Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study

Pierro, Marco; Gentili, Damiano; Liolli, Fabio Romano; Cornaro, Cristina; Moser, David; Betti, Alessandro; Moschella, Michela; Collino, Elena; Ronzio, Dario; Meer, Dennis van der

doi:10.1016/j.renene.2022.03.041

Cited by 43 publications

(16 citation statements)

References 28 publications

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“…It is well known that enlarging the forecast‐controlled area the accuracy of solar/wind predictions decreases due to the so called “smoothing effect.” [ 49 ] This obviously implies the reduction of required flexibility and imbalance volumes. However, as mentioned above, the extension of the forecast footprint will only be possible when all the transmission bottlenecks between the market zones are completely removed (as planned by the Italian TSO).…”

Section: Resultsmentioning

confidence: 99%

“…[ 44 ] Then, we applied previously developed load, PV, and wind power forecast models [The description of forecast models is beyond the scope of this article and will be published in a dedicated paper. However, insights into the solar forecast model can be found in Pierro et al [ 49 ] ] to perform the day ahead netload predictions and we computed the current and future imbalance volumes, costs, and system flexibility requirements assuming capacities growth scenario proposed by the Italian TSO [ 45 ] and the Bloomberg BESS cost evolution [ 46 ] ( Figure ). We assume that the hourly profiles of load, PV, and wind generation remain the same as in 2016 but are rescaled according to the NT scenario to take into account the evolution of the RES penetration and the annual growth of the electric demand.…”

Section: Methodsmentioning

confidence: 99%

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Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

et al. 2022

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Achieving the European Union renewable energy source penetration 2030 targets requires an increase in flexible resources to compensate for the variability/intermittency of solar and wind generation to ensure system safety and balancing. Herein, two readily deployable flexibility solutions to balance demand/supply as an alternative to building additional thermoelectric reserves are proposed. How the transmission system OPERATOR can use the ancillary services provided by a flexible photovoltaic (PV) fleet (solar regulation) or PV/wind fleet (variable renewable energy (VRE) regulation) together with a suitable underforecast and proactive curtailment of variable renewable generation (aka, implicit storage) to reduce current and future Italian imbalances is shown. How these flexibility solutions can become even more effective when combined with a strengthening of the transmission grid is shown. The imbalance reduction achievable by 2030 through solar/VRE regulation strategies would be of the order of 20–50% with zonal balancing and 27–80% with nationwide balancing is found. Imbalance costs would remain comparable with the business‐as‐usual (thermal generation) costs. A proactive curtailment of 5–17% of the total VRE generation is the environmental cost of stabilizing the system using VRE plants, avoiding the construction of thermoelectric reserves.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

et al. 2022

Self Cite

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“…The assumption of homogeneous spatial distribution of the installed capacity can be considered strong. It was motivated by two factors: (i) it ensures methodological coherence with PV, for which, contrary to wind, it is not easy to geolocate the fleet of generators; (ii) the hypothesis of spatially constant installed capacity was found to be reasonable for aggregated capacity factors at national level in previous works (Pierro et al, 2022; Saint‐Drenan et al, 2018). Results described in Section 4.2 show that this assumption only has a visible impact for specific areas where the resource is very particular, and no plant is installed (e.g., mountains).…”

Section: Methods: Description Of the Input Data And Energy Conversion...mentioning

confidence: 99%

C3S Energy: A climate service for the provision of power supply and demand indicators for Europe based on the ERA5 reanalysis and ENTSO‐E data

Dubus,

Saint‐Drenan,

Troccoli

et al. 2023

Meteorological Applications

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The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policymakers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past 40 years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydropower, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long term. This article introduces this service and the resulting dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO‐E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been preferred upon models' accuracy. However, the comparison with ENTSO‐E data shows that the models provide plausible energy indicators and, in particular, allow comparing climate variability effects on power demand and generation in a harmonized manner all over Europe.

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“…Therefore, the research on total DPV power forecasting is more meaningful for the safe and stable operation of the distribution network. A variety of methods have been proposed for regional PV power forecasting, which can be generally divided into (1) forecasting-accumulation methods, which first forecast the output power of each PV site in a region and then aggregate the forecasting results to obtain the regional PV output [17,18]; (2) accumulation-forecasting methods, which first aggregate the PV power of each site to obtain the total regional output, and then use the regional output as input to forecast the regional PV output [19]; (3) clustering-forecasting-accumulation methods, which first group the regional sites into several clusters according to certain rules, the sum of the power over each cluster is then obtained, and forecast the power for each cluster, finally aggregate the forecasting result of each cluster to get the regional power [20]. (4) Up-scaling methods, which first establish the mapping relationship between some representative sites and the total output of the entire region, so that the PV output of the entire region can be more accurately predicted only relying on the data of part sites [21,22].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

Wang,

Fu,

Zhang

et al. 2023

IET Generation Trans & Dist

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Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs’ adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset.

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Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study

Cited by 43 publications

References 28 publications

Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

C3S Energy: A climate service for the provision of power supply and demand indicators for Europe based on the ERA5 reanalysis and ENTSO‐E data

Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

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