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

Kai Zhang, Jingzhi Li, Zhubin He and Wanfeng Yan

Physica A: Statistical Mechanics and its Applications, 2018, vol. 501, issue C, 356-369

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.

Keywords: Microgrid energy dispatching; Markov decision process; Linear approximation approach (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:501:y:2018:i:c:p:356-369

DOI: 10.1016/j.physa.2018.02.196

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