Chemical production sites usually consist of plants that are owned by different companies or business units but are tightly connected by streams of materials and carriers of energy. Distributed optimization, where each entity optimizes its objective and the transfer prices of energy and materials are adapted by a coordinator, is a promising approach to this kind of problems, as confidentiality of internal data can be preserved. In this contribution, we propose an extension of the widely used subgradient methods for inequality constrained distributed QPs, which we call analytical extrapolation (AE). Therein, the analytical structure of the dual function is exploited to speed up convergence. Two strategies for handling changing sets of active constraints are presented. We investigate the performance of our algorithm on test problems, where different problem parameters are varied, and show that the performance of our algorithm is in most cases significantly better than that of other methods.

化学生产场所通常由不同公司或业务部门拥有的工厂组成，但它们之间通过物质流和能源载体紧密相连。分布式优化（每个实体都优化其目标，并且由协调员调整能源和材料的转移价格）是解决此类问题的一种有前途的方法，因为可以保留内部数据的机密性。在此贡献中，我们提出了对不等式约束的分布式QP广泛使用的次梯度方法的扩展，我们称其为分析外推（AE）。其中，利用对偶函数的解析结构来加快收敛速度​​。提出了两种用于处理活动约束的变化集合的策略。我们调查算法在测试问题上的性能，