Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Chi, Lixun; Su, Huai; Zio, Enrico; Qadrdan, Meysam; Li, Xueyi; Zhang, Li; Fan, Lin; Zhou, Jing; Yang, Zhaoming; Zhang, Jinjun

doi:10.1016/j.renene.2021.04.102

Cited by 37 publications

(13 citation statements)

References 56 publications

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“…The authors in [23] use Bayesian learning to obtain the probabilistic statistical features of the measurement data and super Latin sampling to generate the complete measurement data. The reliability assessment is implemented with the extreme learning machine in [24], where the result proves that the data-driven approach is suitable for the prediction analysis of IES. Zhang et al in [25] propose a novel data-driven approach to judge whether the IES is operating in danger and Fu et al in [26] introduce a method to estimate the failure probabilities of IES considering the influence of weather uncertainty, where the data-driven theory plays an irreplaceable role.…”

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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A data‐driven time‐step determination approach for dynamic simulation of heat‐electric coupled system

Guan

Zhou

et al. 2022

IET Renewable Power Gen

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Dynamic simulation of heat-electric coupled system (HECS) can accurately describe the operating condition of networks and equipment of the system, where the space-time step plays a crucial role in the simulation accuracy and speed. To optimally determine the simulation space-time step, this paper identifies the main influencing parameters for simulation elapsed time by distance correlation (DC) analysis and establishes a simulation time prediction model with stepwise regression (SR) method. Meanwhile, the quadratic curve fitting (QCF) method is adopted to obtain the simulation error prediction model. A simulation space-time step optimal determination model considering the specific simulation requirements is further introduced to improve the simulation efficiency based on the aforementioned models. To validate the proposed methodologies, comprehensive analysis of various study cases on a 20-node testbed is presented. The results demonstrate that the average error in elapsed time and error prediction by the proposed model is merely 0.2426 s and 4.84% compared with the real data.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

A data‐driven time‐step determination approach for dynamic simulation of heat‐electric coupled system

Guan

Zhou

et al. 2022

IET Renewable Power Gen

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“…It is assumed that the temperature at the pipe junction is fixed [6] . The convex-concave method is utilized to reduce the SOC relaxation error of the joint electricity-gasthermal optimization [7] . In the water network, to deal with the bilinear terms of water head and flow, the precise SOC relaxation is used to decouple the electrothermal coupling relationship of the cogeneration unit by using the electric boiler, which can improve the operational flexibility of the cogeneration unit, thereby reducing the wind curtailment [8] .…”

Section: Introductionmentioning

confidence: 99%

Integrated Energy System Design of Low-carbon City Based on Multi-energy Complementary of Cooling Heating and Power

Wang

Guan

2023

J. Phys.: Conf. Ser.

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The model of urban integrated energy dispatching system is a mathematical model used to describe the distribution of energy flow in the system, which has the characteristics of high accuracy, strong nonlinearity, and non-convexity. However, in the optimal power flow and other optimization problems, the traditional numerical solution algorithm cannot be directly applied. The strong nonlinearity and non-convexity of the system power flow model bring great difficulties to the solution of the optimization problem. Based on the input and output theory of cooling, heating, and power complementary energy, this paper studies the coupling energy relationship and power flow of various energy devices in an urban multi-source microgrid. It establishes the reliability index of the system energy supply, starting from the integrated energy system structure of low-carbon cities. A bi-level optimization model considering the constraints of energy supply reliability is constructed, which provides a basis for the subsequent establishment of the optimal allocation model.

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“…Thus, developing an appropriate reliability assessment method could benefit the stakeholders by improving the evaluation accuracy. Up to now, several methods have been proposed by researchers (Shariatkhah et al, 2015;Ansari et al, 2020;Cao et al, 2021;Chi et al, 2021). To name a few, H. Wang.…”

mentioning

confidence: 99%

“…In the work of Shariatkhah et al (2015), a sequential Monte Carlo method incorporating Markov chains is proposed to assess the energy supply reliability of IES. Chi et al (2021) evaluated the probability distribution of each functional state of the IES based on machine learning methods and statistical methods. In the work of Ansari et al (2020), a datadriven method via mixture models and importance sampling is proposed to construct time-dependent probability distributions of wind-integrated systems.…”

mentioning

confidence: 99%

Data-Driven Reliability Evaluation of the Integrated Energy System Considering Optimal Service Restoration

Dai

Zeng

et al. 2022

Front. Energy Res.

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The demand for environmental protection and energy utilization transformation has promoted the rapid development of integrated energy systems (IES). Reliability evaluation is a fundamental element in designing IES as it could instruct the planning and operation of IES. This study proposes a novel data-driven reliability improvement and evaluation method considering the three-state reliability model and an optimal service restoration model (OSR). First, a multi-energy flow model is introduced and linearized in order to reduce the computing complexity. Next, a three-state reliability model is developed, considering the transitional process and partial failure mode. Furthermore, an optimal service restoration model is established to determine the best repairment moment for minimizing the load curtailment, and a data-driven reliability evaluation method is developed that integrates OSR and models the stochastic state transition process using the historical measurement data of the smart meters. Finally, the proposed reliability evaluation method is tested on a test IES, and the numerical results validate its effectiveness in evaluating the reliability of IES and improving the overall reliability.

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Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Cited by 37 publications

References 56 publications

A data‐driven time‐step determination approach for dynamic simulation of heat‐electric coupled system

A data‐driven time‐step determination approach for dynamic simulation of heat‐electric coupled system

Integrated Energy System Design of Low-carbon City Based on Multi-energy Complementary of Cooling Heating and Power

Data-Driven Reliability Evaluation of the Integrated Energy System Considering Optimal Service Restoration

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