Chunyang Liu

Harbin Medical University

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Hengxu Zhang

Shandong University

Haoran Zhao

Wuhan University

Xiaohan Shi

Shandong University of Technology

Huaguang Zhang

State Key Laboratory of Synthetical Automation for Process Industries

Hang Tian

Nanjing University

Houzhen Ma

Shandong University

Mengxue Wang

Anhui Medical University

J.X. Sun

First Affiliated Hospital of Harbin Medical University

Jinyi Guo

Harbin Medical University

Huan Xiang

Harbin Medical University

Cooperative Institutions

Shandong University

First Affiliated Hospital of Harbin Medical University

Harbin Medical University

Heilongjiang Institute of Technology

Shandong First Medical University

Northeast Electric Power University

Tsinghua University

Wuhan University of Science and Technology

China Agricultural University

University of Electronic Science and Technology of China

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Interval-Probabilistic Electricity-Heat-Gas Flow Calculation by Dual-Level Surrogate Structure

IEEE Transactions on Smart Grid (2023)

Hang Tian Haoran Zhao Haoran Li Chunyang Liu Jian Chen

The study of highly efficient methods for performing multiple energy flow (MEF) computation with epistemic and aleatory uncertainties, known as interval-probabilistic energy flow (IPEF) calculation, is a fast-growing area of research. However, existing approaches are easily caught in a quandary among accuracy, efficiency, and scope of applicability. This paper presents a dual-level surrogate structure (DLSS) for dealing with the IPEF calculation of an integrated energy system (IES) in an accurate and efficient manner. The structure accommodates electricity, heat, and gas, attaining a high-level balance among precision, speed, and range of applicability. At the lower level of DLSS, a sparse polynomial chaos expansion (sPCE) model is employed to directly map random input variables to output boundary values, saving massive repetition of deterministic calculation. At the upper level of DLSS, a heterogeneous-learning-based multiple energy flow (HL-MEF) model is proposed to accelerate the indispensable deterministic MEF calculation for online sampling, thereby easing the computational burden of sPCE training. Finally, numerical results on a real-world electricity-heat-gas system demonstrate that the proposed DLSS can solve the IPEF problem at nearly 400 times the speed of the standard double-layer Monte Carlo simulation (DLMCS) with guaranteed accuracy.

Uncertainty Quantification

Surrogate model

10.1109/tsg.2023.3263164

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Citations (3)

Impact of surface ultraviolet radiation intensity on hospital admissions for ischemic and hemorrhagic stroke: A large-scale database study using distributed lag nonlinear analysis, 2015-2022, in Harbin, China

Journal of Stroke and Cerebrovascular Diseases (2024)

Shouyue Wu Hongli Zhang Chunyang Liu Qiunan Yang J.X. Sun

Lag time

Intensity

Stroke

Time lag

10.1016/j.jstrokecerebrovasdis.2024.107908

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Citations (0)

A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function

IEEE Transactions on Power Systems (2021)

Chunyang Liu Hengxu Zhang Mohammad Shahidehpour Quan Zhou Tao Ding

Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system.

Microgrid

10.1109/tpwrs.2021.3099336

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Citations (25)

Non-Intrusive Load Monitoring Method for Multi-Energy Coupling Appliances Considering Spatio-Temporal Coupling

IEEE Transactions on Smart Grid (2023)

Hang Liu Chunyang Liu Haoran Zhao Hang Tian Junwei Liu

The multi-energy coupling (MEC) appliances couple electricity, gas, heat, and cold, of which the gas, heat, and cold have long transient processes. However, these transient features have never been considered in past non-intrusive load monitoring (NILM) methods. This paper proposes a novel NILM method for MEC appliances considering spatio-temporal coupling, which combines semi-supervised learning and weakly supervised learning. Firstly, the unlabeled historical events are labeled by graph-based semi-supervised learning (GBSSL) and distinguished into three categories, i.e., reliable, uncertain, and unreliable samples. Then, to fully use of spatio-temporal coupling information, the unreliable samples are discarded and the feature vectors of other samples are expanded into spatio-temporal coupling feature vectors. To improve the accuracy of load identification, this paper proposes a two-stage deep learning framework "Teaching and Mutual Learning" (TML). In the teaching stage, two convolutional neural networks (CNN) are pre-trained with reliable samples. In the mutual learning stage, the noise samples are filtered by using the different learning abilities of the two networks. The effectiveness and superiority of the proposed method are verified on five types of MEC appliances in a park IES.

Transient (computer programming)

Feature (linguistics)

10.1109/tsg.2023.3248679

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A novel analytical unified energy flow calculation method for integrated energy systems based on holomorphic embedding

Applied Energy (2023)

Houzhen Ma Chunyang Liu Haoran Zhao Hengxu Zhang Mengxue Wang

Energy Flow

10.1016/j.apenergy.2023.121163

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Citations (11)

Python Software Package for Automatic Generation and Verification of BPA Simulation Data Aimed at Load Flow Analysis

2021 International Conference on Power System Technology (POWERCON) (2023)

Jifan Zhang Hengxu Zhang Xiaohan Shi Chunyang Liu

With the expansion of the power grid scale and the development of the smart grid, a substantial number of new infrastructure projects are added to the grid annually. To guarantee the safe and stable operation of the power system, it is crucial to construct a power system simulation model based on the actual grid, and promptly input grid parameters into the simulation software, thereby updating the load flow analysis report to provide effective guidance. However, given the stringent requirements of power system simulation software on system data models, the steps involved are complicated and prone to errors. To improve the efficiency of grid data processing, this paper utilizes the Python programming language, oriented towards the BPA calculation program, and proposes an automatic generation and verification method for load flow simulation data files. This can realize the automatic conversion of grid data to simulation parameters intended for load flow analysis. The reliability and practicality of this data generation and verification method are substantiated using the Shandong power grid as an example.

Python

10.1109/powercon58120.2023.10330953

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A MILP-Based Battery Degradation Model for Economic Scheduling of Power System

IEEE Transactions on Sustainable Energy (2022)

Chunyang Liu Houzhen Ma Hengxu Zhang Xiaohan Shi Fang Shi

Battery energy storage (BES) systems play an increasingly important part in power system operation because of their high efficiency and decreasing cost. This article proposes a mixed integer linear programming (MILP) model of battery degradation considering both depth of discharge (DoD) and state of charge (SoC), which can reflect the battery degradation mechanism. To improve computational efficiency for the economic scheduling of power system, a novel one-dimensional linearization is proposed to linearize this two-variable function, which significantly reduces computation burdens without loss of accuracy. Considering the battery degradation cost, a day-ahead economic scheduling model is built and a MILP algorithm is applied to solve it. The effectiveness of the MILP-based battery degradation model for economic scheduling is validated using the modified IEEE 30-bus system. For comparison, other existing battery degradation models are also implemented in the same system.

Linearization

Degradation

State of charge

10.1109/tste.2022.3232370

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Citations (12)

Analytical Dynamic Energy-Carbon Flow Model and Application in Cost Allocation for Integrated Energy Systems

IEEE Transactions on Smart Grid (2023)

Mengxue Wang Haoran Zhao Chunyang Liu Xiaoli Huang Zhengsong Wang

Energy indicators on the demand side, such as consumers' total carbon emissions and energy costs, nodal energy carbon emission intensity (CEI) and energy price (EP), describe the impact of consumers' energy consumption on system carbon emissions and costs, helping consumers with energy consumption decisions. In this paper, an analytical dynamic energy-carbon flow model of integrated energy system (IES) is proposed, which allocates energy and carbon emissions from supply side to demand side. This method is also applied to cost allocation, resulting in accurate energy indicators for consumers. Firstly, a dynamic energy flow model for an IES is established based on the superposition characteristics of subsystems. Then, a dynamic energy flow tracing (DEFT) method that considers pipeline packing, transmission loss and time delay is proposed first to estimate an analytical energy relationship between sources and loads. Combined with the CEI of sources, a dynamic carbon flow model is proposed to allocate the carbon emissions and form CEI on demand side. We also apply this method to cost allocation, providing consumers with total energy costs and real-time EP. The proposed methodology is validated in a modified IES in Jilin Province, China, and the information conveyed by the energy indicators is completely analyzed.

Energy Flow

Carbon fibers

10.1109/tsg.2023.3327082

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Citations (3)