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.
The complexity of the multiperiod dynamic unit commitment problem makes it difficult or even unviable to find the global optimal solution. Ordinal optimization provides a simulation-based approach suitable for solving this kind of problem. It uses crude models and rough estimates to derive a small set of unit commitment schemes for which simulations are necessary and worthwhile to find good enough solutions with drastically reduced computational burden. The 10-100 thermal units standard test example and the case of an actual provincial power system with 128 units verify the feasibility of ordinal optimization to solve the large-scale dynamic unit commitment problem.Yuxin Du (Non-member) received the B.S. degree from Nanchang University, Nanchang, China, in 2015. Now she is pursuing the M.S. degree from
Large-scale wind farms connected to the power grid have brought great challenges to power-system dispatch. In this study, an improved two-stage compensation stochastic optimisation algorithm based on recursive dynamic regression is proposed to solve the day-ahead dynamic economic-dispatching problem considering the high-dimensional correlation of multiple wind farms. First, a copula function is used to describe the correlation of high-dimensional wind farms. Second, a twostage compensation stochastic-optimisation algorithm is proposed to convert the dynamic economic-dispatching model to a twostage model with mutual iteration by decoupling the conventional and stochastic variables. In this decoupled model, the calculation of the expected compensation cost is critical and usually limited by the dimension of the correlated wind farms, which leads to inefficient convergence and long computation times. To solve those problems, a recursive dynamic multivariable linear regression method based on global least squares is proposed to improve the two-stage stochastic optimisation algorithm. This improved two-stage compensation algorithm overcomes the dimensional disaster of traditional stochastic optimisation methods and can solve the dynamic economic dispatching problem considering the high-dimensional correlation of multiple wind farms. Finally, the practicability and efficiency of the proposed algorithm are verified by the examples of an IEEE118 system and an actual provincial system.
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