Not only has the state''s grid operator acknowledged the collective power of DERs aggregated into virtual power plants (VPPs), but it has also launched the country''s first program to integrate aggregations of
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Not only has the state''s grid operator acknowledged the collective power of DERs aggregated into virtual power plants (VPPs), but it has also launched the country''s first
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Consequently, this paper develops a model-free aggregation method for VPPs. The proposed method first develops an input convex neural network (ICNN)-based surrogate for the feasible
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New York''s new Distributed Energy Resource (DER) & Aggregation Participation Model, a first-of-its-kind initiative, integrates aggregated DERs, like virtual power plants (VPPs), into wholesale energy
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This unified entity operates in power system markets, participating in both wholesale and retail transactions, as well as providing services to the operator. Essentially, an aggregator is a company that manages a VPP, which in turn is a collective assembly of DERs .
When the acquired frequency control requirements and resource regulation characteristics change due to changes in grid dynamics or user habits, the virtual power plant can implement a dynamic aggregation mechanism based on a threshold. Then, the dynamic aggregation mechanism is divided into two parts: resources selection and coordination.
A dynamic aggregation mechanism for VPPs is proposed for the first time, which includes dynamic resource selection and coordination processes.
Currently, commonly used aggregation techniques include rule-based aggregation, aggregation with static clustering, and market-based aggregation. With the advancement of smart grid technologies, the proposed method in this paper has the potential for broader applications, enhancing the flexibility and regulation capabilities of VPPs.
In practice, the choice of aggregation techniques for VPPs depends on resource characteristics, grid requirements, and implementation complexity. Currently, commonly used aggregation techniques include rule-based aggregation, aggregation with static clustering, and market-based aggregation.
The proposed method ends up using less than 500 selected flexible resources at the time of aggregation due to the fact that it can only select resources according to the clusters, so it is limited by the number of resources.
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