RED WoLF hybrid energy storage system: Algorithm case study and green competition between storage heaters and heat pump

Abstract

Green house gases reduction is critical in current climate emergency and was declared as major target by United Nations. This manuscript proposes the progressive adaptive recursive multi threshold control strategy for hybrid energy storage system that combines thermal storage reservoirs, heat pumps, storage heaters, photovoltaic array and a battery. The newest control strategy is tested in numerical experiment against primal dual simplex optimisation method as benchmark and previous iterations of RED WoLF threshold approaches. The proposed algorithm allows improvement in reduction of CO2 emissions by 9% comparatively to RED WoLF double threshold approach and by 26% comparatively to RED WoLF single threshold approach. Besides, the proposed technique is at least 100 times faster than linear optimisation, making the algorithm applicable to edge systems. The proposed method is later tested in numerical experiment on two measured datasets from Luxembourg school and office, equipped with batteries and ground source heat pumps. The system allows the reduction of CO2 emission and improvement of self-consumption, size reduction of the photovoltaic array installed at the facilities by at least by half as well as substituting battery storage by thermal storage, reducing the initial investment to the system. Intriguingly, despite 3.6 times difference in efficiency between heat pumps and storage heaters, the system equipped with latter have potential to achieve similar performance in carbon reduction, suggesting that energy storage have more prominent carbon reduction effect, than the power consumption, making cheaper systems with storage heaters a possible alternative to heat pumps.

Keywords

• Hybrid energy storage
• Photovoltaic
• Artificial intelligence
• Green house gases
• Energy management