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Virtual Power Plants

Deep Learning-Based Method for Operation Dispatch Strategy Generation of Virtual Power Plants


Centralized and distributed optimization methods used by traditional virtual power plants (VPPs) in power system dispatching face issues such as high computational complexity, difficulties in privacy protection, and slow iterative convergence. There is an urgent need to propose an accurate and efficient acceleration method for generating VPP operational dispatching strategies. This paper proposes a deep learning-based acceleration method for generating VPP operational dispatching strategies.

By using the equivalent projection method to solve the operation feasible region of the VPP, the objective function and constraints of the VPP are transformed into constraints of coordination variables and submitted to the system dispatching center for optimization, thereby avoiding the slow convergence problem of iterative computation methods. The Kolmogorov–Arnold Network (KAN) is employed to predict the batch operation feasible regions of the VPP, addressing the inefficiency of individually calculating feasible regions. Tests on a 13,659-node system show that the proposed method reduces solution time by 64.40% while increasing the objective function value by only 4.74%, verifying its accuracy and speed.

This paper proposes an accelerated scheduling strategy generation method based on the operation feasible regions of VPP. The approach first employs an equivalent projection method to generate VPP’s operation feasible regions in real-time, aggregating distributed resource constraints into schedulable boundaries. Subsequently, it trains a KAN network-based feasible region predictor to batch-generate time-varying feasible regions for multiple VPPs. Compared with traditional Multilayer Perceptrons (MLPs), the KAN network achieves superior parameter efficiency and prediction accuracy through learnable activation functions. This innovation enables the dispatch center to rapidly acquire information about the entire grid’s adjustable resources.

Constructing a dynamic feasible region scheduling model for VPPs, which converts distributed optimization into centralized scheduling under feasible region constraints, eliminating iterative computations.

Developing a KAN network-based feasible region prediction framework to enable fast and accurate updates of multi-VPP feasible regions. Research Hypothesis. We hypothesize that the integration of equivalent projection and KAN-based learning can accelerate VPP operation strategy generation, significantly reducing computational burden while preserving dispatch feasibility and decision quality. 

Virtual Power Plant, distributed energy resources, smart grid, energy storage systems, demand response, grid stability, renewable integration, energy aggregation, real-time monitoring, decentralized energy, IoT in energy, grid flexibility, battery storage, prosumer networks, virtual grid, AI in energy, load balancing, energy efficiency, peak shaving, digital energy platforms

#VirtualPowerPlant, #SmartGrid, #DistributedEnergy, #EnergyStorage, #DemandResponse, #RenewableEnergy, #GridStability, #EnergyInnovation, #BatteryStorage, #DecentralizedEnergy, #CleanEnergy, #IoTEnergy, #AIinEnergy, #Prosumer, #EnergyEfficiency, #PeakShaving, #DigitalGrid, #SustainableEnergy, #GridFlexibility, #EnergyTransition

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