A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.

Design and Simulation of a Solar Regulator Based on DC-DC Converters Using a Robust Sliding ModeController

The MPPT （maximum power point tracking） is one of the most important features of a regulator system that processes the energy produced by a photovoltaic generator. It is necessary, in fact, to design a controller that is able to set the output value of the voltage and ensure the working within the maximum power point. In this paper, we propose the application of the robust sliding mode control technique to a DC-DC buck converter which is combined with a classical P ＆ O （perturbation and observation） algorithm to enhance the solar system efficiency. Dynamic equations describing the boost converter are derived and a sliding mode controller for a buck converter is designed. It is shown that, this control approach gives good results in terms of robustness toward load and input voltage variations. The effectiveness of the proposed work is verified by the simulation results under PowerSim environment.

Modeling and control of photovoltaic energy conversion connected to the grid

This paper presents modeling and control of a photovoltaie generator （PVG） connected to the grid. The parameters of the PVG have been identified in previous work （series and parallel resistance, reverse saturation current and thermal voltage） using Newton-Raphston and the gradient algorithm. The electrical energy from a PVG is transferred to the grid via two static converters （DC/DC and DC/AC）. The objective of the proposed control strategy is to maximize energy captured from the PVG. The adapted control law for extracting maximum power from the PVG is based on the incremental conductance algorithm. The developed algorithm has the capability of searching the maximum photovoltaic power under variable irradiation and temperature. To control the DC/AC inverter, an intelligent system based on two structures is constructed： a current source control structure and a voltage source control structure. The system has been validated by numerical simulation using data obtained from the PVG installed in the laboratory research （INSAT, Tunisia）.

Implementation of photovoltaic water pumping system with MPPT controls

To increase the output efficiency of a photo- voltaic （PV） system, it is important to apply an efficient maximum power point tracking （MPPT） technique. This paper describes the analysis, the design and the experi- mental implementation of the tracking methods for a stand- alone PV system, using two approaches. The first one is the constant voltage （CV） MPPT method based on the optimum voltage, which was deduced experimentally, and considered as a reference value to extract the optimum power. The second one is the increment conductance （Inc- Cond） MPPT method based on the calculation of the power derivative extracted by the installation. The output controller can adjust the duty ratio to the optimum value. This optimum duty ratio is the input of a DC/DC boost converter which feeds a set of Moto-pump via a DC/AC inverter. This paper presents the details of the two approaches implemented, based on the system perfor- mance characteristics. Contributions are made in several aspects of the system, including converter design, system simulation, controller programming, and experimental setup. The MPPT control algorithms implemented extract the maximum power point （MPP）, with satisfactory performance and without steady-state oscillation. MATLAB/Simulink and dSpace DSll04 are used to conduct studies and implement algorithms. The two proposed methods have been validated by implementing the performance of the PV pumping systems installed on the roof of the research laboratory in INSAT Tunisia. Experimental results verify the feasibility and the improved functionality of the system.

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

This paper proposes a simple and practical approach to model the uncertainty of solar irradiance and determines the optimized day-ahead(DA)schedule of electricity mar-ket.The problem formulation incorporates the power output of distributed solar photovoltaic generator(DSPVG)and forecasted load demands with a specified level of certainty.The proposed approach determines the certainty levels of the random variables(solar irradiance and forecasted load demand)from their probability density function curves.In this process of optimization,the energy storage system(ESS)has also been mod-eled based on the fact that the energy stored during low locational marginal price(LMP)periods and dispatched during high LMP periods would strengthen the economy of DA schedule.The objective of the formulated non-linear optimization problem is to maximize the social welfare of market participants,which incorporates the assured generation outputs of DSPVG,subject to real and reactive power balance and transmission capability constraints of the system and charging/dis-charging and energy storage constraints of ESS.The simulation has been performed on the Indian utility 62-bus system.The results are presented with a large number of cases to demonstrate the effectiveness of the proposed approach for the efficient,economic and reliable operation of DA electricity markets.

Active Power Reserve Photovoltaic Virtual Synchronization Control Technology

The photovoltaic virtual synchronous generator(PV-VSG)solves the problem of lack of inertia in the PV power-generation system.The existing PV plants without energy storage are required to participate in the power grid’s frequency modulation(FM),but existing PV-VSGs with energy storage have high requirements for coordinated control.Therefore,the active power reserve PV-VSG(APR-PV-VSG)is studied.Based on the different methods to obtain the maximum power point(MPP),the peer-to-peer and master-slave APR-PV-VSG strategies are proposed.The PV inverters are deviated from the MPP to reserve active power,which is used as the virtual inertia and primary FM power.These methods equip the PV power station with FM capability.The effectiveness of the proposed control strategies is verified by simulation results.

Dynamic characteristics and improved MPPT control of PV generator

This paper presents a mathematical model of photovoltaic （PV） module and gives a strategy to calculate online the maximum power point （MPP）. The variation of series and shunt resistor are taken into account in the model and are dynamically identified using the Newton-Raphson algorithm. The effectiveness of the proposed model is verified by laboratory experiments obtained by implementing the model on the dSPACE DS1104 board.

Wide spectrum solar energy harvesting through an integrated photovoltaic and thermoelectric system

This paper proposes a power system concept that integrates photovoltaic （PV） and thermoelectric （TE） technologies to harvest solar energy from a wide spectral range. By introduction of the ＇spectrum beam splitting＇ technique, short wavelength solar radiation is converted directly into electricity in the PV cells, while the long wavelength segment of the spectrum is used to produce moderate to high temperature thermal energy, which then generates electricity in the TE device. To overcome the intermittent nature of solar radiation, the system is also coupled to a thermal energy storage unit. A systematic analysis of the integrated system is carried out, encompassing the system configuration, material properties, thermal management, and energy storage aspects. We have also attempted to optimize the integrated system. The results indicate that the system configuration and optimization are the most important factors for high overall efficiency.