Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Abstract

This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

“…Based on the formulated model and weather forecasting data, future steam consumption is estimated. To improve the performance of electric water heating systems in smart homes, Bo Lin et al have developed a model based on nonlinear autoregressive network with external input (NARX) using a neural network for electric water heaters to achieve the comfort requirement with minimum energy consumption [15]. A study on energy savings in building heating systems is presented in [14] by using the model predictive control (MPC) approach.…”

Exploring the Energy Flexibility of Electric Water Heaters

“…Taking into consideration that in the European Union and in the United States of America, domestic hot water heating accounts for 14% and 17% of the electricity consumed in the residential sector [3], respectively, the energy flexibility offered by Electric Water Heaters (EWH) is of significant importance. Such flexibility is provided thanks to the thermal energy storage capabilities of EWH, being its control normally associated to the development of systems aimed to improve self-consumption of photovoltaic [4,5] and wind [6,7] generation or to reduce electricity related costs [8][9][10][11][12][13][14][15].…”

“…The literature [4][5][6][7][8][9][10][11][12][13][14][15] shows that there is a wide variety of methodologies being developed to exploit the energy flexibility provided by EWH while bringing benefits to users depending on specific objectives. For instance, EWH operation has been defined using genetic algorithms [4] or mixed integer linear programming techniques [5] to improve photovoltaic self-consumption.…”

“…Regarding wind generation, external signals have been used both at individual and/or aggregated levels [6,7] to set the operation of EWH in order to increase matching between demand and the availability of the respective primary energy resource. Appliance commitment [8], dynamic programming [9], binary integer programming techniques [10], customer-centered heuristic [11], genetic algorithms [12], binary particle swarm optimization [13], reinforcement learning [9,14] and cost-optimal algorithms [15] have been used to support EWH operation and reduce associated electricity costs.…”

Financial Benefit Analysis of an Electric Water Heater with Direct Load Control in Demand Response

“…Actually, DR helps to modify the users' normal electricity consumption patterns with electricity price schedules patterns or incentive payments to keep lower electricity usage. A price-responsive DR management system is introduced in [6]. DR method is implemented traditionally in the industries and other large factories.…”