Economic dispatching method with robust collaborative consistency
An economic scheduling and consistency technology, applied in the direction of single-network parallel feed arrangement, etc., can solve problems such as communication topology changes, large amount of transmitted data, and failure of the collaborative consensus algorithm to converge
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Embodiment 1
[0045]The Robust Collaborative Consensus Algorithm (RCCA) proposed in this embodiment is firstly an algorithm suitable for decentralized optimal scheduling. It has been improved on the basis of the collaborative consensus algorithm: (1) Introduced consensus The linear gain function effectively suppresses the delay and noise problems of information transmission between agents. (2) At the same time, in order to prevent the time-varying information topology problem caused by the capacity limitation, topology failure and plug-and-play of the intelligent unit, the concept of virtual consistency variable is proposed.
[0046] as attached figure 1 Shown, described a kind of economic scheduling method with robust collaborative consistency, comprises the following steps:
[0047] Step S1, determining the adjacency matrix A of the multi-agent network topology graph, thereby generating a Laplacian matrix L;
[0048] The adjacency matrix A determined in step S1 and the Laplacian matrix ...
Embodiment 2
[0082] In the second embodiment, the decentralized economic dispatch framework model is taken as the research object, and the model includes three power generation intelligent body units. The specific model parameters are shown in Table 1.
[0083] Table 1 Parameters of the decentralized economic dispatch framework model
[0084] In the second embodiment, there are two targets to be optimized, which are the economic target and the emission target. In this embodiment, the method for assigning the power economy / emission target of the intelligent unit in the model to the robust collaborative consistency includes the following steps:
[0085] S1. Determine the adjacency matrix A, thereby generating a Laplacian matrix L.
[0086] Wherein the adjacency matrix A determined in this embodiment is:
[0087] A = 0 1 1 ...
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