Comfort, peak load and energy: Centralised control of water heaters for demand-driven prioritisation

Abstract

Recent advances in smart grid technology enable new approaches to address the problem of load control for domestic water heating. Since water heaters store energy, they are well-suited to load management. However, existing approaches have focused on the electrical supply side, ignoring the obvious link between the user and the grid: individual hot water consumption patterns. This paper proposes a load spreading approach in which water heaters compete for access to the heating medium. The proposed smart grid solution takes grid load limits, real-time temperature measurements, water usage patterns, individual user comfort, and heater meta-data into consideration. The scheduler only turns on the heaters with the highest level of need, but limits the number of on heaters to ensure that the grid load stays below a set limit for a set time. The method is evaluated by simulation against various heater set temperature levels, and for various load limits, and compared with ripple control and actual consumption measured in a field trial of 34 water heaters. The proposed algorithm reduces the load from 62kW to 20, 30, 40, and 50kW (vs. 106kW for full ripple control). The resulting number of unwanted cold events is fewer than for ripple control, and only slightly more than no control, while reducing the total energy by 14% from a user-optimised natural experiment.

Opportunities and Threats of Implementing Drain Water Heat Recovery Units in Poland

“…The level of the warm-water preparation system user safety and comfort should also be considered. Roux et al [42] noted that, regardless of the need to reduce water heating costs, consumers want to have guaranteed stable access to warm water. Cholewa et al [43], on the other hand, emphasized the need to seek solutions that not only enable the reduction of energy consumption, but are also quick to install.…”

How much energy can optimal control of domestic water heating save?

“…They can thus act as a buffer to store energy in the form of heat for delayed use when peak-shifting power scheduling schemes are applied (Du and Lu, 2011;Diduch et al, 2012;Shaad et al, 2012). Scheduling schemes need to account for the device's thermal behaviour, water draw patterns and customer comfort and convenience (Gholizadeh and Aravinthan, 2016;Roux et al, 2018). Thermal models for water heaters, and algorithms for their control, are described extensively in the literature for smart grid applications (Goh and Apt, 2004;Nehrir et al, 2007;Du and Lu, 2011;Lu et al, 2011;Diao et al, 2012;Diduch et al, 2012;Booysen et al, 2013;Boudreaux et al, 2014;Nel et al, 2016a;Kepplinger et al, 2015;Gholizadeh and Aravinthan, 2016;Zuniga et al, 2017;Ahmed et al, 2018;Hohne et al, 2018;Jack et al, 2018;Kapsalis et al, 2018;Lunacek et al, 2018;Kepplinger et al, 2019;Gerber et al, 2019).…”

“…In developing countries, where the user typically pays a time-independent flat fee per kWh, and not a tariff based on time-of-use or congestion, poorer users resort to schedule control simply to reduce their monthly bill, and possibly their environmental footprint (Nel et al, 2016b;Hohne et al, 2019), and not to shift peaks or to avoid congestion charges. In this situation it is the user who bears the burden of the increased energy usage that may result from any demand management schemes (Roux et al, 2018). And given the coal-intensive energy generation typical of these countries, any change in energy used usually implies a proportional change in greenhouse gasses emitted.…”

“…To make the optimal control problem tractable to be solved with dynamic programming, the electric water heater (EWH) thermal dynamics is modelled with a one-node lumped-parameter model. We envisage the use of a smart water heater remote controller to manage the temperature and heating schedule using a minute-based control cycle, such as the one developed by Brown (2015) and used by Roux et al (2018). We evaluate the performance of the proposed algorithm against the unscheduled thermostat-controlled case using one-minute simulation step time.…”

Non-invasive estimation of domestic hot water usage with temperature and vibration sensors

“…Based on known hot water demand patterns, individualised heating schedules and temperature set points can be used by SECs to reduce energy consumption due to water heating without affecting user comfort (Kepplinger et al, 2016;Booysen and Cloete, 2016;Jack et al, 2018;Armstrong et al, 2014). Widespread use of SECs can decrease the strain on the electricity supply grid through peak shaving by implementing demand-side management, which schedules when EWH heating elements can be active, while taking consumer comfort and hot water demand into account (Jack et al, 2018;Roux et al, 2017Roux et al, , 2018. SECs can also decrease the total domestic water consumption by providing users with detailed water consumption feedback.…”