iDR

Abstract

Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data.In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers' houselevel consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider's DR objectives -such as reduction in peak load -while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR's capabilities are demonstrated using real-world house-level as well as appliance-level data.

Economic modelling and optimisation of smart grids using demand response techniques

“…So far, we have assumed that for each consumer the permissible reduction in consumption is expressed in the contract as a percentage aXŽ Õ of the optimal consumption. This was also the assumption made in [CGW+14] in order for the provider to indirectly enforce some limit to the inconvenience caused. However, it is plausible that even a small reduction of the energy consumed can cause great inconvenience, especially in cases where consumers appear to be less flexible to adjustments in their schedule.…”

“…• Optimal Consumption: Consumer has an optimal daily consumption vector and an associated optimal consumption J for the day considered. Following the approach of [LCL11] and [CGW+14], we assume that these are derived by maximizing the total net benefit of the consumer; see below.…”

“…are relevant factors for DR participation. Hence, any effective DR system should to consider the end consumers' electricity usage preferences and their demographic attributes [CGW+14].…”

“…In this section, we build on previous work of ours in collaboration with other researchers from AUEB and IBM Research India [CGW+14], which deals with incentives for single DR events, and present a methodology for designing efficient contract-based ADR programs taking into account consumers' preferences and external context, e.g., the weather. We design ADR programs according to which consumers are both compensated for a specified amount of energy curtailment and assigned an optimal schedule, using a consumer net benefit maximization approach.…”

“…An algorithm to select the optimal set of consumers to be targeted for DR and two accompanying policies that restrict the discomfort caused to consumers, thus providing a guarantee of how annoying DR will be and making participation more attractive. One of the policies was introduced in [LCL11] and employed in [CGW+14] and restricts the imposed reduction in consumption. The other policy is innovative and restricts the reduction in consumer's utility due to DR.…”