publications

2022

1. PSCC

   Towards balanced three-phase charging: Phase optimization in adaptive charging networks

   Ye, Zixin,  Li, Tongxin, and Low, Steven

   Electric Power Systems Research 2022

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2. SIGMETRICS

   Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions

   Li, Tongxin, Yang, Ruixiao, Qu, Guannan, Shi, Guanya, Yu, Chenkai, Wierman, Adam, and Low, Steven

   Proceedings of the ACM on Measurement and Analysis of Computing Systems 2022

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   We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances consistency, which measures the competitive ratio when predictions are accurate, and robustness, which bounds the competitive ratio when predictions are inaccurate.

2021

1. SmartGridComm

   Data-driven Electric Vehicle Charging Station Placement for Incentivizing Potential Demand

   Sun, Chenxi,  Li, Tongxin, and Tang, Xiaoying

   In 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) 2021

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2. TSG

   Learning-based Predictive Control via Real-time Aggregate Flexibility

   Li, Tongxin, Sun, Bo, Chen, Yue, Ye, Zixin, Low, Steven H, and Wierman, Adam

   IEEE Transactions on Smart Grid 2021

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   Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator. However, most of existing aggregate flexibility measures often are slow-timescale estimations and much less attention has been paid to real-time coordination between an aggregator and an operator. In this paper, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In addition to deriving analytic properties of the MEF, combining learning and control, we show that it can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm – the penalized predictive control (PPC), which modifies vanilla model predictive control (MPC). The benefits of our scheme are (1). Efficient Communication. An operator running PPC does not need to know the exact states and constraints of the loads, but only the MEF. (2). Fast Computation. The PPC often has much less number of variables than an MPC formulation. (3). Lower Costs. We show that under certain regularity assumptions, the PPC is optimal. We illustrate the efficacy of the PPC using a dataset from an adaptive electric vehicle charging network and show that PPC outperforms classical MPC.

3. SIGMETRICS

   Information Aggregation for Constrained Online Control

   Li, Tongxin, Chen, Yue, Sun, Bo, Wierman, Adam, and Low, Steven H

   Proceedings of the ACM on Measurement and Analysis of Computing Systems 2021

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   This paper considers an online control problem involving two controllers. A central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller’s goal is to minimize the cumulative cost; however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.

2020

1. PSCC

   Classification of electric vehicle charging time series with selective clustering

   Sun, Chenxi,  Li, Tongxin, Low, Steven H, and Li, Victor OK

   Electric Power Systems Research 2020

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   We develop a novel iterative clustering method for classifying time series of EV charging rates based on their “tail features”. Our method first extracts tails from a diversity of charging time series that have different lengths, contain missing data, and are distorted by scheduling algorithms and measurement noise. The charging tails are then clustered into a small number of types whose representatives are then used to improve tail extraction. This process iterates until it converges. We apply our method to ACN-Data, a fine-grained EV charging dataset recently made publicly available, to illustrate its effectiveness and potential applications.

2. SIGMETRICS

   Competitive algorithms for the online multiple knapsack problem with application to electric vehicle charging

   Sun, Bo, Zeynali, Ali,  Li, Tongxin, Hajiesmaili, Mohammad, Wierman, Adam, and Tsang, Danny HK

   Proceedings of the ACM on Measurement and Analysis of Computing Systems 2020

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   We introduce and study a general version of the fractional online knapsack problem with multiple knapsacks, heterogeneous constraints on which items can be assigned to which knapsack, and rate-limiting constraints on the assignment of items to knapsacks. This problem generalizes variations of the knapsack problem and of the one-way trading problem that have previously been treated separately, and additionally finds application to the real-time control of electric vehicle (EV) charging. We introduce a new algorithm that achieves a competitive ratio within an additive factor of one of the best achievable competitive ratios for the general problem and matches or improves upon the best-known competitive ratio for special cases in the knapsack and one-way trading literatures. Moreover, our analysis provides a novel approach to online algorithm design based on an instance-dependent primal-dual analysis that connects the identification of worst-case instances to the design of algorithms. Finally, we illustrate the proposed algorithm via trace-based experiments of EV charging.

3. TPS

   Decentralized provision of renewable predictions within a virtual power plant

   Chen, Yue,  Li, Tongxin, Zhao, Changhong, and Wei, Wei

   IEEE Transactions on Power Systems 2020

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4. SIGEnergy

   Real-time flexibility feedback for closed-loop aggregator and system operator coordination

   Li, Tongxin, Low, Steven H, and Wierman, Adam

   In Proceedings of the Eleventh ACM International Conference on Future Energy Systems 2020

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   Consider a system operator that wishes to optimize its objectives over time subject to operational constraints as well as private constraints of controllable loads managed by an aggregator. In this paper, we design a real-time feedback signal for the aggregator to quantify and communicate its available flexibility to the system operator. The proposed feedback signal at each time is the conditional probability of future feasible trajectories that will be enabled by the operator’s decision. We show that it is the unique distribution that maximizes a system capacity for flexibility. It allows the system operator to maintain feasibility and enhance future flexibility while optimizing its objectives. We illustrate how the design can be used by the system operator to perform online cost minimization and real-time capacity estimation, while provably satisfying the private constraints of the loads.

5. SIGEnergy

   ORC: An Online Competitive Algorithm for Recommendation and Charging Schedule in Electric Vehicle Charging Network

   Sun, Bo,  Li, Tongxin, Low, Steven H, and Tsang, Danny HK

   In Proceedings of the Eleventh ACM International Conference on Future Energy Systems 2020

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   There is an increasing need for spatial and temporal schedule tailored to the requests and preferences of electric vehicles (EVs) in a network of charging stations. From the perspective of a charging network operator, this paper considers an online decision-making problem that recommends charging stations and the corresponding energy prices to sequential EV arrivals, and schedules the charging allocation to maximize the expected total revenue. To address the uncertainties from future EV arrivals and EVs’ choices with respective to recommendations, we propose an Online Recommendation and Charging schedule algorithm (ORC) that is parameterized by a value function for customized designs. Under the competitive analysis framework, we provide a sufficient condition on the value function that can guarantee ORC to be online competitive. Moreover, we design a customized value function based on the sufficient conditions in an asymptotic case, and then rigorously prove the competitive ratio of ORC in the general case. Through extensive experiments, we show that ORC achieves significant increase of revenues compared to benchmark online algorithms.

6. TSIPN

   Learning graphs from linear measurements: Fundamental trade-offs and applications

   Li, Tongxin, Werner, Lucien, and Low, Steven H

   IEEE Transactions on Signal and Information Processing over Networks 2020

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   We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error. We first derive a necessary condition on the number of measurements. Then, by considering a three-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity for both noisy and noiseless recovery. In the special cases of the uniform distribution on trees with n nodes and the Erdős-Rényi (n,p) class, the fundamental trade-offs are tight up to multiplicative factors with noiseless measurements. In addition, for practical applications, we design and implement a polynomial-time (in n) algorithm based on the three-stage recovery scheme. Experiments show that the heuristic algorithm outperforms basis pursuit on star graphs. We apply the heuristic algorithm to learn admittance matrices in electric grids. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness and robustness of the proposed algorithm for parameter reconstruction.

2019

1. CDC

   Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Application to Electric Grids

   Li, Tongxin, Werner, Lucien, and Low, Steven H

   In 2019 IEEE 58th Conference on Decision and Control (CDC) 2019
2. SIGEnergy

   ACN-Data: Analysis and applications of an open EV charging dataset

   Lee, Zachary J,  Li, Tongxin, and Low, Steven H

   In Proceedings of the Tenth ACM International Conference on Future Energy Systems 2019

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2018

1. ISIT

   Maximum likelihood upper bounds on the capacities of discrete information stable channels

   Li, Tongxin

   In 2018 IEEE Information Theory Workshop (ITW) 2018
2. ITW

   Quadratically constrained channels with causal adversaries

   Li, Tongxin, Dey, Bikash Kumar, Jaggi, Sidharth, Langberg, Michael, and Sarwate, Anand D

   In 2018 IEEE International Symposium on Information Theory (ISIT) 2018
3. DSW

   Robust and consistent clustering recovery via sdp approaches

   Sun, Chenxi,  Li, Tongxin, and Li, Victor OK

   In 2018 IEEE Data Science Workshop (DSW) 2018

2016

1. SPAWC

   Fundamental limits and achievable strategies for low energy compressed sensing with applications in wireless communication

   Li, Tongxin, Bakshi, Mayank, and Grover, Pulkit

   In 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2016

2014

1. ISIT

   Group testing with prior statistics

   Li, Tongxin, Chan, Chun Lam, Huang, Wenhao, Kaced, Tarik, and Jaggi, Sidharth

   In 2014 IEEE International Symposium on Information Theory 2014

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   We consider a new group testing model wherein each item is a binary random variable defined by an a priori probability of being defective. We assume that each probability is small and that items are independent, but not necessarily identically distributed. The goal of group testing algorithms is to identify with high probability the subset of defectives via non-linear (disjunctive) binary measurements. Our main contributions are two classes of algorithms: (1) adaptive algorithms with tests based either on a maximum entropy principle, or on a Shannon-Fano/Huffman code; (2) non-adaptive algorithms. Under loose assumptions and with high probability, our algorithms only need a number of measurements that is close to the information-theoretic lower bound, up to an explicitly-calculated universal constant factor. We provide simulations to support our results.