• Energy Research

AbstractIn photovoltaic (PV) projects, it is important to establish a common practice for professional risk assessment, which serves to reduce the risks associated with related investments. The objective of this paper is to present a methodology on how to improve the current understanding of several key aspects of technical risk management during the PV project lifecycle, with the identification of technical risks and their economic impact. To achieve this, available statistical data of failures during a PV project have been collected with the aim to (i) suggest a guideline for the categorisation of failure and (ii) develop a methodology for the assessment of the economic impact of failures occurring during operation but which might have originated in previous phases. The risk analysis has the aim to assess the economic impact of technical risks and how this can influence various business models and the levelised cost of electricity. This paper presents the first attempt to implement cost‐based failure modes and effects analysis to the PV sector and to define a methodology for the estimation of economic losses because of planning failures, system downtime and substitution/repair of components. The methodology is based on statistical analysis and can be applied to a single PV plant or to a large portfolio of PV plants in the same market segment. The quality of the analysis depends on the amount of failure data available and on the assumptions taken for the calculation of a cost priority number. The overall results can be linked to the cost of periodic and corrective maintenance and form the basis to estimate the impact of various risk and mitigation scenarios in PV business models. Copyright © 2017 John Wiley & Sons, Ltd.

AbstractThe IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

Abstract This article proposes two strategies for the mitigation of power imbalances and related costs resulting from increasing PV penetration onto the Italian grid. New “state of the art” solar and netload day ahead forecast models were developed and applied to real data. These strategies consist of: (1) Improving the accuracy of PV and net load power forecast and enlarging the footprint of the controlled grid area; (2) Transforming unconstrained PV plants into “flexible PV plants”: remotely controlled PV plants that can be proactively curtailed and work with cost-optimized Battery Energy Storage Systems. We demonstrate that the first strategy can effectively limit the imbalance impact when integrating a large share of PV generation, reducing imbalance volumes and costs, both at current and future solar penetration levels. We further demonstrate that the second strategy can entirely eliminate the imbalance impact of PV penetration, hence providing operational certainty to the TSO. Indeed, we show how flexible PV plants can be cost-optimally sized to set the imbalance volume at a desired target value regardless of PV installed capacity, hence allowing massive solar penetration. Finally, we show that the cost of implementing these strategies is less than the current cost of handling such imbalance impacts.

Coloured building integrated photovoltaics (BIPVs) may contribute to meeting the decarbonisation targets of European and other countries. Nevertheless, their market uptake has been hindered by a lack of social acceptance, technical issues, and low economic profitability. Being able to assess in advance the influence of the coloured layers on a module’s power generation may help reduce the need for prototyping, thereby allowing optimisation of the product performance by reducing the time and costs of customised manufacturing. Therefore, this review aims at investigating the available literature on models and techniques used for assessing the influence of coloured layers on power generation in customised BIPV products. Existing models in the literature use two main approaches: (i) detailed optical modelling of the layers in the module’s stack, including coloured layers, and (ii) mathematical elaboration of the final product’s measured characteristics. Combining the two approaches can provide improved future models, which can accurately assess every single layer in the module’s stack starting from measured parameters obtained with simpler equipment and procedures.

Accurate solar radiation estimates in Alpine areas represent a challenging task, because of the strong variability arising from orographic effects and mountain weather phenomena. These factors, together with the scarcity of observations in elevated areas, often cause large modelling uncertainties. In the present paper, estimates of hourly mean diffuse fraction values from global radiation data, provided by a number (13) of decomposition models (chosen among the most widely tested in the literature), are evaluated and compared with observations collected near the city of Bolzano, in the Adige Valley (Italian Alps). In addition, the physical factors influencing diffuse fraction values in such a complex orographic context are explored. The average accuracy of the models were found to be around 27% and 14% for diffuse and beam radiation respectively, the largest errors being observed under clear sky and partly cloudy conditions, respectively. The best performances were provided by the more complex models, i.e., those including a predictor specifically explaining the radiation components’ variability associated with scattered clouds. Yet, these models return non-negligible biases. In contrast, the local calibration of a single-equation logistical model with five predictors allows perfectly unbiased estimates, as accurate as those of the best-performing models (20% and 12% for diffuse and beam radiation, respectively), but at much smaller computational costs.

Abstract Distributed generation from wind and solar acts on regional electric demand as a reduced consumption, giving rise to a “load shadowing effect”. The net load becomes much more difficult to predict due to its dependence on the meteorological conditions. As a consequence, the growing penetration of variable generation increases the imbalance between demand and scheduled supply (net load forecast) and the reserve margins (net load uncertainty). The aim of this work is to quantify the benefit of the use of advanced probabilistic approaches rather than a traditional time-series method to assess the day-ahead reserves. For this purpose, several methods for load and net load uncertainty assessment have been developed and applied to a real case study considering also future solar penetration scenarios. The results show that, when forecasting only the load both traditional and probabilistic methods exhibit similar accuracy. Instead, in the case of net load prediction, i.e. when solar power is present, the probabilistic forecast can effectively limit the reserve margin needed to arrange the imbalance between residual demand and supply. The developed probabilistic approach provides a notable reduction of the Following Reserve which increases with the solar penetration: from 32.5% to 68.3% at 7% and 45% of penetration.

AbstractRecently, the local government of South Tyrol, a province in the alpine area in the north of Italy, has defined the guidelines for an energy and climate package with targets for 2050 where it is stated that-The yearly CO2 emission per capita will be reduced to less than 1.5 tons (less than 4 tons per capita by 2020 as intermediate target)-90% of the energy need will be covered by renewables (at least 75% by 2020 as intermediate target).Specifically, energy production from photovoltaics will contribute towards those targets with a total installed power of 300 MW by 2020 (around 0.6kW per capita considering population as of 2012) and 600 MW by 2050 (around 1.2kW per capita considering population as of 2012). These figures start from a baseline of PV installed power in the province at the end of 2012 of 220 MW (around 0.45kW per capita, around 0.28kW per capita in Italy for comparison). Although both targets seem easily within reach, it is nonetheless important to lay the right foundation through favourable legislation and long term planning.It is within these context that a detailed analysis of the real PV potential (as comprehensive and effectively exploitable) of the area is proposed so to understand how ambitious those targets really are and to provide data and information for future energy and strategy planning. In this work, in an innovative approach, the impact of novel solutions on non-conventional surfaces were also included such as PV installations on transport infrastructure, avalanche barriers, artificial lakes, etc. High altitude installations were also taken into account due to the high irradiance values in the alpine area which are comparable to those in South of Italy (up to 2200 kWh/m2 per year). Limitations due to costs, phasing out of generous incentives programs, restrictive laws (for instance, architectural and environmental constraints) were considered, too.The different technical aspects will be presented from the use of existing solar cadastres and databases to statistical analysis based on the morphology of different urban contexts. The roof (and façade) potential will then be added to the potential from the above mentioned non-conventional surfaces with the final aim of providing a figure for PV potential that takes into account planning permissions, restrictions, structural limitations, current legislation, visual impact and costs.

One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms’ generation is important both for the producers/traders to minimize the imbalance costs and for the transmission system operators to ensure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale photovoltaic (PV) plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of the forecast accuracy and accuracy improvement in the context of the Italian energy market’s regulatory framework. Through this study, we further point out several important criticisms of the Italian “single pricing” system that brings paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome the undesired effects of the current dispatching regulations.