Battery energy storage capacity optimisation for grid-connected microgrids with distributed generators

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Author(s)
Garmabdari, Rasoul
Moghimi, Mojtaba
Yang, Fuwen
Gray, Evan
Lu, Junwei
Year published
2017
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Abstract:
This paper presents a battery capacity optimisation method with the aim of investment and operational cost reduction for grid-connected microgrids consisting of dispatchable generators, renewable energy resources and battery energy storage. The operating cost of grid-connected commercial Microgrids is mainly associated with the purchased energy from the grid and monthly peak demand. Hence, mitigating the peak value by the means of battery energy storage and dispatchable generators during the peak period can effectively reduce the operating cost. However, due to the high cost and short life span of the battery energy ...
View more >Abstract: This paper presents a battery capacity optimisation method with the aim of investment and operational cost reduction for grid-connected microgrids consisting of dispatchable generators, renewable energy resources and battery energy storage. The operating cost of grid-connected commercial Microgrids is mainly associated with the purchased energy from the grid and monthly peak demand. Hence, mitigating the peak value by the means of battery energy storage and dispatchable generators during the peak period can effectively reduce the operating cost. However, due to the high cost and short life span of the battery energy storage systems, the optimum design of energy storages is of the utmost importance to the Microgrids. This paper proposes an efficient iterative method with an inner unit commitment optimisation layer to achieve the optimised battery capacity. In order to implement the inner unit commitment optimisation, the Mixed Integer Quadratic Programming (MIQP) optimisation algorithm is applied and CPLEX solver is chosen to solve the optimisation problem. This approach is applicable and beneficial when dealing with high demands as it economically distributes the load requirement between the battery and dispatchable generators. Finally, the proposed method is applied to determine the battery capacity of the experimental Microgrid at Griffith University. The simulation results for the understudy case verified the efficiency and effectiveness of the proposed approach.
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View more >Abstract: This paper presents a battery capacity optimisation method with the aim of investment and operational cost reduction for grid-connected microgrids consisting of dispatchable generators, renewable energy resources and battery energy storage. The operating cost of grid-connected commercial Microgrids is mainly associated with the purchased energy from the grid and monthly peak demand. Hence, mitigating the peak value by the means of battery energy storage and dispatchable generators during the peak period can effectively reduce the operating cost. However, due to the high cost and short life span of the battery energy storage systems, the optimum design of energy storages is of the utmost importance to the Microgrids. This paper proposes an efficient iterative method with an inner unit commitment optimisation layer to achieve the optimised battery capacity. In order to implement the inner unit commitment optimisation, the Mixed Integer Quadratic Programming (MIQP) optimisation algorithm is applied and CPLEX solver is chosen to solve the optimisation problem. This approach is applicable and beneficial when dealing with high demands as it economically distributes the load requirement between the battery and dispatchable generators. Finally, the proposed method is applied to determine the battery capacity of the experimental Microgrid at Griffith University. The simulation results for the understudy case verified the efficiency and effectiveness of the proposed approach.
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Conference Title
2017 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING CONFERENCE (AUPEC)
Volume
2017-November
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Electrical energy generation (incl. renewables, excl. photovoltaics)