Electric grids are traditionally operated as multi-entity systems with each entity managing a geographical region. Interest and demand for decarbonization and energy democratization is resulting in growing penetration of controllable energy resources. In turn, this process is increasing the number of grid entities. The paradigm shift is also fueled by increased adoption of intelligent sensors and actuators equipped with advanced processing and computing capabilities. While collaboration among power grid entities (agents) reduces energy cost and increases overall reliability, achieving effective collaboration is challenging. The main challenges stem from the heterogeneity of system agents and their collected information. Furthermore, the scale of data collection is constantly increasing and many grid entities have strict privacy requirements. Another challenge is the energy industry's common practice of keeping data in silos. Federated computation is an approach well suited to addressing these issues that are increasingly important for multi-agent energy systems. Through federated computation, agents collaboratively solve learning and optimization problems while respecting each agent's privacy and overcoming barriers of cross-device and cross-organization data isolation. In this paper, we first establish the need for federated computations to achieve energy optimization goals of the future power grid. We discuss practical challenges of performing multi-agent data processing in general. Then we address challenges that arise specifically for orchestrating operation of connected distributed energy resources in the Internet of Things. We conclude this paper by presenting a novel federated computation framework that addresses some of these issues, and we share examples of two initial field test setups in research demonstrations and commercial building applications with Grid Fruit LLC.

中文翻译：

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通过联合计算连接分布式能源灵活性：局限性和可能性

电网传统上作为多实体系统运行，每个实体管理一个地理区域。对脱碳和能源​​民主化的兴趣和需求导致可控能源越来越渗透。反过来，这个过程会增加网格实体的数量。越来越多地采用配备先进处理和计算能力的智能传感器和执行器，也推动了范式转变。虽然电网实体（代理）之间的协作降低了能源成本并提高了整体可靠性，但实现有效协作具有挑战性。主要挑战源于系统代理及其收集的信息的异质性。此外，数据收集的规模不断增加，许多网格实体有严格的隐私要求。另一个挑战是能源行业将数据保存在孤岛中的常见做法。联合计算是一种非常适合解决这些对多智能体能源系统越来越重要的问题的方法。通过联合计算，代理协同解决学习和优化问题，同时尊重每个代理的隐私，克服跨设备和跨组织数据隔离的障碍。在本文中，我们首先确定了对联邦计算的需求，以实现未来电网的能源优化目标。我们一般讨论执行多代理数据处理的实际挑战。然后，我们解决了专门为物联网中互联分布式能源的编排操作而出现的挑战。