In recent past, the number of electric vehicles (EVs) have increased significantly due to their various advantages to environment. With the proper charging and discharging of EVs with vehicle-to-grid (V2G), electrical...
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In recent past, the number of electric vehicles (EVs) have increased significantly due to their various advantages to environment. With the proper charging and discharging of EVs with vehicle-to-grid (V2G), electrical system can get controllable storage/generations and at the same time, EV owners can earn profits. This paper presents a novel dispatch strategy to determine, when and at what rate EV battery should charge/discharge in order to maximize profits to EV owners and responding to power system's requirements while considering the effect of renewable DG power availability. This objective depends on many criteria like buying and selling price of energy, battery state of charge (SoC), Renewable DG power availability, and load leveling. To solve these, a new multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), is developed. The proposed optimal dispatch strategy is applied to 100 and 200 EV fleets with random travel plan. Further, the effect of these fleets with optimal dispatch strategy is tested on IEEE 33 bus distribution system with added DGs. Furthermore, optimal power dispatch of DGs in EV-rich distribution system is obtained by bat optimization algorithm (BOA). The simulation results demonstrate the feasibility and benefits of the proposed technique.
This paper presents a novel dispatch strategy of distributed generators (DGs) in microgrid for reliability improvement. Energy index of reliability is considered for improvement along with minimization of system losse...
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This paper presents a novel dispatch strategy of distributed generators (DGs) in microgrid for reliability improvement. Energy index of reliability is considered for improvement along with minimization of system losses, system voltage deviation, and cost of DGs. These objectives depend on many criteria like price of DG power, renewable DG power availability, and system loading. The dispatch strategy is developed using bat optimization algorithm. The proposed algorithm is implemented for four seasons: winter, spring, summer, and autumn. For each season, hourly optimal schedules are obtained while maintaining reliability and optimizing objectives. Further, the proposed algorithm is tested for future load enhancement and future price of renewable DGs. The effect of the future load and renewable DG price changes on optimal DG dispatch is tested with IEEE 33 bus distribution system having mixed loads like industrial, residential, and commercial. The feasibility and benefits of the proposed technique are demonstrated with obtained results.
Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, ...
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Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, such as temperature and solar irradiance. As an effective solution, maximum power point tracking (MPPT) approaches have been developed and used in PV systems to increase the efficiency subject to the changing climate conditions. In this respect, a combinatorial MPPT technique is presented in this paper based on the fuzzy controller and bat optimization algorithm to desirably tune the control parameters. To this end, the membership functions of the fuzzy logic controller (FLC) are appropriately specified to cope with uncertainties caused by changing climatic conditions. The studied PV system, equipped with the MPPT technique, operates jointly with an electrical energy storage system which is based on a lead-acid battery. By employing this hybrid generation system, the solar power generation intermittency can be well compensated and the stabilized power output can be achieved. The proposed model is then simulated on a typical hybrid energy system, including a PV system and a battery energy storage (BES) system. In this respect, the superior performance of the suggested control scheme is verified through making a comprehensive comparison with other well-known techniques. Besides, the behavior of the system under varying climate conditions is studied and the desired performance of the suggested combinatorial controller is validated. For example, the presented bat-FLC scheme can help the hybrid system reach 99% efficiency for partial shading conditions (PSCs) which is 18% more compared to the prevalent perturb and observe (P&O) technique. (C) 2020 Elsevier Ltd. All rights reserved.
In this paper, we explore directional radar embedded communications where the embedded information symbols is transmitted to the desired receiver located in pre-specified direction(s) while simultaneously lowering the...
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ISBN:
(纸本)9781728116792
In this paper, we explore directional radar embedded communications where the embedded information symbols is transmitted to the desired receiver located in pre-specified direction(s) while simultaneously lowering the probability of interception by undesired receiver(s). We devise a two-dimensional (i.e., range and angle) directional modulation (DM) scheme for information symbols embedded into the radiation of frequency diverse multiple-input multiple-output (FD-MIMO) radar. In FD-MIMO array, orthogonal waveforms are adopted for radar functionality. We implement the communication operation by using bat optimization algorithm for DM which optimized the phase shifters continuously with time. Thus, each pair of symbols is designed and embedded in each orthogonal waveform. When the phase shifters are altered, the steering vector of the FD-MIMO radar is also changed according to the angle and range values specified. The hybrid FD-MIMO enjoys the advantages of MIMO in spatial diversity and the frequency diverse array in range-dependent coherent directional gain. Moreover, satisfactory hit error rate for communication operation is achieved.
Increasing penetration levels of inverter-interfaced generation impose challenging frequency control problems to power system operation since frequency response capabilities are reduced as conventional generation is d...
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Increasing penetration levels of inverter-interfaced generation impose challenging frequency control problems to power system operation since frequency response capabilities are reduced as conventional generation is displaced by renewable energy sources. To face these challenges, alternatives such as battery Energy Storage Systems (BESSs) are necessary to provide advanced and enhanced frequency support. However, improper location and size of BESSs may greatly affect the performance and economic cost of their particular application. Based on this, an approach for the placement and sizing of BESS for primary frequency support in an isolated power system is presented in this study. BESS location and size are determined according to the most severe contingency for generation outage and different penetration levels of converter based renewable generation in the test system. Under these considerations, the transmission system bus with the larger frequency decline is identified for BESS placement. On the other hand, BESS sizing is formulated as a constrained optimization problem, with a defined cost function to be minimized. An iterative process based on the bat optimization algorithm (BOA) is used in this work to determine the selected parameters to be optimized. For comparison purposes, a Genetic algorithm (GA) approach is also included to deal with the formulated optimization problem. Simulation results show that system frequency response can be improved with the approach proposed in this study. Besides, the use of BOA based alternative is seen to perform relatively better than the GA approach in this case. (C) 2018 Elsevier B.V. All rights reserved.
In speech processing for speaker verification, feature subset selection is one of the key components. Feature Subset Selection (FS) also played a vital role in the fields like pattern recognition, image processing, da...
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ISBN:
(纸本)9781509016235
In speech processing for speaker verification, feature subset selection is one of the key components. Feature Subset Selection (FS) also played a vital role in the fields like pattern recognition, image processing, data mining, and gene selection. In a real world problem related to speech domain, speech sample contains a large number of relevant and irrelevant features. To increase the speaker verification rate, one needs to use the optimization technique for feature selection after the feature extraction technique. The ultimate goal is to select the most relevant subset of features for error free optimized classification in the speech domain. In this regard a novel feature subset selection algorithm is proposed using batalgorithm and Multi Objective optimization technique. Results of the experiment shows the proposed algorithm surpassed the accuracy rates shown by the conventional systems.
Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Lo...
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Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, bat optimization algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and Firefly optimizationalgorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches. (C) 2015 Published by Elsevier B.V.
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