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Energy-Efficient Regional Area Metropolitan Optical Access Network (RAMOAN) Using Modified Load Adaptive Sequence Arrangement (M-LASA) Methodology
Energy efficiency in optical networks is one of the important criteria to attain enhanced performance. Better energy efficiency can be attained in optical networks if the resources are properly scheduled. Polling sequences perform resource scheduling based on load requirements, and various methodologies have evolved in recent times for better efficiency. Load adaptive sequence arrangement (LASA) is one of the familiar and efficient methodologies adopted in various methodologies. However, the power consumption and idle time of optical network units are high which should be reduced to attain optimal network performances. Considering this, a modified LASA is presented in this research work for the regional area metropolitan optical access network (RAMOAN). The modified LASA is obtained by integrating the first-in-last-out polling sequence such that the proposed arrangement provides a better coverage radius, minimal power cost, maximum access reach, maximum energy saving, and minimal delay and energy cost. The better performance of the proposed approach is compared with traditional time and wavelength division multiplexed (TWDM), TWDM-protected, and RAMOAN with LASA through simulation analysis to validate the better performances of the proposed model.
An Extensive Study Using the Beetle Swarm Method to Optimize Single and Multiple Objectives of Various Optimal Power Flow Problems
An electric energy generation system, under the economic operation mode, is an imperative mission in the power system function. This article deals with the use of beetle swarm optimization algorithm (BSOA), for optimal power flow (OPF) solution, in an effective approach. BSOA is a competent optimization technique, to handle multimodal, nonlinear, and nondifferentiable objective functions. The proposed OPF is modeled by numerous objective functions, formulations with constraints, examined with thirty-one different cases, on the three distinguished test systems (IEEE 30, 57, and 118-bus), using single and weighted sum multiobjectives. Six new multiobjective cases are also studied. The control variables, such as real generation of power, tap setting ratio of transformers, bus voltages magnitudes, and the values of shunt capacitor, are also optimized. Potency and robustness of this proposed method were investigated and evaluated with more recent findings reported in the literature. This extensive study revealed the preeminence of the presented technique, applied to OPF problem, with intricate and nonsmooth objective functions.
An Ultrashort-Term Wind Power Prediction Method Based on a Switching Output Mechanism
The ultrashort-term wind power prediction (USTWPP) technology assists the grid to arrange spare capacity, which is important to optimize power investment reasonably. To improve the accuracy of USTWPP and optimize power investment requirements, a USTWPP method with dynamic switching of multiple models is proposed. For high wind speed fluctuation samples, the wind speed-power curve (WSPC) is fitted in a large sample of historical data, and the corrected wind speed is the input of WSPC. The spatiotemporal attentive network model (STAN) is built for the prediction of low wind speed fluctuation samples. According to the real-time fluctuation characteristics of the correction wind speed, a switching mechanism between multiple models is established to reconstruct the prediction results along the time axis direction, and the predicted power is set to zero for the samples whose correction wind speed is lower than the cut-in wind speed. We conducted simulation experiments with data provided by a wind farm with an installed capacity of 130.5 MW in China. The normalized root mean square error (NRMSE) for the 4 h ahead predicted power reaches 0.0907, which verified the validity and applicability of the proposed model.
Investigation of a Battery Storage System Aimed at Demand-Side Management of Residential Load
In this paper, an approach is presented for the demand-side management of residential loads in the urban areas of Pakistan using a battery storage system at the feeder level. The proposed storage system will be installed by a private distributor to supply affordable electricity during peak hours. The experimental data used to carry out this research work are the Pakistan Residential Energy Consumption (PRECON) data set. The households of the data set are categorized based on electric power usage through K-means clustering. The clusters are expanded for feeder synthesis to represent small-scale, medium-scale, and large-scale consumption. This expansion is performed through uniform distribution in a Monte Carlo simulation. The techno-economic analysis for the installation of a battery storage system is carried out for each feeder using SAM. The results of the research project elucidated that the load factors of the feeders representing small-scale, medium-scale, and large-scale consumption improved by 1%, 6%, and 7% by using the optimally sized batteries of 50 kW (670 kWh), 90 kW (1207 kWh), and 100 kW (1360 kWh), respectively. The distributor profit and the consumer utility bill savings ranged from US$12 k to US$25 k. The results proved the validity of the used approach to simultaneously reduce the consumer bill, maximize the distributor profit, and improve the feeder load factor. The novelty of this work lies in the location and in the way the system modeling has been performed with limited data.
A Comprehensive Review of Various Topologies and Control Techniques for DC-DC Converter-Based Lithium-Ion Battery Charge Equalization
Worldwide, electric vehicle (EV) sales are booming nowadays due to the rapid increase in the cost of fossil fuels. Lithium-ion batteries are very familiar in the EV industry because of their high energy per unit mass relative to other electric energy storage systems. To obtain the required voltage, several lithium-ion batteries are connected serially. Due to manufacturing inconsistencies, the voltage of serially connected cells is not always equal, which might result in a charge imbalance. This imbalance may reduce the battery’s life span due to the action of undercharging and overcharging. Battery charge equalisation (BCE) is challenging because it requires a constant voltage level in each cell. Various topologies and control strategies have been proposed in the past literature to build and improve the BCE. This study extracts the recently proposed DC-DC converter-based topologies for BCE. This study then gives a comparative analysis of various control strategies used in BCE and ends with implementing control strategies with BCE topology using a DC-DC converter. This study incorporates contextualised topologies used by BCE with design, operation, and applications. Extensive simulation results are provided to compare the performance of DC-DC converter-based BCE topologies in balancing speed. Also, a comprehensive comparison of various converter topologies and control strategies has been carried out for future investigation.
Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System
Advanced power converters are being developed due to the latest developments in power electronics switches. The matrix converter (MC) assessment is the product of this drastic growth. MC is a bidirectional power flow device with a single-step energy conversion mechanism that operates at variable voltage and frequency. The main drawback of MC is the presence of harmonics due to power electronic device switching. A lot of research has been done to decrease the harmonics content present in the MC, but it is restricted to a certain level only. To overcome this limit, the harmonics are minimized in the MC by incorporating soft computing controllers. This article presents particle swarm optimization (PSO), modified PSO (MPSO), and modified hybrid PSO (MHPSO) based controllers for different loads. The proposed MC with a soft computing controller reduces the harmonics by selecting suitable switching pulses for every sample. The simulations are made using the MATLAB/Simulink environment. The results of the proposed MHPSO controller reduce the THD value from 8.654 with a PSO approach to 7.536 in the presence of nonlinear loads.