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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.
If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments.
In the regime of incentive-based autonomous demand response, time dependent prices are typically used to serve as signals from a system operator to consumers. However, this approach has been shown to be problematic from various perspectives. We clarify these shortcomings in a geometric way and thereby motivate the use of power signals instead of price signals. The main contribution of this paper consists of demonstrating in a standard setting that power tracking signals can control flexibilities more efficiently than real-time price signals. For comparison by simulation, German renewable energy production and German standard load profiles are used for daily production and demand profiles, respectively. As for flexibility, an energy storage system with realistic efficiencies is considered. Most critically, the new approach is able to induce consumptions on the demand side that real-time pricing is unable to induce. Moreover, the pricing approach is outperformed with regards to imbalance energy, peak consumption, storage variation, and storage losses without the need for additional communication or computation efforts. It is further shown that the advantages of the optimal power tracking approach compared to the pricing approach increase with the extent of the flexibility. The results indicate that autonomous flexibility control by optimal power tracking is able to integrate renewable energy production efficiently, has additional benefits, and the potential for enhancements. The latter include data uncertainties, systems of flexibilities, and economic implementation.