With the current trend of transforming a centralized power system into a decentralized one for efficiency, reliability, and environmental reasons, the concept of a microgrid that integrates a variety of distributed energy resources into distribution networks is gaining popularity. In this paper, we investigate the energy management of a microgrid with renewable energy sources (RESs), such as wind turbines and solar panels, and co-generation in both grid-connected and islanded modes. To address the uncertainties associated with RES power output and grid-connection condition in the microgrid, we propose a two-stage adaptive robust optimization approach to provide a robust unit commitment schedule for co-generation units, with the objective of minimizing the total operating cost under the worst-case scenario of renewable power output and grid-connection condition. The proposed approach ensures robust microgrid operation in consideration of worst-case scenarios, and the over-conservatism can also be mitigated by introducing âbudget of uncertaintyâ parameters. Finally, a case study is conducted to show the effectiveness of the proposed approach.
@article{guo2018islanding,abbr={TSG},bibtex_show={true},title={Islanding-Aware Robust Energy Management for Microgrids},author={Guo, Yuanxiong and Zhao, Chaoyue},journal={IEEE Transactions on Smart Grid},volume={9},number={2},year={2018},publisher={IEEE},selected={},html={https://ieeexplore.ieee.org/document/7500056},pdf={guo2018islanding_TSG.pdf}}
TSG
Colocation data center demand response using Nash bargaining theory
Yuanxiong Guo,
Hongning Li,
and Miao Pan
IEEE Transactions on Smart Grid,
vol. 9,
no. 5,
pp. 4017â4026,
2018
The huge yet flexible power consumption of data centers makes them promising resources for demand response, particularly for emergency demand response (EDR) which requires a certain amount of load curtailment during emergencies. However, current data centers often participate in EDR by starting up their backup diesel generators, resulting in both high costs and large carbon emissions. In this paper, we focus on cost-effective and eco-friendly demand response in colocation data centers by designing economic incentives for tenants to reduce their loads during emergency periods for EDR. In particular, we model and analyze the interaction among the data center operator and tenants by using Nash bargaining theory, and derive the optimal solutions for the load reduction and reimbursement for each tenant under two different bargaining protocols (i.e., sequential bargaining and concurrent bargaining). We prove that the derived solutions are Pareto-efficient and fair, and therefore self-enforcing and satisfactory for all entities. Numerical results based on trace-driven simulations show that the proposed bargaining approach is beneficial to both the data center operator and tenants, while also reducing the carbon emissions to the environment from data center demand response.
@article{guo2018colocation,abbr={TSG},bibtex_show={true},title={Colocation data center demand response using Nash bargaining theory},author={Guo, Yuanxiong and Li, Hongning and Pan, Miao},journal={IEEE Transactions on Smart Grid},volume={9},number={5},pages={4017--4026},year={2018},publisher={IEEE},selected={},html={https://ieeexplore.ieee.org/document/7803550},pdf={guo2018colocation_TSG.pdf}}
TVT
Protecting operation-time privacy of primary users in downlink cognitive two-tier networks
Xuewen Dong,
Yanmin Gong,
Jianfeng Ma,
and Yuanxiong Guo
IEEE Transactions on Vehicular Technology,
vol. 67,
no. 7,
pp. 6561â6572,
2018
Dynamic Spectrum Sharing (DSS) has a great potential in fully utilizing the scarce spectrum resources, and heterogeneous two-tier network has been regarded as one major solution for achieving it. Without privacy protection in operation-time, however, the primary users will be reluctant to share their spectrum with secondary users. In this paper, we present PriDSS in two-tier wireless networks, the first scheme for the administrator of a dynamic spectrum sharing system to select secondary users in a differentially operation-time private manner. First, we describe the operation-time inference attacks on the traditional secondary users auction without privacy. Then, we bring up a ranking metric to quantify the administratorâs preference for secondary users. Moreover, based on the exponential mechanism, we calculate the probability of each secondary user being selected as a winner through the ranking metric. Finally, a truthful payment method is designed according to that probability. Extensively theoretical analysis and evaluations show that PriDSS can simultaneously achieve truthfulness, approximate social welfare maximization, and differential operation-time privacy.
@article{dong2018protecting,abbr={TVT},bibtex_show={true},title={Protecting operation-time privacy of primary users in downlink cognitive two-tier networks},author={Dong, Xuewen and Gong, Yanmin and Ma, Jianfeng and Guo, Yuanxiong},journal={IEEE Transactions on Vehicular Technology},volume={67},number={7},pages={6561--6572},year={2018},publisher={IEEE},html={https://ieeexplore.ieee.org/abstract/document/8299451},pdf={dong2018protecting_TVT.pdf}}
CNS
Practical Collaborative Learning for Crowdsensing in the Internet of Things with Differential Privacy
Yuanxiong Guo,
and Yanmin Gong
IEEE Conference on Communications and Network Security,
2018
Machine learning is increasingly used to produce predictive models for crowdsensing applications such as health monitoring and query suggestion. These models are more accurate when trained on large amount of data collected from different sources. However, such massive data collection presents serious privacy concerns. The personal crowdsensing data such as photos, voice records, and locations is often highly sensitive, and once being sent out to the collecting companies, falls out of the control of the crowdsensing users who own it. This may preclude the practice of transmitting all user data to a central location and training there using conventional machine learning approaches. In this paper, we advocate an alternative approach that leaves data stored on the user side and learns a shared model by coordinating local training of crowdsensing users in an iterative process. Specifically, we focus on regularized empirical risk minimization and propose an efficient scheme based on decomposition that enables multiple crowdsensing users to jointly learn an accurate learning model for a given learning objective without sharing their private crowdsensing data. We exploit the fact that the optimization problems used in many learning tasks are decomposable and can be solved in a parallel and distributed way by the alternating direction method of multipliers (ADMM). Considering the heterogeneity of different user devices in practice, we propose an asynchronous ADMM algorithm to speed up the training process. Our scheme lets users train independently on their own crowdsensing data and only share some updated model parameters instead of raw data. Moreover, secure computation and distributed noise generation are novelly integrated in our scheme to guarantee differential privacy of the shared parameters in the execution of the asynchronous ADMM algorithm. We analyze the privacy guarantee and demonstrate the privacy-utility trade-off of our privacy-preserving collaborative learning scheme empirically based on real-world data.
@inproceedings{guo2018practical,abbr={CNS},bibtex_show={true},title={Practical Collaborative Learning for Crowdsensing in the Internet of Things with Differential Privacy},author={Guo, Yuanxiong and Gong, Yanmin},booktitle={IEEE Conference on Communications and Network Security},organization={IEEE},year={2018},html={https://ieeexplore.ieee.org/document/8433181},pdf={guo2018practical_CNS.pdf}}
2017
TDSC
My privacy my decision: control of photo sharing on online social networks
Kaihe Xu,
Yuanxiong Guo,
Linke Guo,
Yuguang Fang,
and Xiaolin Li
IEEE Transactions on Dependable and Secure Computing,
vol. 14,
no. 2,
pp. 199â210,
2017
Photo sharing is an attractive feature which popularizes online social networks (OSNs). Unfortunately, it may leak usersâ privacy if they are allowed to post, comment, and tag a photo freely. In this paper, we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize usersâ private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy. We also develop a distributed consensus-based method to reduce the computational complexity and protect the private training set. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebookâs platform.
@article{xu2017my,abbr={TDSC},bibtex_show={true},title={My privacy my decision: control of photo sharing on online social networks},author={Xu, Kaihe and Guo, Yuanxiong and Guo, Linke and Fang, Yuguang and Li, Xiaolin},journal={IEEE Transactions on Dependable and Secure Computing},volume={14},number={2},pages={199--210},year={2017},organization={IEEE},selected={},html={https://ieeexplore.ieee.org/document/7120947},pdf={xu2017my_TDSC.pdf}}
TWC
Dolphins first: Dolphin-aware communications in multi-hop underwater cognitive acoustic networks
Xuanheng Li,
Yi Sun,
Yuanxiong Guo,
Xin Fu,
and Miao Pan
IEEE Transactions on Wireless Communications,
vol. 16,
no. 4,
pp. 2043â2056,
2017
Acoustic communication is the most versatile and widely used technology for underwater wireless networks. However, the frequencies used by current acoustic modems are heavily overlapped with the cetacean communication frequencies, where the man-made noise of underwater acoustic communications may have harmful or even fatal impact on those lovely marine mammals, e.g., dolphins. To pursue the environmental friendly design for sustainable underwater monitoring and exploration, specifically, to avoid the man-made interference to dolphins, in this paper, we propose a cognitive acoustic transmission scheme, called dolphin-aware data transmission (DAD-Tx), in multi-hop underwater acoustic networks. Different from the collaborative sensing approach and the simplified modeling of dolphinsâ activities in existing literature, we employ a probabilistic method to capture the stochastic characteristics of dolphinsâ communications, and mathematically describe the dolphin-aware constraint. Under dolphin-awareness and wireless acoustic transmission constraints, we further formulate the DAD-Tx optimization problem aiming to maximize the end-to-end throughput. Since the formulated problem contains probabilistic constraint and is NP-hard, we leverage Bernstein approximation and develop a three-phase solution procedure with heuristic algorithms for feasible solutions. Simulation results show the effectiveness of the proposed scheme in terms of both network performance and dolphin awareness.
@article{li2017dolphins,abbr={TWC},bibtex_show={true},title={Dolphins first: Dolphin-aware communications in multi-hop underwater cognitive acoustic networks},author={Li, Xuanheng and Sun, Yi and Guo, Yuanxiong and Fu, Xin and Pan, Miao},journal={IEEE Transactions on Wireless Communications},volume={16},number={4},pages={2043--2056},year={2017},publisher={IEEE},selected={},html={https://ieeexplore.ieee.org/document/7728118},pdf={li2017dolphins_TWC.pdf}}
e-Energy
Coalitional datacenter energy cost optimization in electricity markets
Zhe Yu,
Yuanxiong Guo,
and Miao Pan
Proceedings of the Eighth ACM International Conference on Future Energy Systems,
2017
In this paper, we study how datacenter energy cost can be effectively reduced in the wholesale electricity market via cooperative power procurement. Intuitively, by aggregating workloads across a group of datacenters, the overall power demand uncertainty of datacenters can be reduced, resulting in less chance of being penalized when participating in the wholesale electricity market. We use cooperative game theory to model the cooperative electricity procurement process of datacenters as a cooperative game, and show the cost saving benefits of aggregation. Then, a cost allocation scheme based on the marginal contribution of each datacenter to the total expected cost is proposed to fairly distribute the aggregation benefits among the participating datacenters. Finally, numerical experiments based on real-world traces are conducted to illustrate the benefits of aggregation compared to noncooperative power procurement.
@inproceedings{yu2017coalitional,abbr={e-Energy},bibtex_show={true},title={Coalitional datacenter energy cost optimization in electricity markets},author={Yu, Zhe and Guo, Yuanxiong and Pan, Miao},booktitle={Proceedings of the Eighth ACM International Conference on Future Energy Systems},pages={191--202},year={2017},organization={ACM},selected={},html={https://dl.acm.org/doi/abs/10.1145/3077839.3077860},pdf={yu2017coalitional_e-energy.pdf}}
2016
TSG
A privacy-preserving scheme for incentive-based demand response in the smart grid
Yanmin Gong,
Ying Cai,
Yuanxiong Guo,
and Yuguang Fang
IEEE Transactions on Smart Grid,
vol. 7,
no. 3,
pp. 1304â1313,
2016
The advanced metering infrastructure (AMI) in the smart grid provides real-time information to both grid operators and customers, exploiting the full potential of demand response (DR). However, it introduces new privacy threats to customers. Prior works have proposed privacy-preserving methods in the AMI, such as temporal or spatial aggregation. A main assumption in these works is that fine-grained data do not need to be attributable to individuals. However, this assumption does not hold in incentive-based demand response (IDR) programs where fine-grained metering data are required to analyze individual demand curtailments, and hence, need to be attributable. In this paper, we propose a privacy-preserving scheme for IDR programs in the smart grid, which enables the DR provider to compute individual demand curtailments and DR rewards while preserving customer privacy. Moreover, a customer can reveal his/her identity and prove ownership of his/her power usage profile in certain situations, such as legal disputes. We achieve both privacy and efficiency in our scheme through a combination of several cryptographic primitives, such as identity-committable signatures and partially blind signatures. As far as we know, we are the first to identify and address privacy issues for IDR programs in the smart grid.
@article{gong2016privacy,abbr={TSG},bibtex_show={true},title={A privacy-preserving scheme for incentive-based demand response in the smart grid},author={Gong, Yanmin and Cai, Ying and Guo, Yuanxiong and Fang, Yuguang},journal={IEEE Transactions on Smart Grid},volume={7},number={3},pages={1304--1313},year={2016},selected={},html={https://ieeexplore.ieee.org/document/7069275},pdf={gong2016privacy_TSG.pdf}}
IoT-J
Optimal task recommendation for mobile crowdsourcing with privacy control
Yanmin Gong,
Lingbo Wei,
Yuanxiong Guo,
Chi Zhang,
and Yuguang Fang
IEEE Internet of Things Journal,
vol. 3,
no. 5,
pp. 745â756,
2016
Mobile crowdsourcing (MC) is a transformative paradigm that engages a crowd of mobile users (i.e., workers) in the act of collecting, analyzing, and disseminating information or sharing their resources. To ensure quality of service, MC platforms tend to recommend MC tasks to workers based on their context information extracted from their interactions and smartphone sensors. This raises privacy concerns hard to address due to the constrained resources on mobile devices. In this paper, we identify fundamental tradeoffs among three metrics-utility, privacy, and efficiency-in an MC system and propose a flexible optimization framework that can be adjusted to any desired tradeoff point with joint efforts of MC platform and workers. Since the underlying optimization problems are NP-hard, we present efficient approximation algorithms to solve them. Since worker statistics are needed when tuning the optimization models, we use an efficient aggregation approach to collecting worker feedbacks while providing differential privacy guarantees. Both numerical evaluations and performance analysis are conducted to demonstrate the effectiveness and efficiency of the proposed framework.
@article{gong2016optimal,abbr={IoT-J},bibtex_show={true},title={Optimal task recommendation for mobile crowdsourcing with privacy control},author={Gong, Yanmin and Wei, Lingbo and Guo, Yuanxiong and Zhang, Chi and Fang, Yuguang},journal={IEEE Internet of Things Journal},volume={3},number={5},pages={745--756},year={2016},html={https://ieeexplore.ieee.org/document/7365407},pdf={gong2016optimal_IoTJ.pdf}}
TCBB
Private data analytics on biomedical sensing data via distributed computation
Yanmin Gong,
Yuguang Fang,
and Yuanxiong Guo
IEEE/ACM Transactions on Computational Biology and Bioinformatics,
vol. 13,
no. 3,
pp. 431â444,
2016
Advances in biomedical sensors and mobile communication technologies have fostered the rapid growth of mobile health (mHealth) applications in the past years. Users generate a high volume of biomedical data during health monitoring, which can be used by the mHealth server for training predictive models for disease diagnosis and treatment. However, the biomedical sensing data raise serious privacy concerns because they reveal sensitive information such as health status and lifestyles of the sensed subjects. This paper proposes and experimentally studies a scheme that keeps the training samples private while enabling accurate construction of predictive models. We specifically consider logistic regression models which are widely used for predicting dichotomous outcomes in healthcare, and decompose the logistic regression problem into small subproblems over two types of distributed sensing data, i.e., horizontally partitioned data and vertically partitioned data. The subproblems are solved using individual private data, and thus mHealth users can keep their private data locally and only upload (encrypted) intermediate results to the mHealth server for model training. Experimental results based on real datasets show that our scheme is highly efficient and scalable to a large number of mHealth users.
@article{gong2016private,abbr={TCBB},bibtex_show={true},title={Private data analytics on biomedical sensing data via distributed computation},author={Gong, Yanmin and Fang, Yuguang and Guo, Yuanxiong},journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},volume={13},number={3},pages={431--444},year={2016},html={https://ieeexplore.ieee.org/document/7374672},pdf={gong2016private_TCBB.pdf}}
e-Energy
Enabling reliable data center demand response via aggregation
Luyao Niu,
and Yuanxiong Guo
Proceedings of the Seventh ACM International Conference on Future Energy Systems,
2016
Although data centers are recognized as promising resources for demand response (DR), it is not easy for them to participate into DR programs due to their unreliable DR capacities. The unreliability mainly comes from their random workload arrivals. In this paper, we study how to enable reliable data center DR. We focus on the scenario that independent data centers participate into capacity bidding program (CBP) in which they need to sign forward contracts to commit the amount of power reduction during the DR event. We show that due to the uncertainty of DR capacity in real time, it is risky for the data centers to sign contracts in advance. Such risky behaviors are adverse to the profitability of data centers when providing DR resources. Inspired by the intuition that aggregation can reduce uncertainty, we propose that data centers cooperate with others and sign the forward contract collectively based on their aggregated DR capacity to mitigate the uncertainty of DR capacity. A coalitional game is used to model the cooperation among the data centers. We further design a payoff allocation to split the profit generated via cooperation fairly, guaranteeing that no coalition has the incentive to deviate. In addition, we show that the proposed payoff allocation captures the marginal contribution of each data center and is efficient. Finally, trace driven simulation results are presented to demonstrate the effectiveness of the proposed approach. The results show that participating into CBP collaboratively leads to a win-win situation where the data centers obtain higher profits and the utility company gets more reliable DR resources from data centers.
@inproceedings{niu2016enabling,abbr={e-Energy},bibtex_show={true},title={Enabling reliable data center demand response via aggregation},author={Niu, Luyao and Guo, Yuanxiong},booktitle={Proceedings of the Seventh ACM International Conference on Future Energy Systems},pages={1--11},year={2016},organization={ACM},selected={},html={https://dl.acm.org/doi/10.1145/2934328.2934350},pdf={niu2016enabling_e-energy.pdf}}
JSAC
M3-STEP: Matching-Based Multi-Radio Multi-Channel Spectrum Trading With Evolving Preferences
Jingyi Wang,
Wenbo Ding,
Yuanxiong Guo,
Chi Zhang,
Miao Pan,
and Jian Song
IEEE Journal on Selected Areas in Communications,
vol. 34,
no. 11,
pp. 3014â3024,
2016
Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although the existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi-channel transmissions, and so on), they still have to face many practical but challenging issues, such as the new infrastructure deployment, the extra control overhead, and the scalability issues. To address those issues, in this paper, we propose a novel matching-based multi-radio multi-channel spectrum trading (M3-STEP) scheme in cognitive radio (CR) networks. We employ conflict graph to characterize the interference relationship among SUs with multiple CR radios, and formulate the centralized PUsâ revenue maximization problem under multiple constrains. In view of the NP-hardness of solving the problem and no existence of centralized entity, we develop the M3-STEP algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its pairwise stability. Simulation results show that the proposed M3-STEP algorithm achieves close to optimal performance and outperforms other distributed algorithms without considering spectrum reuse.
@article{wang2016m3,abbr={JSAC},bibtex_show={true},title={M3-STEP: Matching-Based Multi-Radio Multi-Channel Spectrum Trading With Evolving Preferences},author={Wang, Jingyi and Ding, Wenbo and Guo, Yuanxiong and Zhang, Chi and Pan, Miao and Song, Jian},journal={IEEE Journal on Selected Areas in Communications},volume={34},number={11},pages={3014--3024},year={2016},publisher={IEEE},selected={},html={https://ieeexplore.ieee.org/document/7583657},pdf={wang2016m3_JSAC.pdf}}
2015
ICDCS
Privacy-preserving machine learning algorithms for big data systems
Machine learning has played an increasing important role in big data systems due to its capability of efficiently discovering valuable knowledge and hidden information. Often times big data such as healthcare systems or financial systems may involve with multiple organizations who may have different privacy policy, and may not explicitly share their data publicly while joint data processing may be a must. Thus, how to share big data among distributed data processing entities while mitigating privacy concerns becomes a challenging problem. Traditional methods rely on cryptographic tools and/or randomization to preserve privacy. Unfortunately, this alone may be inadequate for the emerging big data systems because they are mainly designed for traditional small-scale data sets. In this paper, we propose a novel framework to achieve privacy-preserving machine learning where the training data are distributed and each shared data portion is of large volume. Specifically, we utilize the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation. We show that the proposed scheme is secure in the semi-honest model and use extensive simulations to demonstrate its scalability and correctness.
@inproceedings{xu2015privacy,abbr={ICDCS},bibtex_show={true},title={Privacy-preserving machine learning algorithms for big data systems},author={Xu, Kaihe and Yue, Hao and Guo, Linke and Guo, Yuanxiong and Fang, Yuguang},booktitle={IEEE 35th International Conference on Distributed Computing Systems (ICDCS)},pages={318--327},year={2015},organization={IEEE},selected={},html={https://ieeexplore.ieee.org/document/7164918},pdf={xu2015privacy_ICDCS.pdf}}
2014
TPDS
Energy and network aware workload management for sustainable data centers with thermal storage
Yuanxiong Guo,
Yanmin Gong,
Yuguang Fang,
Pramod P Khargonekar,
and Xiaojun Geng
IEEE Transactions on Parallel and Distributed Systems,
vol. 25,
no. 8,
pp. 2030â2042,
2014
Reducing the carbon footprint of data centers is becoming a primary goal of large IT companies. Unlike traditional energy sources, renewable energy sources are usually intermittent and unpredictable. How to better utilize the green energy from these renewable sources in data centers is a challenging problem. In this paper, we exploit the opportunities offered by geographical load balancing, opportunistic scheduling of delay-tolerant workloads, and thermal storage management in data centers to facilitate green energy integration and reduce the cost of brown energy usage. Moreover, bandwidth cost variations between users and data centers are considered. Specifically, this problem is first formulated as a stochastic program, and then, an online control algorithm based on the Lyapunov optimization technique, called Stochastic Cost Minimization Algorithm (SCMA), is proposed to solve it. The algorithm can enable an explicit trade-off between cost saving and workload delay. Numerical results based on real-world traces illustrate the effectiveness of SCMA in practice.
@article{guo2014energy,abbr={TPDS},bibtex_show={true},title={Energy and network aware workload management for sustainable data centers with thermal storage},author={Guo, Yuanxiong and Gong, Yanmin and Fang, Yuguang and Khargonekar, Pramod P and Geng, Xiaojun},journal={IEEE Transactions on Parallel and Distributed Systems},volume={25},number={8},pages={2030--2042},year={2014},publisher={IEEE},selected={},html={https://ieeexplore.ieee.org/document/6654159},pdf={guo2014energy_TPDS.pdf}}
2013
TPDS
Electricity cost saving strategy in data centers by using energy storage
Yuanxiong Guo,
and Yuguang Fang
IEEE Transactions on Parallel and Distributed Systems,
vol. 24,
no. 6,
pp. 1149â1160,
2013
Electricity expenditure comprises a significant fraction of the total operating cost in data centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this paper, we consider utilizing existing energy storage capabilities in data centers to reduce electricity cost under wholesale electricity markets, where the electricity price exhibits both temporal and spatial variations. A stochastic program is formulated by integrating the center-level load balancing, the server-level configuration, and the battery management while at the same time guaranteeing the quality-of-service experience by end users. We use the Lyapunov optimization technique to design an online algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. We demonstrate the effectiveness of our proposed algorithm through extensive numerical evaluations based on real-world workload and electricity price data sets. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both the spatial and temporal variations in wholesale electricity prices and workload arrival processes.
@article{guo2013electricity,abbr={TPDS},bibtex_show={true},title={Electricity cost saving strategy in data centers by using energy storage},author={Guo, Yuanxiong and Fang, Yuguang},journal={IEEE Transactions on Parallel and Distributed Systems},volume={24},number={6},pages={1149--1160},year={2013},publisher={IEEE},html={https://ieeexplore.ieee.org/document/6226387},pdf={guo2013electricity_TPDS.pdf},selected={}}
TSG
Decentralized coordination of energy utilization for residential households in the smart grid
Yuanxiong Guo,
Miao Pan,
Yuguang Fang,
and Pramod P Khargonekar
IEEE Transactions on Smart Grid,
vol. 4,
no. 3,
pp. 1341â1350,
2013
In this paper, we investigate the minimization of the total energy cost of multiple residential households in a smart grid neighborhood sharing a load serving entity. Specifically, each household may have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. Numerical results based on real-world trace data show that our control algorithm can effectively reduce the total energy cost in the neighborhood.
@article{guo2013decentralized,abbr={TSG},bibtex_show={true},title={Decentralized coordination of energy utilization for residential households in the smart grid},author={Guo, Yuanxiong and Pan, Miao and Fang, Yuguang and Khargonekar, Pramod P},journal={IEEE Transactions on Smart Grid},volume={4},number={3},pages={1341--1350},year={2013},publisher={IEEE},html={https://ieeexplore.ieee.org/abstract/document/6576262},pdf={guo2013decentralized_TSG.pdf},selected={}}
TSG
A market based scheme to integrate distributed wind energy
Zongrui Ding,
Yuanxiong Guo,
Dapeng Wu,
and Yuguang Fang
IEEE Transactions on Smart Grid,
vol. 4,
no. 2,
pp. 976â984,
2013
Efficiently integrating wind energy into the smart grid is gaining momentum under renewable portfolio standard (RPS) with deep wind penetration. Due to the randomness of wind energy production, ancillary service (AS) is needed in large amount to regulate wind power for system stability and reliability. As a result, the cost of wind power depends on the AS market and may be, quite higher than that of conventional power. Therefore, it is challenging to economically integrate wind energy with current power system to satisfy RPS. With the communication, sensing and advanced control features incorporated into the smart grid, the interactions among the grid components will facilitate solving this problem. In this paper, we consider the wind energy integration of small-scale utilities installed with wind turbines and acted as distributed energy resources (DERs). Since wind energy can be integrated to serve customer load or enter a separate green energy market, we propose a theoretical framework to dynamically determine the role of wind energy and provide long-term RPS guarantee. This approach results in a simple dynamic threshold control policy which maximizes the expectation of the profit for a green utility and is easily implemented online.
@article{ding2013market,abbr={TSG},bibtex_show={true},title={A market based scheme to integrate distributed wind energy},author={Ding, Zongrui and Guo, Yuanxiong and Wu, Dapeng and Fang, Yuguang},journal={IEEE Transactions on Smart Grid},volume={4},number={2},pages={976--984},year={2013},publisher={IEEE},html={https://ieeexplore.ieee.org/document/6476051},pdf={ding2013market_TSG.pdf}}
2012
TPDS
Optimal power management of residential customers in the smart grid
Yuanxiong Guo,
Miao Pan,
and Yuguang Fang
IEEE Transactions on Parallel and Distributed Systems,
vol. 23,
no. 9,
pp. 1593â1606,
2012
Recently intensive efforts have been made on the transformation of the worldâs largest physical system, the power grid, into a âsmart gridâ by incorporating extensive information and communication infrastructures. Key features in such a âsmart gridâ include high penetration of renewable and distributed energy sources, large-scale energy storage, market-based online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with real-time electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.
@article{guo2012optimal,abbr={TPDS},bibtex_show={true},title={Optimal power management of residential customers in the smart grid},author={Guo, Yuanxiong and Pan, Miao and Fang, Yuguang},journal={IEEE Transactions on Parallel and Distributed Systems},volume={23},number={9},pages={1593--1606},year={2012},publisher={IEEE},doi={},url={},html={https://ieeexplore.ieee.org/document/6127869},pdf={guo2012optimal_TPDS.pdf},selected={}}
2011
GLOBECOM
Cutting down electricity cost in Internet data centers by using energy storage
Yuanxiong Guo,
Zongrui Ding,
Yuguang Fang,
and Dapeng Wu
Electricity consumption comprises a significant fraction of total operating cost in data centers. System operators are required to reduce electricity bill as much as possible. In this paper, we consider utilizing available energy storage capability in data centers to reduce electricity bill under real- time electricity market. Laypunov optimization technique is applied to design an algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both time- diversity and location-diversity of electricity price.
@inproceedings{guo2011cutting,abbr={GLOBECOM},bibtex_show={true},title={Cutting down electricity cost in Internet data centers by using energy storage},author={Guo, Yuanxiong and Ding, Zongrui and Fang, Yuguang and Wu, Dapeng},booktitle={IEEE Global Telecommunications Conference},pages={1--5},year={2011},organization={IEEE},html={https://ieeexplore.ieee.org/document/6134209},pdf={guo2011cutting_GLOBECOM.pdf},selected={}}