Document Type

Closed Project

Publication Date

Winter 2018


Ramin Neshati

Course Title

Decision Making in Engineering and Technology Management

Course Number

EMGT 530/630


Electric utilities -- Management, Electric utilities -- Planning, Decision making, Hierarchical Decision Model, Smart power grids


Due to deregulation and the advent of intermittent renewable power sources, the classic school of thought on how to optimally manage the electrical power grid has become outdated. To address this reality, utilities are having to ascertain a paradigm shift in their philosophy on how to best manage assets and customer territory. Research highlights that in order to conform to these challenges, utilities have begun focusing on asset efficiency as opposed to the traditional practice of constructing the gird to safely and reliably supply load to meet the greatest single demand in their respective territories. In order to increase efficiency, grid management has to increase flexibility.

One way to obtain this flexibility is from a ‘non-wired’ resource called Demand Response (DR). Even though this resource is beneficial and provides a solution, a key challenge for DR hinges on the implementation of robust market frameworks. Developing such frameworks allows DR to optimally meet adverse grid conditions while simultaneously providing customers the greatest incentive which will ensure high adoption rates and constant participation.

The complexity in designing a robust framework is the fact that energy suppliers and utilities exhibit economic rationale through a profit maximizing lens, while customers exhibit inconsistent valuation of their utility expense resulting in unpredictable behavior. Research identifies the lack of experience among power systems stakeholders and design mechanisms to develop robust frameworks are some of the greatest challenges in creating sustainable DR solutions. This study looks to provide a qualitative-quantitative tool, using the Hierarchical Decision Methodology (HDM), to guide power system decision makers in choosing the optimal DR solution given their unique sets of territorial demands and system needs.


This project is only available to students, staff, and faculty of Portland State University

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