Constructing a Locational Marginal Emission Model Framework for Electrical Distribution Grids: Application to the Province of Alberta Grid for Dual Cost-Emissions Optimization.

构建配电网位置边际排放模型框架：应用于艾伯塔省电网的双成本排放优化。

• 批准号: 577385-2022
• 负责人: Farrokhabadi, MostafaMF
• 金额: $ 8.01万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752346
• 关键词: Constructing; Locational; Marginal; Emission; Model

Widespread electrification plays a vital role in Canada's "2030 Emissions Reduction Plan" success. However, for electrification to deliver its potential, it must be followed by a reduction in the electricity sector's greenhouse gas (GHG) emissions to as close to zero as possible while maintaining the stability and reliability of the energy supply. This grant pertains to the latter. Successful GHG reduction in electrical grid operation depends on several factors. Electricity grid operation is a complex combination of heterogenous processes spanning a time frame of years ahead to a few milliseconds. Many stakeholders influence the operation ranging from policymakers to end consumers. While a plethora of prior art can be found discussing the grids' optimal operation, one main challenge remains barely touched; there exists no framework to incorporate real-time marginal emissions of the system into decision-making by stakeholders. Local direct measurements from generators provide no locational marginal (LM) information that is necessary for optimal real-time operation. The emphasis on "real-time" deals with the fast varying operation trajectory of modern electrical grids, caused by high penetration of stochastic agents such as renewable energies and electric transportation. The emphasis on LM is to enable active participation of distributed energy resources and demand to minimize GHG emissions.This grant will propose a framework for modeling and estimating the locational marginal emissions, and apply it to the province of Alberta, Canada. The multidisciplinary research will use machine learning methods to map real-time and day-ahead LM emissions. The framework will be used to develop an optimal dispatch tool for dual cost-emission minimization while considering grid operational constraints. The objective is for distributed energy resources and end users to understand their impact on GHG emissions and to actively participate in emissions reductions. The work will be done in partnership with industry and provides guidance to grid operators for minimizing GHG emissions. Commercialization of the findings is highly anticipated.

广泛的电气化在加拿大的“ 2030减排计划”的成功中起着至关重要的作用。但是，要使电气发挥潜力，必须将电源部门的温室气体（GHG）排放量降低到尽可能接近零，同时保持能源供应的稳定性和可靠性。这笔赠款与后者有关。电网操作成功的温室气体减少取决于几个因素。电网操作是跨越几年时间到几毫秒的异质过程的复杂组合。许多利益相关者会影响从决策者到最终消费者的行动。虽然可以发现大量的先前艺术讨论网格的最佳操作，但主要的挑战仍然几乎没有触及。没有框架将系统的实时边际排放纳入利益相关者的决策中。发电机的本地直接测量不提供最佳实时操作所需的位置边缘（LM）信息。对“实时”的强调涉及由随机剂（例如可再生能量和电动运输）高渗透引起的现代电网的快速操作轨迹。对LM的重点是使分布式能源的积极参与和最大程度地减少温室气体排放的需求。该赠款将为建模和估算位置边际排放的框架，并将其应用于加拿大艾伯塔省。多学科研究将使用机器学习方法来绘制实时和日期的LM排放。该框架将用于开发最佳的调度工具，以最小化双重成本发射，同时考虑网格操作约束。目的是使分布式能源和最终用户了解其对温室气体排放的影响，并积极参与减少排放。这项工作将与行业合作完成，并为电网运营商提供指导，以最大程度地减少温室气体排放。高度期望发现的商业化。