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首页> 外文期刊>IEEE transactions on industrial informatics >Accelerated Distributed Hybrid Stochastic/Robust Energy Management of Smart Grids
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Accelerated Distributed Hybrid Stochastic/Robust Energy Management of Smart Grids

机译：加速分布式混合动力随机/智能电网的鲁棒能量管理

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摘要

The uncertainties of renewable energy, loads, and electricity prices pose significant challenges to the economical and secure energy management of smart grids. In this article, a hybrid stochastic/robust (HSR) optimization method is developed to minimize the overall cost of all units. The proposed approach takes advantage of stochastic programming, robust optimization, and distributed optimization methods while considering various system constraints. First, stochastic electricity price scenarios are selected by the Latin hypercube sampling method. Second, the uncertainties of renewable energy generation and loads are managed by the proposed robust optimization method under each price scenario. Then, an improved distributed optimization method is proposed to solve the formulated HSR optimization problem, which considerably enhances the convergence with the accelerated gradient method. Numerical case studies of both small-scale and large-scale power systems demonstrate the accuracy, effectiveness, and scalability of the proposed distributed HSR approach. Additionally, the optimality and convergence of this proposed distributed algorithm are mathematically proven and analyzed.
机译：可再生能源，负荷和电价的不确定性对智能电网的经济和安全能源管理构成了重大挑战。在本文中，开发了一种混合随机/鲁棒（HSR）优化方法，以最大限度地降低所有单元的总成本。所提出的方法利用随机编程，强大的优化和分布式优化方法，同时考虑各种系统约束。首先，通过拉丁超立体采样方法选择随机电价方案。其次，可再生能源生成和负载的不确定性由在每个价格方案下所提出的鲁棒优化方法管理。然后，提出了一种改进的分布式优化方法来解决配方的HSR优化问题，这显着提高了加速梯度方法的收敛性。小规模和大型电力系统的数值案例研究证明了所提出的分布式HSR方法的准确性，有效性和可扩展性。另外，该提出的分布式算法的最优性和收敛在数学上被证明并分析。

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