Spatial decision support framework and multi-objective optimization for power transmission and generation planning

Ghandehari Shandiz, Sara

Spatial decision support framework and multi-objective optimization for power transmission and generation planning

dc.contributor.advisor	Bergerson, Joule A.
dc.contributor.advisor	Rosehart, William Daniel
dc.contributor.author	Ghandehari Shandiz, Sara
dc.contributor.committeemember	De La Hoz Siegler, H.
dc.contributor.committeemember	Knight, A. M.
dc.contributor.committeemember	Gokaraju, Ramakrishna Rama
dc.date	2019-06
dc.date.accessioned	2019-01-25T21:21:12Z
dc.date.available	2019-01-25T21:21:12Z
dc.date.issued	2019-01-24
dc.description.abstract	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.	en_US
dc.identifier.citation	Ghandehari Shandiz, S. (2019). Spatial decision support framework and multi-objective optimization for power transmission and generation planning (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/36107
dc.identifier.uri	http://hdl.handle.net/1880/109848
dc.language.iso	en	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Multi-criteria, Decisions making, Multi-objective optimization, Siting, Routing, Power transmission lines, Stochastic uncertainty, Fuzzy linguistic quantifiers, Pareto optimal solutions, Alternative solutions	en_US
dc.subject.classification	Engineering	en_US
dc.title	Spatial decision support framework and multi-objective optimization for power transmission and generation planning	en_US
dc.type	doctoral thesis	en_US
thesis.degree.discipline	Engineering – Chemical & Petroleum	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Doctor of Philosophy (PhD)	en_US
ucalgary.item.requestcopy	true