Learning Price-Elasticity of Smart Consumers in Power Distribution Systems

Demand Response is an emerging technology which will transform the power grid of tomorrow. It is revolutionary, not only because it will enable peak load shaving and will add resources to manage large distribution systems, but mainly because it will tap into an almost unexplored and extremely powerful pool of resources comprised of many small individual consumers on distribution grids. However, to utilize these resources effectively, the methods used to engage these resources must yield accurate and reliable control. A diversity of methods have been proposed to engage these new resources. As opposed to direct load control, many methods rely on consumers and/or loads responding to exogenous signals, typically in the form of energy pricing, originating from the utility or system operator. Here, we propose an open loop communication-lite method for estimating the price elasticity of many customers comprising a distribution system. We utilize a sparse linear regression method that relies on operator-controlled, inhomogeneous minor price variations, which will be fair to all the consumers. Our numerical experiments show that reliable estimation of individual and thus aggregated instantaneous elasticities is possible. We describe the limits of the reliable reconstruction as functions of the three key parameters of the system: (i) ratio of the number of communication slots (time units) per number of engaged consumers; (ii) level of sparsity (in consumer response); and (iii) signal-to-noise ratio.Comment: 6 pages, 5 figures, IEEE SmartGridComm 201

A Dynamic Market Mechanism for Integration of Renewables and Demand Response

The most formidable challenge in assembling a Smart Grid is the integration of a high penetration of renewables. Demand Response, a largely promising concept, is increasingly discussed as a means to cope with the intermittent and uncertain renewables. In this paper, we propose a dynamic market mech- anism that reaches the market equilibrium through continuous negotiations between key market players. In addition to incor- porating renewables, this market mechanism also incorporates a quantitative taxonomy of demand response devices, based on the inherent magnitude, run-time, and integral constraints of demands. The dynamic market mechanism is evaluated on an IEEE 118 Bus system, a high fidelity simulation model of the Midwestern United States power grid. The results show how the proposed mechanism can be utilized to determine combinations of demand response devices in the presence of intermittent and uncertain renewables with varying levels of penetration so as to result in a desired level of Social Welfare.This work was supported in part by the National Science Foundation grants ECCS-1135815 and EFRI-1441301