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Fully decentralised multi-area dynamic economic dispatch for large-scale power systems via cutting plane consensus

机译:通过切平面共识,对大型电力系统进行完全分散的多区域动态经济调度

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This study discusses the implementation of a fully decentralised optimisation for the multi-area dynamic economic dispatch of large-scale power systems based on the cutting plane consensus (CPC) algorithm. Each area constructs a local master problem to approximate the original problem by generating its own cutting planes and receiving cutting planes from other areas, hence an upper-level coordinator is not required. Two modifications of the standard CPC algorithm have been developed. First, only the newest cutting planes are transmitted among different areas to reduce the amount of information transferred. Second, to reduce the required number of iterations, a cutting plane is deleted only if it is inactive over consecutive iterations. Theoretically, the convergence and correctness of this algorithm can be guaranteed for power systems with a wide range of scales without tuning the optimisation parameters, and a high-quality suboptimal solution can still be obtained if the algorithm is prematurely terminated. The proposed method is applied to simulations of a three-area IEEE Reliability Test System and an actual four-area 2298-bus provincial power system to demonstrate its effectiveness.
机译:这项研究讨论了基于剖切面共识(CPC)算法的大规模电力系统多区域动态经济调度的完全分散优化的实现。每个区域通过生成自己的切割平面并从其他区域接收切割平面来构造局部主问题,以近似原始问题,因此不需要上级协调器。已经开发了标准CPC算法的两种修改形式。首先,只有最新的切割平面在不同区域之间传输,以减少传输的信息量。其次,为减少所需的迭代次数,仅当剖切面在连续的迭代中处于非活动状态时才将其删除。从理论上讲,在不调整优化参数的情况下,可以保证该算法在较大规模的电力系统中的收敛性和正确性,并且如果算法过早终止,仍然可以获得高质量的次优解决方案。该方法被应用于三区域IEEE可靠性测试系统和实际的四区域2298总线省级电力系统的仿真,以证明其有效性。

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