Abstract
AbstractThe intermittent and uncertainty of new energy in the grid connection process affects the overall quality of the grid. To resolve the scattered geographical locations, small individual capacities and poor controllability of distributed energy storage (DES) devices, edge computing is applied in conjunction with aggregate service providers to build an edge computing‐based aggregate model of DES. Regarding its characteristics, the schedulable potential of the energy storage device is analysed through modelling. A multi‐objective scheduling model with multiple parties participating in the subject's behaviour is established using the Internet of Things (IoT) optimization strategy. The simulation example of concrete data further verifies the validity of the model. Results demonstrate that the edge computing‐based aggregate model of DES effectively reduces the calculation load's peak‐valley load and reduces the wind power abandonment. In the meantime, the multiple starts and stops of the thermal power unit reduce the operation cost of the unit. The multi‐objective dispatch model can reduce the opportunity cost and payment of DES effectively. This model achieves load peak reduction and valley filling and reduces the peak dispatch cost of the power grid. The research results can provide some ideas for storing and utilizing the new energy.
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