Research on scheduling strategy of heterogeneous flexible load clusters with strong association consider dual-scale similarities

With the increasing marketization of electricity, residential users are gradually participating in various businesses of power utility companies, and there are more and more interactive adjustments between load, source, and grid. However, the participation of large-scale users has also brought challenges to the grid companies in carrying out demand-side dispatching work. The user load response is uneven, and users’ behavioral characteristics are highly differentiated. It is necessary to consider the differences in users’ electricity consumption demand in the design of the peak–valley load time-sharing incentives, and to adopt a more flexible incentive form. In this context, this paper first establishes a comprehensive clustering method integrating k-means and self-organizing networks (SONs) for the two-step clustering and a BP neural network for reverse adjustment and correction. Then, a time-varying incentive optimization for interactive demand response based on two-step clustering is introduced. Furthermore, based on the different clustering results of customers, the peak–valley load time-sharing incentives are formulated. The proposed method is validated through case studies, where the results indicate that our method can effectively improve the users’ load characteristics and reduce the users’ electricity costs compared to the existing methods.

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Research on scheduling strategy of heterogeneous flexible load clusters with strong association consider dual-scale similarities

Cited by 3 publications

References 21 publications

Informing targeted Demand-Side Management: Leveraging appliance usage patterns to model residential energy demand heterogeneity

Informing targeted Demand-Side Management: Leveraging appliance usage patterns to model residential energy demand heterogeneity

A new deep clustering method with application to customer selection for demand response program

A Time-Varying Incentive Optimization for Interactive Demand Response Based on Two-Step Clustering

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