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Hedging strategies for load-serving entities in wholesale electricity markets

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arXiv 2017年

作者： Zhou, Datong P. Dahleh, Munther A. Tomlin, Claire J. Department of Mechanical Engineering University of California Berkeley United States Laboratory for Information and Decision Systems MITCambridge United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

— Load-serving entities which procure electricity from the wholesale electricity market to service end-users face significant quantity and price risks due to the volatile nature of electricity demand and quasi-fixed residential tariffs at which electricity is sold. This paper investigates strategies for load serving entities to hedge against such price risks. Specifically, we compute profit-maximizing portfolios of forward contract and call options as a function of uncertain aggregate user demand and wholesale electricity prices. We compare the profit to the case of Demand Response, where users are offered monetary incentives to temporarily reduce their consumption during periods of supply shortages. Using smart meter data of residential customers in California, we simulate optimal portfolios and derive conditions under which Demand Response outperforms call options and forward contracts. Our analysis suggests that Demand Response becomes more competitive as wholesale electricity prices *** work has been supported in part by the National Science Foundation under CPS:FORCES (CNS-1239166) and CEC Grant 15-083. Copyright © 2017, The Authors. All rights reserved.

关键词： Profitability

Eliciting private user information for residential demand response

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arXiv 2017年

作者： Zhou, Datong P. Balandat, Maximilian Dahleh, Munther A. Tomlin, Claire J. Department of Mechanical Engineering University of California Berkeley United States Laboratory for Information and Decision Systems Mit Cambridge United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

Residential Demand Response has emerged as a viable tool to alleviate supply and demand imbalances of electricity during times when the electric grid is strained. Demand Response providers bid reduction capacity into the wholesale electricity market by asking their customers under contract to temporarily reduce their consumption in exchange for a monetary incentive. This paper models consumer behavior in response to such incentives by formulating Demand Response in a Mechanism Design framework. In this auction setting, the Demand Response Provider collects the price elasticities of demand as bids from its rational, profit-maximizing customers, which allows targeting only the users most susceptible to incentives such that an aggregate reduction target is reached in expectation. We measure reductions by comparing the materialized consumption to the projected consumption, which we model as the "10-in-10"-baseline, the regulatory standard set by the California Independent System Operator. Due to the suboptimal performance of this baseline, we show, using consumption data of residential customers in California, that Demand Response Providers receive payments for "virtual reductions", which exist due to the inaccuracies of the baseline rather than actual reductions. Improving the accuracy of the baseline diminishes the contribution of these virtual reductions. Copyright © 2017, The Authors. All rights reserved.

关键词： Sales

How peer effects influence energy consumption

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arXiv 2017年

作者： Zhou, Datong P. Roozbehani, Mardavij Dahleh, Munther A. Tomlin, Claire J. Department of Mechanical Engineering University of California Berkeley United States Laboratory for Information and Decision Systems Mit Cambridge United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

This paper analyzes the impact of peer effects on electricity consumption of a network of rational, utilitymaximizing users. Users derive utility from consuming electricity as well as consuming less energy than their neighbors. However, a disutility is incurred for consuming more than their neighbors. To maximize the profit of the load-serving entity that provides electricity to such users, we develop a two-stage gametheoretic model, where the entity sets the prices in the first stage. In the second stage, consumers decide on their demand in response to the observed price set in the first stage so as to maximize their utility. To this end, we derive theoretical statements under which such peer effects reduce aggregate user consumption. Further, we obtain expressions for the resulting electricity consumption and profit of the load serving entity for the case of perfect price discrimination and a single price under complete information, and approximations under incomplete information. Simulations suggest that exposing only a selected subset of all users to peer effects maximizes the entity's profit. Copyright © 2017, The Authors. All rights reserved.

关键词： Profitability

Blind regression: Nonparametric regression for latent variable models via collaborative filtering 30

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Blind regression: Nonparametric regression for latent variab...

30th Annual Conference on Neural Information Processing systems, NIPS 2016

作者： Lee, Christina E. Li, Yihua Shah, Devavrat Song, Dogyoon Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We introduce the framework of blind regression motivated by matrix completion for recommendation systems: given m users, n movies, and a subset of user-movie ratings, the goal is to predict the unobserved user-movie ratings given the data, i.e., to complete the partially observed matrix. Following the framework of non-parametric statistics, we posit that user u and movie i have features x1(u) and x2 (i) respectively, and their corresponding rating y(u, i) is a noisy measurement of f(x1(u), x2(i)) for some unknown function f. In contrast with classical regression, the features x = (x1(u), x2(i)) are not observed, making it challenging to apply standard regression methods to predict the unobserved ratings. Inspired by the classical Taylor's expansion for differentiable functions, we provide a prediction algorithm that is consistent for all Lipschitz functions. In fact, the analysis through our framework naturally leads to a variant of collaborative filtering, shedding insight into the widespread success of collaborative filtering in practice. Assuming each entry is sampled independently with probability at least max(m-1+δ,n-1/2+δ) with δ > 0, we prove that the expected fraction of our estimates with error greater than e is less than γ2/ϵ2 plus a polynomially decaying term, where γ2 is the variance of the additive entry-wise noise term. Experiments with the MovieLens and Netflix datasets suggest that our algorithm provides principled improvements over basic collaborative filtering and is competitive with matrix factorization methods. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Collaborative filtering

On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop

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On the nonexistence of event-based triggers that preserve Ga...

American control Conference

作者： Enoch Kung Junfeng Wu Dawei Shi Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Automatic Control The Royal Institute of Technology (KTH) State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509045839

State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

关键词： State estimation Packet drops Event-based triggers State estimation Event-based Filters State variable TRIGGERS Gauss

Two short proofs regarding the logarithmic least squares optimality in Chen, K., Kou, G., Tarn, J. M., Song, Y. (2015): Bridging the gap between missing and inconsistent values in eliciting preference from pairwise comparison matrices, Annals of Operations Research 235(1):155–175

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Annals of Operations Research 2017年第1期253卷 707-708页

作者： Sándor Bozóki Laboratory on Engineering and Management Intelligence Research Group of Operations Research and Decision Systems Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Department of Operations Research and Actuarial Sciences Corvinus University of Budapest Budapest Hungary

Semidefinite approximations of the matrix logarithm

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arXiv 2017年

作者： Fawzi, Hamza Saunderson, James Parrilo, Pablo A. Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Electrical and Computer Systems Engineering Monash University Victoria3800 Australia Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

The matrix logarithm, when applied to Hermitian positive definite matrices, is concave with respect to the positive semidefinite order. This operator concavity property leads to numerous concavity and convexity results for other matrix functions, many of which are of importance in quantum information theory. In this paper we show how to approximate the matrix logarithm with functions that preserve operator concavity and can be described using the feasible regions of semidefinite optimization problems of fairly small size. Such approximations allow us to use off-the-shelf semidefinite optimization solvers for convex optimization problems involving the matrix logarithm and related functions, such as the quantum relative entropy. The basic ingredients of our approach apply, beyond the matrix logarithm, to functions that are operator concave and operator monotone. As such, we introduce strategies for constructing semidefinite approximations that we expect will be useful, more generally, for studying the approximation power of functions with small semidefinite representations. Copyright © 2017, The Authors. All rights reserved.

关键词： Convex optimization

Layered black-box, behavioral interconnection perspective and applications to problems in communications, Part I: I.I.D. Sources

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arXiv 2017年

作者： Agarwal, Mukul Mitter, Sanjoy Sahai, Anant Mumbai Maharashtra410210 India Laboratory for information and decision systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139-4307 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720-1770 United States

In this paper, the problem of communication over an essentially unknown channel, which is known to be able to communicate a source to a destination to within a certain distortion level, is considered from a behavioral, interconnection view-point. Rates of reliable communication are derived and source-channel separation for communication with fidelity criteria is proved. The results are then generalized to the unicast multi-user setting under certain assumptions. Other applications of this problem problem which follow from this perspective are discussed. Copyright © 2017, The Authors. All rights reserved.

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PSSE Redux: Convex relaxation, decentralized, robust, and dynamic approaches

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arXiv 2017年

作者： Kekatos, Vassilis Wang, Gang Zhu, Hao Giannakis, Georgios B. Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States