MPC-based interval number optimization for electric water heater scheduling in uncertain environments

Wang, Jidong; Li, Chenghao; Che, Yanbo; Zhou, Yue; Li, Yinqi

doi:10.1007/s11708-019-0644-9

Cited by 3 publications

(13 citation statements)

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“…Residential WHs have been traditionally considered as suitable candidates in many load incentive-based DR applications [20,32,33,43]. The topic of WH load shifting in response to variable price signals is also broadly covered in the literature [34][35][36][37].…”

Section: E T W Hmentioning

confidence: 99%

“…The authors accounted for the presence of the tap mixer and represented the user comfort as a cumulative di erence between the supplied tap water temperature and the user-desired tap water temperature (similar to [54]). The mixed integer linear problem thus contained a set of linear variables that represented the tank water temperature, and the set of binary variables representing the on/o control actions An interval-based approach to deal with uncertainties in hot water demand, ambient temperature, and real-time price of electricity was employed by [36] for WH scheduling. The model predictive control (MPC) algorithm based on the forecasts and newly updated information was devised for minimizing the electricity bill of a consumer.…”

Section: P C W Hmentioning

confidence: 99%

“…There is a numerous number of existing solutions for incentive-based [20,32,33] and price-based DR of WHs [34][35][36]. Most of the solutions ensure user comfort by keeping the tank water temperature above a prede ned minimum threshold [37][38][39].…”

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confidence: 99%

“…As one can conclude from two tables, the number of deterministic algorithms overwhelms the number of stochastic, arti cial intelligent (AI) and engineering-based solutions. This can be explained by di culties with obtaining the probability distributions or fuzzy membership functions [36], big amount of data required for reinforcement learning (RL) [37] and big computational costs of stochastic solutions [60]. Although most of the solutions are heavily a ected by an unexpected variation of input variables (e.g., an unforseen intense hot water demand), some of them yet speci cally consider such uncertainties [34,36,58,60].…”

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confidence: 99%

“…As contrasted, more engineering solutions [33,47,51] are usually driven by iterative algorithms to adjust controlled signals based on a simple logic. In the reviewed literature, incentive-based approaches pursue di erent utility-oriented goals like peak load reduction [20,43,46], support of frequency [33] and voltage [32] regulation or combination of them [44], whereas the price-based solutions are mainly focused on reduction of a consumer electricity bill [34][35][36]. A couple of strategies use WH load shifting outside high-price periods to diminish stress on the grid [66,68].…”

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Balancing between energy, comfort and money: in control of residential electric tank water heaters

Belov¹

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Samenvattingxi 2 1 I the umbrella of DSM programs. Demand response can give numerous bene ts both for participants and market-wide. Key bene ts can be summarized as follows [6][7][8][9]:of European buildings is expected to decrease in the future because of a renovation of the current building stock, whereas the use of a domestic hot water is expected to increase. As one of the most energy demanding end-uses in a household, water heating deserves special consideration. Water heating systems can have di erent con guration, equipment e ciency, fuel use and pipe insulation. There are di erent ways to con gure a domestic water heating system. The two most popular ways are the tank recharge and instantaneous water heating [18]. Other con gurations include mixed energy systems that use electricity combined with other energy sources (e.g., gas) to produce hot water also coupled with space heating and(or) solar collectors. The water heating usually takes place at one central point, however, sometimes there can be multiple separate heating units at home (e.g., a small tank heater for the kitchen).The breakdown by fuel of the residential energy spent on water heating in 2017 is represented in Figure 1.3. The chart indicates that the biggest fraction of European domestic water heating is powered by gas, while electric water heating accounts for the second biggest share (≈ 20%).

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confidence: 99%

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Balancing between energy, comfort and money: in control of residential electric tank water heaters

Belov¹

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Optimal scheduling method of regenerative electric heating for emergency residential building heating: An affine arithmetic‐based model predictive control approach

Zhang

et al. 2022

IET Energy Syst Integration

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Residential heating faces the challenge of heating interruption when an electric power outage occurs. As a promising heating electrification form, regenerative electric heating (REH) equipped with thermal energy storage (TES) has the flexibility of maintaining the building indoor temperature within the desired range during power outages and reducing the operation cost during normal operation states. However, the allocation and scheduling of the limited thermal energy in TES for the above two purposes is impacted by many uncertainties, for example, outdoor temperature, irradiation, and duration of power outages. Overestimation of the thermal energy required for power outages in the TES can improve the heating supply reliability, but it will also increase the REH operation cost to some extent, and vice versa. To address this problem, an affine arithmetic‐based model predictive control approach (AA‐MPC) for an optimal REH scheduling method is proposed to balance the heating supply reliability during power outages and operation economy of REH at the same time. An REH‐based residential building energy system model is developed to describe the building thermal load associated with the outdoor temperature and irradiation. Then, the required thermal energy for emergency building heating provided by the hot water tank (HWT) is determined using the minimum thermal demand of residents during a power outage, which is constrained by the minimum comfort temperature threshold. Based on this, an AA‐MPC approach that takes the thermal energy for emergency building heating as a time‐varying constraint of the HWT is developed to determine the optimal REH scheduling that considers emergency residential building heating under the above uncertainties. Numerical studies show that the proposed method can maintain minimum thermal demand for at least 2 h when a power outage occurs under uncertainties. At the same time, it can reduce the impact of uncertainties on the operation cost and reduce economic problems caused by emergency heating to a certain extent. Compared to the interval arithmetic‐based model predictive control approach, the operation cost intervals of the proposed method are reduced by 57.3%, 0.3%, and 32.5% under low, middle, and high prediction error levels respectively.

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Deep Reinforcement Learning for Autonomous Water Heater Control

et al. 2021

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Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

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MPC-based interval number optimization for electric water heater scheduling in uncertain environments

Cited by 3 publications

References 25 publications

Balancing between energy, comfort and money: in control of residential electric tank water heaters

Balancing between energy, comfort and money: in control of residential electric tank water heaters

Optimal scheduling method of regenerative electric heating for emergency residential building heating: An affine arithmetic‐based model predictive control approach

Deep Reinforcement Learning for Autonomous Water Heater Control

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