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Guangyu He
The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200200, China

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Journal article
Published: 28 October 2020 in Energies
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Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.

ACS Style

Ce Peng; Guoying Lin; Shaopeng Zhai; Yi Ding; Guangyu He. Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model. Energies 2020, 13, 5629 .

AMA Style

Ce Peng, Guoying Lin, Shaopeng Zhai, Yi Ding, Guangyu He. Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model. Energies. 2020; 13 (21):5629.

Chicago/Turabian Style

Ce Peng; Guoying Lin; Shaopeng Zhai; Yi Ding; Guangyu He. 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model." Energies 13, no. 21: 5629.

Journal article
Published: 05 August 2020 in Applied Energy
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This paper presents a temporally coupled distributed online (TDO) algorithm to aggregate and coordinate numerous networked distributed energy resources (DERs) as a virtual power plant (VPP). A centralized stochastic optimization problem is formulated to minimize the long-term social utility loss while satisfying the voltage security, operational requirements of DERs, and VPP service requests. After that, we propose the TDO algorithm to reformulate the primary problem as an adaptation of online convex optimization (OCO). In particular, the temporally coupled constraints are well separated to each timeslot. In real-time operation, the VPP operator updates the incentives according to the measurement feedback. The smart energy gateways (SEGs) equipped at each node maximize their income and utility based on the received incentive signals through adjusting the setpoints of the governed photovoltaics (PV) inverters and electric vehicles (EVs). Unlike conventional distributed optimization algorithms where complicated iterative procedures between agents are unavoidable, the proposed TDO algorithm is computation- and communication-efficient since the SEG can directly employ the closed-form optimal setpoints without iterative communications once receiving the incentives. Furthermore, we design an incentive scheme to coordinate the SEGs based on the privacy protected nonintrusive measurements instead of direct control. Optimality and convergency of TDO are analyzed mathematically. Finally, the proposed method is corroborated numerically on a modified 33-node test feeder. A larger system is tested to validate the computational time performance.

ACS Style

Shuai Fan; Jiang Liu; Qing Wu; Mingjian Cui; Huan Zhou; Guangyu He. Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm. Applied Energy 2020, 277, 115583 .

AMA Style

Shuai Fan, Jiang Liu, Qing Wu, Mingjian Cui, Huan Zhou, Guangyu He. Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm. Applied Energy. 2020; 277 ():115583.

Chicago/Turabian Style

Shuai Fan; Jiang Liu; Qing Wu; Mingjian Cui; Huan Zhou; Guangyu He. 2020. "Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm." Applied Energy 277, no. : 115583.

Journal article
Published: 27 December 2019 in Electric Power Systems Research
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Large-scale demand response (DR) is a critical enabler to integrate significant renewable energy sources (RES) into power systems. Current customer baseline load (CBL)-based DR schemes face obstacles in large-scale deployments due to their centralized form, unfair DR performance measurement, and poor effect on decision making approach of customers. To bridge the gaps, this paper proposes the concept of customer directrix load (CDL), which is the desired load profile for customers from the view of the entire DR program, and a novel CDL-based DR scheme. Additionally, an optimization problem considering time-coupling constraints is formulated to help customers respond to the CDL. The computationally intensive problem is then translated into a quadratic programming problem in each time slot using Lyapunov optimization approach. A closed-form solution exists and ensures that the optimal decision is reached in real-time efficiently. Test systems are generated using data from PJM and Open Energy Information. The online algorithm and fairness performance of the proposed scheme are validated in a small system through benchmark comparisons. Further tests on a large-scale system show that the CDL-based DR scheme can help the power system integrate considerably more RES.

ACS Style

Shuai Fan; Zuyi Li; Lin Yang; Guangyu He. Customer directrix load-based large-scale demand response for integrating renewable energy sources. Electric Power Systems Research 2019, 181, 106175 .

AMA Style

Shuai Fan, Zuyi Li, Lin Yang, Guangyu He. Customer directrix load-based large-scale demand response for integrating renewable energy sources. Electric Power Systems Research. 2019; 181 ():106175.

Chicago/Turabian Style

Shuai Fan; Zuyi Li; Lin Yang; Guangyu He. 2019. "Customer directrix load-based large-scale demand response for integrating renewable energy sources." Electric Power Systems Research 181, no. : 106175.

Journal article
Published: 20 April 2019 in Applied Energy
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As a typical energy-cyber-physical system (e-CPS), home energy management system (HEMS) plays a critical role in power systems by accommodating higher levels of renewable generation, reducing power costs, and decreasing consumer energy bills. HEMS can help understand the home appliances energy use and learn the users’ preference so as to optimize home appliances operation and achieve higher energy efficiency. HEMS needs massive historical and real-time data for the above applications. Since HEMS is always based on a wireless sensor network, a more effective online data compression approach is necessary. The efficient data compression methods can not only relieve data transmission pressure and reduce data storage overhead, but also enhance data analysis efficiency. This paper proposes an online pattern-based data compression approach for the data generated by home appliances. The proposed approach first discovers the patterns of the time series data and then utilizes these patterns for the online data compression. The pattern discovery method in the proposed approach includes an online adaptive segmenting algorithm with incremental processing technique and a similarity metric based on piecewise statistic distance. The key issues of parameter selection and data reconstruction are also presented. Real-world common home appliance datasets are employed for comparing the performance of the proposed approach with those of six state-of-the-art algorithms. The experimental results demonstrate the outperformance of the proposed approach. Further complexity analysis shows that the proposed approach has linear time complexity. To the best of our knowledge, this is the first paper that performs online data compression based on the extracted patterns of the time series.

ACS Style

Kunqi Jia; Ge Guo; Jucheng Xiao; Huan Zhou; Zhihua Wang; Guangyu He. Data compression approach for the home energy management system. Applied Energy 2019, 247, 643 -656.

AMA Style

Kunqi Jia, Ge Guo, Jucheng Xiao, Huan Zhou, Zhihua Wang, Guangyu He. Data compression approach for the home energy management system. Applied Energy. 2019; 247 ():643-656.

Chicago/Turabian Style

Kunqi Jia; Ge Guo; Jucheng Xiao; Huan Zhou; Zhihua Wang; Guangyu He. 2019. "Data compression approach for the home energy management system." Applied Energy 247, no. : 643-656.

Paper
Published: 28 January 2019 in IEEJ Transactions on Electrical and Electronic Engineering
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As a typical cyber physical system (CPS), a smart grid should facilitate more robust integration and interoperability of information models and subsystems so that the performance of the physical system can be optimized. However, the intrinsic complexity and heterogeneity of CPS are two of the biggest obstacles to interoperation and interconnection among various information models and subsystems. In order to address the dilemma, this paper proposed a systematic, data‐centric system design approach comprehensively including data‐oriented system architecture, data resources abstraction and data‐driven mechanisms. To be specific, the autonomous decentralized system (ADS) theory is introduced to establish the data‐centric system architecture. A MQTT/MQTT‐SN‐based communication protocol, which supports not only IP‐based devices but also non‐IP devices, is proposed to meet the requirement of publish–subscribe mechanism of the ADS‐based system architecture. A data‐centric meta‐interface is proposed to formalize the data resources abstraction. A Lebesgue sampling‐based data exchange mechanism, which is proven tractable but effective, is proposed to realize the data‐driven mechanism. As user energy management system (UEMS) is an integral part of smart grid, a realistic UEMS for heating project is considered a typical case to study the performance of the proposed data‐centric system. This paper is one of the first few papers that systematically propose a systematic data‐centric system design approach for a smart grid, which is supported by aspects of system architecture, communication protocol and data exchange mechanism. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

ACS Style

Kunqi Jia; Zhihua Wang; Shuai Fan; Shaopeng Zhai; Guangyu He. Data‐Centric Approach: A Novel Systematic Approach for Cyber Physical System Heterogeneity in Smart Grid. IEEJ Transactions on Electrical and Electronic Engineering 2019, 14, 748 -759.

AMA Style

Kunqi Jia, Zhihua Wang, Shuai Fan, Shaopeng Zhai, Guangyu He. Data‐Centric Approach: A Novel Systematic Approach for Cyber Physical System Heterogeneity in Smart Grid. IEEJ Transactions on Electrical and Electronic Engineering. 2019; 14 (5):748-759.

Chicago/Turabian Style

Kunqi Jia; Zhihua Wang; Shuai Fan; Shaopeng Zhai; Guangyu He. 2019. "Data‐Centric Approach: A Novel Systematic Approach for Cyber Physical System Heterogeneity in Smart Grid." IEEJ Transactions on Electrical and Electronic Engineering 14, no. 5: 748-759.

Journal article
Published: 16 March 2018 in Applied Sciences
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Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.

ACS Style

Shuai Fan; Guangyu He; Kunqi Jia; Zhihua Wang. A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems. Applied Sciences 2018, 8, 452 .

AMA Style

Shuai Fan, Guangyu He, Kunqi Jia, Zhihua Wang. A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems. Applied Sciences. 2018; 8 (3):452.

Chicago/Turabian Style

Shuai Fan; Guangyu He; Kunqi Jia; Zhihua Wang. 2018. "A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems." Applied Sciences 8, no. 3: 452.