Virtual Power Plant DER Aggregation With Edge-Cloud Scheduling
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Solution Overview
Problem
Distributed energy resources (DERs) face challenges in being fully utilized due to high dispersion, uncertainty, and heterogeneity, making it difficult for small-capacity DERs to participate effectively in power grid regulation, leading to low enthusiasm and inefficient operational burden for system operators.
Innovation Solution
A two-stage self-organizing optimized aggregation method and system for virtual power plants (VPPs) that uses edge computing and deep Bayesian networks to aggregate DERs into unified mathematical models for virtual synchronous generators and energy storage, followed by cloud-end optimization for efficient scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual DERs directly participate in system scheduling, then regulation capability is improved, but computational burden increases and operational efficiency decreases
Solution Approach 1:
The patent segments the scheduling problem into two levels: local edge computing devices handle individual DER aggregation and preliminary optimization, while cloud computing platforms perform global coordination. This segmentation reduces the computational burden on any single system component and improves overall operational efficiency while maintaining regulation capability.
Solution Approach 2:
The patent introduces edge computing devices as intermediary components between individual DERs and the central scheduling system. These edge devices aggregate DER resources locally, perform preliminary optimization, and communicate with the cloud platform, thereby reducing the complexity of direct system-wide scheduling while preserving regulation capabilities.
2Productivity
If DERs are aggregated into VPP, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The VPP system is segmented into multiple hierarchical levels: individual DERs, edge computing devices for local aggregation, and cloud platforms for global coordination. This segmentation manages system complexity by distributing functions across different levels rather than creating a monolithic complex system.
Solution Approach 2:
Edge computing devices autonomously perform local aggregation and optimization of DER resources without requiring constant central control. This self-service capability reduces the overall system complexity by enabling decentralized decision-making while still achieving effective resource utilization.
3Productivity
If small-capacity DERs participate in grid regulation, then energy utilization is improved, but economic benefit is reduced
Solution Approach 1:
The patent merges multiple small-capacity DERs into virtual aggregated units through edge computing devices. This merging allows small DERs to collectively participate in grid regulation and access wholesale market opportunities, thereby improving both energy utilization and economic benefits that would be unavailable to individual small-scale resources.
Solution Approach 2:
The VPP aggregation system provides multi-functionality, enabling DERs to simultaneously participate in multiple markets including spot markets, ancillary services, and wholesale markets. This universal access to different revenue streams improves economic benefits while maintaining high energy utilization through diverse participation opportunities.
Data Source
AI summary
Provided is a two-stage self-organizing optimized aggregation method and system for distributed resources of a virtual power plant. First-stage aggregation oriented to a distribution transformer area is completed by taking a natural physical cluster composed of distributed energy resources in the distribution transformer area as a first stage, performing aggregation by using an edge computing server deployed in the distribution transformer area, constructing a generalized transformer area load model including wind power, photovoltaic power, and a load, aggregating distributed gas turbines and generators of small hydropower stations in the distribution transformer area into a unified virtual synchronous generator model, aggregating distributed energy storage devices in the distribution transformer area into a centralized virtual energy storage model; second-stage aggregation across distribution transformer areas is completed by uploading all parameters of the generalized transformer area load model, the virtual synchronous generator model, and the virtual energy storage model to a cloud end.