Abstract. is paper describes the use of So Systems Methodology (SSM) as a tool for problem structuring, which is the rst phase encompassed in a methodological approach currently under development to provide decision support based on Multi-Criteria Decision Analysis (MCDA) in energy planning problems in an urban context. In order to apply the methodology to a real-world problem, a medium sized Portuguese city has been chosen as the decision setting. SSM is used for characterizing as precisely as possible the decision problem context, identifying the main stakeholders and their relations, and discerning the relevant criteria at stake for each one. Future work directions based on this phase are also envisaged.
The optimization of an economic indicator has traditionally been the sole objective function of mathematical programming models for power generation expansion planning. Recently, however, other evaluation aspects, such as environmental concerns, were also given an explicit role as objective functions in mathematical models. Models become thus more realistic so that decision makers are able to grasp the inherent conflicts and trade‐offs among the distinct objectives in selecting a best compromise plan. A significant change in the planning processes has also occurred concerning new planning methodologies integrating demand‐side management (DSM) techniques, in an attempt to change the levels and forms of electricity use by the consumers. This paper presents a multiple objective linear programming model for power generation expansion planning incorporating DSM. The objective functions are the total expansion cost, the environmental impact associated with the installed power capacity and the environmental impact associated with the energy output. DSM is included by modelling it as a new generating group along with the generating alternatives from the supply side. Five categories of constraints are considered related to the reliability of the supply system, the availability of the generating units, the capacity of the DSM‐equivalent generating group, the total capacity installed throughout the planning period, the pollutant emissions. Some results are presented derived by using an interactive method, aimed at assisting decision makers in a progressive and selective search of good compromise solutions.
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