Optimal power flow (OPF) with close cooperation between the power grid and flexible electricity-intensive chemical processes can reduce costs for the grid and for electricity users. However, this would require sharing detailed chemical process models, which may reveal confidential information and potentially jeopardize competitive advantages for a chemical process operator. We propose an algorithm that enables economically advantageous cooperation without the need to exchange sensitive information. Low-order linear models are used to represent the dynamic behavior of electricity-intensive processes. We integrate these models into the OPF problem and solve the problem using a decoupling strategy based on Benders-type cuts. The cuts introduce limited communication between the chemical processes and the grid without exchanging explicit information pertaining to the process dynamics and performance. Our results reproduce solutions obtained by sharing detailed process models up to a user-defined optimality gap for several test cases that reflect both normal and congested grid states. We also investigate the trade-off between the value of the optimality gap and computational effort. Finally, we study the scaling behavior of the iterative procedure with respect to flexible loads at multiple grid locations.

中文翻译：

---

一种在潮流协同优化中保护敏感过程信息的解耦策略

最佳潮流 (OPF) 与电网和灵活的电力密集型化学过程之间的密切合作可以降低电网和电力用户的成本。然而，这需要共享详细的化学过程模型，这可能会泄露机密信息并可能危及化学过程操作者的竞争优势。我们提出了一种算法，无需交换敏感信息即可实现经济上有利的合作。低阶线性模型用于表示电力密集型过程的动态行为。我们将这些模型集成到 OPF 问题中，并使用基于 Benders 型切割的解耦策略解决问题。削减在化学过程和电网之间引入了有限的通信，而没有交换与过程动态和性能有关的明确信息。我们的结果再现了通过共享详细的过程模型获得的解决方案，直至反映正常和拥塞网格状态的几个测试用例的用户定义的最优差距。我们还研究了最优差距的价值和计算工作量之间的权衡。最后，我们研究了迭代过程相对于多个网格位置的灵活负载的缩放行为。我们还研究了最优差距的价值和计算工作量之间的权衡。最后，我们研究了迭代过程相对于多个网格位置的灵活负载的缩放行为。我们还研究了最优差距的价值和计算工作量之间的权衡。最后，我们研究了迭代过程相对于多个网格位置的灵活负载的缩放行为。