Open Access

ARTICLE

Research on Flexible Load Aggregation and Coordinated Control Methods Considering Dynamic Demand Response

Chun Xiao^1,2,*

1 State Grid Shanxi Marketing Service Center, Taiyuan, 030032, China
2 College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

* Corresponding Author: Chun Xiao. Email:

(This article belongs to the Special Issue: Emerging Technologies for Future Smart Grids)

Energy Engineering 2025, 122(7), 2719-2750. https://doi.org/10.32604/ee.2025.063782

Received 23 January 2025; Accepted 21 March 2025; Issue published 27 June 2025

Abstract

In contemporary power systems, delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids. This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration. In the initial stage, using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads, including temperature-controlled loads, electric vehicles, and energy storage devices, a novel calculation method for their maximum adjustable capacities is devised. Distinct from conventional methods, this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities, thereby enhancing the accuracy and flexibility of demand-side resource management. Subsequently, an SSA-BiLSTM flexible load classification prediction model is established. This model represents an innovative application in the field, effectively combining the advantages of the Sparrow Search Algorithm (SSA) and the Bidirectional Long-Short-Term Memory (BiLSTM) neural network. Furthermore, a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately. This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator, significantly improving the precision of capacity assessment compared to existing methods. Finally, in consonance with the intra-day scheduling plan, a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely, thus achieving real-time dynamic demand response. Through in-depth case analysis and comprehensive comparative studies, the effectiveness of the proposed method is convincingly validated. With its innovative techniques and enhanced performance, it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems, providing a novel and effective solution for optimizing power grid operation and demand-side management.

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