Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT)...
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Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT), small size, and competitive pricing for the overall source. A hybrid energy storage system (ESS) controlled by an intelligent energy management strategy (EMS) may be substantially included in multi-source EV design and development. Therefore, this paper proposes a hybrid chimp optimization algorithm (ChOA) and Levy walk technique to create an optimum EMS. The proposed technique reduces battery power (BP) stress and increases the LT, which is accomplished by using a hybrid ChOA-Levy walk (ChOA-LW) optimizationalgorithm with a rule-based approach in accordance with understanding the performance of LIB and SCAP. In order to optimize the rule-based EMS's control settings, the latter strategy is suggested. The control approach can be implemented online once the offline optimization procedure is finished. The presented technique is evaluated via simulation and on an experimental platform by means of a power emulator testbed of a LIB/SCAP hybrid ESS. In terms of BP stress and LT, the findings are compared with a conventional rule-based approach and a mono-source containing a regular high-power LIB. Results obtained demonstrate the effectiveness of the suggested technique, which enables the requested performance to be satisfied with better energy utilization. The assessment results also show notable LT improvements for the LIB, an improvement of up to 19% over the mono-source in reference to a conventional single cell LIB.
Increasing numbers of microgrids (MGs) and the maturing of technologies would allow MGs to engage in power market bidding as well as ensure their own consumption. A market base microgrid transaction mechanism design a...
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Increasing numbers of microgrids (MGs) and the maturing of technologies would allow MGs to engage in power market bidding as well as ensure their own consumption. A market base microgrid transaction mechanism design according to the blockchain (BC) platform is established in this study and the chimp optimization algorithm is employed for solving the optimal bidding process in the transaction. In this study, a price-based model is presented that organically integrates BC and MG for solving the issue of insufficient utilization of power in MG gaming competitions. Its convergence accuracy and search ability are high. In addition, two of the most significant players on the market will be utilized as research objects in order to build a real multi sources mini-MG network to address the actual time energy costs issue. The example verification is used as a basis for the comparison evaluation.
The research on deriving accurate equivalent circuit of solar photovoltaic (PV) modules is increasing due to the necessity of constructing efficient energy conversion devices. The PV panel manufacturers provide data o...
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The research on deriving accurate equivalent circuit of solar photovoltaic (PV) modules is increasing due to the necessity of constructing efficient energy conversion devices. The PV panel manufacturers provide data on three essential points on current-voltage (I-V) characteristics for standard temperature conditions (STC). Hence, the research on PV modules for different environmental/operational conditions with equivalent mathematical models are quite complex. Therefore, there is a necessity to derive accurate PV equivalent circuit parameters using novel AI-based approaches. This work proposes a novel hybrid meta-heuristic algorithm, hybrid chimp-Sine cosine algorithm (HCSCA), for PV panel equivalent circuit parameter extraction. A well-known single- and double-diode PV models have been investigated with the proposed algorithm for different categories of PV modules, namely monocrystalline, polycrystalline, and thin film. The parameters derived from the proposed approach result in minimum error over different executions in the order of less than 10(-10), which recommends better implementation in the present scenario. The nature of extracted parameters and I-V characteristics of considered PV panels are examined over different runs, which provided satisfactory performance characteristics with the proposed algorithm and recommended for its practical implementation.
The incorporation of renewable energy sources into Electric Vehicle (EV) integration systems to charge the onboard charger will result in a significant increase in the number of EV and industrial equipment in the next...
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The incorporation of renewable energy sources into Electric Vehicle (EV) integration systems to charge the onboard charger will result in a significant increase in the number of EV and industrial equipment in the next years. Wireless Power Transmission (WPT) technology is a significant source of operation in the field of power transmission, with enormous potential in a wide range of applications. The resonance phenomenon has lately acquired favor as a method of transferring electricity to a load efficiently across a large air gap. This research presents an efficient L2CL-LCL correction in WPT for obtaining electricity from the PV array and charging the EV's battery. To demonstrate its advantages, the L2CL-LCL method is compared to the DSLCL compensation method. The system adjusts the transmit current of the primary coil to maintain stable output power, maximize power transfer efficiency, and power transferred to the load using an Improved Weighted chimpoptimization (IWdCH) algorithm that searches for the coupling coefficient 'k' of the elements of the compensation networks. Finally, the proposed system was implemented in Python power electronics software and its superiority was demonstrated by comparing it to conventional optimizationalgorithms. It outperforms conventional optimizationalgorithms in terms of efficiency, with anefficiency of 95 %.
Financial accounting information systems (FAISs) are one of the scientific fields where deep learning (DL) and swarm-based algorithms have recently seen increased use. Nevertheless, the application of these hybrid net...
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Financial accounting information systems (FAISs) are one of the scientific fields where deep learning (DL) and swarm-based algorithms have recently seen increased use. Nevertheless, the application of these hybrid networks has become more challenging as a result of the heightened complexity imposed by extensive datasets. In order to tackle this issue, we present a new methodology that integrates the twin adjustable reinforced chimp optimization algorithm (TARCHOA) with deep long short-term memory (DLSTM) to forecast profits using FAISs. The main contribution of this research is the development of the TAR-CHOA algorithm, which improves the efficacy of profit prediction models. Moreover, due to the unavailability of an appropriate dataset for this particular problem, a newly formed dataset has been constructed by employing fifteen inputs based on the prior Chinese stock market Kaggle dataset. In this study, we have designed and assessed five DLSTM-based optimizationalgorithms, for forecasting financial accounting profit. The performance of various models has been evaluated and ranked for financial accounting profit prediction. According to our research, the best-performing DL-based model is DLSTM-TARCHOA. One constraint of our methodology is its dependence on historical financial accounting data, operating under the assumption that past patterns and relationships will persist in the future. Furthermore, it is important to note that the efficacy of our models may differ based on the distinct attributes and fluctuations observed in various financial markets. These identified limitations present potential avenues for future research to investigate alternative methodologies and broaden the extent of our findings.
Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to opera...
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Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm optimization (CSO), Grey Wolf optimization (GWO) and the proposed chimp optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms.
Renewable energy based power generation has proven to be a viable standalone option in areas where extending the grid is challenging. This study addresses this issue by assessing the possibility of an integrated renew...
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Renewable energy based power generation has proven to be a viable standalone option in areas where extending the grid is challenging. This study addresses this issue by assessing the possibility of an integrated renewable energy system (IRES) to electrify twelve villages in the Uttarakhand state of India. Four battery energy storage (BES) devices, namely Lead-Acid (LA), Sodium-Sulfur (NAS), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered for storage in this study. Using the chimp optimization algorithm (ChOA) on the MATLAB (c) platform, eight different configurations consisting of solar photovoltaic (SPV) array, a micro-hydropower (MHP) plant, and a biogas generator (BGG) are modeled and optimized. The study reveals that the optimal IRES configuration with the lowest cost and highest performance comprises 676 SPV panels (260 kWp), one MHP plant (25 kW), one BGG (40 kW), and 648 NAS batteries (778 kWh). This configuration has a total system life cycle cost (LCC) of INR 68.77 million and cost of energy (COE) of 16.77 INR/kWh at 0 % loss of power supply probability. The opti-mization problem was run 1-50 times and found that the proposed ChOA algorithm is more robust compared to others, displaying the lowest Best, Worst, and Mean values of LCC (across all eight configurations), convergence rapidity (26th iteration), and least computational time (3481 sec). However, GWO (seven configurations), MFO (34th iteration), and GA (4154 sec) stand very close to ChOA performance in terms of providing minimum LCCs, convergence rapidity, and computational time, respectively. Furthermore, surplus energy (SE) is effectively utilized by incorporating electric vehicles (EVs) as dump load in the system. The proposed integrated charging (IC) strategy outperforms other charging strategies by energizing 134 EVs, utilizing 99.59 % of SE, and reducing the total COE to 10.57 INR/kWh. Finally, the proposed IC strategy results in a net saving of 94,479.39 tons of greenhouse gas emi
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