Residential Demand Response Scheduling with Consideration of Consumer Preferences

Abstract

This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances "on" or "off" does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their "flexibility"; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction.

An Energy Box in a Cloud-Based Architecture for Autonomous Demand Response of Prosumers and Prosumages

“…In the case that the householder does not participate in any DR program, the electricity usage is the result of familiar habits. So, taking into account the statistical surveys reported in [21][22][23], the considered load demand profile is that reported in Figure 3 with We considered common household appliances: internal and external lightings, a personal computer, a TV set, a refrigerator, an air-conditioning system, a washing machine, a tumble dryer, a dishwasher and an electric vehicle. In the case that the householder does not participate in any DR program, the electricity usage is the result of familiar habits.…”

“…In the case that the householder does not participate in any DR program, the electricity usage is the result of familiar habits. So, taking into account the statistical surveys reported in [21][22][23], the considered load demand profile is that reported in Figure 3 with a dotted line. The daily energy consumption is 24.97 kWh.…”

Optimisation of photovoltaic system and storage

“…Developing scheduling algorithms taking these factors into account is therefore important. Jovanovic et al [5] proposed an energy scheduling framework that considers different types of households classified by family composition, status and number of occupants. The customer preferences when scheduling the hems activities were also considered, making the constraints of the pv and storage system more acceptable from the point of view of the customer.…”

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“…Jovanovic et al [15] proposed a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. The proposal takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills.…”