We’re guest editing a Special issue in the IBPSA Journal of Building Performance Simulation together with Dr Henze of CU Boulder.
Periods of high demand for electricity raise electricity prices and the overall cost of electric power provision. Flattening, smoothing, and reducing electrical demand reduces operational and capital costs of electricity generation, transmission, and distribution. Demand response is the coordination of electricity consuming agents (i.e. buildings) in order to reshape electrical demand to achieve grid benefits.
Reinforcement learning (RL) has gained popularity in the research community as a model-free and adaptive control paradigm for the built environment. RL has the potential to enable inexpensive plug-and-play building controllers that can be easily implemented without necessitating potentially expensive control models (unlike model predictive control), and to coordinate multiple buildings for demand response and load shaping. Despite its potential, there are still many open questions regarding its plug-and-play capabilities, performance characteristics and limitations, safety of operation, and learning speed.
The aim of this Special Issue is to explore how reinforcement learning algorithms for adaptive control can be used in a portfolio of buildings to coordinate their building energy system behavior to achieve multiple objectives, e.g., peak shaving or load shifting, in pursuit of grid-interactive and efficient building operation.
Link to submission and more info: https://think.taylorandfrancis.com/special_issues/citylearn-challenge/?utm_source=TFO&utm_medium=cms&utm_campaign=JPE14825
Topics of interest
This topical issue on “The CityLearn Challenge — Multi-agent reinforcement learning for community-scale energy management” addresses the following areas:
- Application of the CityLearn OpenAI Gym environment (citylearn.net) to study multi-agent coordination, in particular submissions to the 2019 CityLearn Challenge
- Modeling and simulation of building energy systems to integrate with CityLearn (distributed energy sources, district scale storage and generation, etc)
- Novel reinforcement learning-based control algorithms, with emphasis on applications suitable for the built environment (both residential and commercial)
- Computational and simulation frameworks to study RL in the built environment
- Adaptive and coordinated building control applications at the community scale
- Uncertainty and sensitivity analysis
Other related topics are welcome, authors should generally consider the aims and scope of JBPS which can be found here: https://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tbps20
Abstract (500 words): August 15th, 2020
Submission deadline of full papers: September 30th, 2020
Completion of first-round review: November 30th, 2020
Submission deadline of revised papers: January 15th, 2021
Final notification: February 15th, 2021
Issue published: March 15th, 2021