ORCID Profile
0000-0002-0461-8874
Current Organisation
Princeton University
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Publisher: MDPI AG
Date: 22-02-2018
DOI: 10.20944/PREPRINTS201802.0109.V2
Abstract: Building more energy efficient and sustainable urban areas that will both mitigate the effect of climate change and adapt for the future climate, requires the development new tools and methods that can help urban planners, architect and communities achieve this goal. In the current study, we designed a workflow that links different methodologies developed separately, to derive the energy consumption of a university school c us for the future. Three different scenarios for typical future years (2039, 2069, 2099) were run as well as a renovation scenario (Minergie-P). We analyse the impact of climate change on the heating and cooling demand of the buildings and determined the relevance of the accounting of the local climate in this particular context. The results from the simulations showed that in the future there will a constant decrease in the heating demand while for the cooling demand there will be a significant increase. It was further demonstrated that when the local climate was taken into account there was an even higher rise in the cooling demand but also that the proposed renovations were not sufficient to design resilient buildings. We then discuss the implication of this work on the simulation of building energy consumption at the neighbourhood scale and the impact of future local climate on energy system design. We finally give a few perspective regarding improved urban design and possible pathways for the future urban areas.
Publisher: MDPI AG
Date: 10-04-2018
DOI: 10.3390/SU10041134
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 03-2020
Publisher: American Society of Mechanical Engineers
Date: 26-06-2016
DOI: 10.1115/ES2016-59517
Abstract: Integration of non-dispatchable renewable energy sources such as wind and solar into the grid is challenging due to the stochastic nature of energy sources. Hence, electrical hubs (EH) and virtual power plants that combine non-dispatchable energy sources, energy storage and dispatchable energy sources such as internal combustion generators and micro gas turbines are getting popular. However, designing such energy systems considering the electricity demand of a neighborhood, curtailments for grid interactions and real time pricing (RTP) of the main utility grid (MUG) is a difficult exercise. Seasonal and hourly variation of electricity demand, potential for each non-dispatchable energy source and RTP of MUG needs to be considered when designing the energy system. Representation of dispatch strategy plays a major role in this process where simultaneous optimization of system design and dispatch strategy is required. This study presents a bi-level dispatch strategy based on reinforced learning for simultaneous optimization of system design and operation strategy of an EH. Artificial Neural Network (ANN) was combined with a finite state controller to obtain the operating state of the system. Pareto optimization is conducted considering, lifecycle cost and system autonomy to obtain optimum system design using evolutionary algorithm.
Publisher: Elsevier BV
Date: 11-2019
Publisher: IEEE
Date: 09-2018
Publisher: IEEE
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 17-02-2020
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 03-2017
Publisher: Springer Science and Business Media LLC
Date: 10-04-2023
Publisher: Elsevier BV
Date: 09-2017
Location: United States of America
No related grants have been discovered for Amarasinghage Tharindu Dasun Perera.