LAUSR

Multidisciplinary collaborative optimisation (MCO) is an effective theory to solve the design optimisation problems of complex systems. Here, power-system day-ahead dynamic economic dispatch with integrated wind power is studied. The MCO method is introduced to decouple the scenarios and collaboratively optimise the multiple scenarios to deal with the uncertainty of wind-power generation. Based on this, the dynamic economic-dispatch problem is divided into a system-level model and multiple subdisciplinary optimisation models for the forecasting and error scenarios. A dynamic relaxation algorithm is then introduced to solve the system-level optimisation model. The decoupled subdisciplinary models for the error scenarios are solved by a grid-computation tool in parallel, which greatly improves computational efficiency. Finally, the established model and its corresponding solution method are applied to a 10-machine, 39-bus test system. It is shown that the proposed MCO-based dynamic economic dispatching method performs much better in high-dimensional scenarios, which are the inherent limitations of the traditional centralised multi-scenario method.