Solar Grid Parity dynamics in Italy: A real option approach

Biondi, Tommaso; Moretto, Michele

doi:10.1016/j.energy.2014.11.072

Cited by 70 publications

(22 citation statements)

References 15 publications

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“…The result is a price of electricity expressed in e/kWh, which allows investors to earn their investment, operation and fuel cost back plus their cost-of-capital (Short et 2 8 This is reasonable from the point of view of a PV plant owner who will see a low correlation of his energy price with market risk. See for example Capizzani (2012) and Biondi and Moretto (2015). 2 9 Our estimates for r are in line with Ciabattoni et al (2014).…”

Section: Other Inputs Are the Followingsupporting

confidence: 63%

Investing in Photovoltaics: Timing, Plant Sizing and Smart Grids Flexibility

2016

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In Italy and in many EU countries, the last decade was characterized by a large development of distributed generation power plants. Their presence determined new critical issues for the design and management of the overall energy system and the electric grid due to the presence of discontinuous production sources. It is commonly agreed that contingent problems that a¤ect local grids (e.g. ine¢ ciency, congestion rents, power outages, etc.) may be solved by the implementation of a "smarter" electric grid.The main feature of smarts grid is the great increase in production and consumption ‡exi-bility. Smart grids give producers and consumers, the opportunity to be active in the market and strategically decide their optimal production/consumption scheme. The paper provides a theoretical framework to model the prosumer's decision to invest in a photovoltaic power plant, assuming it is integrated in a smart grid. To capture the value of managerial ‡exibility, a real option approach is implemented. We calibrate and test the model by using data from the Italian energy market. JEL: Q42; C61; D81

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Section: Other Inputs Are the Followingsupporting

confidence: 63%

Investing in Photovoltaics: Timing, Plant Sizing and Smart Grids Flexibility

2016

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“…Electricity prices are also taken into account as a source of uncertainty for renewables by Monjas-Barroso and Balibrea-Iniesta [12], who analyse the mechanisms of public support for renewable energy. Similar work has been carried out by Boomsma et al [13], who analyse investment timing for renewable energy projects under different support schemes, and Biondi and Moretto [14], who evaluate the uncertainties surrounding the Italian PV market. Given these findings, we included electricity price uncertainty in our analysis.…”

Section: Electricity Price Uncertaintymentioning

confidence: 78%

The Implications of Policy Uncertainty on Solar Photovoltaic Investment

Assereto

Byrne

2020

Energies

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Policy and electricity price uncertainty provide disincentives to investors considering renewable energy investments. While electricity price uncertainty impacts on investment decisions relating to any energy investment, whether renewable or non-renewable, policy uncertainty will affect renewable energy investment decisions to a far greater extent. In this study, we consider the two main sources of uncertainty a solar Photovoltaic (PV) project is exposed to: electricity price uncertainty and policy uncertainty. We focus our analysis on utility-scale solar photovoltaics in the Pennsylvania, Jersey, Maryland Power Pool (PJM) electricity market and the New Jersey Solar Renewable Energy Credit (SREC) market. Using Solar Renewable Energy Credits as a proxy for policy, we find that there is considerable volatility in both electricity prices and policy. In a sample covering eleven years, we implement univariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) and combinations of GARCH models with different weighting schemes and find that combination models provide superior forecasts. In renewable energy markets, policy supports have a significant impact on an investment’s profitability. The implication for policymakers is clear: to foster investment in solar PV, policy stability is critical.

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“…The parity is a level at which the average grid energy price is equal to the LCOE of the PV system. 63,64 For this simulation, the N pv and N bat are taken from Table 6, ie, 992 and 41, respectively. The weather on this specific day is clear (normal) because rising and falling of PV_Pwr are smooth as shown in plot (a).…”

Section: Resiliency With the Fleet Of 300 Evs During The Winter Seasonmentioning

confidence: 99%

Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

et al. 2018

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Summary In this paper, the particle swarm optimization (PSO) is used to find optimum size of the photovoltaic (PV) array and energy storage unit (ESU) for PV grid‐connected charging system (in office workplace) for electric vehicles (EV). It is designed in such a way that the EVs are charged at a fixed price (rather than time‐of‐use price) without incurring economic losses to the station owner. The simulation is modeled using the single diode model (for PV) and the state of charge of Li‐ion battery (for ESU and EV). The objective function of the PSO is formulated based on a financial model that comprises of the grid tariff, EV demand, and the purchasing as well as selling prices of the energy from PV and ESU. By integrating the financial model with energy management algorithm (EMA), the PSO computes the minimum number of PV modules (Npv) and ESU batteries (Nbat) for a various number of vehicles and office holidays. The resiliency of the proposed system is validated under different weather conditions, EV fleet, parity levels, energy prices, and operating period. Furthermore, the performance of the proposed system is compared with the standard grid charging system. The results suggest that with the computed Npv and Nbat, the charging price is decreased by approximately 16%, while the EV charging burden on the grid is reduced by 94% to 99%. It is envisaged that this work provides the guidance for the installers to precisely determine the optimum size of the components prior to the physical construction of the charging station.

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Solar Grid Parity dynamics in Italy: A real option approach

Cited by 70 publications

References 15 publications

Investing in Photovoltaics: Timing, Plant Sizing and Smart Grids Flexibility

Investing in Photovoltaics: Timing, Plant Sizing and Smart Grids Flexibility

The Implications of Policy Uncertainty on Solar Photovoltaic Investment

Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

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