Zhang, Jie

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6880

Jie Zhang is an Assistant Professor of Mechanical Engineering. Dr. Zhang's research interests include:

  • Multidisciplinary design optimization,
  • Complex engineered systems,
  • Power & energy systems,
  • Renewable energy,
  • Grid modernization,
  • Big data analytics,
  • Probabilistic design


Recent Submissions

Now showing 1 - 5 of 5
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    Design a J-Type Air-Based Battery Thermal Management System Through Surrogate-Based Optimization
    (Elsevier Ltd, 2019-06-11) Liu, Yuanzhi; Zhang, Jie; Liu, Yuanzhi; Zhang, Jie
    Battery thermal management system is of great importance to the performance and safety of electric vehicles. The conventional U- and Z-type air-based structures may fail to meet the thermal requirements under changing working conditions. This paper proposes a novel J-type air-based battery thermal management system by integrating the U-type and Z-type structures. A comparative parametric study of key design variables and priori optimized structures is first conducted with a newly developed battery electro-thermal model. Based on the parametric analyses, the grouped-channel optimizations are performed using surrogate-based optimization. Results show that there are 35.3%, 46.6%, and 31.18% reduction in temperature rise for U-, Z-, and J-type, respectively. The pros and cons of the J-type structure are further explored by comparing with the optimal U- and Z-type structures. A further J-type optimization regarding the manifold configuration is also conducted to show that the optimal settings of the air-based cooling system vary across working conditions, and the J-type structure is able to be adaptively controlled to satisfy the cooling requirement. Corresponding experiments are also conducted to validate the modeling and optimization results. © 2019 Elsevier Ltd
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    Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization
    (Institute of Electrical and Electronics Engineers Inc.) Sun, Mucun; Feng, Cong; Zhang, Jie; Chartan, E. K.; Hodge, B. -M; Sun, Mucun; Feng, Cong; Zhang, Jie
    Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model. © 2018 IEEE.
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    Grid Optimization of Shared Energy Storage Among Wind Farms Based on Wind Forecasting
    (Institute Of Electrical And Electronics Engineers Inc.) Zhu, K.; Chowdhury, S.; Sun, Mucun; Zhang, Jie; Sun, Mucun; Zhang, Jie
    Energy storage is crucial for source-side renewable energy power plants for enhancing output stability and reducing mismatch between power generation and demand. However, installing large size energy storage systems for renewable energy plants may not be economic, due to high capital cost and ever-increasing human resources and maintenance cost. As a result, in this paper, a shared energy storage system among multiple wind farms is proposed to address this energy management challenge. A state-of-the-art wind power forecasting method with ensemble numerical weather prediction models is used to optimally determine the size of a shared energy storage system (ESS). A number of scenarios are performed to optimize and explore the energy storage size under different economic and storage resource sharing circumstances. The performance of ESS, namely the net revenue of power plants, is explored subject to ESS size and operating constraints of wind farms and power systems. Results of a case study show that sharing of energy storage among multiple wind farms and lower cost of storage progressively enhance the economic benefits of using storage to mitigate over-production/under-forecasting (thus curtailment) and under-production/over-forecasting scenarios.
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    Break-Even Analysis of Battery Energy Storage in Buildings Considering Time-Of-Use Rates
    (IEEE Computer Society) Sun, Mucun; Chang, Chih-Lun; Zhang, Jie; Mehmani, A.; Culligan, P.; Sun, Mucun; Chang, Chih-Lun; Zhang, Jie
    As energy consumption in residential and commercial buildings continues to grow, demand-side management (DSM) for energy systems becomes crucial, because DSM can shift energy use from peak to off-peak hours. In order to realize peak load shifting, energy storage systems (ESSs) can be integrated into buildings to store energy during off-peak hours and discharge energy in peak hours. However, installing a large number of ESSs in individual buildings can complicate DSM and increase the overall capital cost. In this paper, a cost-effective DSM strategy is proposed to address this energy management challenge. The break-even cost of battery storage in a building is explored through a process of two-step optimization in conjunction with different tariff structures. A number of scenarios are performed to conduct cost analyses of the storage-based building energy system under different time-of-use rate structures. The performance of the DSM strategy in the battery break-even cost, is explored using a particle swarm optimization algorithm based on the size of energy storage and priced-based constraints of the energy system. Results of a case study show that the proposed approach can reduce the peak-to-average ratio of the total energy demand to the total energy costs. In addition, as the percentage reductions in yearly maximum energy peaks increase, the optimal battery cost becomes progressively more economical to building owners.
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    Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures
    (MDPI Ag, 2016-02-02) G©mez-L©zaro, E.; Bueso, M. C.; Kessler, M.; Mart©n-Mart©nez, S.; Zhang, Jie; Hodge, B. -M; Molina-Garc©a, A.; Zhang, Jie
    The Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.

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