Suli Zou

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Zou

First Name

Suli

ORCID

0000-0003-1508-8458

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Publications 1 - 10 of 12

Semidecentralized Zeroth-Order Algorithms for Stochastic Generalized Nash Equilibrium Seeking
Zou, Suli; Lygeros, John (2023)
IEEE Transactions on Automatic Control
In this article, we address the problem of stochastic generalized Nash equilibrium (SGNE) seeking, where a group of noncooperative heterogeneous players aim at minimizing their expected cost under some unknown stochastic effects. Each player's strategy is constrained to a convex and compact set and should satisfy some global affine constraints. In order to decouple players' strategies under the global constraints, an extra player is introduced aiming at minimizing the violation of the coupling constraints, which transforms the original SGNE problems to extended stochastic Nash equilibrium problems. Due to the unknown stochastic effects in the objective, the gradient of the objective function is infeasible and only noisy objective values are observable. Instead of gradient-based methods, a semidecentralized zeroth-order method is developed to achieve the SGNE under a two-point gradient estimation. The convergence proof is provided for strongly monotone stochastic generalized games. We demonstrate the proposed algorithm through the Cournot model for resource allocation problems.
Demand Side Management in Direct Current Distribution Networks: Convergence and Optimality
Zou, Suli; Ma, Zhongjing; Zhu, Guorong; et al. (2018)
2018 IEEE International Symposium on Circuits and Systems (ISCAS)
Game-theoretic robust energy coordination for a neighbourhood of smart homes
Zou, Suli; Warrington, Joseph; Lygeros, John (2019)
Proceedings of the 18th European Control Conference (ECC 2019)
Price Control for Heterogeneous Thermostatically Controlled Loads in Communication and Computation Delay Environments
Zou, Suli; Chen, Zhe; Lygeros, John (2020)
2019 IEEE 58th Conference on Decision and Control (CDC)
Game Theoretic Stochastic Energy Coordination under A Distributed Zeroth-order Algorithm
Chen, Yuwen; Zou, Suli; Lygeros, John (2020)
IFAC-PapersOnLine ~ 21st IFAC World Congress
Dealing with the effects from uncertainties properly is a key problem in stochastic energy management problems to achieve safe and efficient operation of the system. In this paper, we study the problem of coordinating multi-period electric vehicles charging amidst uncertainty from the embedded renewable generation in a local distribution network under transformer capacity limits. A stochastic generalized game is presented to formulate the underlying electric vehicle coordination problem wherein the cost function of each player is affected by the intermittent renewable energy supply. Existing algorithms for seeking the equilibrium rely on conditions on the form of the cost functions. In our setting, however, stochastic effects are not known in advance which results in an unknown form of the cost functions. We propose a distributed iterative zeroth-order algorithm, which only relies on the observations of costs, to achieve a stochastic generalized Nash equilibrium of the game under the concept of Gaussian smoothing. Under certain mild assumptions, the proposed algorithm is guaranteed to converge to the neighborhood of the stochastic generalized Nash equilibrium. We demonstrate the algorithm for a distribution network energy management problem with 3 heterogeneous subgroups of electric vehicles.
Consensus-based coordination of electric vehicle charging considering transformer hierarchy
Zou, Suli; Hiskens, Ian A.; Ma, Zhongjing (2018)
Control Engineering Practice
Decentralised gradient projection method for economic dispatch problem with valve point effect
Elsheakh, Yousif; Zou, Suli; Ma, Zhongjing; et al. (2018)
IET Generation, Transmission & Distribution
A Biological Immunity-Based Neuro Prototype for Few-Shot Anomaly Detection with Character Embedding
Ma, Zhongjing; Chen, Zhan; Zheng, Xiaochen; et al. (2024)
Cyborg and Bionic Systems
Anomaly detection has wide applications to help people recognize false, intrusion, flaw, equipment failure, etc. In most practical scenarios, the amount of the annotated data and the trusted labels is low, resulting in poor performance of the detection. In this paper, we focus on the anomaly detection for the text type data and propose a detection network based on biological immunity for few-shot detection, by imitating the working mechanism of the immune system of biological organisms. This network enabling the protected system to distinguish the aggressive behavior of “nonself” from the legitimate behavior of “self” by embedding characters. First, it constructs episodic task sets and extracts data representations at the character level. Then, in the pretraining phase, Word2Vec is used to embed the representations. In the meta-learning phase, a dynamic prototype containing encoder, routing, and relation is designed to identify the data traffic. Compare to the mean-based prototype, the proposed prototype applies a dynamic routing algorithm that assigns different weights to samples in the support set through multiple iterations to obtain a prototype that combines the distribution of samples. The proposed method is validated on 2 real traffic datasets. The experimental results indicate that (a) the proposed anomaly detection prototype outperforms state-of-the-art few-shot techniques with 1.3% to 4.48% accuracy and 0.18% to 4.55% recall; (b) under the premise of ensuring the accuracy and recall, the number of training samples is reduced to 5 or 10; (c) ablation experiments are designed for each module, and the results show that more accurate prototypes can be obtained by using the dynamic routing algorithm.
A Stackelberg game approach for price response coordination of thermostatically controlled loads
Wang, Peng; Zou, Suli; Wang, Xiaojuan; et al. (2018)
Applied Sciences
In this paper, we study the demand response of the thermostatically controlled loads (TCLs) to control their set-point temperatures by considering the tradeoff between the electricity payment and TCL user’s comfort preference. Based upon the dynamics of the TCLs, we set up the relationship between the set-point temperature and the energy demand. Then, we define a discomfort function with respect to the associated energy demand which represents the discomfort level of the set-point temperature. More specifically, the system is equipped with a coordinator named electric energy control center (EECC) which can buy energy resources from the electricity market and sell them to TCL users. Due to the interaction between EECC and TCL users, we formulate the specific energy trading process as a one-leader multiple-follower Stackelberg game. As the main contributions of this work, we show the existence and uniqueness of the equilibrium for the underlying Stackelberg games, and develop a DR algorithm based on the so-called Backward Induction to achieve the equilibrium. Several numerical simulations are presented to verify the developed results in this work.
Parallel demand side auction mechanism for dynamic and efficient resource allocation
Wang, Peng; Zou, Suli; Ma, Zhongjing (2019)
IET Control Theory & Applications

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