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

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

Planning for PV plant performance monitoring by means of unmanned aerial systems (UAS)

The sustainable use of renewables will represent a key challenge in the near future, and relative energy management operations will play a crucial role in energy efficiency and savings for future generations. The operation and maintenance of energy systems are a very high valuable activity to prevent energy losses, and a correct monitoring can detect in advance equipment degradation guaranteeing good performance over time. Present research strives to find out possibility of unmanned aerial vehicle (UAV) use in monitoring applications for energy production sites and to investigate effects of this novel method on energy management procedures. Furthermore, investigation about novel approaches in cooperative inspection of real photovoltaic (PV) plants was carried out by light UAVs and utilize the global positioning system to find out the optimum route mapping during the solar PV modules monitoring. The purpose of this work is to propose a reliable, fast and cost effective method for PV plant planning and monitoring by means of UAS technolog

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)

Transient Analysis of Large Scale PV Systems with Floating DC Section

The increasing penetration of renewable sources with power-electronic interfaces in power systems is raising technical problems and the overall efficiency of photovoltaic systems can decrease dramatically. In this context, the optimal layout for the photovoltaic system is required. The most adequate strategy to connect the renewable system to the electrical power grid or to supply the end users must be adopted. The present paper proposes a design layout of a PV plant using a DC bus system to improve the overall energy conversion efficiency. An analysis of steady-state system stability, voltage drop and DC cable conduction losses is conducted. The leakage currents to the ground are investigated through simulations. Experimental results are shown focused on the analysis of optimal layout of photovoltaic systems under particular operating conditions

Snail Trails and Cell Microcrack Impact on PV Module Maximum Power and Energy Production

—This paper analyzes the impact of the snail trail phenomena on photovoltaic (PV) module performances and energy production. Several tests (visual inspection, maximum power determination, dielectric withstand, wet leakage current, and electroluminescence test) were carried out on 31 PV modules located in a PV plant in Italy. The electroluminescence test highlighted the strong correlation between the appearance of snail trails and presence of damaged cells in PV modules. The daily energy produced by four PV modules affected by snail trails ranged between 68% and 88% of the energy produced by a damage free commercial PV module over the same period

Comparison of different physical models for PV power output prediction

The electricity produced from renewable energy, in particular from wind and photovoltaic plants, has seen exponential rise in the last decade. Consequently, the prediction of power produced from these plants is fundamental for the reliability, safety and stability of the grid. This paper compares three physical models describing the PV cell (corresponding to three-, four- and five-parameter equivalent electric circuit) and two thermal models for the cell temperature estimation (NOCT and Sandia). The models were calibrated and tested towards ten monocrystalline and eight polycrystalline modules installed at SolarTechLab at Politecnico di Milano. The hourly error of the forecasted power output is usually lower than 15Wh, while NMAE% and WMAE% are in the range of 0.5% and 10%. Low errors, calculated with actual weather conditions, suggest that the implemented models are accurate, but they cannot be directly compared with other approaches which adopt weather forecasts. Results show that there is no clear advantage of using complex models, but the data used for the model calibration mostly affect the model accuracy. It was found that forecasted power output are more accurate using experimental data and Sandia's thermal model in monocrystalline cells type, while for the polycrystalline the data from the manufacturer and NOCT have lower errors