Predictive accommodation of spatiotemporal variability in distributed photovoltaic generation

为了实现对全人类至关重要的可持续能源目标，我们必须增加光伏（PV）在配电网中的渗透。然而，光伏发电会随着时间和天气而波动。我将探索如何通过分布式控制本地光伏发电（PVG），存储和负载，在生产消费者框架内适应光伏发电（PVG）的变化。智能多代理系统（MAS）将被表示为交互能量（TE）代理，其操作由响应于动态输入和约束而影响资产的服务支持。该分布式MAS将通过概率PVG预测来通知。工作的性质是通过研究这些TE代理的社区协调来了解日内操作范围内的预测调节潜力，这些TE代理受到不确定变量的影响，如当地能源和电网条件，定价信号和生产者偏好。这些不确定的变量之间存在的依赖关系将作为一个预测控制问题，使用随机规划代理之间。所有智能电网领域都有预期成果。资产成果是模块级电力电子（MLPE）在城市环境中适应光伏电池板的动态遮蔽，并实现智能家庭能源管理系统（HEMS）作为交易代理。服务的贡献是阴天的天空预报，用于生成PVG小时内预报的时序概率模型。在操作上，本研究提供了一个分布式智能电网控制系统，与完整的协议支持，将不同层次的电力系统连接在一起，成为一个可互操作的整体。研究领域的好处是智能电网技术的融合。MLPE方法产生的PVG模型为建筑集成光伏的适应性研究提供了最大的广度，而具有前瞻性逆变器，存储和负载控制的开源HEMS则有利于能源管理领域。PVG中的概率预测是通过能量预测问题的融合而发展成熟的。在运营上，这项研究加速了智能电网向强大的交互式能源互联网的发展。对加拿大的好处超越了能源部门。这项研究将为产消者提供更大的选择和控制权，同时创造一个在产消者和分销服务运营商之间进行调解的能源服务行业。资产技术的进步将使商品解决方案能够帮助消除增加光伏渗透率的瓶颈。能源预测的进步将在关键的日内时间范围内提供更大的业务确定性，以更低的成本加强能源安全。该计划解决了智能电网发展的新兴方法，并将为政策，市场设计和商业模式提供信息，同时培训急需的工程师。全球智能电网市场预计将以每年20%的速度增长，到2023年将达到520亿美元。

In order to achieve sustainable energy goals, which are of importance to all humanity, we must increase photovoltaic (PV) penetration into the distribution grid. However, PV power fluctuates with time & weather. I will Discover how variability in PV generation (PVG) can be accommodated within a prosumer framework through distributed control of local PVG, storage, and loads. An intelligent multi-agent system (MAS) will be expressed as transactive energy (TE) agents, with operations supported by services that impact assets in response to dynamic inputs and constraints. This distributed MAS will be informed by probabilistic PVG forecasting. The nature of the work is to understand the predictive accommodation potential across intraday operating horizons by studying the community coordination of these TE agents subject to uncertain variables, like local energy & grid conditions, pricing signals, and prosumer preferences. The dependencies that exist between these uncertain variables will be addressed as a predictive control problem between agents using stochastic programming. Anticipated outcomes are in all smart grid domains. Asset outcomes are the accommodation of dynamic overshadowing of PV panels in the urban environment by module level power electronics (MLPE), and the realization of an intelligent home energy management system (HEMS) as a transactive agent. The service contribution is of overcast sky predictors used to generate chronological probability models for PVG intrahour forecasting. Operationally, this research provides a distributed smart grid control system, with full protocol support, that ties the different levels of the electrical system together into one interoperable whole. Benefits to the research field are in smart grid technology convergence. The MLPE approach yields a PVG model that gives the greatest breadth to accommodation studies for building-integrated PV, while an open source HEMS with forward-looking inverter, storage and load control benefits the energy management field. Probability prediction in PVG is matured through the fusion of energy forecasting problems. Operationally, this research accelerates smart grid evolution toward a robust transactive energy internet. Benefits to Canada transcend the energy sector. The research will give prosumers greater choice and control, while creating an energy services industry that mediates between them and distribution service operators. Advances in asset technologies will enable commodity solutions that aid in removing bottlenecks to increased PV penetration. Advances in energy forecasting will provide greater operational certainty over critical intraday time horizons, enhancing energy security at lower cost. This program addresses an emerging approach to smart grid evolution, and will inform policy, market design, and business models while training critically needed engineers. The global smart grid market is expected to grow annually by 20%, reaching USD 52 billion by 2023.