Design and Implementation of an Interactive Interface for Demand Response and Home Energy Management Applications

Abstract

Demand response (DR) implementations have recently found wide application areas in the context of smart grids. The effectiveness of these implementations is primarily based on the willingness of end-users to be involved in such programs. In this paper, an interactive and user-friendly interface is presented in order to facilitate and accordingly to increase the participation of end-users in DR programs. The proposed interface has the capability of providing the targeted information about the DR events to end-users and system operators, as well as allowing end-users to interactively monitor and control the progress of their appliances. In addition to its benefits to system operators and thus to the improved operation of power systems, the proposed interface particularly aims to exploit the potential energy-related cost savings by providing the required information and resources to end-users via mobile phone. A separate interface apart from the mentioned end-user oriented interface has also been developed for the system operator to more effectively check the status of DR applications in detail. The capabilities of the proposed concept are evaluated in a real smart home in terms of various aspects.

Empowering the selection of demand response methods in smart homes: development of a decision support framework

“…Both the inputs and the interactions are specified for each phase of the framework. In the Planning phase, users state their objectives, potentially on the HEMS' (graphical) user interface (e.g., (Han et al 2014;Khadar et al 2017;Yener et al 2017)), as this interface is a suitable way to enable the user to transport his or her objectives into the framework. This phase occurs either on the initial use of the DR method or when an "Objective Event" occurs.…”

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

“…The battery SOC after EV charging completes should fulfill customers' demand, which is determined by: (10) where SOC 0 is the initial SOC of EV; L is the travel distance of EV (mile); E EV is the efficiency of driving (mile/kWh); and Q EV is the full capacity of battery (kWh).…”

“…For the aggregated WH model, the demand flow rate is randomly obtained from [26], which includes 18 kinds of demand flow rate for one day. The thermal resistance of the tank insulation obeys a random uniform distribution R~U (10,20). The tank volume obeys a random normal distribution V~N (40,6.272), whose range is approximately 20 to 65 gallons.…”

“…In recent years, it has been suggested to utilize the flexibility potential available from residential demand-side resources. Compared with traditional generation resources, DR resources have some advantages [10,11] in terms of operational control in the power system. Firstly, according to a report by the Federal Utility Regulatory Commission, demand response (DR) is capable of offsetting 20% of the peak demand in the US [12].…”

A Centralized Smart Decision-Making Hierarchical Interactive Architecture for Multiple Home Microgrids in Retail Electricity Market

“…Furthermore, the remarkable participation of householders whose houses are equipped with renewable energy resources and ES in demand response programs, while reducing carbon emissions would make an impact on the market as they are to reduce their cost [13]. So, for expanding these participators in demand-side management programs, the EMS needs an interactive and user-friendly interface with secure communication [14]. Moreover, H-MGs should be armed with a decision support tool for adopting their initial strategies [15] based on local optimization of DER operation and energy usage by a domestic energy management controller [16] that enable them to engage in the market eagerly.…”