Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

Optimization Methods of MPPT Parameters for PV Systems:Review,Classification,and Comparison

To obtain efficient photovoltaic(PV)systems,optimum maximum power point tracking(MPPT)algorithms are inevitable.The efficiency of MPPT algorithms depends on two MPPT parameters,i.e.,perturbation amplitude and perturbation period.The optimization of MPPT algorithms affect both the tracking speed and steady-state oscillation.In this paper,optimization methods of MPPT parameters are reviewed and classified into fixed and variable methods.The fixed MPPT parameters are constant during MPPT performance,and a trade-off should be made between the tracking speed and steady-state oscillation.However,the variable MPPT parameters will be changed to improve both the tracking speed and the steadystate oscillations.Moreover,some of them are simulated,compared,and discussed to evaluate the real contributions of the optimization methods to the MPPT efficiency.Furthermore,significant features of the optimization methods,i.e.,noise immunity,robustness,and computation effort,are investigated.

Power Electronics and Its Application to Solar Photovoltaic Systems in India

Out of many renewable energy resources, solar energy is one of the conspicuous sources of energy which can supply the increasing demand of energy. As of May 2014, India has an installed PV capacity of 2.5 GW. The solar photovoltaic project includes power electronics with high quality performance devices, incorporated with smart energy management principles. Power electronics is used to improve the energy efficiency of apparatus, and help the generation of environmentally clean energy. In this article the explanation of role of power electronics and the discussion about similar and future concepts in solar photovoltaic systems related to reliability and advancement of each technology in India has been presented.

An Introduction to Distribution-Level Solar Energy

Solar energy has been widely used in power generation.With the development of solar energy,the distributed photovoltaic power generation and the distributed grid-connected PV systems become the center of attention.This paper provided a brief introduction to distribution-level solar energy.Firstly,the development of solar energy was analyzed,and the distributed photovoltaic power generation was discussed.Secondly,the distributed grid-connected PV systems and basic theory of photovoltaic solar channel were analyzed.In order to ensure PV power is connected to grid stably and reliably,some related aspects such as the establishment of mathematical model for solar photovoltaic cell,the analysis of I-V characteristics of solar photovoltaic cell,and the tracking of its maximum power point(MPPT)to control the behaviour of the DC/DC converter were discussed.Finally,a simulation model was necessary to be established by using PSCAD/EMTDC function module to verify and simulate the mathematical model and control strategies,and some suggestions were put forward for the sustainable development of solar energy.

Rooftop photovoltaic （PV） halls： Achieving economic energy demand systems for industrial benefit via lowering

Industrial hails are characterized with their retatively high roof-to-floor ratio, which facilitates ready deployment of renewable energy generation, such as photovoltaic （PV） systems, on the rooftop. To promote deployment of renewable energy generation, feed-in tariff （FIT） higher than the electricity rate is available in many countries to subsidize the capital investment. FIT comes in different forms. For net FIT, in order to maximize the economic benefit, surplus electridty generation at each hour is desirable. One way to achieve surplus electricity generation is by increasing generation capacity, which is synonymous to higher capital investment. In fact, surplus electricity generation can also be achieved by lowering the energy demand of the building. This particularly the case for industrial hatls, which are usually subject to high energy demand for space conditioning in order to remove the excess heat gain due to the many power-intensive processes. Building energy performance simulation toots can be used to explore the different building design options that could lower the energy demand. In this paper, single-objective optimization on investment return will be deployed to study the cost effectiveness among different options in lowering energv demand. It Will-be demonstrated with a case study of a warehouse.

Application of metal oxides-based nanofluids in PV/T systems: a review

Having the wide application of metal oxides in energy technologies, in recent years, many researchers tried to increase the performance of the PV/T system by using metal oxide-based nanofluids (NFs) as coolants or optical filters or both at the same time. This paper summarizes recent research activities on various metal oxides (Al2O3, TiO2, SiO2, Fe3O4, CuO, ZnO, MgO)-based NFs performance in the PV/T system regarding different significant parameters, e.g., thermal conductivity, volume fraction, mass flowrate, electrical, thermal and overall efficiency, etc. By conducting a comparative study among the metal oxide-based NFs, Al2O3/SiO2-water NFs are mostly used to achieve maximum performance. The Al2O3-water NF has a prominent heat transfer feature with a maximum electrical efficiency of 17%, and a maximum temperature reduction of PV module of up to 36.9°C can be achieved by using the Al2O3-water NF as a coolant. Additionally, studies suggest that the PV cell’s efficiency of up to 30% can be enhanced by using a solar tracking system. Besides, TiO2-water NFs have been proved to have the highest thermal efficiency of 86% in the PV/T system, but TiO2 nanoparticles could be hazardous for human health. As a spectral filter, SiO2-water NF at a size of 5 nm and a volume fraction of 2% seems to be very favorable for PV/T systems. Studies show that the combined use of NFs as coolants and spectral filters in the PV/T system could provide a higher overall efficiency at a cheaper rate. Finally, the opportunities and challenges of using NFs in PV/T systems are also discussed.

Generating capacity adequacy evaluation of large-scale, grid-connected photovoltaic systems

Large-scale, grid-connected photovoltaic sys- tems have become an essential part of modem electric power distribution systems. In this paper, a novel approach based on the Markov method has been proposed to investigate the effects of large-scale, grid-connected photovoltaic systems on the reliability of bulk power systems. The proposed method serves as an applicable tool to estimate performance （e.g., energy yield and capacity） as well as reliability indices. The Markov method frame- work has been incorporated with the＇ multi-state models to develop energy states of the photovoltaic systems in order to quantify the effects of the photovoltaic systems on the power system adequacy. Such analysis assists planners to make adequate decisions based on the economical expectations as well as to ensure the recovery of the investment costs over time. The failure states of the components of photovoltaic systems have been considered to evaluate the sensitivity analysis and the adequacy indices including loss of load expectation, and expected energy not supplied. Moreover, the impacts of transitions between failures on the reliability calculations as well as on the long- term operation of the photovoltaic systems have been illustrated. Simulation results on the Roy Billinton test system has been shown to illustrate the procedure of the proposed frame work and evaluate the reliability benefits of using large-scale, grid-connected photovoltaic system on the bulk electric power systems. The proposed method can be easily extended to estimate the operating and maintenance costs for the financial planning of the photovoltaic system projects.

Integration of PV system with SMES based on model predictive control for utility grid reliability improvement

This paper describes the integration of a photovoltaic (PV) renewable energy source with a superconducting magnetic energy storage (SMES) system. The integrated system can improve the voltage stability of the utility grid and achieve power leveling. The control schemes employ model predictive control (MPC), which has gained significant attention in recent years because of its advantages such as fast response and simple implementation. The PV system provides maximum power at various irradiation levels using the incremental conductance technique (INC). The interfaced grid side converter of the SMES can control the grid voltage by regulating its injected reactive power to the grid, while the charge and discharge operation of the SMES coil can be managed by the system operator to inject/absorb active power to/from the grid to achieve the power leveling strategy. Simulation results based on MATLAB/Simulink® software prove the fast response of the system control objectives in tracking the setpoints at different loading scenarios and PV irradiance levels, while the SMES injects/absorbs active and reactive power to/from the grid during various events to improve the voltage response and achieve power leveling strategy.

A new method for estimating the longevity and degradation of hotovoltaic systems considering weather states

The power output of solar photovoltaic （PV） systems is affected by solar radiation and ambient temperature. The commonly used evaluation techniques usually overlook the four weather states which are clear, cloudy, foggy, and rainy. In this paper, an ovel analytical model of the four weather conditions based on the Markov chain is proposed. The Markov method is well suited to estimate the reliability and availability of systems based on a continuous stochastic process. The proposed method is generic enough to be applied to reliability evaluation of PV systems and even other applications. Further aspects investigated include the new degradation model for reliability predication of PV modules. The results indicate that the PV module degradation over years, failures, and solar radiation must be considered in choosing an efficient PV system with an optimal design to achieve the maximum benefit of the PV system. For each aspect, a method is proposed, and the complete focusing methodology is expounded and validated using simulated point targets. The results also demonstrate the feasibility and applic- ability of the proposed method for effective modeling of the chronological aspects and stochastic characteristics of solar cells as well as the optimal configuration and sizing of large PV plants in terms of cost and reliability.

A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator

Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.