Stochastic Energy Management in Smart Grid with Cyber-Physical Resilience Enhancement

随着传统电网向未来智能电网的发展，分布式能源资源，包括光伏（PV）面板和风力涡轮机等分布式发电（DG）单元，以及电池、飞轮和具有车到网（V2G）功能的电动汽车（ev）或电动公交车（EBs）等分布式存储（DS）单元，引起了学术界和产业界的广泛关注。由于这些设备的灵活控制和快速响应，它们可以支持需求侧和电网规模服务的广泛应用。除了DG、DS、EV/EB技术和电力电子技术的不断进步外，近年来迫切需要开发可靠和智能的能源管理方案。能源管理面临的最大挑战之一是分布式能源产生和/或需求的随机性。此外，随着包括电动汽车/EBs在内的DS单元在高渗透水平上的集成，充电和放电行为会给智能电网带来很大的不确定性，从而影响系统运行的可靠性和稳定性。此外，冰暴、飓风、地震等自然灾害会对配电系统中的大量元器件造成损坏，智能电网中的数据传输也容易受到严重的网络攻击。因此，开发具有网络物理弹性的创新随机能源管理方案在技术上具有挑战性，但对未来的智能电网至关重要。本研究的长期目标是建立智能能源系统架构，建立先进的信息和通信系统，作为连接所有能源系统（不仅包括电网，还包括其他类型的能源系统，如石油和天然气系统）的“胶水”，使能源高效、可靠、安全、可持续地在工业、商业、住宅和交通领域流动。短期目标是解决上述技术挑战，并在四个方面为具有网络物理弹性的智能电网开发随机能源管理方案，包括分布式能源和网络物理威胁的数据驱动随机建模，具有分布式能源的可持续建筑的随机能源管理，智能配电系统的随机能源管理，智能电网随机能源管理的网络物理弹性增强。拟议研究的跨学科性质将确定整体系统优化的新机会，否则孤立的方法是不可能的。拟议的研究将为电力/能源和信息/通信行业产生新的知识和培训HQP。

As the traditional electrical grid evolves towards future smart grid, distributed energy resources including distributed generation (DG) units such as photovoltaic (PV) panels and wind turbines, and distributed storage (DS) units such as batteries, flywheels, and electric vehicles (EVs) or electric buses (EBs) with vehicle-to-grid (V2G) capabilities, have attracted a great amount of attention from researchers in both academia and industry. Due to the flexible control and fast response of these devices, they can support a wide range of applications for both demand-side and grid-scale services. In addition to the constant progress in DG, DS, EV/EB technologies and power electronics, the development of reliable and intelligent energy management schemes has been in an urgent need in recent years. One of the greatest challenges in energy management is the randomness caused by the power generation and/or demand of distributed energy resources. Also, along with the integration of DS units including EVs/EBs at high penetration level, the charging and discharging behaviour can introduce great uncertainties into the smart grid, which can affect the reliability and stability of system operation. In addition, natural disasters such as ice storms, hurricanes, and earthquakes can cause damages to a large number of components in power distribution systems, while the data transmissions in smart grid may be prone to severe cyber attacks. Therefore, developing innovative stochastic energy management schemes with cyber-physical resilience is technically challenging but of paramount importance for the future smart grid. The long-term goal of the proposed research is to establish an architecture of intelligent energy system and build up an advanced information and communication system as a "glue" to interconnect all the energy systems (including not only electrical grid, but also other types of energy systems such as oil and natural gas systems), such that the energy can flow in industrial, commercial, residential, and transportation sectors in an efficient, reliable, secure, and sustainable way. The short-term objectives are to address the above-mentioned technical challenges and develop stochastic energy management schemes for smart grid with cyber-physical resilience in four thrusts including data-driven stochastic modeling for distributed energy resources and cyber-physical threats, stochastic energy management of sustainable buildings with distributed energy resources, stochastic energy management in smart distribution systems, and cyber-physical resilience enhancement of stochastic energy management in smart grid. The interdisciplinary nature of the proposed research will identify new opportunities for overall system optimization that are otherwise impossible in isolated approaches. The proposed research will generate new knowledge and train HQP for both power/energy and information/communication industries.