This paper proposes a framework for contingency management using smart loads, which are realized through the emerging paradigm of the Internet of Things. The framework involves the system operator, the load serving entities (LSEs), and the end-users with smart home management systems, that automatically control adjustable loads. The system operator uses an efficient linear equation solver to quickly calculate the load curtailment needed at each bus to relieve congested lines after contingencies. Given this curtailment request, an LSE calculates a power allowance for each of its end-use customers. We approximate this large-scale, NP-hard, problem to a convex optimization for efficient computation. A smart home management system determines the appliances allowed to be used in order to maximize the user’s utility within the power allowance given by the LSE. Since the users’ utility depends on the nearfuture utilization of the appliances, we propose the Welch-based Reactive Appliance Prediction (WRAP) algorithm to predict the user behavior and maximize utility. The proposed framework is validated using the New England 39-bus test system. We show that power system components at risk can be quickly alleviated by adjusting a large number of small smart loads. Additionally, WRAP accurately predicts the users’ future behavior, minimizing the impact on their utility.

Collaborazione di Ricerca / Ciavarella, Stefano; Joo, Jhi Young; Silvestri, Simone. - (2015).

Collaborazione di Ricerca

CIAVARELLA, STEFANO;SILVESTRI, SIMONE
2015

Abstract

This paper proposes a framework for contingency management using smart loads, which are realized through the emerging paradigm of the Internet of Things. The framework involves the system operator, the load serving entities (LSEs), and the end-users with smart home management systems, that automatically control adjustable loads. The system operator uses an efficient linear equation solver to quickly calculate the load curtailment needed at each bus to relieve congested lines after contingencies. Given this curtailment request, an LSE calculates a power allowance for each of its end-use customers. We approximate this large-scale, NP-hard, problem to a convex optimization for efficient computation. A smart home management system determines the appliances allowed to be used in order to maximize the user’s utility within the power allowance given by the LSE. Since the users’ utility depends on the nearfuture utilization of the appliances, we propose the Welch-based Reactive Appliance Prediction (WRAP) algorithm to predict the user behavior and maximize utility. The proposed framework is validated using the New England 39-bus test system. We show that power system components at risk can be quickly alleviated by adjusting a large number of small smart loads. Additionally, WRAP accurately predicts the users’ future behavior, minimizing the impact on their utility.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/854820
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