ANOCA: AC Network-aware Optimal Curtailment Approach for Dynamic Hosting Capacity

Grunnleggende konsepter

Proposing a new two-stage optimization framework, ANOCA, based on three-phase AC models to address current gaps in dynamic hosting capacity algorithms.

Sammendrag

The content introduces ANOCA, a novel approach for dynamic hosting capacity optimization. It addresses the limitations of existing algorithms by utilizing a two-stage framework based on three-phase AC models. The framework involves prosumers calculating optimal export setpoints and the utility performing infeasibility-based optimization to ensure grid stability. Different curtailment strategies are explored, including L1, L2, and L∞norm-based dispatch algorithms. Experimental setups and results demonstrate the effectiveness of ANOCA in maintaining grid stability while optimizing power exports.

I. Introduction

• Historical power flow paradigm
• Emergence of distributed energy resources

II. State-of-the-art Solutions

• Static hosting capacity (SHC)
• Dynamic hosting capacity (DHC) algorithms

III. Re-envisioning Dynamic Hosting Capacity with ANOCA

• Proposal of a two-stage optimization framework
• Utilization of three-phase AC models

IV. Experimental Setup

• HEMS and DMS experiment data
• Simulation environment and hardware

V. Experiments

• HEMS and DMS analysis in ANOCA
• Comparative assessment of various curtailment strategies

VI. Conclusions

• Summary of ANOCA framework and curtailment strategies

VII. Acknowledgement

• Recognition of assistance in code development

References

• Citations of relevant works and resources

Statistikk

"The average solution time for HEMS optimizations was 0.107 seconds."
"The average DMS analysis solution time on a large network was 133.83 seconds."

Sitater

"The L1-norm curtailment strategy results in the most sparse curtailment."
"L∞-norm curtailment strategy results in the most equitable curtailment."
"L2-norm curtailment strategy strikes a balance with somewhat equitable curtailment."

Viktige innsikter hentet fra

ANOCA

by Emmanuel O. ... klokken arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18085.pdf

Dypere Spørsmål

How can ANOCA be adapted for different types of distribution grids?

ANOCA can be adapted for different types of distribution grids by customizing the network model and constraints based on the specific characteristics of each grid. For instance, for grids with varying voltage levels or different types of DERs, the AC model used in ANOCA can be adjusted to accurately represent the grid physics. Additionally, the optimization parameters and curtailment strategies can be tailored to suit the requirements and constraints of diverse distribution grids. By incorporating grid-specific data and parameters, ANOCA can be fine-tuned to optimize hosting capacity and manage power flows effectively in various grid scenarios.

What are the potential drawbacks of relying on the L∞-norm curtailment strategy?

While the L∞-norm curtailment strategy offers equitable curtailment by minimizing the maximum curtailment any prosumer faces, it comes with certain drawbacks. One potential drawback is the high volume of net curtailment associated with the L∞-norm strategy. This means that a significant amount of power may need to be curtailed across the grid, leading to potential energy wastage and reduced overall efficiency. Additionally, the computational complexity of implementing the L∞-norm strategy may be higher compared to other strategies, resulting in longer optimization times and increased resource requirements.

How can the ANOCA framework be extended to incorporate real-time grid conditions for more dynamic optimization?

To incorporate real-time grid conditions for more dynamic optimization within the ANOCA framework, several enhancements can be implemented. Firstly, integrating advanced sensors and communication technologies to provide real-time data on grid parameters such as voltage levels, power flows, and DER outputs can enable ANOCA to make more informed decisions. Additionally, implementing predictive analytics and machine learning algorithms can help ANOCA anticipate grid conditions and adjust optimization strategies proactively. Furthermore, developing adaptive control mechanisms that can respond to real-time grid events and dynamically adjust curtailment decisions based on changing conditions will enhance the framework's ability to optimize grid operations in a dynamic environment.