arXiv subjects

Baosen Zhang

Publications and source records attributed to Baosen Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2017

Competition and Efficiency of Coalitions in Cournot Games with Uncertainty

We investigate the impact of coalition formation on the efficiency of Cournot games where producers face uncertainties. In particular, we study a market model where firms must determine their output before an uncertain production capacity is realized. In contrast to standard Cournot models, we show that the game is not efficient when there are many small firms. Instead, producers tend to act conservatively to hedge against their risks. We show that in the presence of uncertainty, the game becomes efficient when firms are allowed to take advantage of diversity to form groups of certain sizes. We characterize the tradeoff between market power and uncertainty reduction as a function of group size. In particular, we compare the welfare and output obtained with coalitional competition, with the same benchmarks when output is controlled by a single system operator. We show when there are $N$ firms present, competition between groups of size $Ω(\sqrt{N})$ results in equilibria that are socially optimal in terms of welfare and groups of size $Ω(N^{2/3})$ are socially optimal in terms of production. We also extend our results to the case of uncertain demand by establishing an equivalency between Cournot oligopoly and Cournot Oligopsony. We demonstrate our results with real data from electricity markets with significant wind power penetration.

cs.GT↗

arXiv2017

A Distributed Online Pricing Strategy for Demand Response Programs

We study a demand response problem from utility (also referred to as operator)'s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the utility to minimize its operational cost considering time-varying demand response targets and responses of consumers. We develop a joint online learning and pricing algorithm. In each time slot, the utility sends out a price signal to all consumers and estimates the cost functions of consumers based on their noisy responses. We measure the performance of our algorithm using regret analysis and show that our online algorithm achieves logarithmic regret with respect to the operating horizon. In addition, our algorithm employs linear regression to estimate the aggregate response of consumers, making it easy to implement in practice. Simulation experiments validate the theoretic results and show that the performance gap between our algorithm and the offline optimality decays quickly.

math.OC↗

arXiv2017

Pricing Residential Electricity Based on Individual Consumption Behaviors

The conventional practice of retail electric utilities is to aggregate customers geographically. The utility purchases electricity for its customers via bulk transactions on the wholesale market, and it passes these costs along to its customers, the end consumers, through their rate plan. Typically, all residential consumers are offered the same per unit rate plan, which leads to cost sharing. Some consumers use their electricity at peak hours, when it is more expensive on the wholesale market, and others consume mostly at off peak hours, when it is cheaper, but they all enjoy the same per unit rate through their utility. This paper proposed a method for the utility to segment a population of consumers on the basis of their individual consumption patterns. An optimal recruitment algorithm was developed to aggregate consumers into groups with a relatively low per unit cost of electricity on the wholesale market. It was then proposed that the utility should group together enough consumers to ensure an adequately low forecast error, which is related to risks it faces in wholesale market transactions. Finally, it was shown that by repeated application of this process, the utility could segment the entire population into groups and offer them differentiated rate plans based on their actual consumption behavior. These groupings are stable in the sense that no one consumer can unilaterally improve her outcome.

math.OC↗

arXiv2017

Participation of an Energy Storage Aggregator in Electricity Markets

An important function of aggregators is to enable the participation of small energy storage units in electricity markets. This paper studies two generally overlooked aspects related to aggregators of energy storage: i) the relationship between the aggregator and its constituent storage units and ii) the aggregator's effect on system welfare. Regarding i), we show that short-term outcomes can be Pareto-inefficient: all players could be better-off. In practice, however, aggregators and storage units are likely to engage in long rather than short-term relationships. Using Nash Bargaining Theory, we show that aggregators and storage units are likely to cooperate in the long-term. A rigorous understanding of the aggregator-storage unit relationship is fundamental to model the aggregator's participation in the market. Regarding ii), we first show that a profit-seeking energy storage aggregator is always beneficial to the system when compared to a system without storage, regardless of size or market power the aggregator may have. However, due to market power, a monopolist aggregator may act in a socially suboptimal manner. We propose a pricing scheme designed to mitigate market power abuse by the aggregator. This pricing scheme has several important characteristics: its formulation requires no private information, it incentivizes a rational aggregator to behave in a socially optimal manner, and allows for regulation of the aggregator's profit.

eess.SY↗

arXiv2017

Linear Estimation of Treatment Effects in Demand Response: An Experimental Design Approach

Demand response aims to stimulate electricity consumers to modify their loads at critical time periods. In this paper, we consider signals in demand response programs as a binary treatment to the customers and estimate the average treatment effect, which is the average change in consumption under the demand response signals. More specifically, we propose to estimate this effect by linear regression models and derive several estimators based on the different models. From both synthetic and real data, we show that including more information about the customers does not always improve estimation accuracy: the interaction between the side information and the demand response signal must be carefully modeled. In addition, we compare the traditional linear regression model with the modified covariate method which models the interaction between treatment effect and covariates. We analyze the variances of these estimators and discuss different cases where each respective estimator works the best. The purpose of these comparisons is not to claim the superiority of the different methods, rather we aim to provide practical guidance on the most suitable estimator to use under different settings. Our results are validated using data collected by Pecan Street and EnergyPlus.

math.OC↗

arXiv2017

Economic Dispatch Considering Spatial and Temporal Correlations of Multiple Renewable Power Plants

The correlations of multiple renewable power plants (RPPs) should be fully considered in the power system with very high penetration renewable power integration. This paper models the uncertainties, spatial correlation of multiple RPPs based on Copula theory and actual probability historical histograms by one-dimension distributions for economic dispatch (ED) problem. An efficient dynamic renewable power scenario generation method based on Gibbs sampling is proposed to generate renewable power scenarios considering the uncertainties, spatial correlation and variability (temporal correlation) of multiple RPPs, in which the sampling space complexity do not increase with the number of RPPs. Distribution-based and scenario-based methods are proposed and compared to solve the real-time ED problem with multiple RPPs. Results show that the proposed dynamic scenario generation method is much more consist with the actual renewable power. The proposed ED methods show better understanding for the uncertainties, spatial and temporal correlations of renewable power and more economical compared with the traditional ones.

math.OC↗

arXiv2017

An Optimal Treatment Assignment Strategy to Evaluate Demand Response Effect

Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a limited number of signals. In addition, customer behavior also depends on a large number of exogenous covariates. These two features lead to a high dimensional inference problem with limited number of observations. In this paper, we formulate this problem by using a multivariate linear model and adopt an experimental design approach to estimate the impact of demand response signals. We show that randomized assignment, which is widely used to estimate the average treatment effect, is not efficient in reducing the variance of the estimator when a large number of covariates is present. In contrast, we present a tractable algorithm that strategically assigns demand response signals to customers. This algorithm achieves the optimal reduction in estimation variance, independent of the number of covariates. The results are validated from simulations on synthetic data.

eess.SY↗

arXiv2017

A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation

A vital aspect in energy storage planning and operation is to accurately model its operational cost, which mainly comes from the battery cell degradation. Battery degradation can be viewed as a complex material fatigue process that based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery degradation assessment. However, the rainflow algorithm does not have a closed form, which makes the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed degradation model to be incorporated in different battery optimization problems and guarantees the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed degradation model in maximizing the battery operating profits as well as extending its lifetime.

math.OC↗

arXiv2017

Optimizing Curbside Parking Resources Subject to Congestion Constraints

To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of this network, canonical queueing network results are not available to us. We present a new kind of queueing network subject to customer rejection due to the lack of available servers. We provide conditions for such networks to be stable, a computationally tractable "single node" view of such a network, and show that maximizing the occupancy through price control of such queues, and subject to constraints on the allowable congestion between queues searching for an available server, is a convex optimization problem. We demonstrate an application of this method in the Mission District of San Francisco; our results suggest congestion due to drivers searching for parking stems from an inefficient spatial utilization of parking resources.

math.OC↗

arXiv2017

Optimal Regulation Response of Batteries Under Cycle Aging Mechanisms

When providing frequency regulation in a pay-for-performance market, batteries need to carefully balance the trade-off between following regulation signals and their degradation costs in real-time. Existing battery control strategies either do not consider mismatch penalties in pay-for-performance markets, or cannot accurately account for battery cycle aging mechanism during operation. This paper derives an online control policy that minimizes a battery owner's operating cost for providing frequency regulation in a pay-for-performance market. The proposed policy considers an accurate electrochemical battery cycle aging model, and is applicable to most types of battery cells. It has a threshold structure, and achieves near-optimal performance with respect to an offline controller that has complete future information. We explicitly characterize this gap and show it is independent of the duration of operation. Simulation results with both synthetic and real regulation traces are conducted to illustrate the theoretical results.

math.OC↗

arXiv2017

Blocking Transferability of Adversarial Examples in Black-Box Learning Systems

Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars. To further broaden the use of ML models, cloud-based services offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service tools as black-box systems. However, ML classifiers are vulnerable to adversarial examples: inputs that are maliciously modified can cause the classifier to provide adversary-desired outputs. Moreover, it is known that adversarial examples generated on one classifier are likely to cause another classifier to make the same mistake, even if the classifiers have different architectures or are trained on disjoint datasets. This property, which is known as transferability, opens up the possibility of attacking black-box systems by generating adversarial examples on a substitute classifier and transferring the examples to the target classifier. Therefore, the key to protect black-box learning systems against the adversarial examples is to block their transferability. To this end, we propose a training method that, as the input is more perturbed, the classifier smoothly outputs lower confidence on the original label and instead predicts that the input is "invalid". In essence, we augment the output class set with a NULL label and train the classifier to reject the adversarial examples by classifying them as NULL. In experiments, we apply a wide range of attacks based on adversarial examples on the black-box systems. We show that a classifier trained with the proposed method effectively resists against the adversarial examples, while maintaining the accuracy on clean data.

cs.LG↗

arXiv2017

Deceiving Google's Perspective API Built for Detecting Toxic Comments

Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently, Google and Jigsaw started a project called Perspective, which uses machine learning to automatically detect toxic language. A demonstration website has been also launched, which allows anyone to type a phrase in the interface and instantaneously see the toxicity score [1]. In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples. We show that an adversary can subtly modify a highly toxic phrase in a way that the system assigns significantly lower toxicity score to it. We apply the attack on the sample phrases provided in the Perspective website and show that we can consistently reduce the toxicity scores to the level of the non-toxic phrases. The existence of such adversarial examples is very harmful for toxic detection systems and seriously undermines their usability.

cs.LG↗

arXiv2017

Information Management for Decentralized Energy Storages under Market Uncertainties

In this paper, we propose a model of decentralized energy storages, who serve as instruments to shift energy supply intertemporally. From storages' perspective, we investigate their optimal buying or selling decisions under market uncertainty. The goal of this paper is to understand the economic value of future market information, as energy storages mitigate market uncertainty by forward-looking strategies. At a system level, we evaluate different information management policies to coordinate storages' actions and improve their aggregate profitability: (1) providing a publicly available market forecasting channel; (2) encouraging decentralized storages to share their private forecasts with each other; (3) releasing additional market information to a targeted subset of storages exclusively. We highlight the perils of too much market information provision and advice on exclusiveness of market forecast channel.

math.OC↗

arXiv2017

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance.

stat.ML↗

arXiv2016

Online Active Linear Regression via Thresholding

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional settings. Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality --- significantly reducing both the mean and variance of the squared error.

stat.ML↗

arXiv2016

Learning Temporal Dependence from Time-Series Data with Latent Variables

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes. A special case of wide interest and applicability is the setting where the noise is Gaussian and relationships are Markov and linear. We study this setting with two additional features: firstly, each random process has a hidden (latent) state, which we use to model the internal memory possessed by the variables (similar to hidden Markov models). Secondly, each variable can depend on its latent memory state through a random lag (rather than a fixed lag), thus modeling memory recall with differing lags at distinct times. Under this setting, we develop an estimator and prove that under a genericity assumption, the parameters of the model can be learned consistently. We also propose a practical adaption of this estimator, which demonstrates significant performance gains in both synthetic and real-world datasets.

cs.LG↗

arXiv2016

Leveraging energy storage to optimize data center electricity cost in emerging power markets

Energy storage in data centers has mainly been used as devices to backup generators during power outages. Recently, there has been a growing interest in using energy storage devices to actively shape power consumption in data centers to reduce their skyrocketing electricity bills. In this paper, we consider using energy storage in data centers for two applications in a joint fashion: reducing peak demand charges and enabling data centers to participate in regulation markets. We develop an optimization framework that captures the cost of electricity, degradation of energy storage devices, as well as the benefit from regulation markets. Under this frame- work, using real data Microsoft data center traces and PJM regulation signals, we show the electricity bill of a data center can be reduced by up to 20%. Furthermore, we demonstrate that the saving from joint optimization can be even larger than the sum of individually optimizing each component. We quantify the particular aspects of data center load profiles that lead to this superlinear gain. Compared to prior works that consider using energy storage devices for each single application alone, our results suggest that energy storage in data centers can have much larger impacts than previously thought possible.

cs.DC↗

arXiv2016

To Observe or Not to Observe: Queuing Game Framework for Urban Parking

We model parking in urban centers as a set of parallel queues and overlay a game theoretic structure that allows us to compare the user-selected (Nash) equilibrium to the socially optimal equilibrium. We model arriving drivers as utility maximizers and consider the game in which observing the queue length is free as well as the game in which drivers must pay to observe the queue length. In both games, drivers must decide between balking and joining. We compare the Nash induced welfare to the socially optimal welfare. We find that gains to welfare do not require full information penetration---meaning, for social welfare to increase, not everyone needs to pay to observe. Through simulation, we explore a more complex scenario where drivers decide based the queueing game whether or not to enter a collection of queues over a network. We examine the occupancy-congestion relationship, an important relationship for determining the impact of parking resources on overall traffic congestion. Our simulated models use parameters informed by real-world data collected by the Seattle Department of Transportation.

eess.SY↗