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
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Browsing Zhang, Jie by Subject "Energy storage"
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Item 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, JieAs 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.Item 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, JieEnergy 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.