Browsing by Author "Gokaraju, Ramakrishna Rama"

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    Spatial decision support framework and multi-objective optimization for power transmission and generation planning
    (2019-01-24) Ghandehari Shandiz, Sara; Bergerson, Joule A.; Rosehart, William Daniel; De La Hoz Siegler, H.; Knight, A. M.; Gokaraju, Ramakrishna Rama
    Energy system decisions involve taking into account various and often conflicting criteria and to satisfy the values and preferences of a wide variety of people that are considered as the stakeholders of energy system projects. In this thesis, the models developed to support the energy system decision-making are analyzed and integrated frameworks are proposed for the problems of power transmission and generation expansion planning. Different transmission siting approaches are implemented and evaluated based on their computational complexity, number of generated route alternatives and their overall suitability. In general, the results show that the method that generates a database of routes that are not limited to macro-corridors perform better than the other methods with regard to the evaluation metrics. To study the uncertainties present in transmission siting decisions, an integrated fuzzy-stochastic framework was developed. The proposed framework incorporates both probabilistic uncertainties in the performance measures of alternatives and the linguistic stakeholders’ values. It is found out that certain route alternatives rank higher than the other routes even if the inputs of the siting model that are the evaluation of the criteria and stakeholders’ weights are considered uncertain. The selected top alternatives can provide insights for the siting problem to be refined with real stakeholders’ data in the further analyses and through stakeholder discussions. In a separate part of this thesis, the decisions about power generation expansion plan are investigated. The optimization formulation is compared against a model called MGA for the problem that the number of descriptive objectives is more than one. It is observed that even though increasing the number of objectives makes the model more complicated it does not necessarily provide more valuable information. MGA is able to find alternative diverse generation expansion plans that perform well with regard to a set of modelled and unmodelled objectives. It is also inferred from this analysis that parametric uncertainties may change the solutions of multi-objective models and if the solutions change with respect to the decision variables space, their objective values change correspondingly. Therefore, multi-objective solutions are more sensitive to the parametric uncertainties.