Control Of Electric Water Heaters Research Articles

Large-Scale Modeling and DR Control of Electric Water Heaters With Energy Star and CTA-2045 Control Types in Distribution Power Systems

This article proposes a generalized energy storage model for battery energy storage systems, electric water heaters (EWH), and heating, ventilation, and air-conditioning (HVAC) systems to enable demand response control complying to Energy Star and CTA-2045 standards. The demand response control has been implemented in the DER integration testbed, which was originally developed by Electric Power Research Institute, to demonstrate that the “energy content” and “energy take” for BESS and EWH with mixing valve technology are comparable for typical residential ratings. A distribution power system was modeled using the modified IEEE 123-bus feeder system, measured residential loads, and EWH power simulated based on realistic hot water draws from CBECC-Res software. The demand response control, which complies to CTA-2045 standards was implemented to the EWHs considering the energy take values. Results demonstrate that the EWHs can be controlled to postpone the peak power at the distribution system level and provide a large amount of energy storage, while maintaining system robustness. The impact on occupant comfort was also analyzed.

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

In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy.

Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering

Previous studies have shown that electric water heaters (EWHs) have strong potential in demand-side management applications more precisely because they offer energy storage capability, so, can be employed as shift loads. However, the challenge of EWH curtailment strategies is to minimize the impact on the hot water availability while shaving the peak of consumption during critical periods. The success of such strategies depends highly on the knowledge of the consumption behavior of each user. Thus, appropriated modeling and consumption analysis could yield better management strategies. This study proposes an electric water heater control strategy based on the dynamic programming and power consumption profile classification. An adaptive clustering process allows recognizing the clients who contribute to the highest power consumption during the peak periods. The analysis and simulation indicate that an appropriate control on the group of users could be implemented to reduce the peak demand and to meet the hot water demand. A k-means clustering algorithm has been used for cluster analysis. The silhouette method has been applied to estimate the appropriate number of clusters.

Active Demand-Side Management System to Facilitate Integration of RES in Low-Voltage Distribution Networks

This paper presents a demand-side management system designed for areas with high penetration of renewable energy sources. The system is based on the centralized direct control of electric water heaters (EWHs) with the objective to find the optimal dispatch of the EWHs so that the peak imports and exports of the controlled area are minimized. The EWH dispatch is computed utilizing two-stage optimization: the first stage computes the EWH load distribution on a longer time horizon, whereas the second stage corrects the dispatch in real time based on the instantaneous import or export. In cooperation with a local distribution system operator, a pilot installation of the proposed system has been implemented in a part of the distribution system of the Czech Republic. The simulations of a full participation scenario indicate that the system is capable of significant reduction of the peak imports and exports as well as the reduction of the overall energy transfers from and to the controlled area and associated power losses. The results also show that the proposed load control reduces voltage fluctuations in the controlled area.

A column generation method for optimal load management via control of electric water heaters

Electric water heaters have been the focus of several previous studies where they are viewed as energy storing devices to be considered for peak load shaving within device interruption based load management programs. It is well known that such controls, when applied indiscriminately, can cause payback phenomena which could actually worsen existing peak loads. Thus, optimal switching strategies must be devised. A column generation based optimization scheme which combines the advantages of linear programming and dynamic programming approaches, both previously proposed in load management literature, is presented. The method is successfully tested on a representative set of residential electric water heating load mixes. The results are analyzed in the paper, both from the computational and load management point of view.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Peak Shaving--A Way to Fight Rising Costs

The background of rising costs in the production, transmission, and distribution of electric power and energy is discussed and, in particular, the significant increases in the cost of generation. It discusses the adverse annual load pattern experienced by Buckeye Power, Inc. and the electric water heater control program of load management adopted by Buckeye to fight the rising costs of providing electric service.