Technological change in energy - PROMETHEUS
The substitution between different fuels/technological options and technological change in PROMETHEUS is modelled through a mechanism that is similar for both final energy demand and energy supply (power generation and hydrogen production). Central to this mechanism is the notion of the “gap”, which is defined in terms of the difference between energy demand and the amount of energy that can be satisfied using existing equipment from the previous year, which is not scrapped. The overall scrapping rate of each technology includes normal scrapping, due to plants reaching the end of their lifetimes, and premature scrapping, due to changes in variable and fuel costs which render the continuation of the plant's operation economically unsustainable. The inclusion of the latter form of scrapping is important in order to enable the modelling of rapid technical transformation in case of strong climate action or rapidly increasing fossil fuel prices, as the renewal of equipment stock accelerates.
Competition between technologies to cover energy demand in each sector occurs in terms of market shares within the gap. The allocation of new investments is modelled as a quasi cost-minimizing function (based on the Weibul specification) and is driven by the total cost of the competing options, which includes the discounted and annualised capital costs, fixed and variable Operating and Maintenance costs, carbon costs and costs to purchase the required energy carriers. Technology diffusion therefore depends both on economic considerations (e.g. the relative costs and competitiveness of alternative technological options) but also on various other factors including mimetism, information, trade, infrastructure development, TRL levels, and network effects, which are captured by the "maturity" parameters,reflecting the relative“maturity” factor of each technology in terms of readiness of consumers to adopt them.
Traditional technology dynamics has long recognised the importance of learning by experience in the improvement of the cost and technical performance of technologies. However, it is also widely accepted that (public and private) R&D can contribute directly to technological improvement and in order to address policy questions concerning the efficacy of R&D, it is clear that R&D must figure explicitly in the technology dynamics specification. The core in the endogenous technological change modelling adopted in PROMETHEUS is the two factors learning curve (TFLC) specification and the endogenisation of the technical progress through both learning by research and learning by experience. Under this scheme, an R&D action leads directly to technological improvement, which in turn enhances competitiveness of a particular option and leads to increased technology take-up. This latter increase sets in motion learning by experience, which results in further technological improvement, further up-take etc. In this sense, learning by doing acts as an accelerator of the impact of initial R&D effects. Clearly, the cycle is characterised by dampening effects that result in finite overall impacts. This dampening notwithstanding, the inclusion of such mechanisms in the model does tend to introduce elements of instability, in particular “lock-in” effects –massive R&D funding on some technological options may lockout other options that fail to benefit from the learning by experience they could have enjoyed, had such initial R&D infusion not taken place. There is sufficient evidence that this scheme is an accurate representation of the way technical progress has occurred in the past. PROMETHEUS also incorporates the notion of technical potential (floor costs), as they emerge from perspective technological analysis.
Taking into account the fact that technological change is a process characterized by fundamental uncertainty, critical parameters for the effects of R&D effort, technology adoption and cost efficiency are explicitly modelled enabling the quantification of the variance and covariance associated with the adoption of particular technologies. The parameters of the two factor learning curves in PROMETHEUS are jointly distributed random variables and they covary. The PROMETHEUS outlook also incorporates modelling of the size and direction of R&D, which are endogenous to the model. By analysing historical observations of R&D on energy technologies and utilizing perspective analysis, relations have been established, linking R&D to economic factors and particularly measures of energy cost.
PROMETHEUS augments the traditional TFLC specification (i.e. technology costs depend on the accumulated technology production/capacity and on cumulative R&D expenditire) by incorporating clustering effects, which are essential in cases of a rapid energy system transformation.The idea is that technological progress in a specific direction enhances cost efficiency of similar technologies, to a degree which depends on the “proximity” of the corresponding technologies. A technology cluster is a group of technologies that share a common component. A technology can belong to different clusters when it is composed of different components, e.g. a natural gas combined cycle is part of the gas turbine, recovery boiler and steam turbine clusters. The common component is assumed to be the learning technology and each component has its own learning curve specifications. Technical progress leads to the improvement of different cost components, i.e. capital, fixed O&M and variable O&M cost and technical efficiency. Thus learning parameters have been estimated for each of the above components. The improvement in different cost components leads to a reduction of the overall cost of the technology and hence to increased competitiveness, in particular for low-carbon technologies (that are currently immature and have a high innovation and deployment potential).
In PROMETHEUS technology dynamics for 51 technological options for electricity production, hydrogen production/storage/delivery and passenger cars were estimated. These include:
- Capital costs parameters for 44 technological options
- Fixed O&M costs for 34 technologies; although they are basically labour costs, technical progress has been assumed based on the increased automation, reliability and the economies of scale
- Variable cost parameters for 12 technologies, adjusted for efficiency.
- Efficiency parameters for 20 technologies
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|Institution||E3Modelling (E3M), Greece, https://e3modelling.com/modelling-tools.|
|Solution concept||Partial equilibrium (price elastic demand)|
|Anticipation||Energy system simulation.Foresight is included only is some sub-modules (i.e. electricity generation)|