scispace - formally typeset
I

Iain MacGill

Researcher at University of New South Wales

Publications -  237
Citations -  6168

Iain MacGill is an academic researcher from University of New South Wales. The author has contributed to research in topics: Electricity market & Electric power industry. The author has an hindex of 34, co-authored 221 publications receiving 5023 citations.

Papers
More filters
Journal ArticleDOI

Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

TL;DR: This work improves the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles and simultaneously scheduling all DER schedules, to investigate the potential consumer value added by coordinated DER scheduling.
Journal ArticleDOI

Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization

TL;DR: In this paper, the authors investigated the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 hours and achieved near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem.
Journal ArticleDOI

Simulations of Scenarios with 100% Renewable Electricity in the Australian National Electricity Market

TL;DR: In this article, the authors developed scenarios for 100% renewable generation to meet current electricity demand in the five Australian states and one territory spanned by the National Electricity Market (NEM).
Journal ArticleDOI

The potential impacts of grid-connected distributed generation and how to address them: A review of technical and non-technical factors

TL;DR: In this paper, the potential negative impacts at high penetrations include voltage fluctuations, voltage rise and reverse power flow, power fluctuations, power factor changes, frequency regulation and harmonics, unintentional islanding, fault currents and grounding issues.
Journal ArticleDOI

Least cost 100% renewable electricity scenarios in the Australian National Electricity Market

TL;DR: In this paper, a genetic algorithm and an existing simulation tool are used to identify the lowest cost (investment and operating) scenarios of renewable technologies and locations for NEM regional hourly demand and observed weather in 2010 using projected technology costs for 2030.