Intelligent Electric Water Heater Control with Varying State Information

The increasing share of renewable energy sources in the electricity grid results in a higher degree of uncertainty regarding electrical energy production. In response to this, flexibility of the demand has been proposed as part of the solution. An important source of flexibility available at the residential consumer side are thermostatically controlled loads (TCLs). In this paper the activation of this source of flexibility is achieved by applying batch reinforcement learning (BRL) to an electric water heater (EWH) in a Time of Use (ToU) setting. The cost performance of six BRL agents with six different state spaces is compared quantitatively. In every case, the BRL agent can successfully shift energy consumption within 20–25 days. The performance of an agent with access to multiple temperature sensors along the height of the EWH is comparable to the performance of an agent with access to only the highest temperature sensor. This indicates manufacturing costs related to sensors can be reduced while maintaining the same performance. Additionally, results show that the inclusion of a theoretical state of charge value in the state space increases performance by more than 8% compared to the performance of the other BRL agents. It is therefore argued that an estimation of the state of charge should be included in future work as it would increase cost performance.