Output Power Of Generator Research Articles

Design and analysis of Lithium-ion pouch cell with LMO-NMC blended cathode using coupled thermo-electro-chemical model

In this study, a Lithium-ion pouch cell is studied considering a blended cathode, made of dual intercalating cathode materials. The proposed battery designs are thoroughly investigated through the pseudo-two-dimensional thermo-electro-chemical coupled model, developed with the help of the COMSOL Multiphysics computational tool. Therefore, the major objective of this study is to analyse the thermo-electro-chemical performance of Lithium-ion pouch cell made of LMO-NMC blended cathodes, for different electrode designs arrived by varying the design parameters. The battery performance attributes include: discharge voltage, energy capacity, power output, and heat generation. Furthermore, a parametric study on the design parameters of blended electrode, such as: thickness of electrode, particle radius, volume fraction of active material, and blend ratio is also carried out to study their influence on the battery performance. The results indicate that the cell with blended cathode incorporates the properties of its constituent materials. A 50:50 LMO-NMC blended cathode is observed to improve the energy capacity of the cell by 26.48% with respect to the cell with LMO cathode. Also, at 10C discharge rate, the improvement in capacity utilization and reduction in heat generation are observed to be 12.87% and 12.77%, respectively, as compared to the cell with NMC cathode. Furthermore, all the electrode design parameters are found to significantly influence the battery performance, particularly at higher discharge rates. Moreover, inter dependency among the design parameters has also been identified. Therefore, the present study can help the designers of Lithium-ion batteries to come up with efficient designs considering blended cathodes.

Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.

Combined Heat and Power Units Economic Dispatch (CHPUED) is a challenging non-convex optimization challenge in the power system that aims at decreasing the production cost by scheduling the heat and power generation outputs to dedicated units. In this article, a Kepler optimization algorithm (KOA) is designed and employed to handle the CHPUED issue under valve points impacts in large-scale systems. The proposed KOA is used to forecast the position and motion of planets at any given time based on Kepler's principles of planetary motion. The large 48-unit, 96-unit, and 192-unit systems are considered in this study to manifest the superiority of the developed KOA, which reduces the fuel costs to 116,650.0870 USD/h, 234,285.2584 USD/h, and 487,145.2000 USD/h, respectively. Moreover, the dwarf mongoose optimization algorithm (DMOA), the energy valley optimizer (EVO), gray wolf optimization (GWO), and particle swarm optimization (PSO) are studied in this article in a comparative manner with the KOA when considering the 192-unit test system. For this large-scale system, the presented KOA successfully achieves improvements of 19.43%, 17.49%, 39.19%, and 62.83% compared to the DMOA, the EVO, GWO, and PSO, respectively. Furthermore, a feasibility study is conducted for the 192-unit test system, which demonstrates the superiority and robustness of the proposed KOA in obtaining all operating points between the boundaries without any violations.

An Interference Optimization – Induced Electrical Turbine Fault Prediction and Analysis Method

Predicting electrical turbine faults is decisive for consistent operation and power generation output. Based on the operative cycles of the electrical turbine, the faults are predicted to prevent power generation interruptions. This paper introduces an Interference Optimization-based Fault Prediction Method (IO-FPM) for serving smooth operation purposes. In this method, the inferred optimization using classifier tree learning is induced for segregating the operating cycles of the turbine. The maximum and minimum threshold conditions for turbine operation using resistance and magnitude of the blades are accounted for each operation cycle. The classifier performs segregation based on low and high thresholds for predicting failure cycles. Such cycles are altered using pre-maintenance intervals and mechanical fault diagnosis at an early stage. This prevents turbine failure regardless of external influencing factors.

Retracted: Analysis of the Characteristics in Linear Generator for EDS Maglev Based on Harmonic Magnetic Field Method

The optimal non-contact onboard power source for superconducting electrodynamics suspension (EDS) maglev is the linear generator. As a result, the development of the linear generator has been significantly aided by the research of its properties. A method for figuring out the maximum output power of the EDS maglev's linear generator is suggested. It has the advantages of a straightforward formulation, quick calculation time, and acceptable precision. First, the electromagnetic model of the linear generator taking into account the eddy current loss of the outer vessel is constructed in accordance with the structure of EDS maglev. Second, the magnetic flux density of superconducting (SC) coils and the multi-structure figure-8 shaped coils containing harmonic components are expressed using Fourier coefficients utilizing the harmonic magnetic field approach, and this is done by incorporating the structural correction coefficient of SC coils. Third, it is proposed to use variable resistance to equivalent eddy current losses to calculate the linear generator's maximum output power. Finally, the finite element (FEM) simulation and the measurement data of the Yamanashi line are used to confirm that the theory is valid.

Optimal Configuration for Soft Open Point Based on Scenario Generation of Wind Power and Photovoltaic Output

Soft Open Point (SOP) is a new type of fully controlled power electronic device. The use of SOPs can greatly improve the efficiency, flexibility, and controllability of distribution networks. However, considering that SOPs are expensive and have high maintenance costs, the installation capacity and location of SOPs need to be optimized. In this paper, we propose an SOP optimization configuration method considering the stochasticity and correlation of wind and PV power generation. First, considering the uncertainty and negative correlation of wind and PV power output, the probability density distribution function is generated using Copula theory and historical data. Then, the optimal location of the SOPs is determined by a cluster discovery algorithm and the Power Loss Sensitivity Index (PLSI). Finally, the capacity of the SOP is determined by minimizing the integrated cost of the network over a year, and the capacity of the SOP is solved by the conic curve programming method. The superiority of the proposed method is verified in this study at the IEEE-33 node test system.

Dual‐Objective Optimization for Maximizing Photovoltaic Output Power in a Two‐Stage Single‐Phase Grid‐Tied System

The effective utilization of photovoltaic systems (PVS) is significantly affected by partial shading (PS). Although total cross‐tied topology minimizes the power loss, the partial shading condition (PSC) decreases the output power generation and exhibits multiple power peaks in the Power Voltage characteristics. To address this issue, a dual‐objective optimization technique based on reduced search space equilibrium optimization (RSS‐EO) for a single‐phase, two‐stage grid‐connected PVS, is presented. The proposed technique accomplishes two objectives 1) maximizing power generation by reconfiguring the PV modules using a switching matrix based on irradiance equalization and 2) extracting the global maximum power (GMP) with faster convergence, negligible steady‐state oscillations, and reduced steady‐state error. The simulation and experimental results validate the effectiveness of the proposed RSS‐EO technique in efficiently distributing shade over the entire PV array and tracking GMP. The power enhancements achieved by RSS‐EO, particle swarm optimization, and Munkres are 23.03%, 20.02%, and 19.01%, respectively. Additionally, the proposed method exhibits an average mismatch power loss of 738 W, a fill factor of 58.1%, and a percentage power loss of 26.83%. Furthermore, the proposed algorithm is evaluated based on energy calculations and shows better performance by achieving the shortest payback period of 1.6 years.

High output power moist-electric generator based on synergistic nanoarchitectonics for effective ion regulation

Moisture is ubiquitous in nature and life processes and contains huge amounts of energy. Moist-electric generation is an emerging energy technology that collects energy from the environment and converts it into electrical energy through the interaction of moisture with materials. However, the low power density of moist electric generators (MEG) hinders their further application, and improving the performance of MEG devices remains a challenge. Herein, we propose a synergistic nanoarchitectonics strategy to fabricate MEG by optimizing the paths of the ion generation, transmission and interception processes to obtain excellent power output performance. Highly hydrophilic poly (4-styrensulfonic acid) nano film was used to cover the surface of the porous polyurethane foam framework to form PU@PSS active composite with abundant movable ions, which also aided in water diffusion and provided evaporation channels. Gridded aluminum and PEDOT:PSS nano layer were used as the top electrode and interception layer because of their metallic activities and hydrogen ion adsorption capacities, respectively. Due to its effective water circulation and ion regulation properties, the MEG worked in an all-weather environment and converted energy from air moisture into high-power electrical energy. In addition, the MEG converted ambient heat energy to increase the efficiency of ion transport, thereby increasing the output power. A single MEG spontaneously produced a maximum open-circuit voltage of 1.25 V·cm−2 and a short-circuit current of 150 μA·cm−2 in a heated condition, and provided an ultra-high output power of 1.875 W·m−2 which is an order of magnitude higher than the output power provided by similar MEG devices. Thus, the strategy adopted achieved an ultra-high power output of moist-electric generation, which will inspire the development of next-generation MEG.

Parametric Design of a New Float-Type Wave Energy Generator and Numerical Simulation of Its Hydrodynamic Performance

A novel float-type device for wave energy power generation, designed specifically for offshore wave environments, is introduced as an innovative technology in wave energy utilization. Herein, we present the design concept, structural composition, and energy conversion process of the device, and conduct mathematical modeling and theoretical research on its kinematic and dynamic characteristics. At the same time, we use a numerical wave pool based on the STAR-CCM+ boundary wave making method and damping dissipation method to analyze the motion response and output power of the wave energy generator in a five-order Stokes wave environment within one wave cycle and the entire operating cycle. Finally, in order to develop the best design strategy, we study the effect of changing the structural parameters of the power generation device on the hydrodynamic performance of the device.

Highly Stretchable, Knittable, Wearable Fiberform Hydrovoltaic Generators Driven by Water Transpiration for Portable Self-Power Supply and Self-Powered Strain Sensor.

The development of excellently stretchable, highly mobile, and sustainable power supplies is of great importance for self-power wearable electronics. Transpiration-driven hydrovoltaic power generator (HPG) has been demonstrated to be a promising energy harvesting strategy with the advantages of negative heat and zero-carbon emissions. Herein, this work demonstrates a fiber-based stretchable HPG with the advantages of high output, portability, knittability, and sustainable power generation. Based on the functionalized micro-nano water diffusion channels constructed by the discarded mask straps (MSs) and oxidation-treated carbon nanomaterials, the applied water can continuously produce electricity during the spontaneous flow and diffusion. Experimentally, when a tiny 0.1mL of water encounters one end of the proposed HPG, the centimeter-length device can yield a peak voltage of 0.43V, peak current of 29.5µA, and energy density of 5.833mWhcm-3. By efficiently integrating multiple power generation units, sufficient output power can be provided to drive commercial electronic devices even in the stretched state. Furthermore, due to the reversibility of the electrical output during dynamic stretching-releasing, it can passively convert physiological activities and motion behaviors into quantifiable and processable current signals, opening up HPG's application in the field of self-powered wearable sensing.

Asymmetric Nanoporous Alumina Membranes for Nanofluidic Osmotic Energy Conversion.

The potential of harnessing osmotic energy from the interaction between seawater and river water has been recognized as a promising, eco-friendly, renewable, and sustainable source of power. The reverse electrodialysis (RED) technology has gained significant interest for its ability to generate electricity by combining concentrated and diluted streams with different levels of salinity. Nanofluidic membranes with tailored ion transport dynamics enable efficient harvesting of renewable osmotic energy. In this regard, anodic aluminum oxide (AAO) membranes with abundant nanochannels provide a cost-effective nanofluidic platform to obtain structures with a high density of ordered pores. AAO can be utilized in constructing asymmetric composite membranes with enhanced ion flux and selectivity to improve output power generation. In this review, we first present the fundamental structure and properties of AAO, followed by summarizing the fabrication techniques for asymmetric membranes using AAO and other nanostructured materials. Subsequently, we discuss the materials employed in constructing asymmetric structures incorporating AAO while emphasizing how material selection and design can resist and promote efficient energy conversion. Finally, we provide an outlook on future applications and address the challenges that need to be overcome for successful osmotic energy conversion.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

Generation cost optimisation of hydrothermal system using arithmetic optimisation algorithm considering transmission loss and valve point loading effect

Hydrothermal scheduling is a crucial issue in the field of power system economics. The goal of short-term hydrothermal scheduling is to reduce the total cost of generation by optimising the hourly output of power generation for specific time intervals. This paper presents a new population-based approach called the arithmetic optimisation algorithm for solving the short-term hydrothermal scheduling problem. To verify the performance of the arithmetic optimisation approach, six different test systems with different cost functions were investigated. It has been observed that the proposed AOA reduced the generation cost by 3.41%. Furthermore, upon considering non-linearity in the fitness function by involving the valve-point effect, AOA diminished the generation cost by 8.54%. An appreciable 25.08% reduction in the algorithm execution time was also observed when the proposed AOA was used as the optimisation tool. Numerical results and non-parametric statistical analysis claim the superiority of the proposed approach.

Reliability assessment of the ocean thermal energy conversion systems through Monte Carlo simulation considering outside temperature variation

AbstractThe ocean thermal energy conversion (OTEC) systems, as renewable energy-based power plants, have the potential to play a significant role in meeting future electricity demands due to the vast expanse of the world's oceans. These systems employ the temperature difference between surface ocean waters and deep ocean waters to drive a thermodynamic cycle and produce electricity. The temperature of deep ocean waters, approximately 1000 m below the surface, is approximately 4 °C, while surface ocean temperatures typically range between 20 and 30 °C. The generated power of OTEC systems is dependent on these temperature differences and may vary with changes in surface ocean temperatures. In this study, the main focus is to find the impact of temperature variation on the failure rates of OTEC system components and the generated power output of these plants. The findings indicate that as the demand for the power system increases, its reliability decreases. In order to improve the reliability of the power system, the integration of a new generation unit, such as the close cycle OTEC power plant under investigation, could be necessary. The findings also indicate the importance of considering temperature variation in the evaluation of the reliability of such types of power plants based on renewable energy.

The Influence of the Installation Condition and Performance of Bifacial Solar Modules on Energy Yield

Compared with typical mono-facial photovoltaic (PV) solar modules, bifacial solar modules can make full use of reflected or scattered light from the ground and the surroundings to yield more electrical energy. The electrical energy on the rear side depends on multiple factors, such as the IV parameters of modules, packaging materials, and installation circumstances. In this work, the power generation output is simulated and researched using the PV-SYST software program, based on the different electrical parameters of bifacial solar modules and the installation conditions of the given PV systems. The influencing factors that affect the electrical energy are further analyzed using power-loss diagrams. The results show that improving the surface albedo can raise additional energy by 8.3%, thus behaving significantly better than the mono-facial module. Furthermore, improving the siting height and incidence angle modifier (IAM) of the modules can increase the additional energy by 3.1%. In contrast, adjusting the output current or voltage of the modules adds some energy, while the modules are of the same nominal power value. It was observed that the energy level of a photovoltaic system mainly depends on the installation circumstances, but the electrical parameters of the modules themselves contribute little.

Research and application of intelligent information processing system in power hierarchical control

Abstract In this paper, we propose a distributed secondary FM control method for microgrids to address the problem that the operating cost of microgrids increases significantly under load dropout and use the consistency algorithm to make the system reasonably distribute the load power to re-achieve the stable state. In the actual microgrid, the communication delay discusses the influence of information transmission and system stability. Simulation experiments are used to verify and set different convergence coefficients to obtain a suitable range of convergence coefficients. Then, the microgrid economic power allocation algorithm was used to describe the operating cost of microsources and limit the generator output power of the microgrid system to obtain the microgrid economic power allocation values. The cost amount of MG1 is $1327.3 in the equal micro-increase rate consistency calculation. The control condition test reduced 73.14 kWh in the overall load outage of intelligent microgrid control. The power system’s efficiency can be improved through a control method using intelligent information processing.

State of the art approach for comprehensive power system security assessment—Real case study

Power system security assessment plays a critical role in ensuring the safety and stability of the electric grid, evaluated by the operating state’s proximity to the loadability limits. However, with the increasing integration of intermittent renewable energy sources and rising electricity demands, there is a growing need for fast and reliable algorithms to perform power system security assessments effectively. This work reviews state-of-the-art approaches for steady-state security assessment and demonstrates the advantages of the recently proposed Transversality Enforced Newton Raphson (TENR) algorithm. The algorithm provides better computational tractability, scalability, and the capability to incorporate various technological constraints like limits on voltage setpoints, transmission line thermal limits, generator power outputs, etc. This work presents a generalization of the TENR algorithm, proof of convergence, and an adaptive step-size strategy with improved computational performance. The proposed algorithm was used to study maximum loadability, maximum boundness of the nodal power injections, and total transfer capability on a realistic case study of the Russian Far-East power system model, which expects substantial development in the nearest future.

Optimizing the Load Frequency of a Two-Area Interlinked Power System using Artificial Intelligence Techniques

Power costs are increasing on a daily basis, generating changes in system frequency and causing serious concerns with system stability. It has become a major problem to offer customers with uninterrupted and high-quality power. To mitigate these issues, a linked power system's load distribution and network frequency should be constantly reviewed. Load frequency control adjusts the generator's energy output and tie line power between prescribed limits. As well as regulating generator output power, load frequency control also adjusts tie line power. The disturbance in the frequency due to different load changes is regulated using the proposed scheme. In this article, a two-area load frequency control system is constructed and evaluated using various control approaches, including a proportional integral derivative (PID) controller, a proportional integral (PI) controller, a fuzzy logic-based controller, and an Artificial Neural Network (ANN). The goal is to assess the power system's resilience under different loading conditions with these control schemes. The performance of the controllers is compared based on peak-undershoot, peak-overshoot, and settling time, focusing on tie line power and frequency response. To achieve this, the design is implemented using MATLAB/SIMULINK software.

Analysis of the Output Characteristics of a Vertical Photovoltaic System Based on Operational Data: A Case Study in Republic of Korea

The proliferation of renewable energy sources to achieve carbon neutrality has rapidly increased the adoption of photovoltaic (PV) systems. Consequently, specialized solar PV systems have emerged for various installation purposes. This study focuses on grid connecting vertically installed bifacial PV modules facing east and west by establishing a test bed within Republic of Korea. Based on weather and generation data collected in Republic of Korea, located in the middle of latitude 34.98° N, from January to July 2023, we analyzed and compared the generation patterns, peak generation, peak hours, and total generation of conventional and vertical PV systems. Moreover, PVsyst was used to model the solar PV generation and analyze the consistency and viability of vertical PV generation by comparing actual operational data with simulation results. The vertical PV system demonstrated a peak power generation of 89.1% compared with the conventional PV system with bifacial modules. Based on operational data from January to July, the power generation output of the vertical PV system decreased to 65.7% compared with that of the conventional system with bifacial modules. This corresponded to 78.8% to 80.2% based on the PVsyst simulation results. In particular, the investigations related to the peak generation levels and occurrence times of vertical PV systems provide insights into the practicality of vertical solar PV systems and their potential for improving the PV hosting capacity.

Corona: An effective polarization strategy of polymer composites with high-k filler for piezoelectric nanogenerators

Piezoelectric nanogenerators (PENGs) are mushrooming owing to demand of swift transformation of power supply from fossil fuel to green energy. Piezoelectric ceramics are enthusiastically chased because glorious PENGs demand for high piezoelectricity. Generally, brittleness of ceramics can be compensated by polymer, while the dielectric mismatch between low permittivity of polymer and high permittivity of ceramics plaguing researchers so long. Traditional contact poling is always interrupted by abrupt dielectric breakdown due to electric field concentration at interface between polymer and fillers. Here, we propose a strategy of using corona polarization instead of traditional contact polarization to insistently polarize large-area polymer composite films with high-k filler. The corona polarization technique can ignore the negative effect of local short circuit on polarization failure owing to prominent lateral insulation between deposited charges on surface of composites film, consequently obtaining well-performed piezoelectricity. Henceforth, polarization failure did not occur using corona even under poling voltage of 26 kV. The corona-poled PVDF/Y-ZnO PENG with only 15 wt% content of fillers generated output power density of ∼1064 μW/cm3. Our work will pilot the development of polymer composites with high-k filler and piezoelectric nanogenerators.

Offshore wind power output prediction based on convolutional attention mechanism

ABSTRACT To solve the problem that offshore wind power generation is susceptible to extreme weather conditions that result in low accuracy of monitoring data, this work proposes a method for predicting offshore wind power generation output by combining convolution and attention mechanisms. The method combines bidirectional long and short-term memory (BiLSTM) network with attention mechanism (AM). The input data are first weighted with the AM for reducing the predictive weight of the interference data, and then the attention mechanism-weighted data are fed into the BiLSTM network. The bi-directional propagation neural network can effectively utilize all the input information, resulting in higher prediction accuracy. The method of combining BiLSTM network with AM is compared with the method employing long and short-term memory network, gated recurrent unit, and bidirectional long and short-term memory network alone through simulation. The mean square error (MSE) of the BiLSTM combined with AM method is 41.28% smaller than the MSE of the best LSTM method among the compared methods and 58.43% smaller than the average of the compared methods. The R2-R-Square is 14.21% larger than the R2-R-Square of the LSTM and 32.91% larger than the average of the compared methods. The results show that the proposed method of combining convolution and attention mechanism for offshore wind power generation output prediction has higher prediction accuracy.