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Large share of solar energy imposes a higher system flexibility to resolve the increased demand/supply imbalance due to the inherent intermittency and variability of the resource. In this work, we demonstrate that the additional solar-induced flexibility requirement can be fully provided by a special kind of solar farms, namely flexible PV. These plants are able to provide ancillary services by proactive generation curtailment and storage power injection and they can be managed exactly as the secondary reserve currently used. At the current and future penetration levels, we sized the flexible PV fleet required to reduce the Italian imbalance by 36 % (with respect to its 2016 value) while keeping the curtailment at 6 % of the national PV generation. We show how this result can be achieved at an equal or lower dispatching cost than current cost (depending on the solar share). In addition, we found that a fleet composed of many flexible PV plants with different capacity randomly distributed throughout the country provides an optimal solar regulation performance. Finally, we showed that the effectiveness of the proposed imbalance mitigation strategy depends only slightly on the year-specific load, wind, PV and energy prices profiles used to size the capacity of the flexible fleet.

Calibration of the existing building simulation model is key to correctly evaluating the energy savings that are achievable through retrofit. However, calibration is a non-standard phase where different approaches can possibly lead to different models. In this study, an existing residential building is simulated in parallel by four research groups with different dynamic simulation tools. Manual/automatic methodologies and basic/detailed measurement data sets are used. The calibration is followed by a validation on two evaluation periods. Monitoring data concerning the windows opening by the occupants are used to analyze the calibration outcomes. It is found that for a good calibration of a model of a well-insulated building, the absence of data regarding the users’ behavior is more critical than uncertainty on the envelope properties. The automatic approach is more effective in managing the model complexity and reaching a better performing calibration, as the RMSE relative to indoor temperature reaches 0.3 °C compared to 0.4–0.5 °C. Yet, a calibrated model’s performance is often poor outside the calibration period (RMSE increases up to 10.8 times), and thus, the validation is crucial to discriminate among multiple solutions and to refine them, by improving the users’ behavior modeling.

Resilience and the efficiency of transportation systems are crucial for the economic development of geographical areas, and network analysis applied to railways can provide insight into the importance of branch lines and their impacts on the entire system. This paper explores the behavior of the ERC measure, a local robustness measure, on the railway network in Lombardy, Italy, and analyzes the impacts of deactivating stations or journeys on the network’s robustness. Changes in the topological properties of the network were studied by simulating potential external disturbances and analyzing the impact of deleting the most connected stations or railway lines. The numerical results show how the measures provided effectively identify critical stations and journeys within the network structure and outperform classical topological metrics. Since ERC measures take into account all of the alternative paths present in the network, they can provide valuable information for rerouting traffic along alternative paths in case of failures or disruptions. The paper’s original contribution lies in demonstrating the effectiveness of the ERC measure in identifying critical stations and journeys within the network structure.

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.

The high share of PV energy requires greater system flexibility to address the increased demand/supply imbalance induced by the inherent intermittency and variability of the solar resource. In this work, we have developed a methodology to evaluate the margins for imbalance reduction and flexibility that can be achieved by advanced solar/wind forecasting and by strengthening the national transmission grid connecting the Italian market areas. To this end, for the forecasting of the day-ahead supply that should be provided by dispatchable generators, we developed three advanced load/PV/wind forecasting methodologies based on a chain or on the optimal mix of different forecasting techniques. We showed that, compared to the baseline forecast, there is a large margin for the imbalance/flexibility reduction: 60.3% for the imbalance and 47.5% for the flexibility requirement. In contrast, the TSO forecast leaves only a small margin to reduce the imbalance of the system through more accurate forecasts, while a larger reduction can be achieved by removing the grid constrains between market zones. Furthermore, we have applied the new forecasting methodologies to estimate the amount of imbalance volumes/costs/flexibility/overgenerations that could be achieved in the future according to the Italian RES generation targets, highlighting some critical issues related to high variable renewable energy share.

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.

This work describes a progressive strategy to achieve 100% penetration of intermittent renewables at minimal cost. The strategy works to optimally transform variable renewable energy (RE) into firm, effectively dispatchable generation. This functional dispatchability enables large-scale displacement of conventional generation at equal or lower production cost. By way of this strategy, we delineate a pathway for a full renewable energy transition of the Italian electric mix. In order to achieve 100% renewables in any grid, the fundamental imbalance between the supply of the resource and demand must be alleviated. We propose a transition that starts by addressing net load forecast imbalances resulting from renewables’ prediction errors and ends with the transformation of intermittent renewables into firm, effectively dispatchable generation sources.

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, and hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is the plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to achieving least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in the absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future costs of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.

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 insure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale 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 forecast accuracy and accuracy improvement in the context of the Italian energy market regulatory framework. Through this study, we further point out some several important criticisms of the Italian “single pricing” system that brings to 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 these undesired effects of the current dispatching regulations.

Dye-sensitized solar cell technology is having an important role in renewable energy research due to its features and low-cost manufacturing processes. Devices based on this technology appear very well suited for integration into glazing systems due to their characteristics of transparency, color tuning and manufacturing directly on glass substrates. Field data of thermal and electrical characteristics of dye-sensitized solar modules (DSM) are important since they can be used as input of building simulation models for the evaluation of their energy saving potential when integrated into buildings. However, still few studies in the literature provide this information. The study presented here aims to contribute to fill this lack providing a thermal and electrical characterization of a DSM in real operating conditions using a method developed in house. This method uses experimental data coming from test boxes exposed outdoor and dynamic simulation to provide thermal transmittance (U-value) and solar heat gain coefficient (SHGC) of a DSM prototype. The device exhibits a U-value of 3.6 W/m2·K, confirmed by an additional measurement carried on in the lab using a heat flux meter, and a SHGC of 0.2, value compliant with literature results. Electrical characterization shows an increase of module power with respect to temperature resulting DSM being suitable for integration in building facades.

The increased penetration of photovoltaic power introduces new challenges for the stability of the electrical grid, both at the local and national level. Many different effects are caused by high solar power injection into the electric grid. Among them, the increased risk of imbalance between the actual and scheduled power transmission is of particular relevance. The consequence is the need to exchange larger amounts of dispatchable power on the balancing energy market. The aim of this work is to analyze and quantify the effects of PV penetration in a target region and to evaluate the energy and economic benefits of using day-ahead PV forecast for power transmission scheduling. For this purpose, we developed several data-driven methods for transmission scheduling that include day-ahead PV power forecasts. We compared the resulting operational imbalances from these new models against two reference models currently used by the local grid operators. In the case of no PV generation in the target area, the more accurate reference model leads to an imbalance of 3.6% of the peak power transmission while more accurate data-driven method reduces the imbalance to 3.2%. When the distributed PV capacity is not zero, the imbalance of the reference model grows from 5.15% (at the current penetration of 7%) to 9.8% (at the maximum planned regional penetration of 45%). When we apply the new scheduling model, imbalances are reduced to respectively 3.5% and 5.8% at 7% and 45% of penetration. Since in Italy the costs of imbalances resulting from distributed PV are borne by ratepayers, these costs are estimated to be respectively 2.3% and 15% of the average electricity bill at 7% and 45% penetration if the reference scheduling is used. When applying the new model these costs are respectively reduced to 1.2% and 8.5%. © 2018 Elsevier Ltd

Nowadays, there is a wide debate in literature related to the silicon thin films seasonal performance. Amorphous modules seem to react positively to the temperature, while the temperature parameters indicate a negative thermal response. Periodic fluctuations of nominal power due to light soaking and thermal annealing effects are observed. On the other hand, the module temperature reached in some open rack plants seems too low to activate annealing power regeneration process so that the seasonal performance trend may depend mainly on other effects such as spectral or irradiance. In the following paper, a model that allows to calculate the impact of all the phenomena that affect the photovoltaic performance is used. The light soaking and thermal annealing contributions are measured from outdoor data using two different methods. Both methods lead to similar results, and the model is able to reproduce the seasonal performance with an acceptable level of reliability on the day, hour, minute time scale. An analysis of each effect contribution to the seasonal performance is also provided. Thus, main open questions related to a-Si thin films performance such as positive reaction to temperature and seasonal fluctuations are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

In the paper two models implemented to forecast the hourly solar irradiance with a day in advance are described. The models, based on Artificial Neural Networks (ANN), are generated by a master optimization process that defines the best number of neurons and selects a suitable ensemble of ANN. The two models consist of a Statistical (ST) model that uses only local measured data and a Model Output Statistics (MOS) that corrects Numerical Weather Prediction (NWP) data. ST and MOS are tested for the University of Rome “Tor Vergata” site. The models are trained and validated using one year data. Through a cross training procedure, the dependence of the models on the training year is also analyzed. The performance of ST, NWP and MOS models, together with the benchmark Persistence Model (PM), are compared. The ST model and the NWP model exhibit similar results. Nevertheless different sources of forecast errors between ST and NWP models are identified. The MOS model gives the best performance, improving the forecast of approximately 29% with respect to the PM.

The photovoltaic (PV) system performance essentially depends on the modules response to five effects: spectral, reflection, temperature, irradiance, and nominal power variations. Providing a full characterization of modules behavior in terms of the impact of these effects on real operating conditions performance is very important both to compare different PV technologies and to choose the best technology for a specific site, position, and installation feature. In this work, a systematic approach is used. A theoretical model to calculate the performance ratio related to each effect is proposed. The model is used to compare and to explain the annual behavior of two different technologies: a multicrystalline silicon module (mc-Si) and a double junction amorphous silicon module (a-Si/DJ). The basic features of these modules performance are observed.

New generation photovoltaic (PV) devices such as polymer and dye sensitized solar cells (DSC) have now reached a more mature stage of development, and among their various applications, building integrated PVs seems to have the most promising future, especially for DSC devices. This new generation technology has attracted an increasing interest because of its low cost due to the use of cheap printable materials and simple manufacturing techniques, easy production, and relatively high efficiency. As for the more consolidated PV technologies, DSCs need to be tested in real operating conditions and their performance compared with other PV technologies to put into evidence the real potential. This work presents the results of a 3 months outdoor monitoring activity performed on a DSC mini-panel made by the Dyepower Consortium, positioned on a south oriented vertical plane together with a double junction amorphous silicon (a-Si) device and a multi-crystalline silicon (m-Si) device at the ESTER station of the University of Rome Tor Vergata. Good performance of the DSC mini-panel has been observed for this particular configuration, where the DSC energy production compares favorably with that of a-Si and m-Si especially at high solar angles of incidence confirming the suitability of this technology for the integration into building facades. This assumption is confirmed by the energy produced per nominal watt-peak for the duration of the measurement campaign by the DSC that is 12% higher than that by a-Si and only 3% lower than that by m-Si for these operating conditions. Copyright © 2013 John Wiley & Sons, Ltd.

Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for dayahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively. Copyright © 2017 by ASME.

Rating systems for assessing the environmental impact of buildings are technical instruments that aim to evaluate the environmental impact of buildings and construction projects. In some cases, these rating systems can also cover urban-scale projects, community projects, and infrastructures. These schemes are designed to assist project management in making the projects more sustainable by providing frameworks with precise criteria for assessing the various aspects of a building’s environmental impact. Given the growing interest in sustainable development worldwide, many rating systems for assessing the environmental impact of buildings have been established in recent years, each one with its peculiarities and fields of applicability. The present work is motivated by an interest in emphasizing such differences to better understand these rating systems and extract the main implications to building design. It also attempts to summarize in a user-friendly form the vast and fragmented assortment of information that is available today. The analysis focuses on the six main rating systems: the Building Research Establishment Environmental Assessment Methodology (BREEAM), the Comprehensive Assessment System for Built Environment Efficiency (CASBEE), the Deutsche Gesellschaft für Nachhaltiges Bauen (DGNB), the Haute Qualité Environnementale (HQETM), the Leadership in Energy and Environmental Design (LEED), and the Sustainable Building Tool (SBTool). © 2017 by the authors; licensee MDPI. This article is an open Access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid, increase the amount of energy reserve and the energy imbalance cost. On regional scale, the estimation of the solar power generation from the real time environmental conditions and the solar power forecast is essential for Distribution System Operators, Transmission System Operator, energy traders, and Aggregators. In this context, a new upscaling method was developed and used for estimation and forecast of the photovoltaic distributed generation in a small area of Italy with high photovoltaic penetration. It was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. Two different approaches were investigated. The simplest and more accurate approach requires a low computational effort and very few input information should be provided by users. The power estimation model provided a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 h) obtained a RMSE of 5%–7% and a skill score with respect to the smart persistence from −8% to 33.6%. The one and two days ahead forecast achieved a RMSE of 7% and 7.5% and a skill score of 39.2% and 45.7%. The smoothing effect on cluster scale was also studied. It reduces the RMSE of power estimation of 33% and the RMSE of day-ahead forecast of 12% with respect to the mean single cluster value. Furthermore, a method to estimate the forecast error was also developed. It was based on an ensemble neural network model coupled with a probabilistic correction. It can provide a highly reliable computation of the prediction intervals. © 2017 Elsevier Ltd

The aim of the paper is to compare several data-driven models using different Numerical Weather Prediction (NWP) input data and then to build up an outperforming Multi-Model Ensemble (MME) and its prediction intervals. Statistic, stochastic and hybrid machine-learning algorithms were developed and the NWP data from IFS and WRF models were used as input. It was found that the same machine learning algorithm differs in performance using as input NWP data with comparable accuracy. This apparent inconsistency depends on the capability of the machine learning model to correct the bias error of the input data. The stochastic and the hybrid model using the same WRF input, as well as the stochastic and the non-linear statistic models using the same IFS input, produce very similar results. The MME resulting from the averaging of the best data-driven forecasts, improves the accuracy of the outperforming member of the ensemble, bringing the skill score from 42% to 46%. To reach this performance, the ensemble should include forecasts with similar accuracy but generated with the higher variety of different data-driven technology and NWP input. The new performance metrics defined in the paper help to explain the reasons behind the different models performance. © 2016 Elsevier Ltd.

This paper describes a methodology to evaluate the storage capacity that should support a photo-voltaic (PV) power plant in order to reduce the power imbalance generated by the forecast error. This is obtained through a probabilistic analysis performed on an ensemble of synthetic data. The synthetic data signals are generated starting from at least 1 year of measured data and reproduce the same statistical distribution and the same Fourier power spectrum (FPS) shape.

In this paper a new hybrid Model Output Statistics (MOS), named MOS cascade, is developed to refine the day-ahead forecast of the global horizontal irradiance provided by the Weather Research and Forecast (WRF) model. The proposed approach is based on a sequence of two different MOS. The first, called MOSRH, is a new physically based algorithm, built to correct the treatment of humidity in the WRF radiation schemes. The second, called MOSNN, is based on artificial intelligence techniques and aims to correct the main systematic and learnable errors of the Numerical Weather Prediction output. The 1-day and 2-day forecast accuracies are analyzed via direct comparison with irradiance data measured in two sites, Rome and Lugano. The paper shows that a considerable reduction in error was achieved using MOSRH model and MOS cascade. The differences between the two sites are discussed in details. Finally, the results obtained are compared with the benchmark accuracy reached for the data available for the average climate in Southern Spain and Switzerland.

In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata” site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM.

The paper deals with an extensive photovoltaic (PV) modules monitoring activity carried out at the outdoor station ESTER (Solar Energy TEst and Research) of the University of Rome Tor Vergata, Italy. The purpose of the work was to evaluate and compare the performance of PV silicon modules of polycrystalline (poli-Si) and amorphous (a-Si) technologies during a medium-term outdoor exposure at optimized tilt angle, facing south. Two PV modules, one polycrystalline silicon and one double-junction amorphous silicon, have been exposed since May 2009 until Oct. 2010. A complete characterization of the weather conditions at the site during the test has been performed, and the most relevant parameters for the performance comparison of the two technologies have been derived. In order to compare different technologies and power productions, the energy yield (Y) and performance ratio (PR) for the two modules have been evaluated on a monthly and yearly basis. The typical seasonal trend of PR has been observed for the polycrystalline module, essentially due to the temperature influence on the module performance. For the a-Si module, instead, a degradation trend has been observed for the first months of operation. Subsequently, a significant recovery in the PR and energy production has been registered.

Solar spectral irradiance measurements on a routinely basis are relevant to study the influence of solar spectrum on the photovoltaic (PV) module performance, especially for thin film and third generation PV. Two spectroradiometers from EKO were added to the instrumentation available at the ESTER outdoor station of the University of Rome Tor Vergata. A detailed characterisation of the spectral irradiance at the site was carried on during more than 6 months of monitoring activity measuring spectral solar irradiance in the range 350–1700 nm with a time interval of 10 min on a horizontal plane. A wide variety of spectra were acquired in various weather conditions, and indications about the spectra behaviour on a daily and seasonal basis were obtained. Moreover, information about the effect of the weather conditions on the solar radiation spectral distribution were identified. The Average Photon Energy index was used as an indicator of the spectra characteristics. The same index was also used to evidence the solar spectrum influence on polycrystalline and double junction amorphous silicon PV modules. Copyright © 2012 John Wiley & Sons, Ltd.