Abstract-In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified.Index Terms-Smart grid, user inconvenience, peak load, customer engagement plan, demand response. NOMENCLATURE KNumber of operable states of thermostat loads.Number of operable states of thermostat load i of customer j. k Index of each operable state of thermostat load. J Number of customers in the residential community. I Set of flexible loads for which customer engagement plans are defined. I T Set of thermostat loads for which customer engagement plans are defined. I S Set of shiftable loads for which customer engagement plans are defined.Set of thermostat loads used for cooling for which customer engagement plans are defined.Set of thermostat loads used for heating for which customer engagement plans are defined. Constant value representing the maximum temperature deviation for thermostat load i. △θ j,i Inconvenience severity experienced by customer j for thermostat load i. θ j,i (t) Output temperature of thermostat load i of customer j. θ Actual start time of shiftable load i of customer j.

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Abstract-In this paper we develop an algorithm for peak load reduction to reduce the impact of increased air conditioner usage in a residential smart grid community. We develop Demand Response Management (DRM) plans that clearly spell out the maximum duration as well as maximum severity of inconvenience. We model the air conditioner as a power throttling device and for any given DRM plan we study the impact of increasing the number of power states on the resulting peak load reduction. Through simulations, we find out that adding just one additional state to the basic ON/OFF model, which can throttle power to 50% of the rated air conditioner power, can result in significant amount of peak reduction. However, the peak load that can be reduced is diminishing with the increase in number of states. Furthermore, we also observe the impact of inconvenience duration and inconvenience severity in terms of peak load reduction. These observations can serve as useful guidelines for developing appropriate DRM plans.

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