Component Level Analysis and System Optimization for a Net Positive Energy Infrastructure

利用可再生能源，如太阳能，风能，生物质能和地热能为基础设施提供动力，需要一种设计工具，该工具允许根据给定的地质和能源需求数据，在可再生能源和相关的能源转换和储存技术中进行灵活选择。长丰能源股份有限公司，加拿大上市公司，一直在积极探索碳中和和净正碳足迹基础设施的新兴市场机会。该公司已经确定了两个主要的紧急研发需求：（i）使用现有的调度策略对能源系统（及其组件）进行理论/数值设计和分析，以及（ii）通过实施新的控制算法对实际系统进行评估和优化。这个由滑铁卢大学领导的CRD项目将通过调查复杂基础设施的可再生能源网络的可行性，边界和性能来直接解决第一项任务。最近开发的智能能源网络（SEN）概念将被实施，其功能将使用现有的基础设施EVOLV 1进行验证，该基础设施旨在实现净正碳足迹。该项目将通过模拟拟议SEN系统的运行和性能来解决第二项任务，使用来自配备太阳能，地热和电动汽车充电的EVOLV 1的实际运行数据。将增加生物质气化，以增加废物的热能利用。基于规则和机器学习（更具体地说，强化学习或RL）的控制策略将被应用于改善能源和环境性能。该CRD项目的总体目标是为行业合作伙伴开发一种设计工具，以设计碳中和和净积极建筑。该项目的重要成果包括：（i）实现净正碳足迹设计的关键考虑因素和指标;（ii）能源管理的规模和建模方法;以及（iii）基于SEN的能源系统的最佳控制算法和硬件实现。

Utilizing renewable energy such as solar, wind, biomass and geothermal to power an infrastructure requires a design tool which allows, based on the given geological and energy demand data, a flexibility in choosing among renewable energy sources and associated energy conversion and storage technologies. Changfeng Energy Inc., a Canadian public company, has been actively exploring emerging-market opportunities in carbon neutral and net positive carbon footprint infrastructures. The company has identified two major urgent RD needs: (i) to pursue theoretical/numerical design and analysis of an energy system (and its components) using an existing dispatching strategy and (ii) to carry on assessment and optimization of a practical system via implementation of novel control algorithms. This CRD project, led by the University of Waterloo, will directly address the first task through investigating the feasibility, boundary and performance of a renewable based energy network for a sophisticated infrastructure. A recently developed smart energy network (SEN) concept will be implemented and its functions will be validated using an existing infrastructure EVOLV1 which was designed to achieve the net positive carbon footprint. This project will address the second task through simulating the operation and performance of the proposed SEN system, using the real-life operation data from EVOLV1 which is equipped with solar, geothermal, and electric vehicle charging. Biomass gasification will be added, in order to increase the thermal energy use from the waste. Rule based and machine learning (more specifically, Reinforcement Learning or RL) control strategies will be applied to improve both energy and environmental performances. The overall objective of this CRD project is to develop a design tool for the industry partner to design carbon neutral and net positive buildings. Important outcomes of this project include (i) critical considerations and indicators for achieving net positive carbon footprint design, (ii) sizing and modeling methods for energy management; and (iii) an optimal control algorithm and hardware implementation for the SEN-based energy system.