Demand response with heuristic control strategies for modulating heat pumps. (15th March 2019)

• Record Type: Journal Article
• Authors: Dengiz, Thomas; Jochem, Patrick; Fichtner, Wolf
• Is Part Of: Applied energy. Volume 238(2019)
• Page Start: 1346
• Page End: 1360
• Publication Date: 2019-03-15
• Subjects: Demand response -- Modulating heat pumps -- Heuristic control strategy -- Control and communication architecture -- Smart grid -- Thermal storages; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.12.008
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Control strategies for using flexibilities of modulating heat pumps are developed. A privacy preserving control and communication architecture is used. Combination of central and decentralized control approaches. Comparison to a conventional control and exact optimization approaches. Results reveal a good trade-off between quality and computational time. Abstract: The flexibility of electrical heating devices can contribute to overcoming the challenges caused by increasing shares of volatile renewable energy sources in the energy system. Especially modulating heat pumps are suitable for using intelligent control strategies that vary the pumps' power output based on demand response signals. In this paper, we define optimization problems for minimizing the heating costs and the surplus energy of a residential area, and we introduce novel heuristic control strategies for modulating heat pumps to solve these problems. The heuristic control strategies make use of a privacy preserving control and communication architecture that combines central and decentralized control approaches. All buildings use an underfloor heating system and a domestic hot water tank as thermal storages. Compared to a conventional control strategy, the results show average cost reductions of between 4.1% and 13.3% for the cost minimization heuristics, and average improvements of between 38.3% and 52.6% for the surplus energy minimization heuristic. Contrary to approaches for finding the globally optimal solution, the introduced heuristic control strategies have significantly lower computational times and do not require perfect foresight regarding future demands and electricity generation.