A Multi-Objective Optimization Planning of Park-level Integrated Energy System Based on Two-tier Planning Model

Hao, Luxi; Wu, Jun; Li, Baolin; Xu, Jianbing; Liu, Chang; Gan, Peiying; Wang, Zhidong; Zhang, Yan

doi:10.1109/cieec47146.2019.cieec-2019330

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Considering that the single charging loss in the objective function of the inner layer model is a nonlinear term, a linear transformation method is used to construct a typical 0-1 mixed integer linear programming (MILP) problem [36]. This problem can be modeled using the Yalmip toolbox based on MATLAB, and the Gurobi solver is invoked to solve it [37]. Since the outer optimization model is a typical single-objective nonlinear optimization problem, the MATLAB-based genetic algorithm toolkit gatbx is proposed to solve [38].…”

Section: Solving Model Based On Dqnmentioning

confidence: 99%

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Chen

Jiang

et al. 2022

Entropy

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Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.

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Section: Solving Model Based On Dqnmentioning

confidence: 99%