Power Maximization in Standalone Photovoltaic System: An Adaptive PSO Approach

A control strategy for power maximization which is an important mechanism to extract maximum power under changing environmental conditions using Adaptive Particle Swarm Optimization (APSO) is proposed in this paper. An Adaptive Inertia Weighting Factor (AIWF) is utilised in the velocity update equation of traditional PSO for the improvement in speed of convergence and precision in tracking Maximum Power Point (MPP) in standalone Photovoltaic system. Adaptation of weights based on the success rate of particles towards maximum power extraction is the most promising feature of AIWF. The inertia weight is kept constant in traditional PSO for the complete duration of optimization process. The MPPT in PV system poses a dynamic optimization problem and the proposed APSO approach paves way not only to track MPP under uniform irradiation conditions, but also to track MPP under non uniform irradiation conditions. Simulations are done in MATLAB/Simulink environment to verify the effectiveness of proposed technique in comparison with the existing PSO technique. With change in irradiation and temperature, the APSO technique is found to provide better results in terms of tracking speed and efficiency. Hardware utilizing dSPACE DS1104 controller board is developed in the laboratory to verify the effectiveness of APSO method in real time.

Power Grid Frequency Regulation Strategy for Photovoltaic Plant Based on Multi-Objective Harris Hawks Optimization

To settle the issue of balance between two objectives, i.e., photovoltaic (PV) power station output power maximization and frequency regulation (FR) signals response, a novel PV reconfiguration strategy is proposed in this work, which maximizes the output power through PV reconfiguration, and meanwhile utilizes the energy storage system (ESS) to decrease the PV plant generated power’ deviation from FR signals. Above all, a model of PV-storage power station reconfiguration is designed to minimize the power bias of both rated power and FR signals. Then, the multi-objective Harris hawks optimization (MHHO) is used to obtain the Pareto front which can optimize the above two objectives due to its high optimization efficiency and speed. Subsequently, the optimal compromise solution is selected by the decision-making method of VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Aiming to substantiate the efficacy of the proposed technique, the case studies are carried out under partial shading condition (PSC) with constant and time-varying FR signals. The simulation results show that, compared with the situation without optimization, the power deviations of the two objectives are reduced by 25.11 and 75.76% under constant FR signals and 23.27 and 55.81% under time-varying FR signals by proposed method, respectively.

Numerical Analysis and Parameter Optimization of a Portable Two-Body Attenuator Wave Energy Converter

Easily portable, small-sized ocean wave energy converters (WECs) may be used in many situations where large-sized WEC devices are not necessary or practical. Power maximization for small-sized WECs amplifies challenges that are not as difficult with large-sized devices, especially tuning the device’s natural frequency to match the wave frequency and achieve resonance. In this study, power maximization is performed for a small-sized, two-body attenuator WEC with a footprint constraint of about 1m. A thin, submerged tuning plate is added to each body to increase added mass without significantly increasing hydrostatic stiffness in order to reach resonance. Three different body cross-section geometries are analyzed. Device power absorption is determined through time domain simulations using WEC-Sim with a simplified two-degree-of-freedom (2DOF) model and a more realistic three-degree-of-freedom (3DOF) model. Different drag coefficients are used for each geometry to explore the effect of drag. A mooring stiffness study is performed with the 3DOF model to investigate the mooring impact. Based on the 2DOF and 3DOF power results, there is not a significant difference in power between the shapes if the same drag coefficient is used, but the elliptical shape has the highest power after assigning a different approximate drag coefficient to each shape. The mooring stiffness study shows that mooring stiffness can be increased in order to increase relative motion between the two bodies and consequently increase the power.

Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment

Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4D, 5D, and 6D) are compared to a non-optimized 7D deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4D deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases.

Design Scalability Study of the G-Shaped Piezoelectric Harvester Based on Generalized Classical Ritz Method and Optimization

This paper studies the design scalability of a -shaped piezoelectric energy harvester (EH) using the generalized classical Ritz method (GCRM) and differential evolution algorithm. The generalized classical Ritz method (GCRM) is the advanced version of the classical Ritz method (CRM) that can handle a multibody system by assembling its equations of motion interconnected by the constraint equations. In this study, the GCRM is extended for analysis of the piezoelectric energy harvesters with material and/or orientation discontinuity between members. The electromechanical equations of motion are derived for the PE harvester using GCRM, and the accuracy of the numerical simulation is experimentally validated by comparing frequency response functions for voltage and power output. Then the GCRM is used in the power maximization design study that considers four different total masses—15 g, 30 g, 45 g, 60 g—to understand design scalability. The optimized EH has the maximum normalized power density of 23.1 × 103 kg·s·m−3 which is the highest among the reviewed PE harvesters. We discuss how the design parameters need to be determined at different harvester scales.

Thermodynamic Optimization of an Air bottoming Cycle for Waste Heat Recovery from Preheater Tower in a Cement Industry

This work evaluated the air bottoming cycles(ABC) as a technology for waste heat recovery (WHR) at the level of the preheater tower in a cement industry. An optimization code has been developed in MATLAB environment and linked with REFPROP database as a way to design and calculate the different parameters and points of the cycle. The theory of power maximization is adopted and the genetic algorithm is employedasa way to maximize the net power output of the cycle, while a case study of a real cement plant has been taken into consideration for the examination purpose. Results showed that the integration of the ABC cycle for energy valorization contributes to covering around 8.5% of the industry need for electrical energy, by generating an amount of power that can achieve 1.07 MW.In addition, although the cycle has shown a low efficiency, it can be a practical WHR solution especially in case of water deficiency.