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Grid-scale electrical energy storage (EES) is a key component in cost-effective transition scenarios to renewable energy sources. The requirement of scalability favors EES approaches such as pumped-storage hydroelectricity (PSH) or compressed-air energy storage (CAES), which utilize the cheap and abundant storage materials water and air, respectively. To overcome the site restriction and low volumetric energy densities attributed to PSH and CAES, liquid-air energy storage (LAES) has been devised; however, it suffers from a rather small round-trip efficiency (RTE) and challenging storage conditions. Aiming to overcome these drawbacks, a novel system for EES is developed using solidified air (i.e., clathrate hydrate of air) as the storable phase of air. A reference plant for solidified-air energy storage (SAES) is conceptualized and modeled thermodynamically using the software CoolProp for water and air as well as empirical data and first-order approximations for the solidified air (SA). The reference plant exhibits a RTE of 52% and a volumetric storage density of 47 kWh per m3 of SA. While this energy density relates to only one half of that in LAES plants, the modeled RTE of SAES is comparable already. Since improved thermal management and the use of thermodynamic promoters can further increase the RTEs in SAES, the technical potential of SAES is in place already. Yet, for a successful implementation of the concept - in addition to economic aspects - questions regarding the stability of SA must be first clarified and challenges related to the processing of SA resolved.
Hot water heat pumps are well suited for demand side management, as the heat pump market faced a rapid growth in the past years with the trend to decentralized domestic hot water use. Sales were accelerated through wants and needs of energy conservation, energy efficiency, and less restrictive rules regarding Legionella. While in literature the model predictive control potential for heat pumps is commonly shown in simulations, the share of experimental studies is relatively low. To this day, experimental studies considering solely domestic hot water use are not available. In this paper, the realistic achievable model predictive control potential of a hot water heat pump is compared to the standard hysteresis control, to provide an experimental proof. We show for the first time, how state-of-the-art approaches (model predictive control, system identification, live state estimation, and demand prediction) can be transferred from electric hot water heaters to hot water heat pumps, combined, and implemented into a real-world hot water heat pump setup. The optimization approach, embedded in a realistic experimental setting, leads to a decrease in electric energy demand and cost per unit electricity by approximately 12% and 14%, respectively. Further, an increase in efficiency by approximately 13% has been achieved.