A pathway to rapid global solar energy deployment? Exploring the solar dominance hypothesis

Goodstein, Eban; Lovins, L. Hunter

doi:10.1016/j.erss.2019.05.007

Cited by 24 publications

(10 citation statements)

References 9 publications

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“…All these considerations have in common that the PV financial assumptions are critical for the results and conclusions. Such insights are complemented by Goodstein and Lovins in postulating a solar dominance hypothesis . This already starts to become reality, as since 2017, the newly added global solar PV capacity is higher than the net power capacity change of coal, gas, oil and nuclear together…”

Section: Introductionmentioning

confidence: 99%

Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity

Vartiainen

Masson²,

Breyer

et al. 2019

Progress in Photovoltaics

317

187

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Solar photovoltaics (PV) is already the cheapest form of electricity generation in many countries and market segments. Market prices of PV modules and systems have developed so fast that it is difficult to find reliable up to date public data on real PV capital (CAPEX) and operational expenditures (OPEX) on which to base the levelised cost of electricity (LCOE) calculations. This paper projects the future utility-scale PV LCOE until 2050 in several European countries. It uses the most recent and best available public input data for the PV LCOE calculations and future projections. Utility-scale PV LCOE in 2019 in Europe with 7% nominal weighted average cost of capital (WACC) ranges from 24 €/MWh in Malaga to 42 €/MWh in Helsinki. This is remarkable since the average electricity day-ahead market price in Finland was 47 €/MWh and in Spain 57 €/MWh in 2018. This means that PV is already cheaper than average spot market electricity all over Europe. By 2030, PV LCOE will range from 14 €/MWh in Malaga to 24 €/MWh in Helsinki with 7% nominal WACC. This range will be 9 to 15 €/MWh by 2050, making PV clearly the cheapest form of electricity generation everywhere. Sensitivity analysis shows that apart from location, WACC is the most important input parameter in the calculation of PV LCOE. Increasing nominal WACC from 2 to 10% will double the LCOE.Changes in PV CAPEX and OPEX, learning rates, or market volume growth scenarios have a relatively smaller impact on future PV LCOE.

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Section: Introductionmentioning

confidence: 99%

Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity

Vartiainen

Masson²,

Breyer

et al. 2019

Progress in Photovoltaics

317

187

View full text Add to dashboard Cite

show abstract

“…The cost of solar energy is now typically lower than traditional fossil fuel sources. While the sunlight itself is completely free and abundant, the price of the panels has dropped significantly over the last few decades, and is likely to continue to drop further (Warburg 2019;Goodstein and Lovins 2019). Taking into consideration the upfront purchase and installation costs, a levelized cost of energy (LCOE) can be calculated.…”

Section: Cost Of Solar Energymentioning

confidence: 99%

Financial and Environmental Cost-Benefit Analyses Support the Continuation of Government Incentives for Solar Power

Himes

Withers

Black

2020

JSPG

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Solar power is a source of clean, renewable energy that can help consumers reduce their carbon footprints and curb climate change. In recent years, government incentives, along with improvements in efficiency and reduced costs, have made solar power financially beneficial for consumers as well. In order to accurately analyze the costs and benefits of a potential solar project, many factors must be considered. These include the size of the solar array, the amount of sun exposure, the lifespan of the panels and inverters, the lifespan of the roof or other foundation, the current and future price of utility-provided electricity, and the amount of government incentives offered. These factors are described in detail throughout this article, and the analysis reveals that government incentives for solar power projects make a substantial positive impact on the financial feasibility. We find that the US federal and state governments should continue their incentive programs in order to prompt more organizations to transition to solar power and thereby reduce greenhouse gas emissions.

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“…Around the world, the full deployment of solar energy is being facilitated by several factors including, but not limited to, the reduced price of solar panels; environmental, political and social concerns; and solar energy undercutting utility prices, inter alia. According to [ 1 ] global installed capacity will double every two years; however, significant factors have been identified which impede the speed at which solar dominance can be achieved: (i) lack of investments in efficiency, (ii) insufficient government incentives, and (iii) regulatory constraints. Small-scale Photovoltaic (PV) installations such those in the residential sector benefit from self-consumption by shifting a load from hours when electricity prices are high to hours when the PV energy is being generated, thereby achieving electricity bill savings.…”

Section: Introductionmentioning

confidence: 99%

Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants

Moreno

Santos

Martín

et al. 2021

Sensors

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Solar energy penetration has been on the rise worldwide during the past decade, attracting a growing interest in solar power forecasting over short time horizons. The increasing integration of these resources without accurate power forecasts hinders the grid operation and discourages the use of this renewable resource. To overcome this problem, Virtual Power Plants (VPPs) provide a solution to centralize the management of several installations to minimize the forecasting error. This paper introduces a method to efficiently produce intra-day accurate Photovoltaic (PV) power forecasts at different locations, by using free and available information. Prediction intervals, which are based on the Mean Absolute Error (MAE), account for the forecast uncertainty which provides additional information about the VPP node power generation. The performance of the forecasting strategy has been verified against the power generated by a real PV installation, and a set of ground-based meteorological stations in geographical proximity have been used to emulate a VPP. The forecasting approach is based on a Long Short-Term Memory (LSTM) network and shows similar errors to those obtained with other deep learning methods published in the literature, offering a MAE performance of 44.19 W/m2 under different lead times and launch times. By applying this technique to 8 VPP nodes, the global error is reduced by 12.37% in terms of the MAE, showing huge potential in this environment.

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A pathway to rapid global solar energy deployment? Exploring the solar dominance hypothesis

Cited by 24 publications

References 9 publications

Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity

Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity

Financial and Environmental Cost-Benefit Analyses Support the Continuation of Government Incentives for Solar Power

Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants

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