Renewable energy technology diffusion model for techno-economics feasibility

Kumar, Rajesh; Agarwala, A.K.

doi:10.1016/j.rser.2015.10.109

Cited by 31 publications

(19 citation statements)

References 23 publications

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“…less investments in renewables. Kumar & Agarwala (2016) further pointed out that renewable energy technology diffusion rate is higher due to technological improvements resulting in cost reductions and government policies supportive of renewable energy development and utilization.…”

Section: Technological Innovationmentioning

confidence: 99%

Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL

Silva

Cerqueira

Ogbe

2018

Energy

224

113

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Given the importance of renewable energy in the discussion of a reliable and sustainable energy future, it is imperative to understand its main determinants and to draw result implications for energy policy. This study analyses these determinants for Sub-Saharan Africa. Using a panel data technique, namely the panel-ARDL model for a period covering 1990-2014, the results suggest that economic development (per capita GDP) and an increased use of energy aid renewable energy development while population growth impedes it. Furthermore, the study investigates the potentials and current status of renewable energy in Sub-Saharan Africa. This article shows that although the region has great potential for developing renewable energy such as wind, biomass, solar and hydropower, dispersed throughout the continent, this potential has not been fully explored, even though many resources are plentifully available, and evidence good economic potential.

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Section: Technological Innovationmentioning

confidence: 99%

Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL

Silva

Cerqueira

Ogbe

2018

Energy

224

113

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“…In [27] an integrated approach for the RES technologies diffusion was considered regarding technology, conversion, availability of sources, cost and policy. The authors pointed out to the continuing barriers to the large-scale adoption of RES energy in India.…”

Section: Technology Diffusionmentioning

confidence: 99%

Long-Term Projection of Renewable Energy Technology Diffusion

2019

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The EU aims at increasing the use of renewable energy sources (RES), mainly solar-photovoltaic (PV) and wind technologies. Projecting the future, in this respect, requires a long-term energy modeling which includes a rate of diffusion of novel technologies into the market and the prediction of their costs. The aim of this article has been to project the pace at which RES technologies diffused in the past or may diffuse in the future across the power sector. This analysis of the dynamics of technologies historically as well as in modeling, roadmaps and scenarios consists in a consistent analysis of the main parameters of the dynamics (pace of diffusion and extent of diffusion in particular markets). Some scenarios (REMIND, WITCH, WEO, PRIMES) of the development of the selected power generation technologies in the EU till 2050 are compared. Depending on the data available, the learning curves describing the expected development of PV and wind technologies till 2100 have been modeled. The learning curves have been presented as a unit cost of the power versus cumulative installed capacity (market size). As the production capacity increases, the cost per unit is reduced thanks to learning how to streamline the manufacturing process. Complimentary to these learning curves, logistic S-shape functions have been used to describe technology diffusion. PV and wind generation technologies for the EU have been estimated in time domain till 2100. The doubts whether learning curves are a proper method of representing technological change due to various uncertainties have been discussed. A critical analysis of effects of the commonly applied models for a long-term energy projection (REMIND, WITCH) use has been conducted. It has been observed that for the EU the analyzed models, despite differences in the target saturation levels, predict stagnation in the development of PV and wind technologies from around 2040. Key results of the analysis are new insights into the plausibility of future deployment scenarios in different sectors, informed by the analysis of historical dynamics of technology diffusion, using to the extent possible consistent metrics.

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“…Today, Bass model is still widely used in the research of innovation, technology diffusion and marketing. Kumar [22] proposed energy models to enhance knowledge and skills in the efficient transfer and management of technology for optimally allocating different types of technology feasibility Bertorri [23] introduced a network structure into the Bass Model and investigated numerically the dynamics in the case of networks with different link density. In information age, Bass Model also fits to capture the underlying mechanism of information diffusion on SNS (Social Network Site).…”

Section: General Bass Modelmentioning

confidence: 99%

Decision Making in Crowdfunding Under Risk Analysis

Chen¹,

Chen²,

Chen³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Crowdfunding is an emerging international financial activity often performed via internet mediated platform. With the rapid growth of this financial system, rising risks would influence participant's decision making. In this study, we examine the process of a typical crowdfunding activity, pre-ordering pledging as well as its coming risks. Based on the analysis, we combine Evolutionary Stable Strategy (ESS) and General Bass Model (GBM) to build decision-making models for pre-ordering pledging theoretically where risk factors are taken into account. Finally, evolutionary game simulation system is built to simulate the dynamic decision-making behavior in a risk changing environments. The simulation results demonstrate that the currency exchange rate give great impact on international participant's decision-making behavior in crowdfunding. Low exchange rate brings less investment decision from the investor and high exchange rate leads to overheated investment which challenges funder's diligence. Project system risk may infest the participant's decisions-making process and cause ambiguity at the end. Limitation and managerial suggestions are discussed.

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Renewable energy technology diffusion model for techno-economics feasibility

Cited by 31 publications

References 23 publications

Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL

Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL

Long-Term Projection of Renewable Energy Technology Diffusion

Decision Making in Crowdfunding Under Risk Analysis

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