智能充电系统动态协调250辆电动车，提升效率与电网稳定
Context-Aware Agentic Power Resources Optimisation in EV using Smart2ChargeApp
- 多智能体协同框架融合深度Q网络与图神经网络，实时处理20项环境变量
- 实现92%协调成功率、15%能效提升、20%电网压力下降
- 适合电网运营商、充电站及车队管理方参考，推动绿色出行
本文提出一种上下文感知的多智能体协同动态资源分配（CAMAC-DRA）框架，用于优化基于Smart2Charge应用的智能电动汽车充电生态系统。该系统在250辆电动车与45个充电站组成的网络中运行，通过融合深度Q网络、图神经网络和注意力机制，实时处理包括天气、交通、电网负荷和电价在内的20项上下文特征。框架以加权协调机制和共识协议平衡五类利益相关者：电动车用户（25%）、电网运营商（20%）、充电站运营方（20%）、车队运营方（20%）及环境因素（15%）。基于包含441,077次充电交易的真实数据集验证表明，相比DDPG、A3C、PPO和传统GNN方法，本框架达到92%的协调成功率、15%能效提升、10%成本降低、20%电网压力减少，且收敛速度提升2.3倍，训练稳定性达88%，样本效率为85%。真实场景验证显示，净现值为-122,962美元，可再生能源集成使成本降低69%。其核心贡献在于实现上下文感知的多利益方动态协调，在适应实时变化的同时平衡多重目标，为智能充电与交通电气化提供突破性解决方案。
原文摘要 · Abstract (English)
This paper presents a novel context-sensitive multi\-agent coordination for dynamic resource allocation (CAMAC-DRA) framework for optimizing smart electric vehicle (EV) charging ecosystems through the Smart2Charge application. The proposed system coordinates autonomous charging agents across networks of 250 EVs and 45 charging stations while adapting to dynamic environmental conditions through context-aware decision-making. Our multi-agent approach employs coordinated Deep Q\-Networks integrated with Graph Neural Networks and attention mechanisms, processing 20 contextual features including weather patterns, traffic conditions, grid load fluctuations, and electricity pricing.The framework balances five ecosystem stakeholders i.e. EV users (25\%), grid operators (20\%), charging station operators (20\%), fleet operators (20%), and environmental factors (15\%) through weighted coordination mechanisms and consensus protocols. Comprehensive validation using real-world datasets containing 441,077 charging transactions demonstrates superior performance compared to baseline algorithms including DDPG, A3C, PPO, and GNN approaches. The CAMAC\-DRA framework achieves 92\% coordination success rate, 15\% energy efficiency improvement, 10\% cost reduction, 20% grid strain decrease, and \2.3x faster convergence while maintaining 88\% training stability and 85\% sample efficiency. Real-world validation confirms commercial viability with Net Present Cost of -\$122,962 and 69\% cost reduction through renewable energy integration. The framework's unique contribution lies in developing context-aware multi-stakeholder coordination that successfully balances competing objectives while adapting to real-time variables, positioning it as a breakthrough solution for intelligent EV charging coordination and sustainable transportation electrification.
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