## 7 Conclusions  
Electricity customers can obtain more and more interesting benefits from energy management and trading with the help of demand aggregators to overcome traditional and new DR barriers. The development of prosumers is a key factor in integrating renewables and increasing the decarbonization of our society. However, some control is needed to filter the unpredictability of renewables and its consequences to the overall power system in the future. The contribution of demand-side flexibility appears as a cornerstone for this deployment. The energy aggregator acts as an enrolling participant in DR and assists customers (and prosumers) in getting more profit from their flexible demand. Aggregators must achieve cost-effectiveness for DR. For this objective, they must develop customer participation in new and complex products to consolidate DR as a real option in the medium term and increase the small levels of participation and contribution to decrease peak demand (4-6%). To afford this new scenario, the development and refinement of new tools and methodologies that can explain and test the dynamic of loads and their environment are needed. EWHs, especially HPWH, have interesting characteristics for DR. First, the possibility to take more profit from their inherent capacity to store energy. They can act as \"virtual batteries\" and can balance renewables, whereas they contribute to energy efficiency and electrification (decarbonization). Finally, they have two conversion devices with different characteristics and dynamics of response (resistor and heat pumps), which can have advantages in different products and markets, especially in short-term response, which is basically unexplored for these segments. This paper presents a simulation model (based on PBLM and aggregation methodologies, which are well-known in conventional DR) that can be adapted to be used in several DR policies and scenarios with different time horizons and scope, from a very fast response considering the delay of the compressor to a fast response considering stratification and hybrid switching of heat sources and a medium-term response through the change of storage patterns to follow renewables. The advantages of a grey-box approach such as PBLM are the universality of the model (suitable for several markets and policies), the use of well-known individual models (PBLM), and the easy and fast interpretation of results to react to DR control signals (flexibility of response) while maintaining comfort to ensure \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Indicator** & **Baseline** & \(\mathbf{-10\%}\) & \(\mathbf{-20\%}\) & \(\mathbf{-30\%}\) & \(\mathbf{-40\%}\) & \(\mathbf{-50\%}\) \\ \hline Total demand (kWh) & 159.6 & 168.9 & 165.7 & 169.0 & 165.4 & 171.3 \\ Maximum peak (kW) & 18.9 & 17.8 & 16.0 & 14.8 & 12.4 & 12.3 \\ Real peak reduction (\%) & - & \(-5.8\%\) & \(-15.0\%\) & \(-21.8\%\) & \(-34.4\%\) & \(-34.6\%\) \\ Temperature max (\({}^{\circ}\)C) & 58.9 & 58.5 & 57.8 & 57.6 & 57.1 & 58.1 \\ Temperature min (\({}^{\circ}\)C) & 54.0 & 53.7 & 53.0 & 52.8 & 51.9 & 51.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of HPWH to peak shaving strategy. Figure 16: DR control of HPWH control group (QHCG) to meet the peak shaving target (**a**) demand and (**b**) average temperature of the mixed water zone (X\({}_{2}\)). the satisfaction and engagement of customers in DR. The proposed model shows good accuracy in the evaluation of water temperatures and demand (maximum MAPE errors around 2.35%). The potential of the model to evaluate the flexibility of these efficient loads has been tested and demonstrated in two scenarios. First, results for the increased self-consumption of renewables show an increase in self-consumption of around 15-30%, depending on the month. This is relevant because other flexible loads can increase the flexibility and complexity of DR policies for both customers and aggregators. This will be a topic for future research. The second scenario is peak clipping.