Probabilistic analysis of weather data for a hybrid solar/wind energy system

In this paper, a procedure for the probabilistic treatment of solar irradiance and wind speed data is reported as a method of evaluating, at a given site, the electric energy generated by both a photovoltaic system and a wind system. The aim of the proposed approach is twofold: first, to check if the real probability distribution functions (PDFs) of both clearness index and wind speed overlap with Hollands and Huget and Weibull PDFs, respectively; and then to find the parameters of these two distributions that best fit the real data. Further, using goodness-of-fit tests, these PDFs are compared with another set of very common PDFs, namely the Gordon and Reddy and Lognormal functions, respectively. The results inform the design of a pre-processing stage for the input of an algorithm that probabilistically optimizes the design of hybrid solar wind power systems. In this paper, the validity of the proposed procedure was tested using long-term meteorological data from Acireale (Italy). Copyright © 2010 John Wiley & Sons, Ltd

Development of Models for On-line Diagnostic and Energy Assessment Analysis of PV Power Plants: The Study Case of 1 MW Sicilian PV Plant

Abstract For photovoltaic (PV) power plants, every kilowatt-hour is important, because only kilowatt-hours that are fed into the grid are remunerated. A plant's operator can only adopt prompt measures to eliminate operational faults when these are immediately signaled. In fact, just reading the feed-in meter each month is not sufficient to promptly recognize faults and to avoid the loss of yields. Many inverters record the most important operational data, automatically evaluate the data and, in case of a fault, send the operator notifications via email or text message. However, it only allows obvious faults to be recorded. On the other hand, continuous, absolute and comparative measurements are necessary to ensure the maximum efficiency and availability. Based on real time and historical data, a technical plant manager should inform the operators of any fault which occurs or even take independent measures to rectify it. In this context, suitable models are developed and applied to a 1 MW power plant where a SCADA, Supervisory Control And Data Acquisition, has been installed and operational data are available on a Web page. In order to evaluate the performance of the PV system, firstly the daily corrected Performance ratio has been evaluated. Then, different approaches to estimate the AC and the DC power of the PV plant have been developed. Basing on the difference between the measured and the estimated power, a statistical approach is proposed; it allows to define suitable thresholds on the AC and DC power in order to individuate a fault when occurs. The experimental results show the effectiveness of the proposed approach

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported

Floating photovoltaic plants and wastewater basins: an Australian project

Abstract Floating photovoltaic is a new design solution for photovoltaic (PV) power plants; Floating PV systems (FPVSs) are normally installed on water bodies such as natural lakes or dams reservoirs, and offshore solutions are also investigated. Such technology has attracted increased worldwide attention since 2007 and medium and large FPVSs have already been deployed in several countries, such as Japan, South Korea, India and USA. The cost effectiveness of FPVS increases dramatically if the floating structure performs also other tasks, for instance the reduction of water evaporation. In this context, the possibility to integrate PV plants with the existing basins for wastewater treatment is explored; a compact FPVS without tracking with optimal orientation and distance among rows is suggested as the most simple and economic design solution. Some test cases in South Australia are suggested and analysed

Submerged PV Solar Panel for Swimming Pools: SP3

Abstract The possibility to use photovoltaic (PV) modules submerged in water or simply covered by a water veil suggest the possibility to use this renewable energy source (RES) integrated with swimming pools or with decorative pools and fountains. SP3 solution (Submerged PV Solar Panel for Swimming Pools) is discussed for underflow pools as well as for pools with skimmer. The extension of this concept to the possibility to store solar radiation for heating the water of the pool is explored using the results of experimentation already done for hybrid photovoltaic/thermal (PV/T) modules. Simulation results for Mediterranean latitudes are discussed

Web interactive non intrusive load disaggregation system for active demand in smart grids

A Smart Grid combines the use of traditional technology with innovative digital solutions, making the management of the electricity grid more flexible. It allows for monitoring, analysis, control and communication within the supply chain to improve efficiency, reduce the energy consumption and cost, and maximize the transparency and reliability of the energy supply chain. The optimization of energy consumption in Smart Grids is possible by using an innovative system based on Non Intrusive Appliance Load Monitoring (NIALM) algorithms, in which individual appliance power consumption information is disaggregated from single-point measurements, that provide a feedback in such a way to make energy more visible and more amenable to understanding and control. We contribute with an approach for monitoring consumption of electric power in households based on both a NILM algorithm, that uses a simple load signatures, and a web interactive systems that allows an active role played by users

RAST: RoundAbout Solar Tracking

Abstract Roundabouts became popular worldwide as a tool to reduce the number of car accidents and the EU encourages and promotes this technology as a solution to the problems of urban and extra-urban traffic. RAST (Roundabout Solar Tracking) systems are designed to exploit the available space in roundabouts, which are already equipped and monitored, in order to produce electricity with a photovoltaic single axis tracking system. The energy produced can be used directly by the surrounding facilities or stored and consumed later or channelled to nearby car charge points. The amount of energy that can be produced on a single roundabout is limited by the land size and is normally in the range 100-400 kWp, but the number of suitable roundabouts in cities is high. Therefore, RAST could make an important contribution to the energy production

real time fault detection in photovoltaic systems

Abstract In this paper, a method for real time monitoring and fault diagnosis in photovoltaic systems is proposed. This approach is based on a comparison between the performances of a faulty photovoltaic module, with its accurate model by quantifying the specific differential residue that will be associated with it. The electrical signature of each default will be fixed by considering the deformations induced on the I-V curves. Some faults, such as: interconnection resistance faults and different shading patterns are considered. The proposed technique can be generalized and extended to more types of faults. The fault diagnosis will be determined by fixing a normal and a fault threshold for each fault. These thresholds are calculated based on the Euclidean norm between ideal and normal measurement or between ideal and fault mode measurement. Each threshold is set in a range bounded by the minimum and maximum values of the differential residue obtained for the considered fault. The proposed approach provides identification of faults by calculating their specific threshold ranges. This method allows the instantaneous monitoring of the electrical power delivered by the photovoltaic system

Load Demand Disaggregation Based on Simple Load Signature and User's Feedback

Abstract A detailed and on-line knowledge of the electrical load demand by the users is a critical issue for an effective and responsive deployment of home/building energy management. An approach based on the application of Non Intrusive Appliance Load Monitoring (NIALM) techniques copes with the goal of disaggregating composite loads; but to get a high level of precision, NIALM algorithms need a complete load signature and complex optimization algorithms to find the right combination of single loads that fits the real electrical measurements. On the other hand, it is practically impossible to get the detailed signature of all appliances inside a house/building and sophisticated optimization algorithm are not suitable for on-line applications. To overcome such problems a straightforward NIALM algorithm is proposed, it is based on both a simple load signature, rated active and reactive power and a heuristic disaggregation algorithm. Of course, it is expected that on real applications, this approach cannot reach very high performances; this is the reason why an active involvement of users is considered. The users' feedback aims to: correct the load signatures, reduce the error of disaggregation algorithm and increase the active participation of users in saving energy politics. The NIALM algorithm has been accurately tested numerically using as input load curves generated randomly but under given constraints. In this way, the causes of inefficiency of the proposed approach are quantitatively analyzed both separately and in different combinations. Finally, the increase of the efficiency of the NIALM algorithm due to the application of different feedback actions is evaluated and discussed