1 Insights into Scaling Virtual Power Plants Real-World Findings for Successful Deployment January 2025 Angela Long (Rockcress Consulting) Ryan Long (Rockcress Consulting) PacifiCorp Stacy Miller, U.S. DOE Tolu Omotoso, NRECA Tomas Smith, Puget Sound Energy 5 5 REAL-WORLD FINDINGS FOR SUCCESSFUL DEPLOYMENT Insights into Scaling Virtual Power Plants Executive Summary locationality to their VPP). PGE is enhancing operations at the grid edge for flexibility. This involves real-time computation and communication at the device level to manage customer usage patterns dynamically

of “Digital Network Management and Smart Service operation“ for the Smart Port Platform ， presents real operation scenarios such as port communication and perception network, loading, unloading, transportation to complete remote loading and unloading of containers ； (2) Crane Monitoring : Real-time monitoring of port machinery operating status information, with data collection, analysis, management in multiple dimensions with the help of 5G network, video, sensor and control technology Real-time synergy with the terminal operation system (TOS), enabling remote container loading and unloading

18 技能泛化：组合泛化实现未知技能的学习 n RA-P (IROS 2025, NeurIPS 2024 OWA): composable generalizable agents in real world n Decompose complicated tasks into fine-grained primitive skills, generalizable to new physical 2025 技能泛化：组合泛化实现未知技能的学习 n RA-P (IROS 2025, NeurIPS 2024 OWA): composable generalizable agents in real world n Decompose complicated tasks into fine-grained primitive skills, generalizable to new physical 2025 技能泛化：组合泛化实现未知技能的学习 n RA-P (IROS 2025, NeurIPS 2024 OWA): composable generalizable agents in real world n n Decompose complicated tasks into fine-grained primitive skills, generalizable to new

Systems (MES) track and document the transforma- tion of raw materials into finished goods, providing real-time production manage- ment to drive enterprise-wide compliance, quality and efficiency. Distributed Monitoring provides seamless connectivity to machines on the plant floor, delivering transparent, real-time operational KPIs like Overall Equipment Effectiveness (OEE). Design & Visualization tools that acquire, process and monitor operating data. Smart Manufacturing is the intelligent, real-time orchestration and optimization of business, physical and digital processes within factories

115367  SPE Paper 111990  SPE Paper 127517  SPE Paper 127520  New Tool Manages Big Data in Real Time, AAPG Explorer, January 2016 ADNOC  Shortfall identification and management cycle time improved enabled by the OSDU Data Platform. − ‘’Realtime as the standard”: Making sure that any amount of real time data can be handled by the OSDU Data Platform and linked Edge facilities. − ‘’Support for End-to-End kicks for client data  False alarm ratio is less than 8% Automatic Early Kick Alarming Real-time Drilling Data Physics based modeling Time-series Data mining 04 AI 实例：早期井涌预警系统

具身智能目前最⼤的瓶颈在于数据，各层面均处于早期阶段，如何权衡 真实数据和模拟数据，已成为⾏业内发展路径差异的关键分歧 18 信息来源：量⼦位智库，1）Sim2Real（Simulation to Reality）是指将⼈⼯智能模型从模拟环境（simulation）训练中获得的知识和能⼒转移到现实世界（real world）中应用的过程 数据构成 精简度 • 数据规模小：具身智能的数 据积累需要从零做起，数据 稀少，尤其端到端的⾼质量 误差积累 • 成本⾼速度慢， 需雇佣⼤量数采 ⼈员，搭建采集 环境，以⼈/天 为单位采集数据 • 基于虚拟场景模 拟现实中的物理 规则或特定任务 环境（如⼯厂、 家庭） • 中，Sim2Real1的 分布偏移问题难 以解决 • 低成本且更敏捷， 仿真数据的成本、 采集速度远低于 真实数据 • 通过训练其他⼈ 类或者本体的任 务操作类视频进 ⾏学习 • 低，数据质量参 差不齐，需要⼤ 视频 数据 • 真实数据是最佳 选择，仿真数据 主要是出于数采 成本过⾼的现实 选择，⼤部分具 身智能厂商都在 同时使用真实数 据和仿真数据， 但侧重不同 • 仿真路线的核⼼ 是解决Sim2Real 中间的差距，也 需要结合少量⾼ 质量的真实数据 做数据增强 数据积累 规模 数据闭环 成熟度 空间智能概览 自动驾驶 3D⽣成 具身智能 扩展现实（XR） 世界模型 扩展现实（XR）

application functions. 实现了系统的闭环、实时在线、全自动运行，可在 1 秒钟内 快 速完成优化计算和指令下达。 The system has achieved closed-loop, real-time online, and fully automatic operation, with the ability to complete optimization calculations versatility and easy expandability of functions 配套终端功能灵活定制 Customizable Terminal Functions 实时准确响应调度指令 Real-time and Accurate Response to Dispatch Instructions 平台建设亮点 Highlights of the platform construction - VPP analysis based on different regional advantages VPP can establish efficient connection and real-time monitoring between various types of distributed resources, carrying out trading of distributed

platform has functions such as real-time collection, monitoring, analysis, and management of environmental data. Scientific applications can achieve intelligent control and real-time monitoring of environmental

Suite – Kernel Real-Time Application 32/64 bit Kernel DLL MDF/PCAPng Kithara RealTime Suite – API User Mode Control Layer and External Libraries Communication and Fieldbus Layer Real-Time Layer Hardware Servers 边缘服务器 确定性实时控制 工厂 OT/IT 网络 边缘工作节点 [n] m g m t Real Time HW/SW General Purpose Compute AI Control HMI m g m t Real Time HW/SW General Purpose Compute AI Control HMI 边缘工作节点[1] 械臂的整体运动，从而整体实现使 用自然语言对机械臂的操控。 LLM RTmotion EtherCAT/CANopen ASR Code Generator Real-Time Non-Real-Time CPU P & E Cores iGPU CPU E Cores NPU Intel® Core™ Ultra ARL-H Audio Prompts

process to prompt the generator to generate images progressively resembling the distribu- tion of real data [7, 8]. Moreover, the diffusion models stand out as the most revolutionary technologies that SketchyGAN [37] focuses on the sketch-to-image generation task and aims to achieve more diversity and real- ism. Currently, ControlNet [38] can control diffusion mod- els by adding extra conditions. The sketch-to-image mindsets and explore new design spaces. Simul- taneously, diffusion probabilistic models can generate real- istic architectural rendering images suitable for Virtual Re- ality (VR) or Augmented Reality (AR)