Bi-objective optimization modeling of virtual power plants based on the NSGA-II algorithm
仮想発電所のバイオブジェクト最適化モデリング：NSGA-IIアルゴリズムに基づいて (AI 翻訳)
Meng-Sian Chen, Jing Wang, Mengfei Peng
🤖 gxceed AI 要約
日本語
仮想発電所（VPP）の計画・運用において、経済性と低炭素性の両立を図るため、NSGA-IIアルゴリズムを用いたバイオブジェクト最適化モデルを提案。風力・太陽光・蓄電池・ガス発電機を含むVPPを対象に、総コスト最小化と炭素排出最小化のトレードオフを解析。ケーススタディにより、適切な重み設定で両立可能な構成案が得られることを実証。
English
This paper proposes a bi-objective optimization model for virtual power plants (VPPs) integrating wind, PV, storage, and gas units, using the NSGA-II algorithm to minimize total cost and carbon emissions. A case study reveals a trade-off between cost and emissions, and demonstrates that balanced resource configurations can be achieved by setting appropriate objective weights. The model supports low-carbon transition in power systems.
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、再エネ大量導入に伴いVPPの重要性が増しており、経済性と脱炭素の両立は電力事業者にとって喫緊の課題。本研究の最適化手法は、日本におけるVPP計画・運用に直接応用可能で、SSBJやTCFDに基づく排出量削減目標達成に有用な知見を提供する。
In the global GX context
Globally, virtual power plants are gaining traction as enablers of renewable integration and grid flexibility. This study's bi-objective framework, balancing cost and carbon, is relevant for utilities and aggregators complying with ISSB, TCFD, or CSRD requirements. The NSGA-II approach offers a practical tool for optimizing VPP portfolios under carbon constraints.
👥 読者別の含意
🔬研究者:Provides a bi-objective optimization model using NSGA-II for VPP configuration, demonstrating trade-off analysis between cost and emissions.
🏢実務担当者:Useful for VPP developers and energy managers to identify resource mixes that balance economic and decarbonization goals.
🏛政策担当者:Offers insights into how multi-objective optimization can support low-carbon power system planning, relevant for energy policy design.
📄 Abstract（原文）
With the in-depth advancement of power market-oriented reform, virtual power plants, as new types of market entities that aggregate multiple types of distributed resources, play an important role in improving system flexibility and promoting low-carbon transition. Aiming at the coexistence of cost control and carbon emission constraints faced by source–grid– load–storage integrated virtual power plants in the processes of planning and operation, this paper constructs a virtual power plant optimization configuration model with bi-objective consideration of economic efficiency and low-carbon performance from the perspective of integrated energy systems. First, the investment costs, operating costs, and carbon emissions of multiple types of resources in virtual power plants, including wind power, photovoltaic power generation, energy storage, and gas-fired units, are systematically modeled. On this basis, a bi-objective optimization model with the objectives of minimizing total cost and minimizing carbon emissions is established, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted for solution. Finally, a virtual power plant containing multiple energy resources is taken as a case study to analyze the trade-off relationship between cost and carbon emissions under different resource configuration schemes. The results show that there is an obvious conflict between total cost and carbon emissions. By setting reasonable objective weights, resource configuration schemes that take both economic efficiency and low-carbon performance into account can be obtained, thereby verifying the effectiveness and applicability of the proposed model.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1117/12.3111161first seen 2026-07-20 05:20:40
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