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This paper proposes a distributed direct load control scheme for large-scale residential demand response (DR) built on a two-layer communication-based control architecture. The lower-layer network is within each building, where the energy management controller (EMC) uses wireless links to schedule operation of appliances upon request according to a local power consumption target. The upper-layer network links a number of EMCs in a region whose aggregated demand is served by a load aggregator. The load aggregator wants the actual aggregated demand over this region to match a desired aggregated demand profile. Our approach utilizes the average consensus algorithm to distribute portions of the desired aggregated demand to each EMC in a decentralized fashion. The allocated portion corresponds to each building's aforementioned local power consumption target which its EMC then uses to schedule the in-building appliances. The result will be an aggregated demand over this region that more closely reaches the desired demand. Numerical results show that our scheme can alleviate the mismatch between the actual aggregated demand and the desired aggregated demand profile.

This paper proposes a Real-Time Pricing (RTP)-based power scheduling scheme as demand response for residential power usage. In this scheme, the Energy Management Controller (EMC) in each home and the service provider form a Stackelberg game, in which the EMC who schedules appliances' operation plays the follower level game, and the provider who sets the real-time prices according to current power usage profile plays the leader level game. The sequential equilibrium is obtained through the information exchange between them. Simulation results indicate that our scheme can not only save money for consumers, but also reduce peak load and the variance between demand and supply, while avoiding the "rebound" peak problem.

Abstract-We consider mechanisms to optimize electricity consumption both within a home and across multiple homes in a neighborhood. The homes are assumed to use energy management controllers (EMCs) to control the operation of some of their appliances. EMCs, which are a feature of the emerging SmartGrid, use both prices and user preferences to control power usage across the home. We first present a simple optimization model for determining the timing of appliance operation to take advantage of lower electricity rates during off-peak periods. We then demonstrate, using simulation, that the resulting solution may in fact be more peaky than the "non-scheduled" solution, thereby negating some of the benefits (for the utility) of offpeak pricing models. We then propose a distributed scheduling mechanism to reduce peak demand within a neighborhood of homes. The mechanism provides homes a guaranteed base level of power and allows them to compete for additional power to meet their needs. Finally, we introduce a more powerful EMC optimization model, based on dynamic programming, which, unlike our first optimization model, accounts for the potential for electricity capacity constraints.

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