Research on Dynamic Electricity Price Game Modeling and Digital Control Mechanism for Photovoltaic-Electric Vehicle Collaborative System
太陽光発電-電気自動車連携システムの動的電力価格ゲームモデリングとデジタル制御メカニズムに関する研究 (AI 翻訳)
Zixiu Qin, Hai Wei, Xiaoning Deng, Yi Zhang, Xuecheng Wang
🤖 gxceed AI 要約
日本語
本論文は、太陽光発電（PV）と電気自動車（EV）の連携システムにおける動的価格最適化モデルを提案する。Stackelbergゲーム理論に基づき、PV充電スタンド事業者をリーダー、EVユーザーをフォロワーとし、心理的期待と応答偏差を考慮した三段階応答構造を導入。差分解法を用いて最適化した結果、PV自己消費率が約90.5%に向上し、負荷ピークを18-20時から10-15時にシフト。炭素削減コストは定額料金比4.1%、時間帯別料金比1.9%削減し、経済性と環境性の両立を示した。
English
This paper proposes a dynamic pricing optimization model for PV-EV charging stations using Stackelberg game theory, incorporating user psychology and response deviations. Simulation results show PV self-consumption reaches 90.5%, load shifts from 18-20 to 10-15, and carbon reduction cost is reduced by 4.1% vs flat pricing and 1.9% vs time-of-use pricing, demonstrating economic and environmental benefits.
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
日本でもPVとEVの連携は重要課題であり、本論文の動的価格設定手法は、FIT終了後のPV自家消費拡大やEV充電インフラの効率運用に示唆を与える。ただし、日本の電力市場やユーザー行動特性を考慮した調整が必要である。
In the global GX context
This paper provides a practical game-theoretic framework for dynamic pricing in PV-EV systems, relevant to global efforts in grid integration of renewables and EVs. The methodology can inform charging infrastructure operators and policymakers seeking to balance renewable self-consumption, grid stability, and user costs.
👥 読者別の含意
🔬研究者:Offers a bilevel optimization approach with behavioral modeling for EV charging response.
🏢実務担当者:Provides a pricing strategy that improves PV self-consumption and reduces carbon costs for charging station operators.
🏛政策担当者:Demonstrates how dynamic pricing can align EV charging with renewable generation, aiding grid stability and decarbonization.
📄 Abstract（原文）
Electric vehicles (EVs) and renewable energy generation are widely regarded as key drivers of low-carbon transformation in the transportation and energy sectors due to their emission reduction potential and environmental benefits. However, the inherent intermittency and volatility of photovoltaic (PV) power, coupled with increasingly stochastic and disorderly EV charging demand, pose significant challenges to grid stability and local renewable energy utilization. To address these issues, this paper proposes a dynamic pricing optimization approach based on a Stackelberg game framework, in which the PV charging station operator acts as the leader and EV users as followers. Unlike conventional models, the proposed framework explicitly incorporates user psychological expectations and response deviations through a three-stage “dead-zone-linear-saturation” responsiveness structure, thereby capturing the uncertainty and partial rationality of EV charging behavior. The upper-level objective seeks to maximize operator profit and enhance PV self-consumption, while the lower-level objective minimizes user energy cost under price-responsive charging decisions. The bilevel optimization problem is solved via a differential evolution (DE) algorithm combined with YALMIP+CPLEX. Simulation results for a regional PV-EV charging station show that the proposed strategy increases PV self-consumption to about 90.5% and shifts the load peak from 18:00–20:00 to 10:00–15:00, effectively aligning charging demand with PV output. Compared with both flat and standard time-of-use (TOU) tariffs, the dynamic pricing scheme yields higher operator profit (about 7% improvement over flat pricing) while keeping total user energy expenditure essentially unchanged. In addition, the cumulative carbon reduction cost over the operating cycle is reduced by approximately 4.1% relative to flat pricing and 1.9% relative to TOU pricing, demonstrating simultaneous economic and environmental benefits of the proposed game-based dynamic pricing framework.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/wevj17020072first seen 2026-05-15 17:29:15
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