The Implications of Policy Uncertainty on Solar Photovoltaic Investment

Assereto, Martina; Byrne, Julie

doi:10.3390/en13236233

Cited by 7 publications

(3 citation statements)

References 69 publications

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“…The electricity market in the United States has undergone major deregulatory reforms since the 1990s, aiming to increase competition and benefit consumers. This rising competitiveness altered the price dynamics, resulting in an unavoidable increase in pricing volatility [7,79]. At the same time, electricity prices incorporate all the supply/demand shocks, which leads to highly complex characteristics i.e., autocorrelation, heteroscedasticity, nonlinearity, etc.…”

Section: Discussionmentioning

confidence: 99%

Modeling Dynamic Multifractal Efficiency of US Electricity Market

2021

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The dramatic deregulatory reforms in US electricity markets increased competition, resulting in more complex prices compared to other commodities. This paper aims to investigate and compare the overall and time-varying multifractality and efficiency of four major US electricity regions: Mass Hub, Mid C, Palo Verde, and PJM West. Multifractal detrended fluctuation analysis (MFDFA) is employed to better quantify the intensity of self-similarity. Large daily data from 2001 to 2021 are taken in order to make a more conclusive analysis. The four electricity market returns showed strong multifractal features with PJM West having the highest multifractality (corresponding to lowest efficiency) and Mass Hub having the lowest multifractality (i.e., highest efficiency). Moreover, all series exhibited mean reverting (anti-persistent) behavior in the overall time period. The findings of MFDFA rolling window suggest Palo Verde as the most volatile index, while a significant upward trend in the efficiency of Mass Hub and PJM West is observed after the first quarter of 2014. The novel findings have important implications for policymakers, regulatory authorities, and decision makers to forecast electricity prices better and control efficiency.

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Section: Discussionmentioning

confidence: 99%

Modeling Dynamic Multifractal Efficiency of US Electricity Market

2021

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“…The countries reached saturation of the prosumer photovoltaic market 3-5 years after implementing FIT programs. To be profitable, FIT contracts must be concluded for a relatively long period-at least 10-15 years [47]. Some authors argue that tariffs based on net metering are more advantageous for residential roof-top PV systems, as they make it possible to avoid significant overproduction of energy from such local sources because it becomes unprofitable for prosumers [48,49] Preferential tariffs applied in individual countries were adjusted to the situation in their energy market and were to ensure grid parity for PV energy production at given investment costs [50].…”

Section: System Costs and Government Support Programs For The Develop...mentioning

confidence: 99%

Factors Determining the Development of Prosumer Photovoltaic Installations in Poland

2022

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The development of energy production from renewable sources includes the production of energy from photovoltaic installations by prosumers. In Europe, RES development is driven by political goals and requires subsidies during the deployment period, at least as long as the cost of renewable electricity does not reaches grid parity. The study attempts to determine the importance of factors in the development of energy production by prosumers from PV installations in Polish regions. In 2019, the ‘Moj Prad’ program was introduced, applying subsidies to investment costs and the settlement of energy production in the net-metering system. Almost 900 thousand prosumer PV installations were built by the end of 2021, with a total capacity of 5.9 GW. Solar energy share grew from 0.1 to 2.1%. Spatial econometrics models were use in research to determine factors of prosumer PV systems development in Poland (at NUTS-2). Spatial regimes were found in the studied regions, as indicated by a positive autocorrelation (0.75). Considering the pseudo-R-square co-efficient, we can conclude that the spatial error, i.e., factors not included in the GNS model, constitutes approximately 10%. The economic variables included in the Mansky model, i.e., level of salaries and GDP, explain 90% of the variability of installed PV capacity (Nagelkerke pseudo-R-squared value is 0.906). The level of development of prosumer photovoltaic installations (in W per capita) in regions depends primarily on economic factors represented by the level of salaries in a given region. With the increase in salaries by one unit, we also have an increase in installed power capacity in watts per person by 3.52. Surprisingly, the region’s overall wealth did not matter, as the relative number of installations in regions with lower GDP was higher than in others. One can explain that the individual income of households is more important for increasing the number of prosumer installations than the income of the regional economy. The increase in the number of installations in one region contributed to the subsequent increase in their number in neighboring regions.

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“…Chang et al calculate the average payback period of a solar PV system in Texas to be in the range of 2 to 20 years and emphasize the importance of incentives to the payback period for solar PV systems [13]. Through analyzing the solar PV market in the U.S., Assereto et al find that the most concerned factors for renewable-energy investors are appropriate market conditions and policies, as well as electricity price stability [14]. Souza et al demonstrate that the countries that excelled in global renewable-energy markets are almost the same as those with appropriate regulations, which have favored technological upgrading and local value chain development [15].…”

Section: Solar Photovoltaic Investmentmentioning

confidence: 99%

Solar Photovoltaic Investment Changes across China Regions Using a Spatial Shift-Share Analysis

Sheng

Liu

et al. 2021

Energies

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Solar photovoltaic (PV) has become the fastest-growing new energy in China and one of the main contributors to China’s clean energy transition. From 2013 to 2019, China’s solar PV installed capacity grew from 15,890 MW to 204,180 MW, increasing by 11.85 times. To explore solar PV investment changes across China regions, we use spatial shift-share analysis model to decompose solar PV investment changes from 2013 to 2019 into four components: national energy investment growth effect (NEG), national energy investment structure effect (NES), neighbor–nation solar PV investment competitive effect (NNC), and region–neighbor solar PV investment competitive effect (RNC). Based on the decomposition results, we find that the value of NNC of most western provinces is negative for the entire period, while the NNC of most central and eastern provinces is in the middle and lower range. There is little difference in RNC among these regions. While comparing the influence caused by the four effects, NNC and RNC play dominant roles in solar PV investment changes in eastern and central provinces, which means NEG and NES have relatively small impacts. By contrast, NEG and NES affect the solar PV investment changes at a larger scale in most western provinces. Comparing the NNC and RNC, we find that RNC played a prominent role in the eastern and central regions, while NNC played a dominant role in the west.

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The Implications of Policy Uncertainty on Solar Photovoltaic Investment

Cited by 7 publications

References 69 publications

Modeling Dynamic Multifractal Efficiency of US Electricity Market

Modeling Dynamic Multifractal Efficiency of US Electricity Market

Factors Determining the Development of Prosumer Photovoltaic Installations in Poland

Solar Photovoltaic Investment Changes across China Regions Using a Spatial Shift-Share Analysis

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