Strategic Networks
Poster Submission
Title: Multi-Objective Optimization Dispatch for Microgrids with a High
Penetration of Renewable Generation
Author: Michael Ross
Abstract:
Renewable energy-based generation, such as wind and solar generation, is becoming more
prevalent in distribution systems because it can give rise to many benefits; for example,
reduced greenhouse gas emissions. However, the unpredictable and intermittent nature of their
prime movers also introduces negative effects to the system; for example, variable generation
output. Therefore, Microgrid control and management strategies must be implemented to
reduce the negative effects while maximizing the benefits through coordinated control amongst
available distributed energy resources (such as Energy Storage Systems and demand response).
Although most commercially available Microgrid controllers only minimize the cost of energy,
results from Project 2.1 identify many benefits that can be achieved by a Microgrid, including
reduced energy costs, improved reliability, reduced peak loading, minimized power fluctuations,
and reduced greenhouse gas emissions. In order to achieve these benefits, a Microgrid dispatch
algorithm is formulated as a Multi-Objective Optimization (MOO) that aims to maximize the
utility of the Microgrid by optimizing the aforementioned benefits.
The MOO is implemented through scalarization of the benefits with objective and subjective
valuation parameters so that it can be solved as a single-objective optimization. Objective
valuation parameters employ the quantification of standard utility measurements of the
benefits; for example, cost of non-delivered energy. Subjective valuation parameters are
normalized weighting values that the Microgrid owner places on the benefits, since every
Microgrid is implemented for unique purposes. This approach allows the multiple objectives to
be directly compared, and the controller’s dispatch algorithm is adaptable and customizable to
each Microgrid implementation, allowing the Microgrid to serve its desired purpose(s).
Project Number: 1.4
University: McGill University
Supervisor: Professor Géza Joós