An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system\u27s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques

The diffusion of disruptive technologies

We identify novel technologies using textual analysis of patents, job postings, and earnings calls. Our approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from our data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread across space. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these technology hubs are more likely to arise in areas with universities and high skilled labor pools

Improving the efficiency, power quality, and cost-effectiveness of solar PV systems using intelligent techniques

Growing energy demand, depleting fossil fuels, and increasing environmental concerns lead to adaptation to clean and sustainable energy sources. Renewable energy sources are now believed to play a critical role in diminishing the deteriorating environment, supplying power to remote areas with no access to the grid, and overcoming the energy crisis by reducing the stress on existing power networks. Therefore, an upsurge in renewablesbased energy systems development has been observed during the previous few decades. In particular, solar PV technology has demonstrated extraordinary growth due to readily available solar energy, technological advancement, and a decline in costs. However, its low power conversion efficiency, intermittency, high capital cost, and low power quality are the major challenges in further uptake. This research intends to enhance the overall performance of PV systems by providing novel solutions at all levels of a PV system hierarchy. The first level investigated is the solar energy to PV power conversion, where an efficient maximum power point tracking (MPPT) method is developed. Secondly, the dc to ac power conversion is explored, and an optimal PV system sizing approach with abidance to power quality constraints is developed. Finally, smart power management strategies are investigated to utilise the energy produced by solar PV efficiently, such that the minimum cost of energy can be achieved while considering various technical constraints. The methods involve Genetic Algorithm (GA) for finding the optimal parameters, mathematical models, MATLAB/Simulink simulations of solar PV system (including PV arrays, dc/dc converter with MPPT, batteries, dc/ac inverter, and electric load), and experimental testing of the developed MPPT method and power management strategies at the smart energy lab, Edith Cowan University. Highly dynamic weather and electricity consumption data encompassing multiple seasons are used to test the viability of the developed methods. The results exhibit that the developed hybrid MPPT technique outperforms the conventional techniques by offering a tracking efficiency of above 99%, a tracking speed of less than 1s and almost zero steady-state oscillations under rapidly varying environmental conditions. Additionally, the developed MPPT technique can also track the global maximum power point during partial shading conditions. The analyses of power quality at the inverter’s terminal voltage and current waveforms revealed that solar PV capacity, battery size, and LC filter parameters are critical for the reliable operation of a solar PV system and may result in poor power quality leading to system failure if not selected properly. On the other hand, the optimal system parameters found through the developed methodology can design a solar PV system with minimum cost and conformance to international power quality standards. The comparison between the grid-connected and stand-alone solar PV system reveals that for the studied case, the grid-connected system is more economical than the stand-alone system but outputs higher life cycle emissions. It was also found that for grid tied PV systems, minimum cost of energy can be achieved at an optimal renewable to grid ratio. Additionally, applying a time varying tariff yields a slightly lower energy cost than the anytime flat tariff. A sensitivity analysis of the reliability index, i.e., loss of power supply probability (LPSP), demonstrates that for the stand-alone PV systems, there is an inverse relationship between LPSP and cost of energy. Contrarily, for grid-connected systems, the cost of energy does not vary significantly with the change in LPSP

Azetidinium as Cation in Lead Mixed Halide Perovskite Nanocrystals of Optoelectronic Quality

The dataset is part of the research work submitted for publication titled "Azetidinium as Cation in Lead Mixed Halide Perovskite Nanocrystals of Optoelectronic Quality

Optimal sizing and energy scheduling of grid-supplemented solar PV systems with battery storage: Sensitivity of reliability and financial constraints

Establishing reliable, clean, and inexpensive solar PV systems is a complex interplay between the level of reliability (LPSP), financial constraints, and CO2 emissions. This paper investigates the impact of these factors on stand-alone (SA) and grid-supplemented (GS) solar PV systems over multiple seasons. The research uses established hardware models, detailed power management strategies as well as realistic Australian grid tariffs and Genetic Algorithms to find the minimum Cost of Energy (COE) subject to LPSP and financial constraints. The developed power management strategies are also tested experimentally on a real solar PV system. The results indicate that the grid-supplemented system yields 30% lower COE compared to the stand-alone at baseline (LPSP\u3c0.01) but achieves this at the expense of 17% higher life cycle emissions (LCE, kgCO2-eq/kWh). The results also revealed that in the grid-supplemented systems, only optimum renewable to grid penetration ratios can yield minimum COE which were found to be 95% and 5% respectively in this case study. A typical increase in the COE with tighter LPSP is found for the stand-alone systems. Whilst in the grid-supplemented systems, higher system\u27s reliability can be achieved at almost the same COE but with increased emissions. In terms of tariff structures, the time of use tariff structure offers a marginally lower COE (0.30$/kWh) compared to the anytime flat tariff (COE = 0.32$/kWh), but with the latter outperforming in terms of LCE. The analyses presented help identifying the parameters to be considered in establishing more cost-effective solar PV systems

A novel approach for optimal sizing of stand-alone solar PV systems with power quality considerations

Designing reliable and cost-effective solar PV systems with compliance to national and international power quality standards is critical for their economical, efficient, and safe operation. The conventional approaches currently being used to optimally size the solar PV systems generally ignore power quality criteria during the initial design phase. This paper fills this gap by presenting a novel Genetic Algorithm (GA) based strategy to design a stand-alone solar PV system featuring optimal system size with conformance to power quality standards. MATLAB/Simulink environment was used to develop a detailed model of solar PV system, including a robust control mechanism for maximum power point tracking, battery bi-directional control, and inverter output control. An optimisation algorithm based on the single-objective GA was then designed to find the optimal PV array size, battery capacity, and the values of harmonic filter components so that the total investment cost is minimised and load demand as well as power quality criteria over the studied period are satisfied. The simulation results, corroborated with insightful discussions demonstrate that ignoring power quality criteria by selecting improper non-optimal system parameters can lead to inadmissible voltages and THD levels in the system. In contrast, the proposed design strategy can successfully devise a solar PV system that is optimally sized and complies with power quality standards