Relevant insights into the long-term competitiveness of renewable energy production
The primary goal of this model is to bridge the gap between the literature covering analyzes of renewable potentials on the one hand and modeling an energy system with endogenous technological change on the other.
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To this end, in the first step of innovation, it is necessary to focus on the extended value chain (research, development, production, and distribution, sales and post-sales) of all products and technologies involved in the energy convergence process, driven by electrification. On the other hand, the second part, Development and Application, deals with the identification and quantitative assessment of digital services related to energy convergence, which will be fully developed in the future and will therefore be sold in the next few years (3-5 years).
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The implications of these fundamental but very uncertain developments on the future competitiveness of electricity generation in energy convergence are examined.
It is necessary to focus on the extended value chain of all products and technologies involved in the energy convergence process
I,D & A (Innovation, Development & Application)
The developing numerical model for this purpose provides a framework for assessing technology, which explicitly considers renewable-specific attributes. Also, certain model properties that require consideration when designing models with endogenous experience curves and time-induced changes in the cost reduction rate are first examined in an analytical setting followed by application.
In addition to its methodological contributions, it seeks to produce relevant insights into the long-term competitiveness of renewable energy production. From a methodological perspective, the main contributions relate to the area of endogenous learning and uncertainty in optimizing the energy system model.
Analytics then application
The developing numerical model for this purpose provides a framework for assessing technology, which explicitly considers renewable-specific attributes. Also, certain model properties that require consideration when designing models with endogenous experience curves and time-induced changes in the cost reduction rate are first examined in an analytical setting followed by application..
Technological change and the experience curve
Cost reduction through technological advances is a key driver of renewable competitiveness. The main objective is, therefore, to generally provide background on technological change with a specific focus on the experience curve. The basic concept of the experience curve is the driver behind reducing the costs described by the curve and the advantages and disadvantages of using experience curves as a predictive tool. A review of the literature to identify appropriate learning rates (leads to Education) for the generation of renewable energy technologies is conducted by covering the methods available to implement technological change in economic models and discuss options for addressing non-convexity in bottom-up models.
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There is a constant debate about how technological change is reflected in models and the advantages and pitfalls of such an approach.
