Abstract:The variations in irradiance produced by changes in cloud cover can cause rapid fluctuations in the power generated by large photovoltaic (PV) plants. As the PV power share in the grid increases, such fluctuations may adversely affect power quality and reliability. Thus, energy storage systems (ESS) are necessary in order to smooth power fluctuations below the maximum allowable. This article first proposes a new control strategy (step-control), to improve the results in relation to two state-of-the-art strategies, ramp-rate control and moving average. It also presents a method to quantify the storage capacity requirements according to the three different smoothing strategies and for different PV plant sizes. Finally, simulations shows that, although the moving-average (MA) strategy requires the smallest capacity, it presents more losses (2-3 times more) and produces a much higher number of cycles over the ESS (around 10 times more), making it unsuitable with storage technologies as lithium-ion. The step-control shown as a better option in scenery with exigent ramp restrictions (around 2%/min) and distributed generation against the ramp-rate control in all ESS key aspects: 20% less of capacity, up to 30% less of losses and a 40% less of ageing. All the simulations were based on real PV production data, taken every 5 s in the course of one year (2012) from a number of systems with power outputs ranging from 550 kW to 40 MW.
OPEN ACCESSEnergies 2014, 7 6594
The quality assurance procedures associated with the financing of large PV plants are becoming increasingly more relevant to the PV scene in general. In this context, PV performance modelling is required in order to predict the energy yield and to rate the operating plant performance. Despite the availability of PV performance models since the early days of photovoltaics, the emergence of new proposals and the current debate on the development of an energy rating standard means that this can still be considered an open question. In the specific context of Quality Assurance Procedures, PV performance models must not only be accurate but must also be based on features specifically supported by manufacturers (datasheet information), in order to maintain the chain of responsibility in the event of failure. This paper reviews the currently available PV performance models with regard to accuracy and also compliance with datasheet specifications and guarantees. Accuracy is assessed through a meticulous measurement campaign conducted on PV arrays of four different technologies at a PV plant located in Navarra (northern Spain). The models reviewed are classified into physical models, based on the full I-V curve, and empirical models, which are solely based on the maximum power point (MPP). Despite the fact that physical models and MPP models with more than three parameters are currently widely used, this paper shows that empirical models with just three independent parameters suffice to accurately describe the relationship between PV array performance and operating conditions and are more easily derived from standard datasheet information. This result suggests that 3-parameter empirical models are the best option for PV performance modelling in the context of technical quality assurance procedures.
In-field photovoltaic (PV) module and array characterization is becoming increasingly important within the particular framework of quality assurance procedures at large commercial PV plants. In this context, the correct measurement of the module temperature is critical in order to reduce uncertainty and increase repeatability of results. In the case of large PV array characterization, the measurement provided by the sensors may not be representative of the PV array as a whole given the great diversity of operating conditions occurring throughout its surface area. Spatial temperature differences within a PV array of less than 5 K are typically considered in practice. However, the temperature differences observed at a commercial PV plant in Amareleja (Portugal) are more than twice those typically assumed. Such high differences may considerably increase the uncertainty in the determination of the PV array operating temperature and, hence, in its standard test conditions power characterization. This paper quantifies the uncertainty associated with the in-field measurement of the operating temperature of large PV arrays and their individual PV modules as a function of the type, number, and location of the temperature sensors used. Furthermore, the incident irradiance and particular wind speed conditions over the PV array have been clearly identified as the main causes of these temperature differences, showing that the optimum conditions to perform the PV array characterization do not correspond to low wind speed conditions, as recommended by many authors.
Forecast procedures for large ground mounted PV plants or smaller BIPV or BAPV systems may use a parametric or a nonparametric model of the PV system. In this paper, both approaches are used independently to calculate the energy delivered to the grid on an hourly basis in forecast procedures that use meteorological variables from a Numerical Weather Prediction model as inputs, and their performances against real generation data from six PV plants are analyzed. The parametric approach relies on mathematical models with several parameters that describe the PV systems and it was implemented in MATLAB®, whereas the nonparametric approach is based on Quantile Regression Forests with training and forecast stages and its code was built in R. The parametric approach presented more significant bias on its results, mostly due to the input data and the transposition model of irradiance from a horizontal surface to the plane of the PV array.
A model to simulate the fluctuations generated by a fleet of dispersed photovoltaic (PV) plants solely based on irradiance data measured at one single location is proposed. This simple model has been satisfactorily tested to quantify the power variability of a generic PV fleet, simply by defining two parameters: mean PV plant size and the number of plants in the PV fleet. Specifically, the model provides series of simulated power outputs that may be used in the grid operator simulation programmes, reproducing critical parameters, such as daily maximum fluctuation or the reserves required to offset these fluctuations. The model is created and validated against experimental 1-s data collected throughout 2013 at six PV plants in Spain dispersed over 1100 km 2 , totaling 17 MWp. Likewise, the model has been succesfully tested against another irradiance dataset, four sites across the state of Colorado, USA, and spread over 2400 km 2 .
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