Research on Flexible Control Strategy of Controllable Large Industrial Loads Based on Multi-Source Data Fusion of Internet of Things

With the rapid development of the power grid, the high penetration of new energy sources and the diversity of loads have further aggravated the uncertainty of “source-load”, which has brought huge challenges to the peak shaving of the power grid. In order to ensure the reliable operation of the power system during the electricity peak, this paper combines the IoT technology to propose a flexible control strategy for controllable large industrial loads that considers the interrupt priority. Firstly, the perception and fusion framework of large industrial load information is constructed based on the IoT technology. After that, the improved TOPSIS method is adopted to establish the evaluation model of the adjustable potentials of large industrial loads and the load interruption priority is further divided. Finally, a three-stage rolling regulation model for controllable large industrial loads to participate in the deep peak shaving of the power grid is constructed to achieve the goal of bidirectional peak shaving on the power generation side and the demand side. The case uses an improved IEEE 30-node system for simulation. As the simulation results show, the method proposed in this paper can not only take the cost of peak load regulation into account, but also effectively achieve the goal of ‘peak shaving and valley filling’.

Abstract licence: CC BY-NC-ND

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