Optimal operation scheduling of electric water heaters under dynamic pricing - Kapsalis, Vassilis; Hadellis, Loukas (Sustainable cities and society, 2017)
•An optimal algorithm for the operation scheduling of electric water heaters (EWHs) is presented.•The scheduling problem is modeled as a single-source shortest path problem of a weighted directed acyclic graph (WDAG).•The proposed algorithm achieves the minimization of the energy cost while maximizing the user’s comfort level.•It operates both in day-ahead and in real-time mode, adjusting the temperature set points of smart EWHs and the ON/OFF operation of conventional EWHs.•The performance of the scheduling algorithm is evaluated using simulation, under several realistic scenarios. This paper presents an optimal operation scheduling algorithm and evaluates its performance under a day-ahead real-time pricing (DA-RTP) tariff as well as under a combination of a DA-RTP and a time varying bound on power consumption (demand limit). The scheduling algorithm is based on Dijkstra’s algorithm and can be applied to continuously controlled loads (e.g., electric water heaters – EWHs, heating, ventilation and air conditioning systems – HVACs), which belong to the category of thermostatically controlled devices (TCAs). In this paper, the algorithm deals with the operation scheduling of EWHs, which are devices with a lot of flexibility as they possess high nominal power ratings and can be used as thermal energy buffers. User’s preferences regarding the preferred water temperature, the tolerable temperature range and the acceptable deviation from the minimum energy cost are mapped to the relative weights of energy and comfort cost of the objective function, which is strived to be minimized by the scheduling algorithm, in order to optimize energy cost as well as user’s comfort. Simulation results that verify the performance of the scheduling algorithm are presented under various realistic scenarios which study the effect of the upper temperature set point and the rated power on both the energy cost and the perceived comfort level. Any deviations between the real-time and the predicted hot water demand due to forecasting errors trigger a real-time adjustment of the operation scheduling. Regarding the implementation issue, the algorithm may be used for the control of smart EWHs by optimally adjusting the temperature set point at each time slot as well as for the direct ON/OFF control of the heating element of legacy EWHs.