Development of advanced heat pump and thermal storage systems capable of cost effective demand side management

Abstract

The EU renewable energy directive (2009/28/EC) has set a range of national targets to collectively raise the average renewable share across Europe to 20% by 2020. Northern Ireland and Ireland have set their own ambitions on 40% electricity consumption from renewables by 2020 and 10% and 12% renewable heat by 2020 respectively for Northern Ireland and Ireland. Air source heat pumps have been identified as a major contributor towards reaching renewable heat targets and decarbonisation of heat. In order to maintain a working grid however system constraints result in limitations on wind generation often resulting in curtailment overnight in low demand hours with high wind. Full electrification of heat also gives rise to the necessity to increase generation and reinforce the electricity grid. A more cost-effective solution would be a more sophisticated method which would balance energy production and demand through energy storage. Using high temperature heat pumps domestic retrofit application is possible and when combined with energy storage, demand side management (DSM) control strategies, and user incentives it could be possible to provide a deployable means of balancing non-dispatchable wind generation with heating demand.

The research presents operational performance of a retrofit high temperature cascade airsource heat pump (ASHP) and thermal store which uses publicly available real-time and forecast electricity grid data for Northern Ireland using a low cost network connected Raspberry Pi computer to automate dynamic charging and discharging of a thermal store based on the state of the grid demand in real-time. The ASHP can reach a flow temperature of 80°C at 11kW nominal output, thus avoiding replacement of radiators in the occupied test house. The ASHP impact on peak grid demand is minimised by shifting thermal energy stored at low grid demand. Operational data shows the system is capable of providing 8.7% of the daily demand from storage but the overall system COP drops to 1.91 compared to a COP of 2.27 when heating the house directly. The impact is an increase of on average 1.2p/kWh thermal delivered to the house compared to direct only heating. However, the CO2e intensity of the combined system was 30 gCO2e/kWh thermal less intensive than a gas boiler tested in the same house.
The ASHP was also run as a hybrid coupled with the test house existing gas boiler in parallel mode (one heat source active at any one time only). In this situation the gas boiler effectively replaced the thermal store to provide DSM avoiding the HP impacting on peak grid demand. In addition, the hybrid was run using a real-time electricity market price signal as an vii alternative to a real-time grid demand signal. A small test was also run with the HP in series with the gas boiler. Due to the lack of a combined control, this mode resulted in reduced operating efficiency, however this could be removed with simple control optimisation.
The Raspberry Pi enabled low cost robust fully automated smart grid capability for relatively little cost. The occupants of the test house were unaware of the source of the heat and maintained the existing heat controls. Occupant thermal comfort was never compromised, and occupants were never encouraged or discouraged to use their heating system any differently to the gas boiler installation. The research shows that carbon intensity of domestic heating can be lowered when using an ASHP. Coupled with well-designed thermal storage the impact of electrified heating on the grid network can also be managed with simple and cheap technology without compromising on user thermal comfort. This will come with an efficiency compromise due to storage losses however if storage coincides with otherwise curtailed wind energy overall grid efficiency will increase.

Date of Award	Nov 2018
Original language	English
Sponsors	Department for the Economy
Supervisor	Neil Hewitt (Supervisor), Ming Jun Huang (Supervisor) & Caterina Brandoni (Supervisor)