The increase in extreme weather conditions and greater penetration of intermittent renewable energy, such as solar and wind, have resulted in electric grid balancing challenges, and greenhouse gas emissions remain high during peak hours. Residential load flexibility programs help mitigate these challenges by shaping the electricity demand of major appliances to better match grid-level renewable electricity generation profiles. This study evaluates the load flexibility of a multi-function heat pump (MFHP) system responding to a dynamic price signal. The residential MFHP uses a single air-source heat pump outdoor unit to efficiently meet both water heating and space conditioning needs. The MFHP does not require electric resistance heaters for emergency heat or defrost, potentially avoiding the need for electrical panel upgrades commonly required when replacing gas appliances. A 14 kW (48 kBtu/h) MFHP system is installed in an occupied residential building in California. To achieve load flexibility, a rule-based control algorithm adjusts water heating and space cooling setpoints with the objective of minimizing electric energy cost in response to the dynamic price signal. Model-based control approaches require training data and forecasting, increasing the complexity and commissioning time. The simplicity of this rule-based control approach will be easier to adopt for widescale use because it has lower computational complexity, works with equipment from different manufacturers, and does not require in-depth programming and commissioning. The dynamic price signal used by the algorithm consists of a forecast of hourly prices for the next 24-hour period. The rule-based algorithm determines hourly setpoint schedules for the next 24-hour period based on the dynamic price signal for the water heating and space conditioning and communicates them to the MFHP thermostats.
| File Size : | 1
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| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 9 |
| Product Code(s) : | D-CH-24-C029 |
| Published : | 2024 |
