Multi-objective chance constrained programming model for operational-planning of V2G integrated microgrid

This paper presents a novel chance constrained programming model for the operational-planning of a microgrid containing renewable energy sources like wind and solar, and vehicle to grid (V2G) storage. The model investigates the economic implications of the V2G storage on the microgrid operation. The...

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Published in:DRPT 2015 : proceedings of the 5th IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies : Nov. 26-29, 2015, Changsha, China pp. 443 - 448
Main Authors: Waqar, Asad, Shaorong Wang, Khalid, Muhammad S., Xiaohan Shi
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2015
Subjects:
chance constrained programming
Decision support systems
Markov chain Monte Carlo
Markov processes
Metropolis-Hastings algorithm
microgrid
Microgrids
Monte Carlo methods
operational-planning
Power industry
Programming
renewable energy
Renewable energy sources
V2G
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Abstract This paper presents a novel chance constrained programming model for the operational-planning of a microgrid containing renewable energy sources like wind and solar, and vehicle to grid (V2G) storage. The model investigates the economic implications of the V2G storage on the microgrid operation. The microgrids consisting of intermittent renewable energy sources always need additional backup to mitigate the fluctuating power. This additional backup results in increased investment and operational costs. The V2G storage in such a situation can act as energy buffer and can supply the microgrid with balancing services at low costs. However the availability of the power from the V2G storage is also intermittent in nature because of the random plug-in pattern of electric vehicles. In this paper a microgrid containing a wind, PV and diesel generation and energy storage (V2G/flxed) is connected to an external grid. The external grid has the problem of capacity shortage. Because of the intermittent energy sources, a novel multi-objective stochastic chance constrained programming model is developed for the operational-planning of microgrid. The multiple objectives include the minimization the total net present cost and the operating hours of the diesel generation subject to constraints. The time series data for wind speed, solar radiation, V2G plug-in pattern and load fluctuation is estimated by using the Metropolis-Hastings algorithm, which is a Markov chain Monte Carlo Method. Due to the conflicting objectives, the non-dominant solutions are sorted out and presented using the Pareto front. The simulation results show confidence levels of provision of power by V2G storage and renewable energy sources. The results further show substantial economic implications of V2G storage on microgrid operation in terms of minimization of the total net present cost.
AbstractList This paper presents a novel chance constrained programming model for the operational-planning of a microgrid containing renewable energy sources like wind and solar, and vehicle to grid (V2G) storage. The model investigates the economic implications of the V2G storage on the microgrid operation. The microgrids consisting of intermittent renewable energy sources always need additional backup to mitigate the fluctuating power. This additional backup results in increased investment and operational costs. The V2G storage in such a situation can act as energy buffer and can supply the microgrid with balancing services at low costs. However the availability of the power from the V2G storage is also intermittent in nature because of the random plug-in pattern of electric vehicles. In this paper a microgrid containing a wind, PV and diesel generation and energy storage (V2G/flxed) is connected to an external grid. The external grid has the problem of capacity shortage. Because of the intermittent energy sources, a novel multi-objective stochastic chance constrained programming model is developed for the operational-planning of microgrid. The multiple objectives include the minimization the total net present cost and the operating hours of the diesel generation subject to constraints. The time series data for wind speed, solar radiation, V2G plug-in pattern and load fluctuation is estimated by using the Metropolis-Hastings algorithm, which is a Markov chain Monte Carlo Method. Due to the conflicting objectives, the non-dominant solutions are sorted out and presented using the Pareto front. The simulation results show confidence levels of provision of power by V2G storage and renewable energy sources. The results further show substantial economic implications of V2G storage on microgrid operation in terms of minimization of the total net present cost.
Author Waqar, Asad
Xiaohan Shi
Shaorong Wang
Khalid, Muhammad S.
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  givenname: Asad
  surname: Waqar
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  organization: Huazhong Univ. of Sci. & Technol., Wuhan, China
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  surname: Shaorong Wang
  fullname: Shaorong Wang
  organization: Huazhong Univ. of Sci. & Technol., Wuhan, China
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  fullname: Khalid, Muhammad S.
  organization: Huazhong Univ. of Sci. & Technol., Wuhan, China
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  surname: Xiaohan Shi
  fullname: Xiaohan Shi
  organization: Shandong Univ., Jinan, China
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PublicationTitle DRPT 2015 : proceedings of the 5th IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies : Nov. 26-29, 2015, Changsha, China
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Snippet This paper presents a novel chance constrained programming model for the operational-planning of a microgrid containing renewable energy sources like wind and...
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StartPage 443
SubjectTerms chance constrained programming
Decision support systems
Markov chain Monte Carlo
Markov processes
Metropolis-Hastings algorithm
microgrid
Microgrids
Monte Carlo methods
operational-planning
Power industry
Programming
renewable energy
Renewable energy sources
V2G
Title Multi-objective chance constrained programming model for operational-planning of V2G integrated microgrid
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