When it comes to complex, important/significant problems, people often disagree. Not because they are unreasonable, but because they have different interests and the issues at stake are very significant, with much to gain or lose. Uncertainty about future conditions only exacerbates the difficulty. In the end, decisions are generally made in the political arena after a great deal of public debate by the stakeholders concerned.

GaBE's goal is to support the public energy policy debate by giving a range of stakeholders a basis of commonly accepted results. When the emphasis is on the energy system level instead of an individual energy technology level, GaBE has adopted a layered methodology called multi-scenario modeling, as shown in Figure 1.

Figure 1:

As this diagram shows, the first layer of the methodology is interaction with a group of stakeholders to find out their primary concerns and issues, and choose a wide/broad range of indicators or criteria for the analysis. The stakeholder interaction process is iterative, and the GaBE analysis team then meets again with stakeholders to agree on scenario design and key analytic assumptions, to communicate results and to assist with decision support (scenario ranking). The key steps for common stakeholder acceptance of results are comprehensive choice of criteria and scenario design, and agreement on (or sensitivity analysis of) key assumptions. Figure 2 shows this stakeholder interaction process, along with a typical selection of stakeholders.

Figure 2:

The second level of this process is analytic - gathering the data, performing the multi-scenario modeling and interpreting the large number of results. Naming and composing the data set for each scenario is key to automating the process. In the jargon of this methodology, multiple options are combined into many different strategies, multiple uncertainties are combined into different futures, and strategies and futures are combined into scenarios, as shown in Figure 3 . In general, options are controllable choices, like a fuel or technology choice or an operating procedure, while future uncertainties reflect a lack of knowledge. This depends partly on the point of view; a politician's policy may be a businessman's uncertainty, but this does not affect the actual modeling

Figure 3:

Finally, the core of the methodology is a scenario-modeling engine that can be repetitively used to analyze a large number of scenarios. Two major types of modeling engines exist, divided roughly into simulation and optimization models. The major difference is usually in how the model plans to meet uncertain future needs. A simulation model follows rules and some preset plan, while an optimization model seeks to follow its objective function (generally least cost or highest value). The dichotomy is not complete; for example, simulation models often optimize some aspects of system operation. Although simulation models are often used to produce a larger number of scenarios (cut?* - and optimization models are often used for the sensitivity analysis of fewer scenarios), the choice really depends on model suitability to the problem or situation analyzed. GaBE has used both electric sector simulation and energy-economic optimization models in its analyses.

PSI has linked this multi-scenario methodology to its other analytic capabilities/strengths to give a full range of internal and external costs and burdens. By calculating costs, environmental impacts, and health and safety burdens per unit of energy for individual technologies, and combining these results with the use of each technology over time, it is possible to produce indicators or results for a very wide range of criteria for each of many scenarios. The question then is how to assist stakeholders in discovering the tradeoffs present in the mass of results, and to assist them in each incorporating their own preferences to individually rank different strategies. This process is further discussed on the page covering multi-criteria decision support.