A novel system for providing explicit demand response from domestic natural gas boilers

Georgios I. Tsoumalis, Zafeirios N. Bampos, Pandelis N. Biskas, Stratos D. Keranidis, Polychronis A. Symeonidis and Dimitrios K. Voulgarakis

Applied Energy, 2022, vol. 317, issue C, No S0306261922004421

Abstract: This paper presents a novel, fully automated system for the implementation of explicit gas Demand Response in the Gas Balancing Market, with real-time control instructions provided to domestic gas boilers. The Demand Response is applied in both the upward and downward direction, enabling the respective gas consumer to provide both upward and downward Balancing Services to the Gas Transmission System Operator. The system targets at the welfare maximization of domestic gas consumers, i.e., the revenues attained by providing Balancing Services to the Gas Transmission System Operator minus the cost of gas supply, while maintaining the house’s indoor temperature within the residents’ comfort limits. Real-time data are derived from interconnected commercial thermostats and used by two Recurrent Neural Networks for each house, in order to attain forecasts for the indoor temperature change and the expected boiler’s modulation levels for the next future time intervals. Such forecasted condition changes within each house are then considered in an optimization model that results in the optimal instruction signal that must be provided to the boiler. The signal concerns the boiler operation level for the following 5 min duration. A Genetic Algorithm is employed for the optimization problem solution. The whole system is deployed using a containerized software architecture to ensure scalability and full-service availability. The implemented real-world tests exhibit that domestic consumers can increase their profits from both gas consumption minimization and from the provision of gas DR services in real-time.

Keywords: Artificial intelligence; Energy-aware systems; Gas demand response; Real time control; Gas consumption baseline; Genetic algorithm (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2022.119038

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