Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector

Abstract

This paper provides a research plan focusing on the application of self-learning techniques for energy systems integration in the residential building sector. Demand response is becoming increasingly important in the evolution of the power grid since demand no longer necessarily determines system supply but is now more closely constrained by generation profiles. Demand response can offer energy flexibility services across wholesale and balancing markets. Different applications have focused on the Internet of Things in demand response to assist customers, aggregators and utility companies to manage the energy consumption and energy usage through the adjustment of consumer behaviour. Even though there is extensive work in the literature regarding the potential of the commercial and the residential building sectors to provide flexibility, to date there is no standardised framework to evaluate this flexibility in a customer-tailored way. At the same time, demand response events may affect occupant comfort expectations hindering the utilisation of flexibility that building energy systems can provide. In this research, the integration of machine learning algorithms into building control systems is investigated, in order to unify the monitoring and control of the separate systems under a holistic approach. This will allow the operation of the systems to be optimised with respect to reducing their energy consumption and their environmental footprint in tandem with the maximisation of flexibility, while maintaining occupant comfort.

A multiplex, multi-timescale model approach for economic and frequency control in power grids

“…In addition, ILC is applied to an uninterruptible power supply 6 and for optimal residential load scheduling 44 . In building automation, data-driven methods for demand response in the residential building sector are taken into account 45 ; ILC also addresses frequency control with high penetration of wind integration 46 ; it is further applied to energy management in electric vehicles 47 . Hence, most of the literature combining energy management and ILC focus on single nodes in a grid without emphasis on the overall grid perspective.…”

Iterative learning control in prosumer-based microgrids with hierarchical control

“…Aamir et al (2016) use ILC for an uninterruptible power supply and Chai et al (2016) for optimal residential load scheduling. In building automation, Bampoulas et al (2019) are using data-driven methods for demand response in the residential building sector, Vázquez-Canteli and Nagy (2019) give a review on reinforcement learning for demand response and Yan et al (2010) apply ILC to large-scale heating, ventilating and air-conditioning systems. In Guo et al (2015), ILC is used for frequency control of power grids with high penetration of wind integration.…”

Characterisation and Quantification of Energy Flexibility in the Residential Sector

“…Considering the large number of sites involved, the practical evaluation of energy flexibility would require the extension of the considered case-specific approach to a generic methodology. This methodology will aim to characterise and quantify energy flexibility by using datadriven methods, as described thoroughly in (Bampoulas et al 2019).…”