Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow
Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow
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DOI：
10.23919/acc45564.2020.9147814
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Yue-Chun Chen;A. Bernstein;Adithya M. Devraj;Sean P. Meyn
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文献类型：
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作者：
Yue-Chun Chen;A. Bernstein;Adithya M. Devraj;Sean P. Meyn
本文研究了网络系统的实时优化问题，并开发了在线算法，在不明确了解系统模型的情况下将系统引导到最优轨迹。该问题被建模为具有时变性能目标和工程约束的动态优化问题。算法的设计充分利用了在线零阶原始-对偶投影梯度法。具体地说，涉及目标函数的梯度的原始步骤(因此需要网络系统模型)被其零级近似所取代，该零级近似具有使用确定性扰动信号的两个函数评估。使用系统输出的测量来执行评估，从而产生反馈互连，其中优化算法用作反馈控制器。本文对这种互连的稳定性和跟踪特性提供了一些见解。最后，将该方法应用于电力系统实时最优潮流问题，并在IEEE 37节点配电试验馈线上验证了该方法在参考功率跟踪和电压调节方面的有效性。
This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.