摘要：Grid-edge technologies (GET) enable and amplify the impact of three emerging energy system trends: electrification, decentralisation, and digitalisation. Smart grid integrated heat pumps with thermal energy storage enable both the electrification of heating and decentralised demand response. Such power-to-heat technologies simultaneously decarbonise heating and facilitate the grid integration of more variable renewable electricity in a cost-effective manner. This may help to explore and exploit untapped wind generation potential. This study explores the flexibility potential of a domestic scale heat pump with thermal energy storage in a typical Irish home in December. The system is simulated to investigate demand-side flexibility and sensitivity to both heat pump and thermal storage capacities for three days with wind energy shares of 7%, 25%, and 60%. Using real-time electricity prices and optimising for operational cost, the implicit demand flexibility potential is quantified with different combinations of heat pump power and storage capacity. The results suggest that 33-100% of critical loads can be shifted dynamically to low-cost periods. Optimised system design depends on local climate, heat demand profile, optimisation horizon, and the type of heat pump. Optimisation with genetic algorithm yielded near-global optimal results approximately 40 times faster than with exhaustive enumeration.
其他摘要：Grid-edge technologies (GET) enable and amplify the impact of three emerging energy system trends: electrification, decentralisation, and digitalisation. Smart grid integrated heat pumps with thermal energy storage enable both the electrification of heating and decentralised demand response. Such power-to-heat technologies simultaneously decarbonise heating and facilitate the grid integration of more variable renewable electricity in a cost-effective manner. This may help to explore and exploit untapped wind generation potential. This study explores the flexibility potential of a domestic scale heat pump with thermal energy storage in a typical Irish home in December. The system is simulated to investigate demand-side flexibility and sensitivity to both heat pump and thermal storage capacities for three days with wind energy shares of 7%, 25%, and 60%. Using real-time electricity prices and optimising for operational cost, the implicit demand flexibility potential is quantified with different combinations of heat pump power and storage capacity. The results suggest that 33-100% of critical loads can be shifted dynamically to low-cost periods. Optimised system design depends on local climate, heat demand profile, optimisation horizon, and the type of heat pump. Optimisation with genetic algorithm yielded near-global optimal results approximately 40 times faster than with exhaustive enumeration.
