Efficiency analysis of PV power plants shaded by MV overhead lines

This paper deals with the occurrence of hot spot phenomena in photovoltaic (PV) systems under partial shading caused by objects on some parts of the modules. An interesting case of diffuse shadows is determined by overhead distribution lines whose path crosses or are in the proximity of the PV power plants. Investigating the impact of these shadows on reducing the power production of PV or on damaging the PV modules as the modules’ temperature is increasing, is of high interest. At the SolarTech laboratory of Politecnico di Milano, the conditions for hot spot phenomena occurrence due to the overhead lines shading the PV cells were reproduced. Two experimental campaigns were carried out to investigate the current–voltage and power–voltage characteristics, and the energy production. In each experimental campaign, the built shading structure was considered fixed and different shading conditions were created based on the natural displacement of the sun. The hot spot phenomena was revealed on a field PV installation in Italy, caused my medium voltage overhead lines shading the PV cells, using infrared imagery

Day-ahead PV power forecast by hybrid ANN compared to the five parameters model estimated by particle filter algorithm

A comparison between the hybrid method (PHANN – Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These methods have been employed to perform the dayahead forecast of the output power of a photovoltaic plant. The aim of this work is to assess the forecast accuracy of the two methods

ANN sizing procedure for the day-ahead output power forecast of a PV plant

Since the beginning of this century, the share of renewables in Europe's total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance

On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&amp;O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&amp;O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.</p

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

The optimum PV plant for a given solar DC/AC converter

In recent years, energy production by renewable sources is becoming very important, and photovoltaic (PV) energy has became one of the main renewable sources that is widely available and easily exploitable. In this context, it is necessary to find correct tools to optimize the energy production by PV plants. In this paper, by analyzing available solar irradiance data, an analytical expression for annual DC power production for some selected places is introduced. A general efficiency curve is extracted for different solar inverter types, and by applying approximated function, a new analytical method is proposed to estimate the optimal size of a grid-connected PV plant linked up to a specific inverter from the energetic point of view. An exploitable energy objective function is derived, and several simulations for different locations have been provided. The derived analytical expression contains only the available data of the inverter (such as efficiency, nominal power, etc.) and the PV plant characteristics (such as location and PV nominal power)

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new &ldquo;mixed method&rdquo; which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively)