Systems analysis for progress and innovation in energy technologies for integrated assessment (SAPIENTIA)

The aim of the PROMETHEUS stochastic outlook is to provide assessments on the likelihood of key assumptions underpinning the Baseline and provide assessments on ranges of key results thus giving indications as to the uncertainty associated with them. Unlike most ranges routinely reported in forecasting exercises PROMETHEUS assessments are characterised by a certain degree of rigour as they will have specific probabilities associated with them (quantiles). The stochastic outlook has been build under some key assumptions, including a median climate policy stance for the different regions of the world and a common technology-by-technology R&D Outlook (both government and private) for the 2050 horizon. The PROMETHEUS baseline projections were used to establish a benchmark against which the impact of R&D policies on a wide range of quantifiable SD indicators has been evaluated. The PROMETHEUS stochastic model has been applied to examine the two alternative R&D scenarios considered in SAPIENTIA: a High R&D scenario, implying the doubling of total energy-related R&D (Government and Private for the whole world) on the technologies covered within SAPIENTIA over the period 2006-2025, and a Zero GERD scenario, implying the elimination of Government energy-related R&D (GERD) worldwide from the whole Outlook (to 2050). The stochastic model has also been applied to perform extensive experiments in the form of R&D "shocks" which aimed at examining the impact on sustainability indicators of injecting R&D expenditure on individual technologies. After suitable analytical treatment PROMETHEUS results have been used to provide the essential parameters of the decision support tool.This tool has then been used for carrying out "real life" R&D portfolio exploration.

A review of sustainable-development indicators and European policy objectives has been made. On the basis of this review, concrete definitions of sustainable-development indicators were formulated that can serve as objectives in formal energy modelling activities such as SAPIENTIA.

In the context of SAPIENTIA, the PROMETHEUS stochastic World energy model has been extended to the longer term. This has entailed significant changes in the PROMETHEUS modeling system with regard to the identification and introduction of new technologies and the modification of the structure of the model. Accordingly, PROMETHEUS has been extended to cover a more detailed transportation sector and to incorporate a Hydrogen Production, Storage and Delivery sub-model. In all, 22 additional technologies are represented in the new version (4 large scale power generation, 4 very low emission vehicles, one distributed power generation and 9 Hydrogen Production technologies). Other important developments on the PROMETHEUS model include the endogenisation of climate policy (where climate change -as it is perceived to occur- affects the climate policy intensity) and the endogenisation of R&D, by making it dependent on energy costs, while renewable and CO2 capture technologies shares in total R&D are affected by energy costs and the carbon value (climate policy intensity). Finally, PROMETHEUS has been extended to incorporate technology dynamics for 51 technological options for electricity production, hydrogen production and passenger cars. 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 7 technologies, adjusted for efficiency. Efficiency parameters for 20 technologies

Methods for Multi Criteria Decision Analysis (MCDA) have been designed in order to designate a preferred solution, to classify the alternatives in a small number of categories and/or to rank the alternatives in a subjective order of preference. MCDA can result in improvement of the satisfaction with the decision process, enhancing the communication in the group of decision makers, improvement of the quality of the decision itself and increased productivity of the decision makers. Considerable literature on multiple criteria decision making exists, both in terms of theory and applications. ECN, a partner with experience on Multi Criteria Decision Analysis methods has focused on the following activities: a. Define relevant criteria to select the most appropriate method(s) to be used in conjunction with the ISPA tool. b. Undertake a brief literature study and survey on Multi Criteria Decision Analysis and other related decision analytic methods in particular as applied in or related to energy policy making and supporting models.

Insertion in the TIMES model of the identified important non-power technologies-Incorporation into the model of causality chains leading from R&D to impact on Sustainable Developmet indicators-Inclusion of additional equations to handle 2FLC in TIMES

Regarding the Global MARKAL MACRO (GMM) model, the following extensions were considered: a.the development of a stand-alone transportation model and its integration to GMM after its completion, calibration and testing and after including technology spillovers b.the construction of a link between GMM and the TFLC interface (called Soft-link) c.the development of a simulation routine (CLIMATE) capable of estimating objectives related to climate change.

A list of technologies has been defined. Based on statistical information a description of their technical and economic characterisation has been done. A survey information on prospected technical and economic parameters has been given to complete the technology database for SAPIENTIA.

LEPII-EPE has extended the POLES technology database TechsDB to incorporate the definite set options for Hydrogen and carbon capture and sequestration technologies. The updated TechsDB covers the 45 POLES technologies, combining past data or estimates with expert judgements on future technology performance.

The specification and estimation of the two factor learning curve relations for use in the large models as well as in PROMETHEUS is the backbone for meaningful policy analysis in the context of the SAPIENTIA project as they constitute the main vehicle and first step through which R&D actions translate into impacts. Some specific properties have been sought for the specification of the two factor learning curve formulation: the TFLCs should incorporate both learning-by-doing and learning-by-research (which is crucial in order to be able to perform the R&D policy exercises), endogenise as much of the technical progress as possible, constrain to technical possibilities as they emerge from perspective analysis, include Clustering as fully as available information allows, take carefully into account initial conditions regarding cumulative R&D and equipment stock and capture as much of the above with as few parameters as possible (to render their model incorporation practically feasible). A general algebraic specification has been derived and considerable effort has been devoted to standardise the formulation as much as possible. Some exceptions however were deemed necessary due to specifities of the technologies. As a result, the technologies are classified into five categories, the Cluster Matrix Technologies, for which a cluster matrix is supplied and the general algebraic formulation can be applied, the Stand Alone Technologies, which are orthogonal technologies and do not need to consider clustering, the Perfect Clustering Technologies, the technical and economic characteristics of which are directly related to the corresponding characteristics of other technologies in the same cluster (for example the wind offshore and wind onshore technologies), the On-board storage technologies, (a sub-category of the perfect clustering technologies) that are shared by different types of vehicles and finally the Fuel Cell technologies. Particular attention was given to the estimation of the TFLCs so as to ensure that apart from statistical fit they also displayed sufficient robustness for use in the wide variety of models and especially that they performed credibly in view of the R&D policy analysis. For the estimation historical time-series for R&D, equipment stock, capital costs and projections of technical and economic characteristics of technologies were used (as derived from the TECHPOL database), projections of installed equipment from the provisional POLES Baseline, projections of public and private R&D by technology elaborated by ICCS/NTUA, and finally cluster information from MARKAL. Clustering of technologies has been incorporated through learning by doing. The learning parameters were estimated by applying Maximum likelihood estimation over the historical period; yet this was by no means the only estimation criterion. All properties sought in the TFLCs specification figured among the objectives of the estimation. In addition, simultaneous equations estimation has also been applied (along clusters) in order to improve estimates and obtain appropriate co-variances of learning parameters. In all, technology dynamics have been estimated for a total of 51 technological options covering power generation, CO2 capture and sequestration, Hydrogen-related technologies, conventional and non-conventional vehicles and Fuel Cells. Learning parameters were estimated for capital costs, fixed Operation and Maintenance costs, variable Operation and Maintenance costs, efficiencies and CO2 capture rates.

For indicators out of different areas such as, macro/socio-economic, climate, energy, transport and health data are identified or collected to measure them. Based on available data, causal links between model or technology and sustainability indicators are identified. The Data collection for the remaining indicators were conducted and accordingly causal links were determined.

LEPII-EPE has focused on the update and enhancement of the POLES 2050 Reference Case with a view to specifically serve the needs of the SAPIENTIA project. This has involved significant changes to the POLES modelling system, first because new technologies (not potentially significant for the medium term (2030) but much more important for the further future) had to be identified and introduced in the model and because the proper structure of the model had to be significantly modified. To this end the POLES World Model has been extended to represent the Hydrogen Economy and to include decentralised electricity generation and Carbon Capture and Sequestration technologies (CCS) for H2 and electricity production.

A substantially extended version of the energy-system ERIS model that allows a comprehensive assessment of impacts of alternative policy instruments in the areas of climate change and security of energy supply. To address the objectives of the SAPIENTIA project and most importantly in order to adequately represent the causal chains from R&D and D&D impact on sustainability indicators, ERIS has been substantially restructured, expanded and recalibrated, and new features have been added.

The PROMETHEUS stochastic model has provided the expected impacts and variance-covariance matrices of the impacts that were used in constructing ISPA, the policy integration tool used to perform Integrated R&D Policy Exploration. In SAPIENTIA ICCS-NTUA has undertaken a new specification for ISPA in which the expected impact on a given objective is a non-linear function of the R&D allocated to this technology. No numerical problems arise, and seemingly convexity is retained. However, a considerable increase in complexity is evident. In general, results are defendable and exploration has proceeded in a similar fashion as with the earlier version of ISPA, but they are generally highly sensitive to budget size. The integrated exploration procedure adopted using the ISPA tool includes the following steps: construction of a feasible set by exploiting synergies among the objectives,improvement of the solution obtained by consolidating synergies and relaxing bounds, and, finally, improving the solution by sacrificing the probability (or the threshold) requirements for some objectives. Guidance in the relaxation and the sacrificing has been provided by the shadow costs. A Mixed Integer Programming (MIP) specification of the ISPA model has also been tested, which provides complete flexibility on the joint distributions of the impacts and expands the possibilities for adopting different risk averse stances. However, the MIP version has some disadvantages, such as computational difficulties, difficulties in introducing non-linearities, and, limited possibilities for using shadow costs for guiding the exploration. The exploration procedure adopted using the MIP version of ISPA includes the following steps: setting ambitious expectation targets for all objectives, then giving emphasis on one objective at a time by seeking higher expectations and lower risks, and, consolidating gains both in terms of expectations and risks by exploiting synergies between the objectives. The solution obtained allocated 20-26% of the budget to non-conventional vehicles, 22-26% to nuclear, 25-27% to renewables, and 17-21% to clean coal technologies. Fuel cells share is around 10% when the market impact objective is emphasized, but drops to 3.5% when emphasis is placed on other targets. The solution obtained is diversified in terms of technologies included and it is fairly stable despite the shifts in the emphasis on impacts.

The impact of research, development and demonstration (R&D&D) on sustainable development objectives was examined. These involved: * construction of a global baseline scenario of energy and transport technology deployment and diffusion. * application of the ERIS model to this baseline scenario to evaluate the impact of research and development (R&D) and demonstration and deployment policies on indicators of long-term energy sustainability, including climate change and security of energy supply.

IIASA-ECS has undertaken work in two principal areas. The first is to explain the basic concepts involved in multi-objective stochastic modelling, and the second is to develop an interactive software ("ISPA Tool") to run the ISPA model and to promote an appreciation of its capabilities. ISPA is a stochastic multi-objective optimization model of R&D spending on energy supply technologies. The ISPA Tool allows non-expert users to apply ISPA and analyse its results. To enable potential future users of ISPA to understand the concepts used in the model, IIASA-ECS has developed an introductory tutorial on probabilistic concepts. Based on these terms, a non-technical description of the ISPA model is also given.

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