AI 中文总结
本文提出认知能源管理（CEM）框架，将目标导向推理与持续适应嵌入能源管理循环，通过智能港口能源系统的六小时船舶周转场景演示，实现从可行性到前瞻性能源治理的跨越。
AI 中文摘要
现代能源管理系统，即便在先进的能源互联网（EI）基础设施内，本质上仍属于反应式、受优化约束的系统，无法对上下文、意图或不确定性进行推理。虽然EI范式已建立起强大的网络物理架构，用于通过软件定义的分组网络互连分布式能源资源，但此类系统应如何智能地思考、适应和管理能源决策仍是一个未解决的挑战。本文提出认知能源管理（CEM），这是一个新的概念框架，通过重新定义能源系统在复杂操作环境中的感知、推理、学习和行动方式来解决这一缺口。CEM以EI的网络物理基础为根基，超越了传统优化，将目标导向推理与持续适应嵌入能源管理循环，定位为基于EI的基础设施的认知治理层。我们正式定义CEM，通过结构化对比将其与基于规则和基于优化的范式区分，并阐明其核心架构层。为演示该框架的实用价值，我们开发了一个基于智能港口能源管理的玩具问题——智能港口是现代基础设施中操作要求最高的EI节点环境之一。具体而言，我们对预测性船舶周转场景进行建模，其中启用CEM的系统在六小时的操作时域内规划能源采购、储能预充电和负荷调度。该演示表明，CEM推动EI从追求可行性转向智能、前瞻性的能源治理。
英文摘要
Modern energy management systems, even within advanced energy internet (EI) infrastructures, remain fundamentally reactive, optimization-bound, and incapable of reasoning about context, intent, or uncertainty. While the EI paradigm has established a powerful cyber-physical architecture for interconnecting distributed energy resources via software-defined packetized networks, the question of how such systems should think, adapt, and govern energy decisions intelligently remains an open challenge. This paper introduces cognitive energy management (CEM); a new conceptual framework that addresses this gap by redefining how energy systems perceive, reason, learn, and act within complex operational environments. Grounded in the EI cyber-physical foundation, CEM extends beyond conventional optimization by embedding goal-directed reasoning and continuous adaptation into the energy management loop, positioning itself as the cognitive governance layer of EI-based infrastructures. We formally define CEM, distinguish it from rule-based and optimization-based paradigms through structured comparison, and articulate its core architectural layers. To demonstrate the framework's practical value, we develop a toy problem grounded in smart port energy management; one of the most operationally demanding EI node environments in modern infrastructure. Specifically, we model a predictive vessel turnaround scenario in which a CEM-enabled system plans energy procurement, storage pre-charging, and load scheduling across a six-hour operational horizon. The demonstration illustrates how CEM moves the EI beyond feasibility-seeking toward intelligent, anticipatory energy governance.
