Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm

Reynolds, Jonathan ORCID: https://orcid.org/0000-0001-9106-9246, Ahmad, Muhammad ORCID: https://orcid.org/0000-0002-7269-4369, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 and Hippolyte, Jean-Laurent ORCID: https://orcid.org/0000-0002-5263-2881 2019. Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm. Applied Energy 235 , pp. 699-713. 10.1016/j.apenergy.2018.11.001

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Abstract

Decentralisation of energy generation and distribution to local districts or microgrids is viewed as an important strategy to increase energy efficiency, incorporate more small-scale renewable sources and reduce greenhouse gas emissions. To achieve these goals, an intelligent, context-aware, adaptive energy management platform is required. This paper will demonstrate two district energy management optimisation strategies; one that optimises district heat generation from a multi-vector energy centre and a second that directly controls building demand via the heating set point temperature in addition to the heat generation. Several Artificial Neural Networks are used to predict variables such as building demand, solar photovoltaic generation, and indoor temperature. These predictions are utilised within a Genetic Algorithm to determine the optimal operating schedules of the heat generation equipment, thermal storage, and the heating set point temperature. Optimising the generation of heat for the district led to a 44.88% increase in profit compared to a rule-based, priority order baseline strategy. An additional 8.04% increase in profit was achieved when the optimisation could also directly control a proportion of building demand. These results demonstrates the potential gain when energy can be managed in a more holistic manner considering multiple energy vectors as well as both supply and demand.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of First Compliant Deposit:	24 November 2018
Date of Acceptance:	1 November 2018
Last Modified:	08 May 2023 12:36
URI:	https://orca.cardiff.ac.uk/id/eprint/117074

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