Electrical and Computer Engineering Seminar Series

Research Seminars from EPECentre PhD Students

Speaker

Euan McGill and Luke Schwartfeger

Institute

PhD students, Electric Power Engineering Centre (EPECentre), Department of Electrical and Computer Engineering, University of Canterbury

Time & Place

Fri, 17 Aug 2018 14:00:00 NZST in Link 309 Lecture Theatre

Abstract

 

A More Representative Approach to Load Modelling Within Low Voltage Distribution Network Analysis

Euan McGill

Within low voltage distribution network analysis, load modelling typically assumes uniform distribution of the transformer load among all downstream premises. This simplifying assumption considerably underestimates load diversity. Failing to account for load diversity in low voltage networks may result in an underestimation of extreme voltages. In New Zealand, smart meter data offers an effective means to capture load diversity within network modelling. One limitation regarding the use of smart meter data is temporal resolution. Statuary limits for steady state voltage are generally applied to 1 or 10 minute averaged root mean squared voltages. Using smart meter load profiles with 30-minute temporal resolution may consequently mask over potential violations. This work presents a method for increasing the temporal resolution of smart meter load profiles from 30 minutes to 1 minute, in order to be used within load modelling applications.

 

Modelling Hydro Electric Energy Systems – How, why, and New Zealand’s case

Luke Schwartfeger

When I naively agreed to research “long term variability” of New Zealand’s electricity system and how it would be affected by more renewable generation, little did I know that I was walking into a field of research that seems to have many different approaches to solve the same problem. This problem is hydro scheduling in an electricity system and involves deciding on the optimal manner to dispatch hydro generation to supply electricity demand over a long time horizon.

As a generation technology, hydro generation is unique since it can be naturally coupled with storage in the form of a water held in a reservoir. With storage, the operation of hydro generation must consider the future consequences of the dispatch decisions so to avoid running out of water. The basis for the majority of hydro scheduling modelling is Dynamic Programming.

This presentation will be an overview of Dynamic Programming as a method to solve the hydro scheduling problem, and how/why so many different approaches have been proposed.