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Community Energy Storage System, Smart grid, General Robust Optimization

Wang, Weiran

Supervisor and department: Jie, Chen Electrical and Computer Engineering Hao, Liang Electrical and Computer Engineering

Examining committee member and department: Di, Niu Electrical and Computer Engineering Petr, Musilek Electrical and Computer Engineering

Department: Department of Electrical and Computer Engineering

Specialization: Energy Systems

Date accepted: 2017-01-19T15:59:53Z

Graduation date: 2017-06:Spring 2017

Degree: Master of Science

Degree level: Master's

Abstract: Due to the integration of renewable energy sources such as wind turbines, significant technical challenge exists for the energy management in the future power distribution systems and-or microgrids. The efficiency and reliability of the energy management may be jeopardized by the randomness of the power production from renewable energy sources. In order to address this challenge and to harness renewable power, community energy storage CES systems with dispatchable capacities can be installed to buffer the intermittent supply from renewable energy sources In Part I, we focus on the stochastic model of CES system with wind power generation. The power generation of each wind turbine is modeled using a Markov modulated rate process MMRP, while the CES system is modeled as a queuing system. Based on a diffusion approximation of the queue length, a closed-form representation of the cumulative distribution function CDF of the SoC of the CES system can be derived. In Part II, we focus on the optimal energy management of the CES systems in a microgrid. During the normal operation of the microgrid, the dispatchable outputs of the CES systems are controlled to minimize the overall operation cost of the microgrid. When a fault occurs in the main grid, the microgrid operates in an islanded mode, and energy stored in the CES systems can be utilized to supply the loads in the microgrid for reliability improvement. To control the amount of energy stored in the CES systems, two kinds of SoC thresholds are introduced, which correspond to hard reservation and soft reservation of energy. Accordingly, the stochastic model of the CES system developed in Part I is extended to embed the impact of the two kinds of thresholds. To take account of the potential bias in the forecast of wind power generation, the energy management problem is solved based on a general robust optimization technique. The performance of the stochastic model and optimization technique is evaluated based on the IEEE 123 bus test feeder as well as the wind power generation data of Changling Wind Farm.

Language: English

DOI: doi:10.7939-R3416TB5J

Rights: This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.





Author: Wang, Weiran

Source: https://era.library.ualberta.ca/


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Stochastic Modeling and Optimization for Community Energy Storage Systems by Weiran Wang A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in ENERGY SYSTEMS Department of Electrical and Computer Engineering University of Alberta c Weiran Wang, 2017 Abstract Due to the integration of renewable energy sources such as wind turbines, significant technical challenge exists for the energy management in the future power distribution systems and-or microgrids.
In particular, the efficiency and reliability of the energy management may be jeopardized by the randomness of the power production from renewable energy sources.
In order to address this challenge and to harness renewable power, community energy storage (CES) systems with dispatchable capacities can be installed to buffer the intermittent supply from renewable energy sources.
Yet, how to manage the CES systems still requires extensive research, as the dispatchable capacity of each CES system depends on its state-of-charge (SoC), which is also random in nature. This thesis consists of two parts.
In Part I, we focus on the stochastic model of CES system with wind power generation.
The power generation of each wind turbine is modeled using a Markov modulated rate process (MMRP), while the CES system is modeled as a queuing system.
Based on a diffusion approximation of the queue length, a closed-form representation of the cumulative distribution function (CDF) of the SoC of the CES system can be derived.
The analytical model is validated by a case study based on the wind power generation data obtained from Changling Wind Farm in Jilin Province of Northeast China. In Part II, we focus on the optimal energy management of the CES systems in a microgrid.
During the normal operation of the microgrid, the dispatchable outputs of the CES systems are controlled to minimize the overall operation cost of the microgrid. When a fault occurs in the main grid, the microgrid oper...





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