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A Collaborative Low-Carbon Dispatch Method for Virtual Power Plants and Traditional Power Grids Based on MDP and an Improved NSGA-II Algorithm

MDPと改良NSGA-IIに基づく仮想発電所と従来型電力系統の協調的低炭素ディスパッチ手法 (AI 翻訳)

Dai Z, Zhang F, Tan J, Chen Y

Research Squareプレプリント2026-06-18#炭素価格経営インパクト: コスト削減対象セクター: power
DOI: 10.21203/rs.3.rs-9550459/v1
原典: https://doi.org/10.21203/rs.3.rs-9550459/v1

🤖 gxceed AI 要約

日本語

本論文は、仮想発電所（VPP）と従来型電力系統の協調的低炭素ディスパッチ手法を提案する。マルコフ決定過程（MDP）と改良NSGA-IIを組み合わせ、不確実性下での多目的最適化を実現。段階的炭素取引メカニズムを導入し、経済性と排出削減のパレート最適フロンティアを獲得。IEEE 30母線システムでの検証により、従来手法より優れたトレードオフとロバスト性を示した。

English

This paper proposes a collaborative low-carbon dispatch method for VPPs and traditional power grids, integrating Markov Decision Process (MDP) and an improved NSGA-II to handle source-load uncertainty and multi-objective optimization. A stepped carbon trading mechanism incentivizes emission reduction. Case studies on a modified IEEE 30-bus system demonstrate Pareto-optimal trade-offs between cost and emissions with robustness to forecast errors.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本ではVPP実証やGX-ETSなどカーボンプライシング導入が進む中、本手法は段階的炭素取引を考慮したVPPと系統の協調制御を提案。日本の電力システムにおける再生可能エネルギー統合と排出削減の実践に示唆を与える。

In the global GX context

Globally, this work contributes to the growing literature on low-carbon dispatch with carbon pricing. The MDP-NSGA-II framework offers a robust solution for integrating VPPs into grid operations under uncertainty, relevant to regions expanding carbon markets and renewable penetration.

👥 読者別の含意

🔬研究者:Provides a novel optimization framework (MDP + enhanced NSGA-II) for multi-objective low-carbon dispatch, useful for researchers in power systems and carbon trading.

🏢実務担当者:Demonstrates a practical method for VPP operators to balance cost and emissions, potentially improving operational efficiency and carbon compliance.

🏛政策担当者:Highlights how stepped carbon trading can incentivize emission reductions in power dispatch, informing carbon market design.

📄 Abstract（原文）

<title>Abstract</title> <p>The integration of high-penetration renewable energy and distributed resources via Virtual Power Plants (VPPs) necessitates advanced dispatch strategies that simultaneously address economic efficiency and carbon emission reduction under uncertainty. This paper proposes a novel collaborative low-carbon dispatch framework for a VPP and the traditional power grid. The core methodology integrates a Markov Decision Process (MDP) to model sequential decision-making under source-load uncertainty with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for solving the resulting multi-objective optimization problem. The improved NSGA-II incorporates an MDP-guided initialization strategy and adaptive genetic operators to enhance convergence and solution diversity. A stepped carbon trading mechanism is integrated into the model to strengthen the economic incentive for emission reduction. Case studies on a modified IEEE 30-bus system demonstrate that the proposed framework outperforms traditional deterministic and single-objective dispatch methods. It successfully obtains a Pareto-optimal frontier that provides superior trade-offs between operational cost and carbon emissions, while also exhibiting greater robustness to renewable forecast errors. The results validate that the collaborative strategy effectively leverages VPP flexibility to support the grid, achieving a "win-win" outcome for both economic and environmental objectives.</p>

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