NSF-CSIRO: RAI4IoE: Responsible AI for Enabling the Internet of Energy

能源行业正在经历两个关键驱动因素的重大变革：建设零碳能源行业和能源基础设施的数字化转型。人工智能技术和能源服务市场的进步进一步推动了这两种驱动因素的融合，导致能源领域出现了一个新的研究领域——能源互联网（IoE）。通过物联网，可以利用先进的5G-6G网络和人工智能技术，将电动汽车、蓄电池、风力涡轮机和光伏等可再生分布式能源（DERs）连接和集成，实现可靠的能源分配。这使得DER所有者作为产消者参与能源市场并获得经济激励。贫困地区本质上是资产驱动的，面临着公平的挑战（即公平、多样化和包容性）。如果没有公平的机会，享有特权的个人、群体和组织就会以牺牲弱势群体的利益为代价参与并从中受益。DER资源的实时管理不仅给物联网带来了公平性问题，还收集了高度敏感的位置、时间、活动相关数据，这些数据需要负责地处理（例如隐私、安全），用于人工智能增强的预测，用于优化和优先化服务，以及用于灵活资源的自动化管理。这个美澳联合项目计划为能源互联网开发公平和负责任的人工智能框架、技术和算法，被称为RAI4IoE，旨在通过为每个公民提供安全、隐私保护和公平的接入DERs网络来改善“能源贫困”。这项研究的结果将推动负责任的人工智能作为开发和部署物联网系统和服务的首要原则，促进DER集成，促进与产消费者、聚合商和网络运营商的深度接触，并实现可再生能源供应的灵活性市场。为了促进所有DER所有者和用户公平参与自动化灵活性市场，AI支持的IOE应该由负责任的AI框架和分布式监控、调度、管理和使用DER的指导方针来管理，同时在开放和不断发展的IOE生态系统中通过确保AI公平性和保护AI隐私和AI安全来行使和保障负责任和公平的AI。该项目将为物联网开发负责任的人工智能框架、算法和合规性评估方法，旨在通过为每个公民提供安全、隐私保护和公平的接入物联网网络来改善“能源贫困”。该项目将沿着三个维度开发创新解决方案。首先，它开发了一个公平的人工智能框架，以确保所有人的物联网，包括使资产贫乏的客户能够参与全球DER模型的分布式学习，并将隐私和公平意识的DER数据收集与政策驱动的数据治理相结合。其次，它开发了一套负责任的人工智能算法和模型，以提高物联网对破坏性事件的端到端弹性，包括不规则、稀疏或损坏的数据、数据和算法偏差、隐私侵犯和其他欺诈性DER活动。第三，通过将可解释的人工智能与软件测试和验证方法相结合，开发了一套负责任和公平的人工智能合规方法。该研究成果将引领新一代人工智能增强的分布式能源管理系统。这项研究还将为具有不同背景的研究生和本科生提供学习负责任的人工智能算法开发的独特机会，以及从广泛的跨学科角度公平获取der的重要性。这是美国和澳大利亚研究人员之间的一个联合项目，由美国国家科学基金会和澳大利亚联邦科学与工业研究组织（CSIRO）的负责任和公平人工智能合作机会资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The energy sector is going through substantial changes fueled by two key drivers: building a zero-carbon energy sector and the digital transformation of the energy infrastructure. The advances in AI technology and energy as a service market further fuel the convergence of these two drivers, resulting in the emergence of a new field of research in the energy sector – the Internet of Energy (IoE). With IoE, renewable distributed energy resources (DERs), such as electric cars, storage batteries, wind turbines and photovoltaics, can be connected and integrated for reliable energy distribution by leveraging advanced 5G-6G networks and AI technology. This allows DER owners as prosumers to participate in the energy market and derive economic incentives. DERs are inherently asset-driven and face equitable challenges (i.e., fair, diverse and inclusive). Without equitable access, privileged individuals, groups and organizations can participate and benefit at the cost of disadvantaged groups. The real-time management of DER resources not only brings out the equity problem to the IoE, it also collects highly sensitive location, time, activity dependent data, which requires to be handled responsibly (e.g., privacy, security and safety), for AI-enhanced predictions, for optimization and prioritization services, and for automated management of flexible resources. This US-Australia joint project plans to develop Equitable and Responsible AI framework, techniques and algorithms for the Internet of Energy, coined as RAI4IoE, aiming to elevate "energy poverty" by providing secure, privacy-preserving and equitable access to the networks of DERs for every citizen. The outcome of this research will advance the knowledge of responsible AI as the first principle in developing and deploying the IoE systems and services, in facilitating DER integration, promoting deep engagement with prosumers, aggregators and network operators, and enabling flexibility market of renewable energy supply.To facilitate equitable participation of all DER owners and users in the automated flexibility market, AI enabled IOE should be governed by the responsible AI frameworks and guidelines for distributed monitoring, scheduling, management, and consumption of DERs, while exercising and guaranteeing responsible and equitable AI through ensuring AI fairness and safeguarding AI privacy and AI security in an open and continuously evolving IoE ecosystem. This project will develop responsible AI frameworks, algorithms and compliance evaluation methods for the IoE, aiming to elevate "energy poverty" by providing secure, privacy-preserving and equitable access to the networks of DERs for every citizen. The project will develop innovative solutions along three dimensions. First, it develops an equitable AI framework for ensuring IoE for all, including enabling asset-poor clients to participate in distributed learning of global DER models, and integrating privacy and fairness-aware DER data collection with policy-driven data governance. Second, it develops a suite of responsible AI Algorithms and Models to increase the end-to-end resilience of IoE against disruptive events, including irregular, sparse or corrupted data, biases in data and algorithms, privacy violations, and other fraudulent DER activities. Third, it develops a suite of responsible and equitable AI compliance methods by combining explainable AI with software testing and verification methods. The research findings will lead to new generations of AI-enhanced distributed energy resource management systems. This research will also provide graduate and under-graduate students with diverse backgrounds the unique opportunities to learn responsible AI algorithm development, and the importance of equitable access to DERs from a broad cross-disciplinary perspective.This is a joint project between U.S. and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organization (CSIRO).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.