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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1416223
This article is part of the Research Topic Distributed Learning, Optimization, and Control Methods for Future Power Grids, Volume II View all 12 articles

Double layer optimization scheduling of power system based on node carbon emission flow demand response

Provisionally accepted
Lixia Wang Lixia Wang 1Haodong Zhao Haodong Zhao 2*Dawei Wang Dawei Wang 1Fang Dong Fang Dong 3Tianmin Feng Tianmin Feng 2Rui Xiong Rui Xiong 2
  • 1 State Grid Liaoning Electric Power Supply Co.,Ltd., Shenyang, China
  • 2 STATE GRID Shenyang electric power supply company, Shenyang, China
  • 3 State Grid Liaoning Electric Power Research Institute, Shenyang, China

The final, formatted version of the article will be published soon.

    This study proposes a novel bi-level optimization dispatch strategy based on nodal carbon potential demand response to achieve low-carbon emissions and bolster the economy in power systems. With the increasing urgency of global climate change, the low-carbon transition of the power industry has become a pressing need. The aim of this study is to enhance the low-carbon economic benefits of power systems, thereby supporting the national "dual carbon" strategy.Initially, the carbon emission flow is tracked using the principle of proportional sharing, forming a nodal carbon potential model that details the variation patterns of nodal carbon potential in both spatial and temporal dimensions. Subsequently, carbon flow analysis is integrated into the load-side demand response mechanism. By employing nodal carbon potential, a demand response carbon emission model is established, clarifying the scheduling differences under various carbon potential intensities and constructing a bi-level optimization dispatch model based on nodal carbon potential demand response. The upper level of the model represents the optimal economic dispatch for grid operators, while the lower level corresponds to the economic dispatch for load aggregators. Finally, the proposed method is validated using an enhanced IEEE 30-node system.The motivation for this research lies in addressing the pressing need for low-carbon transitions in power systems, driven by global climate change concerns. This study aims to bridge the gap in existing research by integrating carbon flow analysis with demand response mechanisms, offering a novel approach to optimize power system dispatch. The innovations of this study are:(1) integrating carbon emission flow analysis with demand response for more precise carbon tracking; (2) proposing a demand response model based on nodal carbon potential to enhance scheduling flexibility and economic efficiency; (3) designing a bi-level optimization dispatch model to improve the low-carbon economic benefits of power systems. The results demonstrate that the proposed method significantly reduces carbon emissions and enhances economic performance.

    Keywords: Carbon emission flow, Diverse Flexible Loads, demand response, Electric Vehicles, Bi-level optimization dispatch

    Received: 12 Apr 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Wang, Zhao, Wang, Dong, Feng and Xiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Haodong Zhao, STATE GRID Shenyang electric power supply company, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.