12 results on '"Wang, Zhaojian"'
Search Results
2. New framework of low-carbon city development of China: Underground space based integrated energy systems
- Author
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Qin, Boyu, Li, Hengyi, Wang, Zhaojian, Jiang, Yuan, Lu, Dechun, Du, Xiuli, and Qian, Qihu
- Published
- 2024
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3. Distributed optimal load frequency control considering nonsmooth cost functions
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Wang, Zhaojian, Liu, Feng, Zhao, Changhong, Ma, Zhiyuan, and Wei, Wei
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- 2020
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4. Effectiveness of inactivated COVID-19 vaccines against SARS-CoV-2 Omicron subvariant BF.7 among outpatients in Beijing, China.
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Yang, Hui, Wang, Zhaojian, Zhang, Ying, Xu, Man, Wang, Yushu, Zhang, Yi, An, Zhuoling, and Tong, Zhaohui
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SARS-CoV-2 Omicron variant , *COVID-19 vaccines , *BOOSTER vaccines , *VACCINE effectiveness , *VACCINATION - Abstract
• Three-dose inactivated vaccines was effective against Omicron subvariant BF.7. • The efficacy was more effective in population with underlying diseases. • No protective effect was observed in the two-dose vaccination group. • No effectiveness was observed in population without underlying diseases. To evaluate the effectiveness of inactivated vaccines against SARS-CoV-2 Omicron subvariant BF.7. Information was extracted from outpatients diagnosed with COVID-19 between December 19, 2022 and January 5, 2023 at a single center. Univariate and multivariate logistic regression were performed and three adjusted models were conducted. Vaccine effectiveness (VE) was defined as (1 − OR) × 100 %. Our study comprised a total of 752 outpatients. After adjusting for factors with a P-value < 0.10 in univariable logistic regression, the VE of booster vaccine was 65.4 % (95 % CI6.1–87.3 %, P = 0.037) in comparison with unvaccinated group. Results of the other two adjusted models were similar, which were 66.3 % (95 % CI: 9.0–87.6 %, P = 0.032) and 64.8 % (95 % CI: 3.6–87.1 %, P = 0.042), respectively. Stratified analysis based on underlying diseases indicated that inactivated vaccines did not provide any protection to patients without underlying diseases. In the population with underlying diseases, the VE of booster vaccination was 68.2 % (95 % CI: 8.4–88.9 %, P = 0.034) after adjustment. However, full vaccination did not demonstrate any protection in all models. There was an effectiveness of three-dose inactivated vaccines against Omicron subvariant BF.7. Our findings supported the importance of booster vaccination. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. Exponential stability of partial primal–dual gradient dynamics with nonsmooth objective functions.
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Wang, Zhaojian, Wei, Wei, Zhao, Changhong, Ma, Zhiyuan, Zheng, Zetian, Zhang, Yunfan, and Liu, Feng
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EXPONENTIAL stability , *CONVEX sets , *NONSMOOTH optimization - Abstract
In this paper, we investigate the continuous time partial primal–dual gradient dynamics (P-PDGD) for solving convex optimization problems with the form min x ∈ X , y ∈ Ω f (x) + h (y) , s. t. A x + B y = C , where f (x) is strongly convex and smooth, but h (y) is strongly convex and non-smooth. Affine equality and general convex set constraints are included. We prove the existence of the solution to P-PDGD and its exponential stability. Then, bounds on decaying rates are provided. Moreover, it is also shown that the decaying rates can be regulated by setting the stepsize. [ABSTRACT FROM AUTHOR]
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- 2021
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6. Asynchronous distributed voltage control in active distribution networks.
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Wang, Zhaojian, Liu, Feng, Su, Yifan, Yang, Peng, and Qin, Boyu
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VOLTAGE control , *POWER resources , *ALGORITHMS - Abstract
With the proliferation of distributed energy resources (DERs), voltage regulation in active distribution networks (ADNs) has been facing a great challenge. This paper derives an asynchronous distributed voltage control strategy based on the partial primal–dual gradient algorithm, where both active and reactive power of DERs are considered. Different types of asynchrony due to imperfect communication or other practical limits, such as random time delays and non-identical sampling/computation rates, are fitted into a unified analytic framework. The proposed asynchronous algorithm is then converted into a fixed-point problem by leveraging the operator splitting method, which leads to the convergence proof. Moreover, an online implementation method is introduced to make the controller adjustable to time-varying environments. Finally, numerical experiments are carried out on a rudimentary 8-bus and the IEEE-123 bus system to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2020
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7. Distributed load-side control: Coping with variation of renewable generations.
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Wang, Zhaojian, Mei, Shengwei, Liu, Feng, Low, Steven H., and Yang, Peng
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INTERNAL auditing , *RENEWABLE natural resources , *GENERATIONS , *ELECTRIC charge , *CLOSED loop systems , *ELECTRIC vehicles , *TRACKING control systems - Abstract
This paper addresses the distributed frequency control problem in a multi-area power system taking into account of unknown time-varying power imbalance. Particularly, fast controllable loads are utilized to restore system frequency under changing power imbalance in an optimal manner. The imbalanced power causing frequency deviation is decomposed into three parts: a known constant part, an unknown low-frequency variation and a high-frequency residual. The known steady part is usually the prediction of power imbalance. The variation may result from the fluctuation of renewable resources, electric vehicle charging, etc., which is usually unknown to operators. The high-frequency residual is also unknown and treated as an external disturbance. Correspondingly, in this paper, we resolve the following three problems in different timescales: (1) allocate the steady part of power imbalance economically; (2) mitigate the effect of unknown low-frequency power variation locally; (3) attenuate unknown high-frequency disturbances. To this end, a distributed controller combining consensus method with adaptive internal model control is proposed. We first prove that the closed-loop system is asymptotically stable and converges to the optimal solution of an optimization problem if the external disturbance is not included. We then prove that the power variation can be mitigated accurately. Furthermore, we show that the closed-loop system is robust against both parameter uncertainty and external disturbances. The New England system is used to verify the efficacy of our design. [ABSTRACT FROM AUTHOR]
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- 2019
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8. On Nash–Stackelberg–Nash games under decision-dependent uncertainties: Model and equilibrium.
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Zhang, Yunfan, Liu, Feng, Wang, Zhaojian, Chen, Yue, Feng, Shuanglei, Wu, Qiuwei, and Hou, Yunhe
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EQUILIBRIUM , *NASH equilibrium , *GAMES - Abstract
In this paper, we discuss a class of two-stage hierarchical games with multiple leaders and followers, which is called Nash–Stackelberg–Nash (N–S–N) games. Particularly, we consider N–S–N games under decision-dependent uncertainties (DDUs). DDUs refer to the uncertainties that are affected by the strategies of decision-makers and have been rarely addressed in game equilibrium analysis. In this paper, we first formulate the N–S–N games with DDUs of complete ignorance, where the interactions between the players and DDUs are characterized by uncertainty sets that depend parametrically on the players' strategies. Then, a rigorous definition for the equilibrium of the game is established by consolidating generalized Nash equilibrium and Pareto-Nash equilibrium. Afterward, we prove the existence of the equilibrium of N–S–N games under DDUs by applying Kakutani's fixed-point theorem. Finally, an illustrative example is provided to show the impact of DDUs on the equilibrium of N–S–N games. [ABSTRACT FROM AUTHOR]
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- 2022
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9. A distributed incremental update scheme for probability distribution of wind power forecast error.
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Jia, Mengshuo, Shen, Chen, and Wang, Zhaojian
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WIND forecasting , *WIND power , *MACHINE learning , *PROBABILITY theory , *PARAMETER estimation , *DISTRIBUTED parameter systems , *DISTRIBUTED algorithms - Abstract
• A distributed strategy to avoid collecting raw data from different parties. • An incremental learning algorithm for real-time parameter estimation. • Continuous update for the probability distribution of wind power forecast error. • Correlation among distributed wind generations is considered. Due to the uncertainty of distributed wind generations (DWGs), a better understanding of the probability distributions (PDs) of their wind power forecast errors (WPFEs) can help market participants (MPs) who own DWGs perform better during trading. Under the premise of an accurate PD model, considering the correlation among DWGs and absorbing the new information carried by the latest data are two ways to maintain an accurate PD. These two ways both require the historical and latest wind power and forecast data of all DWGs. Each MP, however, only has access to the data of its own DWGs and may refuse to share these data with MPs belonging to other stakeholders. Besides, because of the endless generation of new data, the PD updating burden increases sharply. Therefore, a distributed strategy is used to avoid raw data collection. In addition, the incremental learning strategy is further applied to reduce the updating burden. Finally, a distributed incremental update scheme is proposed to make each MP continually acquire the latest conditional PD of its DWGs' WPFE. Specifically, the Gaussian-mixture-model-based (GMM-based) joint PD is first used to characterize the correlation among DWGs. Then, a distributed modified incremental GMM algorithm is proposed to enable MPs to update the parameters of the joint PD in a distributed and incremental manner. After that, a distributed derivation algorithm is further proposed to make MPs derive their conditional PD of WPFE from the joint one in a distributed way. Combining the two original algorithms, the complete distributed incremental update scheme is finally achieved, by which each MP can continually obtain its latest conditional PD of its DWGs' WPFE via neighborhood communication and local calculation with its own data. The effectiveness, correctness, and efficiency of the proposed scheme are verified using the dataset from the NREL. [ABSTRACT FROM AUTHOR]
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- 2020
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10. A class of multi-parametric quadratic program with an uncertain objective function.
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Wei, Wei, Wu, Danman, Wang, Zhaojian, Shafie-khah, Miadreza, and Catalão, João P.S.
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ELECTRICITY pricing , *ECONOMIC models , *UNCERTAINTY - Abstract
• The property of solutions of a class of multi-parametric quadratic program with objective uncertainty. • Variable and parameter transformations that reformulate the problem as a traditional one. • Application of the proposed model to the economic evaluation of a residential energy system. In this paper we analyze a class of multi-parametric quadratic program (mpQP) with parameters in the objective function. Except for parameters in coefficients associated with the linear term, the coefficient of the quadratic term, which is a positive definite matrix, is multiplied by a scalar parameter, while the quadratic coefficient of a standard mpQP is deterministic. We reveal the optimal solution is a linear fractional function in the parameters, and the critical regions remain polyhedral. The discussed mpQP can be reformulated as a standard mpQP via variable and parameter transformations. The proposed method is used to evaluate the economic operation of a residential energy system under time-and-level-of-use electricity pricing, highlighting the potential application in practical problems. [ABSTRACT FROM AUTHOR]
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- 2020
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11. Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints.
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Zhu, Dafeng, Yang, Bo, Ma, Chengbin, Wang, Zhaojian, Zhu, Shanying, Ma, Kai, and Guan, Xinping
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INDUSTRIAL management , *INDUSTRIAL districts , *PARK management , *SPREAD (Finance) , *ENERGY management , *DISTRIBUTED algorithms , *ONLINE algorithms - Abstract
Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid–ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically. • A systematic online optimization framework ensuring provable performance for multi-energy system management is presented. • A method is proposed for estimating users' willingness to shift inelastic loads via public data. • The energy storage balance and real-time supply–demand balance can be achieved by two-timescale optimization. • Fast distributed method is proposed to deal with temporally-coupled constraints. [ABSTRACT FROM AUTHOR]
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- 2022
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12. Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park.
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Zhu, Dafeng, Yang, Bo, Liu, Yuxiang, Wang, Zhaojian, Ma, Kai, and Guan, Xinping
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INDUSTRIAL districts , *REINFORCEMENT learning , *ENERGY management , *DEEP learning - Published
- 2022
- Full Text
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