arXiv subjects

Eilyan Bitar

Publications and source records attributed to Eilyan Bitar.

27 records · Page 2Linked to original sources

arXiv2018

Risk-Sensitive Learning and Pricing for Demand Response

We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of $T$ days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over $T$ days that is at most $O(\sqrt{T}\log(T))$, relative to an oracle pricing policy that knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense.

cs.LG↗

arXiv2018

Robust AC Optimal Power Flow

There is a growing need for new optimization methods to facilitate the reliable and cost-effective operation of power systems with intermittent renewable energy resources. In this paper, we formulate the robust AC optimal power flow (RAC-OPF) problem as a two-stage robust optimization problem with recourse. This problem amounts to a nonconvex infinite-dimensional optimization problem that is computationally intractable, in general. Under the assumption that there is adjustable generation or load at every bus in the power transmission network, we develop a technique to approximate RAC-OPF from within by a finite-dimensional semidefinite program by restricting the space of recourse policies to be affine in the uncertain problem data. We establish a sufficient condition under which the semidefinite program returns an affine recourse policy that is guaranteed to be feasible for the original RAC-OPF problem. We illustrate the effectiveness of the proposed optimization method on the WSCC 9-bus and IEEE 14-bus test systems with different levels of renewable resource penetration and uncertainty.

math.OC↗

arXiv2017

An Online Learning Approach to Buying and Selling Demand Response

We adopt the perspective of an aggregator, which seeks to coordinate its purchase of demand reductions from a fixed group of residential electricity customers, with its sale of the aggregate demand reduction in a two-settlement wholesale energy market. The aggregator procures reductions in demand by offering its customers a uniform price for reductions in consumption relative to their predetermined baselines. Prior to its realization of the aggregate demand reduction, the aggregator must also determine how much energy to sell into the two-settlement energy market. In the day-ahead market, the aggregator commits to a forward contract, which calls for the delivery of energy in the real-time market. The underlying aggregate demand curve, which relates the aggregate demand reduction to the aggregator's offered price, is assumed to be affine and subject to unobservable, random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the aggregator, we investigate the extent to which the aggregator might dynamically adapt its offered prices and forward contracts to maximize its expected profit over a time window of $T$ days. Specifically, we design a dynamic pricing and contract offering policy that resolves the aggregator's need to learn the unknown demand model with its desire to maximize its cumulative expected profit over time. In particular, the proposed pricing policy is proven to incur a regret over $T$ days that is no greater than $O(\log(T)\sqrt{T})$.

eess.SY↗

arXiv2017

Decentralized Stochastic Control of Distributed Energy Resources

We consider the decentralized control of radial distribution systems with controllable photovoltaic inverters and energy storage resources. For such systems, we investigate the problem of designing fully decentralized controllers that minimize the expected cost of balancing demand, while guaranteeing the satisfaction of individual resource and distribution system voltage constraints. Employing a linear approximation of the branch flow model, we formulate this problem as the design of a decentralized disturbance-feedback controller that minimizes the expected value of a convex quadratic cost function, subject to robust convex quadratic constraints on the system state and input. As such problems are, in general, computationally intractable, we derive a tractable inner approximation to this decentralized control problem, which enables the efficient computation of an affine control policy via the solution of a finite-dimensional conic program. As affine policies are, in general, suboptimal for the family of systems considered, we provide an efficient method to bound their suboptimality via the optimal solution of another finite-dimensional conic program. A case study of a 12 kV radial distribution system demonstrates that decentralized affine controllers can perform close to optimal.

math.OC↗

arXiv2017

Financial Storage Rights in Electric Power Networks

The decreasing cost of energy storage technologies coupled with their potential to bring significant benefits to electric power networks have kindled research efforts to design both market and regulatory frameworks to facilitate the efficient construction and operation of such technologies. In this paper, we examine an open access approach to the integration of storage, which enables the complete decoupling of a storage facility's ownership structure from its operation. In particular, we analyze a nodal spot pricing system built on a model of economic dispatch in which storage is centrally dispatched by the independent system operator (ISO) to maximize social welfare. Concomitant with such an approach is the ISO's collection of a merchandising surplus reflecting congestion in storage. We introduce a class of tradable electricity derivatives ---referred to as financial storage rights (FSRs)--- to enable the redistribution of such rents in the form of financial property rights to storage capacity; and establish a generalized simultaneous feasibility test to ensure the ISO's revenue adequacy when allocating such financial property rights to market participants. Several advantages of such an approach to open access storage are discussed. In particular, we illustrate with a stylized example the role of FSRs in synthesizing fully hedged, fixed-price bilateral contracts for energy, when the seller and buyer exhibit differing intertemporal supply and demand characteristics, respectively.

math.OC↗

arXiv2016

The Marginal Value of Networked Energy Storage

We consider the problem of characterizing the locational marginal value of energy storage capacity in electric power networks with stochastic renewable supply and demand. The perspective taken is that of a system operator, whose objective is to minimize the expected cost of firm supply required to balance a stochastic net demand process over a finite horizon, subject to transmission and energy storage constraints. The value of energy storage capacity is defined in terms of the optimal value of the corresponding constrained stochastic control problem. It is shown to be concave and non decreasing in the vector of location-dependent storage capacities -- implying that the greatest marginal value of storage is derived from initial investments in storage capacities. We also provide a characterization of said marginal value, which reveals its explicit dependency on a specific measure of nodal price variation. And, more generally, we derive an upper bound on the locational marginal value of energy storage capacity in terms of the total variation of the corresponding nodal price process, and provide conditions under which this bound is tight.

math.OC↗

arXiv2016

Deadline Differentiated Pricing of Deferrable Electric Loads

A large fraction of the total electric load is comprised of end-use devices whose demand for energy is inherently deferrable in time. Of interest is the potential to leverage on such latent flexibility in demand to absorb variability in power supplied from intermittent renewable generation. The challenge, however, lies in designing incentives to reliably induce the desired response in demand. With an eye to electric vehicle charging, we propose a novel forward market for differentiated electric power services, where consumers consent to deferred service of pre-specified loads in exchange for a reduced per-unit price for energy. The longer a consumer is willing to defer, the larger the reduction in price. The proposed forward contract provides a guarantee on the aggregate quantity of energy to be delivered by a consumer-specified deadline. Under the earliest-deadline-first (EDF) scheduling policy, which is shown to be optimal for the supplier, we explicitly characterize a non-discriminatory, deadline-differentiated pricing scheme that yields an efficient competitive equilibrium between the supplier and consumers. We further show that this efficient pricing scheme, in combination with EDF scheduling, is incentive compatible (IC) in that every consumer would like to reveal her true deadline to the supplier, regardless of the actions taken by other consumers.

math.OC↗

arXiv2014

Nondegeneracy and Inexactness of Semidefinite Relaxations of Optimal Power Flow

The Optimal Power Flow (OPF) problem can be reformulated as a nonconvex Quadratically Constrained Quadratic Program (QCQP). There is a growing body of work on the use of semidefinite programming relaxations to solve OPF. The relaxation is exact if and only if the corresponding optimal solution set contains a rank-one matrix. In this paper, we establish sufficient conditions guaranteeing the nonexistence of a rank-one matrix in said optimal solution set. In particular, we show that under mild assumptions on problem nondegeneracy, any optimal solution to the semidefinite relaxation will have rank greater than one, if the number of equality and active inequality constraints is at least twice the number of buses in the network. The sufficient condition holds for arbitrary network topologies (including tree networks). We empirically evaluate the practical implications of these results on several test cases from the literature.

math.OC↗

arXiv2014

An Abrupt Change Detection Heuristic with Applications to Cyber Data Attacks on Power Systems

We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.

math.DS↗