Sancho-Tomás, A., Chapman, J., Sumner, M. et al. (1 more author) (2017) Extending no-MASS : multi-agent stochastic simulation for demand response of residential appliances. In: Barnaby, C.S. and Wetter, M., (eds.) Proceedings of the 15th IBPSA Conference. Building Simulation 2017, 07-09 Aug 2017, San Francisco, CA, USA. International Building Performance Simulation Association - IBPSA , pp. 189-198. ISBN 9781775052005
Abstract
Demand Response (DR) has been proposed as an efficient and inexpensive solution to face the challenges of the evolving power system: to reduce peak demands and thus demands for additional generating capacity, and to improve localized energetic autonomy. The success of DR programs is highly dependent on the acceptance and reactions of end-users: their willingness to devolve control and/or to proactively adjust their energy using behaviours. However, experimental trials to identify the best DR configuration for each type of consumer is very costly. Here simulation has a valuable role to play, in identifying promising DR strategies, taking into account occupants characteristics. This paper describes and evaluates a proof-of-concept extension of a multi-agent stochastic simulation platform that simulates occupants behaviours (No-MASS) to also simulate DR strategies. This is applied in the first instance to electrical devices in households: categorizing these as demand devices (e.g. large and small appliances, heating and hot water systems), supply devices (e.g. PV, cogeneration, wind turbines) and electrical storage devices (e.g. batteries, HEVs). The device-specific DR strategies are learned by our device-agents using machine (specifically reinforcement) learning, to maximize a function rewarding the utilization of locally generated energy. We close this paper by discussing our next steps to add further functionality to No-MASS for DR simulation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 The Authors. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Jun 2021 09:27 |
Last Modified: | 23 Jun 2021 09:27 |
Published Version: | http://www.ibpsa.org/?page_id=962 |
Status: | Published |
Publisher: | International Building Performance Simulation Association - IBPSA |
Refereed: | Yes |
Identification Number: | 10.26868/25222708.2017.056 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175269 |