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1. Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty

Author

Li, Ran, Zhang, Haipeng, Sun, Mingyang, Teng, Fei, Wan, Can, Pineda, Salvador, and Kariniotakis, Georges

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Better forecasts may not lead to better decision-making. To address this challenge, decision-oriented learning (DOL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-to-end model. Applications of DOL in power systems have been developed in recent years. For renewable-rich power systems, uncertainties propagate through sequential tasks, where traditional statistical-based approaches focus on minimizing statistical errors at intermediate stages but may fail to provide optimal decisions at the final stage. This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem. Secondly, this paper extensively reviews DOL techniques and their applications in power systems while highlighting their pros and cons in relation to SBL. Finally, this paper identifies the challenges to adopt DOL in the energy sector and presents future research directions.

Published
2024

2. Nonparametric Stochastic Analysis of Dynamic Frequency in Power Systems: A Generalized Ito Process Model

Author

Wan, Can, Ren, Yupeng, and Ju, Ping

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

The large-scale integration of intermittent renewable energy has brought serious challenges to the frequency security of power systems. In this paper, a novel nonparametric stochastic analysis method of system dynamic frequency is proposed to accurately analyze the impact of renewable energy uncertainty on power system frequency security, independent of any parametric distribution assumption. The nonparametric uncertainty of renewable generation disturbance is quantified based on probabilistic forecasting. Then, a novel generalized Ito process is proposed as a linear combination of several Gaussian Ito processes, which can represent any probability distribution. Furthermore, a stochastic model of power system frequency response is constructed by considering virtual synchronization control of wind power. On basis of generalized Ito process, the complex nonlinear stochastic differential equation is transformed into a linear combination of several linear stochastic differential equations to approximate nonparametric probability distribution of the system dynamic frequency. Finally, the validity of the proposed method is verified by the single-machine system and IEEE 39-Bus system.

Published
2023

3. Convex Relaxation of Combined Heat and Power Dispatch

Author

Jiang, Yibao, Wan, Can, Botterud, Audun, Song, Yonghua, and Shahidehpour, Mohammad

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

Combined heat and power dispatch promotes interactions and synergies between electric power systems and district heating systems. However, nonlinear and nonconvex heating flow imposes significant challenges on finding qualified solutions efficiently. Most existing methods rely on constant flow assumptions to derive a linear heating flow model, sacrificing optimality for computational simplicity. This paper proposes a novel convex combined heat and power dispatch model based on model simplification and constraint relaxation, which improves solution quality and avoids assumptions on operating regimes of district heating systems. To alleviate mathematical complexity introduced by the commonly used node method, a simplified thermal dynamic model is proposed to capture temperature changes in networked pipelines. Conic and polyhedral relaxations are then applied to convexify the original problems with bilinear and quadratic equality constraints. Furthermore, an adaptive solution algorithm is proposed to successively reduce relaxation gaps based on dynamic bivariate partitioning, improving solution optimality with desirable computational efficiency. The proposed method is verified on a 33-bus electric power system integrated with a 30-node district heating system and compared to nonlinear programming solvers and constant-flow-based solutions.

Published
2020
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4. Efficient Robust Dispatch of Combined Heat and Power Systems

Author

Jiang, Yibao, Wan, Can, Botterud, Audun, Song, Yonghua, and Dong, Zhao Yang

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

Combined heat and power systems facilitate efficient interactions between individual energy sectors for higher renewable energy accommodation. However, the feasibility of operational strategies is difficult to guarantee due to the presence of substantial uncertainties pertinent to renewable energy and multi-energy loads. This paper proposes a novel efficient robust dispatch model of combined heat and power systems based on extensions of disturbance invariant sets. The approach has high computational efficiency and provides flexible and robust strategies with an adjustable level of conservativeness. In particular, the proposed robust dispatch method obtains operational strategies by solving a nominal uncertainty-free dispatch problem, whose complexity is identical to a deterministic problem. The robustness against uncertainties is enhanced by endowing the nominal dispatch model with properly tightened constraints considering time-variant uncertainty sets. Towards this end, a novel direct constraint tightening algorithm is developed based on the dual norm to calculate multi-period tightened constraints efficiently without linear programming iterations. Furthermore, the budget uncertainty set is newly combined with constraint tightening to flexibly adjust the conservativeness level of robust solutions. The effectiveness of the proposed robust method is demonstrated in simulation studies of a test system in terms of computational efficiency, decision robustness and cost optimality., Comment: 8 pages, 12 figures

Published
2020
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7. A Multi-model Combination Approach for Probabilistic Wind Power Forecasting

Author

Lin, You, Yang, Ming, Wan, Can, Wang, Jianhui, and Song, Yonghua

Subjects
Computer Science - Learning, Statistics - Applications
Abstract

Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. As the complicated characteristics of wind power prediction error, it would be difficult to develop a universal forecasting model dominating over other alternative models. Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models those provide different kinds of probability density functions to improve the probabilistic forecast accuracy. Three probabilistic forecasting models based on the sparse Bayesian learning, kernel density estimation and beta distribution fitting are used to form the combined model. The parameters of the MMC model are solved based on Bayesian framework. Numerical tests illustrate the effectiveness of the proposed MMC approach.

Published
2017

9. Voltage-Price Coupling in Distribution Networks

Author

Li, Biao, Wan, Can, Yu, Peng, Xu, Qianwen, Song, Yonghua, Li, Biao, Wan, Can, Yu, Peng, Xu, Qianwen, and Song, Yonghua

Abstract

Demand-side users are practical and economical resources for participating in voltage regulation of distribution networks, whose regulation effect is closely related to the price. This paper proposes a novel voltage-price coupling (VPC) mechanism to construct a fair voltage-based nodal pricing method for distribution networks to encourage demand-side users to participate in voltage regulation to improve voltage quality. The voltage impact factor is established to fairly characterize the impact of each nodal power on the voltage deviation in the distribution network. Further, the VPC mechanism is constructed to highly couple the nodal price with the voltage in both spatial and time dimensions by establishing distribution rules of prices at different nodes and different horizons. The VPC mechanism forms a fair and adaptable voltage-based nodal pricing method for distribution networks, which also considers the impact on the existing pricing mechanism, and the balance between the voltage regulation effect and user experience. Then, a price-driven voltage regulation (PVR) model is proposed for the distribution network to reduce voltage deviations and user costs, which integrates the demand-side resource management, voltage regulation, and voltage-based nodal pricing. Case studies verify the effectiveness of the VPC mechanism and the PVR model., QC 20240412

Published
2024
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10. Mediation of depressive symptoms in the association between blood urea nitrogen to creatinine ratio and cognition among middle-aged and elderly adults: evidence from a national longitudinal cohort study.

Author

Feng, Qiaoduan, Yang, Shaokun, Ye, Shaohua, Wan, Can, Wang, Hongjian, and You, Jinsong

Subjects
BLOOD urea nitrogen, MENTAL depression, MIDDLE-aged persons, CENTER for Epidemiologic Studies Depression Scale, COGNITION
Abstract

Background: The relationships between BUNCr (blood urea nitrogen and creatinine ratio) and cognitive function, as well as depressive symptoms, remain unclear. We aim to investigate the association between BUNCr and cognition, as well as depressive symptoms, and to identify the mechanisms underlying these relationships. Methods: We utilized data from the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2020. Cognitive function was assessed using the Telephone Interview of Cognitive Status (TICS) scale, while depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). We employed multivariate linear regression models to examine the association between BUNCr and cognitive function, as well as depressive symptoms. Additionally, causal mediation analysis was conducted to identify potential mediation effects of depressive symptoms between BUNCr and cognition. Results: We observed a negative association between BUNCr and cognitive function (coefficient: -0.192; 95% confidence interval [CI]: -0.326 ∼ -0.059) and a positive relationship between BUNCr and depressive symptoms (coefficient: 0.145; 95% CI: 0.006 ∼ 0.285). In addition, the causal mediation analysis revealed that depressive symptoms (proportion mediated: 7.0%) significantly mediated the association between BUNCr and cognition. Conclusion: Our study has unveiled that BUNCr is inversely associated with cognitive function and positively linked to depressive symptoms. Moreover, we found that depressive symptoms significantly mediated the association between BUNCr and cognition. These findings provide new evidence and insights for the prevention and management of cognitive function and dementia. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Melatonin attenuates scopolamine‐induced cognitive dysfunction through SIRT1/IRE1α/XBP1 pathway.

Author

Liu, Xiao‐Qi, Huang, Shun, Zheng, Jia‐Yi, Wan, Can, Hu, Tian, Cai, Ye‐Feng, Wang, Qi, and Zhang, Shi‐Jie

Abstract

Background: The prevalence of dementia around the world is increasing, and these patients are more likely to have cognitive impairments, mood and anxiety disorders (depression, anxiety, and panic disorder), and attention deficit disorders over their lifetime. Previous studies have proven that melatonin could improve memory loss, but its specific mechanism is still confused. Methods: In this study, we used in vivo and in vitro models to examine the neuroprotective effect of melatonin on scopolamine (SCOP)‐induced cognitive dysfunction. The behavioral tests were performed. 18F‐FDG PET imaging was used to assess the metabolism of the brain. Protein expressions were determined through kit detection, Western blot, and immunofluorescence. Nissl staining was conducted to reflect neurodegeneration. MTT assay and RNAi transfection were applied to perform the in vitro experiments. Results: We found that melatonin could ameliorate SCOP‐induced cognitive dysfunction and relieve anxious‐like behaviors or HT22 cell damage. 18F‐FDG PET‐CT results showed that melatonin could improve cerebral glucose uptake in SCOP‐treated mice. Melatonin restored the cholinergic function, increased the expressions of neurotrophic factors, and ameliorated oxidative stress in the brain of SCOP‐treated mice. In addition, melatonin upregulated the expression of silent information regulator 1 (SIRT1), which further relieved endoplasmic reticulum (ER) stress by decreasing the expression of phosphorylate inositol‐requiring enzyme (p‐IRE1α) and its downstream, X‐box binding protein 1 (XBP1). Conclusions: These results indicated that melatonin could ameliorate SCOP‐induced cognitive dysfunction through the SIRT1/IRE1α/XBP1 pathway. SIRT1 might be the critical target of melatonin in the treatment of dementia. [ABSTRACT FROM AUTHOR]

Published
2024
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16. A two‐stage scheduling model for urban distribution network resilience enhancement in ice storms.

Author

Zhao, Yuheng, Wan, Can, Wang, Chong, Wang, Naiyu, Deng, Ruilong, and Li, Binbin

Subjects
ICE storms, PHOTOVOLTAIC power generation, MIXED integer linear programming, GENERATIVE adversarial networks, MONTE Carlo method, POWER resources, INTEGER programming, STOCHASTIC programming
Abstract

This paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Hydroxysafflor Yellow A Exerts Neuroprotective Effect by Reducing Aβ Toxicity Through Inhibiting Endoplasmic Reticulum Stress in Oxygen–Glucose Deprivation/Reperfusion Cell Model

Author

Ma, Hui-Han, primary, Wen, Jun-Ru, additional, Fang, Hao, additional, Su, Shan, additional, Wan, Can, additional, Zhang, Chao, additional, Lu, Fang-Mei, additional, Fan, Ling-Ling, additional, Wu, Guang-Liang, additional, Zhou, Zi-Yi, additional, Qiao, Li-Jun, additional, Zhang, Shi-Jie, additional, and Cai, Ye-Feng, additional

Published
2023
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24. Optimal Scheduling of Integrated Energy Systems With Multiple CCHPs for High Efficiency and Low Emissions

Author

Xie, Haimin, Liu, Hui, Wan, Can, Goh, Hui Hwang, Rahman, Saifur, Xie, Haimin, Liu, Hui, Wan, Can, Goh, Hui Hwang, and Rahman, Saifur

Abstract

In order to reach carbon neutrality, there is growing interest in reducing greenhouse gas (GHG) and improving energy efficiency. One way to address this issue is the optimal scheduling of the integrated energy system (IES) with multiple combined cooling heating and power (CCHP) systems as proposed in this article. We model IES as a device with multiple input/output ports by the energy hub (EH) framework and propose a multiobjective optimal model to improve energy efficiency and reduce GHG emissions. The proposed model is constructed as a mixed-integer nonlinear programming (MINLP) due to considering nonlinear couplings of multiple energy flows and the unit commitment of multiple CCHP systems. To improve the computational efficiency, the proposed MINLP model is transformed into a nonlinear programming (NLP) model by a fast unit commitment technique based on the approximation of the aggregated online capacity. Finally, simulation results show the effectiveness of the proposed approach in reducing GHG emissions and improving energy efficiency as well as computational efficiency.

Published
2023

25. Probabilistic Forecasting-Based Reserve Determination Considering Multi-Temporal Uncertainty of Renewable Energy Generation

Author

Xu, Yuqi, Wan, Can, Liu, Hui, Zhao, Changfei, and Song, Yonghua

Abstract

Operating reserve, among the most important ancillary services, is a powerful prescription for mitigating increasing uncertainty of renewable generation. Traditional reserve determination neglects uncertainty of renewable generation variations within the dispatch interval, which cannot guarantee sufficient reserve deliverability in real-time operation. This article proposes a probabilistic forecasting-based reserve determination method to efficiently deal with multi-temporal uncertainty of renewable energy in power systems. A novel probabilistic forecasting approach is proposed by integrating Dirichlet process Gaussian mixture model into bootstrap-based extreme learning machine. A unified uncertainty model is constructed for depicting both interval-averaged uncertainty and intra-interval uncertainty by combing the probabilistic forecasts and linearized Itô-process model. In current business practices of many independent system operators, the coordinated determination of two well-designed reserve products, including regulating reserve and ramp capability reserve, is formulated in a two-stage robust optimization framework. A Bernstein polynomial-based model reformulation approach is then employed to handle the existing heterogeneity in the sub-models of each stage. Consequently, the integrated reserve determination is embedded in a generic two-stage robust optimization problem, which can be efficiently solved by the adopted modified column-and-constraint generation method. Finally, numerical simulations are implemented to validate the effectiveness and profitability of the proposed approach.

Published
2024
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26. Evaluation of Grid Support Capacity From Electrified Railways

Author

He, Zihan, Wan, Can, Liu, Hui, and Ju, Ping

Abstract

High power level and temporal operation flexibility endow electric trains with great potential for improving grid resilience. It becomes crucial to quantify the grid support capacity (GSC) from large-scale electrified railways. This article innovatively proposes a hierarchical train coordination and trajectory regulation method to evaluate and deliver the GSC from electrified railways. In the light of the mechanical energy consumption analysis of train running, the energy-oriented train operation model is proposed to avoid the nonlinear differential dynamics of train kinetics when coordinating large-scale trains. Then the aggregation and disaggregation of trains for grid support can be formulated as linear and convex quadratic programming problems respectively. To closely follow the reference power and enhance the feasibility of train operation, a novel iterative discretization approach is proposed to generate the precise reference trajectory. A sliding mode controller with disturbance bound adaption is devised to overcome the modeling errors and disturbances when deploying GSC in realistic environment. Comprehensive numerical examples verify the effectiveness of the proposed scheme, and demonstrate the significant grid support potential from collaborative electrified railways.

Published
2024
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39. Polarization-independent plasmon-induced transparency and slow light effects in a fully continuous symmetric cross-shaped monolayer graphene structure.

Author

Wan, Can, Xiong, Cuixiu, Tan, Meng, Wei, Chengya, Wang, Jie, and Zhang, Saiwen

Abstract

A fully continuous geometric center symmetric cross-shaped graphene structure is proposed. Each cross-shaped graphene unit cell is composed of a central graphene region and four completely symmetric graphene chips, where each graphene chip acts as both bright and dark modes simultaneously, while the central graphene region always acts as the bright mode. Through destructive interference, the structure can realize the single plasmon-induced transparency (PIT) phenomenon, where the optical responses are independent of the polarization direction of the linearly polarized light due to the symmetry of the structure. Combining numerical simulations with coupled mode theory (CMT) calculations, the modulation of the Fermi energy of graphene to the optical spectra is investigated. The results show that the spectra are blue shifted as the Fermi energy increases, and the absorption of the two absorption peaks is basically equal (48.7%) when the Fermi energy increases to 0.667 eV. Theoretical calculations show that the slow light performance of the designed structure enhances with the increase of Fermi energy, where the maximum group index is high up to 424.73. Furthermore, it is worth noting that the electrode can be made very small due to its fully continuous structure. This work provides guidance in terms of terahertz modulators, tunable absorbers, and slow light devices. [ABSTRACT FROM AUTHOR]

Published
2023
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40. Efficacy and safety of a sequential treatment with clearing heat and eliminating phlegm and tonifying and activating blood circulation in treating acute ischemic stroke: study protocol for a randomized controlled trial

Author

Zhou, Ziyi, Wan, Can, Zhao, Yuanqi, Liu, Xiangzhe, Gao, Ying, A N, Hongwei, L I, Lejun, Zhang, Huili, Y U, Xiaofei, Zhang, Xinchun, Wang, Huijuan, Shi, Qing, Wei, Chunhua, Chen, Jie, Huang, Wenguo, Chen, Junbin, H U, Mingzhe, and Cai, Yefeng

Subjects
Hospitalization, Stroke, Hot Temperature, Treatment Outcome, Double-Blind Method, Qi, Humans, Multicenter Studies as Topic, Original Article, Ischemic Stroke, Randomized Controlled Trials as Topic
Abstract

OBJECTIVE: To investigate the short-term efficacy and safety outcomes following a sequential treatment with clearing heat and eliminating phlegm (CHEP) formula and tonifying Qi and activating blood circulation (TQABC) formula in patients with acute ischemic stroke (AIS) within a 72 h time window. METHODS: In this randomized, multicenter, double-blinded, placebo-controlled trial, 500 participants will be randomly assigned in a ratio of 1∶1 to the CHEP+ TQABC group or control group. In addition to guideline-based standard medical care, participants in the treatment group will receive the CHEP formula for the first 5 consecutive days followed by the TQABC formula for another 10 consecutive days, while those in the control group will receive CHEP formula placebo and TQABC formula placebo consecutively. The primary outcome measure will be the comparison of the change in the National Institutes of Health Stroke Scale score from baseline to 15 days after randomization. The secondary outcome measures will include the scores on the modified Rankin Scale, Barthel Index, Patient-Reported Outcomes, TCM symptom pattern (Zheng-hou) evaluation Scale, and the incidence of in-hospital complications. Safety assessment will include the physical examination, laboratory detection, any adverse events or serious adverse events, and the proportion of any complications during hospitalization. DISCUSSION: The results of this study will provide objective and scientific data with which to assess the efficacy and safety of a sequential treatment based on “integrating disease and symptom pattern” for patients with AIS.

Published
2022

41. Data-Driven Nonparametric Probabilistic Optimal Power Flow: An Integrated Probabilistic Forecasting and Analysis Methodology

Author

Li, Yunyi, Wan, Can, Cao, Zhaojing, and Song, Yonghua

Abstract

With large-scale integration of renewable energy such as wind power, probabilistic analysis of optimal power flow becomes crucial for the decision-making of power systems. This paper proposes a novel data-driven integrated probabilistic forecasting and analysis (IPFA) methodology for the nonparametric probabilistic optimal power flow (N-POPF), which internalizes the probabilistic forecasting and nonparametric distributional description forms into uncertainty analysis. The proposed IPFA methodology fully utilizes the uncertainty analysis method to guide the model-free nonparametric probabilistic forecasting of wind power, and then conducts the N-POPF analysis effectively based on the uncertainty information contained in historical data. A comprehensive uncertainty evaluation criterion based on point estimate method and information entropy is proposed to assess both the inherent uncertainty and uncertainty influence of input random variables. Then a model-free multivariate probabilistic forecasting method is established to directly support the solving of N-POPF problems with similar historical measurements. Finally, with deterministic optimal power flow problems corresponding to the selected historical samples, a weighted combination approach for the power flow results is developed to derive the quantiles of the output random variables. Comprehensive experiments on IEEE 24-bus and 118-bus test systems validate the superiority of the proposed IPFA methodology in estimation accuracy and computational efficiency.

Published
2023
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