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Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning

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  • Zhang, Kai
  • Li, Jingzhi
  • He, Zhubin
  • Yan, Wanfeng

Abstract

In this paper, a stochastic optimization framework is proposed to address the microgrid energy dispatching problem with random renewable generation and vehicle activity pattern, which is closer to the practical applications. The patterns of energy generation, consumption and storage availability are all random and unknown at the beginning, and the microgrid controller design (MCD) is formulated as a Markov decision process (MDP). Hence, an online learning-based control algorithm is proposed for the microgrid, which could adapt the control policy with increasing knowledge of the system dynamics and converges to the optimal algorithm. We adopt the linear approximation idea to decompose the original value functions as the summation of each per-battery value function. As a consequence, the computational complexity is significantly reduced from exponential growth to linear growth with respect to the size of battery states. Monte Carlo simulation of different scenarios demonstrates the effectiveness and efficiency of our algorithm.

Suggested Citation

  • Zhang, Kai & Li, Jingzhi & He, Zhubin & Yan, Wanfeng, 2018. "Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 356-369.
    • Handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:356-369
    DOI: 10.1016/j.physa.2018.02.196
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    Cited by:

    1. Minjeong Sim & Dongjun Suh & Marc-Oliver Otto, 2021. "Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building," Sustainability, MDPI, vol. 13(15), pages 1-18, August.
    2. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    3. Ruifeng Shi & Shaopeng Li & Changhao Sun & Kwang Y. Lee, 2018. "Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-22, August.
    4. Sepideh Rezaeeian & Narges Bayat & Abbas Rabiee & Saman Nikkhah & Alireza Soroudi, 2022. "Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs," Energies, MDPI, vol. 15(15), pages 1-18, July.
    5. Jun Dong & Yaoyu Zhang & Yuanyuan Wang & Yao Liu, 2021. "A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR," Sustainability, MDPI, vol. 13(18), pages 1-28, September.
    6. Saif Jamal & Nadia M. L. Tan & Jagadeesh Pasupuleti, 2021. "A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications," Sustainability, MDPI, vol. 13(18), pages 1-31, September.
    7. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    8. Zhang, Hao & Cai, Guixin, 2020. "Subsidy strategy on new-energy vehicle based on incomplete information: A Case in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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