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Energy Management of Islanded Nanogrids Through Nonlinear Optimization Using Stochastic Dynamic Programming

机译:利用随机动态规划的非线性优化能源管理纳米格栅

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摘要

Islanded nanogrids (NGs) are autonomous systems consisting of small-scale generation units including renewable energy sources and traditional fuel generators and energy storage systems (ESS) that typically serve few buildings or loads. This work aims at developing and validating a new optimal energy management (EM) algorithm for an islanded NG. To minimize the generator's operating cost and maximize battery availability at each operating cycle, dynamic programming (DP) framework is employed to solve the underlying optimization problem. The goal of the proposed approach is to ensure the use of maximum available solar power and to achieve optimal battery state of charge. To meet that goal, the management of the ESS is formulated as a stochastic optimal control problem, where nonlinearities in the battery discharging process are considered. A Markov model is constructed for predicting the probability distribution of the solar production used in the stochastic DP formulation. Simulation results are given to illustrate the efficacy of the proposed DP-based approach compared to a rule-based algorithm. Finally, a hardware-in-the-loop system is used to evaluate the real-time operation of the proposed EM algorithm.
机译:岛状纳米林(NGS)是由小型生成单元组成的自主系统,包括可再生能源和传统燃料发生器和能量存储系统(ESS),通常为少数建筑物或负载服务。这项工作旨在开发和验证岛立NG的新的最佳能源管理(EM)算法。为了最大限度地减少发电机的运行成本并最大限度地提高每个操作周期的电池可用性,采用动态编程(DP)框架来解决底层优化问题。该方法的目标是确保使用最大可用的太阳能和实现最佳电池充电状态。为了满足该目标,ESS的管理被制定为随机最佳控制问题,其中考虑了电池放电过程中的非线性。制造马尔可夫模型,用于预测随机DP配方中使用的太阳能生产的概率分布。给出了与基于规则的算法相比,仿真结果说明了所提出的基于DP的方法的功效。最后,使用硬件循环系统来评估所提出的EM算法的实时操作。

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