This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural networks. We encode the feasible space of optimization problems characterized by both tractable and intractable constraints, e.g. differential equations, to a neural network. Leveraging an exact mixed-integer reformulation of neural networks, we solve mixed-integer linear programs that accurately approximate solutions to the originally intractable non-linear optimization problem. We apply our methods to the AC optimal power flow problem (AC-OPF), where directly including dynamic security constraints renders the AC-OPF intractable. Our proposed approach has the potential to be significantly more scalable than traditional approaches. We demonstrate our approach for power system operation considering N-1 security and small-signal stability, showing how it can efficiently obtain cost-optimal solutions which at the same time satisfy both static and dynamic security constraints.
翻译:本文介绍一个框架,通过使用神经网络,捕捉先前难以解决的优化限制,并将它们转化为混合整数线性程序。我们把以可移动和难以解决的限制(如差异方程式)为特征的优化问题的可行空间编码为神经网络。我们利用精确的混合整数重组神经网络,解决准确接近原先难以解决的非线性优化问题的解决办法的混合整数线性程序。我们将我们的方法应用于空调最佳电流问题(AC-OPF),直接包括动态安全限制,使AC-OPF难以解决。我们提议的方法比传统方法大得多。我们展示了我们考虑到N-1安全和小信号稳定性的动力系统操作方法,表明它如何能够有效地获得成本优化的解决方案,同时满足静态和动态安全限制。