTe QDSSCs, 6 CdTe QDSSCs were designed by using different size CdTe QDs which were synthesized by Cannula method. In addition, the FTO/TiO2 surface was treated with TiCl4 in CdTe QDSSCs designed. The decrease in the size of the CdTe QD has been found to increase the PCE of CdTe QDSSC. As a result, the PCE of CdTe QDSSC was increased to 0.385% by using 2.80 nm CdTe QDs synthesized by Cannula method. This PCE is the highest value in CdTe QDSSCs designed using CdTe QDs synthesized in the literature by hot injection method. The current density (J(sc)) and open circuit voltage (V-oc) of the designed CdTe QDSSC were found to be 1.292 mA/cm(2) and 0.724 V, respectively.Duzce Universitesi Scientific Research Project [2016.06.03.472]; Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211/C]This study was supported by Duzce Universitesi Scientific Research Project [2016.06.03.472] and Scientific and Technological Research Council of Turkey (TUBITAK) 2211/C Domestic Priority Areas Ph.D. Scholarship Program

Optimal power scheduling of an off-grid renewable hybrid system used for heating and lighting in a typical residential house

13th International Conference on Environment and Electrical Engineering (EEEIC) -- NOV 01-03, 2013 -- Wroclaw, POLANDWOS: 000345761400064In Turkey, there is a significant increase in demand for electricity due to population and economic growth taken place in recent years. This is a major economic factor affecting electricity price on market as well as other factors such as an increase in oil and natural gas prices. To be more specific around 50% of electricity is generated by natural gas generators and 95% of natural gas is imported from other countries. However, Turkey has considerable amount of wind and solar energy potentials to generate electricity through wind turbines and photo-voltaic arrays either on-grid or off-grid. An off-grid hybrid system is more attractive in rural areas where access to grid is limited or unavailable. This study focuses on a power scheduling in a simple renewable hybrid system in order to minimize the operational unit cost using the binary-coded genetic algorithm instead of using mixed integer linear programming. The preliminary results indicated that the binary-coded genetic algorithm produced encouraging and meaningful outcomes to minimize operational unit cost in a typical renewable microgrid photo-voltaic/wind hybrid system

Home Appliances in the Smart Grid: A Heuristic Algorithm-Based Dynamic Scheduling Model

Customers and power utilities alike will benefit from smart grid technology by lowering energy prices and regulating generating capability. The accuracy of information sharing between main grids and smart meters is critical to the performance of scheduling algorithms. Customers, on the other hand, are expected to plan loads, respond to electricity demand alerts, engage in energy bidding, and constantly track the utility company's energy rates. Consumer loyalty can be improved by strengthening the connectivity infrastructure between the service provider and its customers. We suggest a heuristic demand-side control model for automating the scheduling of smart home appliances in order to optimize the comfort of the customers involved. Simulation findings show that the suggested hybrid solution will reduce the peak-to-average ratio of overall energy demand while still lowering total energy costs without sacrificing consumer convenienc