scispace - formally typeset
P

Pierluigi Mancarella

Researcher at University of Melbourne

Publications -  322
Citations -  14581

Pierluigi Mancarella is an academic researcher from University of Melbourne. The author has contributed to research in topics: Demand response & Electric power system. The author has an hindex of 51, co-authored 303 publications receiving 10667 citations. Previous affiliations of Pierluigi Mancarella include University of Manchester & Polytechnic University of Turin.

Papers
More filters
Journal ArticleDOI

Assessment of the Capacity Credit of Renewables and Storage in Multi-Area Power Systems

TL;DR: In this paper, the authors proposed a new methodology to quantify the capacity credit of renewable energy sources and storage in a multi-area power system, with applications to wind, solar, and pumped hydro storage plants.
Proceedings ArticleDOI

Exploiting electric heat pump flexibility for renewable generation matching

TL;DR: In this paper, a high resolution and granular domestic energy consumption model is applied, which uses a four-node electrical analogue to represent the thermal characteristics of domestic dwellings, and a control algorithm is designed to match the clusters electric load with renewable generation profile.
Proceedings ArticleDOI

System level cost and environmental performance of integrated energy systems: An assessment of low-carbon scenarios for the UK

TL;DR: In this article, the authors investigated the system performance of different UK future scenarios that have been proposed for 2035 from technical, environmental and economic points of view, and a model that can simulate these different future scenarios with detailed analyses of the integrated energy interactions was created in the EnergyPLAN tool.
Journal ArticleDOI

From Security to Resilience: Technical and Regulatory Options to Manage Extreme Events in Low-Carbon Grids

TL;DR: In this article, the authors propose a new and effective way to deal with high-impact, low-probability (HILP) events in power systems, which are characterized by higher operational uncertainty and a risk profile that is correspondingly difficult to assess.