Feed-in tariff contract schemes and regulatory uncertainty

Barbosa, Luciana; Nunes, Cláudia; Rodrigues, Artur; Sardinha, Alberto

doi:10.1016/j.ejor.2020.04.054

Cited by 23 publications

(9 citation statements)

References 25 publications

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“…Deterministic approaches, for example, are unable to provide a precise analysis of the SES network planning problem under uncertain environments. Although previous studies have discussed a number of uncertain programming methods, such as stochastic models [ 10 , 16 , 28 , 29 ] and simulation models [ [21] , [22] , [23] , 30 ], these methods are accompanied by two major drawbacks: (i) a large amount of historical data is required to estimate the probability distribution for the uncertain parameters, which is generally not available for most real cases; (ii) a large number of scenarios are used to model uncertain parameters that can lead to computation time challenges in the original model. In this study, an NSRF model is proposed based on the combination of a scenario-based robust optimization model by Ref.…”

Section: Scenario-based Robust Fuzzy Programming Approachmentioning

confidence: 99%

“…The COVID-19 pandemic is considered a risk event that has affected all aspects of life and led to critical network disruptions. Several previous studies have developed uncertain programming models to overcome the uncertainty in parameters and risks in the power system planning field with or without government subsidies [ [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. For example, Tsao el al.…”

Section: Introductionmentioning

confidence: 99%

“…Regulatory uncertainty in the feed-in tariff model was highlighted by Ref. [ 21 ] by using a Poisson process for incentivizing RE projects. The authors in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…The disruption risks, however, were not addressed in the studies by Refs. [ 21 , 22 ]. In a different study, a simulation approach was applied to evaluate the battery distribution network based on the resilience indices considering the material input risks [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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COVID-19: Government subsidy models for sustainable energy supply with disruption risks

Tsao

Thanh

Chang

et al. 2021

Renewable and Sustainable Energy Reviews

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The outbreak of the COVID-19 pandemic poses great challenges to the current government subsidy models in the renewable energy sector for recovering in the post-pandemic economy. Although, many subsidy models have been applied to accelerate renewable energy investment decisions. However, it is important to develop a new model to ensure the sustainability of the renewable energy supply network under disruptions on both the supply and demand sides due to hazardous events. This study investigates different subsidy models (renewable credit, supplier subsidy, and retailer subsidy) to find a win-win subsidy model for sustainable energy supply under disruption risks. The objective is to determine the optimal capacity of renewable energy added to the grid, the optimal wholesale price of the power plant, and the optimal retail price of the aggregator under different subsidy models to maximize the economic, social, and environmental benefits of the whole network. A novel scenario-based robust fuzzy optimization approach is proposed to capture the uncertainties of business-as-usual operations (e.g., some relevant costs and demand) and hazardous events (e.g., COVID-19 pandemic). The proposed model is tested in a case study of the Vietnamese energy market. The results show that for a high negative impact level of hazardous events on the supply side, the renewable credit and supplier subsidy models should be considered to recovery the renewable energy market. Further, the proposed approach has a better performance in improving the power plant's robust profit for most of the hazard scenarios than the robust optimization model.

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Section: Scenario-based Robust Fuzzy Programming Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Regulatory uncertainty in the feed-in tariff model was highlighted by Ref. [ 21 ] by using a Poisson process for incentivizing RE projects. The authors in Ref.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

COVID-19: Government subsidy models for sustainable energy supply with disruption risks

Tsao

Thanh

Chang

et al. 2021

Renewable and Sustainable Energy Reviews

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“…For utility models see for instanceBirge (2002);Alvarez and Stenbacka (2003);Evans et al (2008);Chronopoulos et al (2011);Grasselli (2011);Choi et al (2017).2 For other risk-neutral models see Morellec (2007, 2013);Maier et al (2020) and for practical applications seeBajeux-Besnainou et al (2010);Conrad (2018);Barbosa et al (2020);Szabó et al (2020) and many others.3 Since its pay-off has two stochastic components, the real option can be regarded either as an exchange option or as an option with stochastic strike. These concepts have been applied in real option theory before.…”

mentioning

confidence: 99%

Risk-Adjusted Valuation for Real Option Decisions

Alexander¹,

Chen²,

Ward³

2021

Preprint

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We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity. The investor's subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor's and the market's view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical realoption valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.

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