Towards Carbon-Free Electricity: A Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization
This paper introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focuses on three key aspects: carbon accounting, carbon-aware decision-making, and carbon-electricity market design. It addresses existing problems, methods, and proposes solutions. In contrast to traditional pool-based emissions models, our framework proposes a novel flow-based emissions model. This model incorporates the underlying physical power grid and power flows, allowing for accurate carbon accounting at both temporal and spatial scales. This, in turn, facilitates informed decision-making to achieve grid decarbonization goals. The framework is built on a flow-based accounting methodology and utilizes the carbon-aware optimal power flow (C-OPF) technique as a theoretical foundation for decarbonization decision-making. Additionally, the paper explores the potential design of carbon-electricity markets and pricing mechanisms to incentivize decentralized decarbonization actions. The critical issues of data availability, infrastructure development, and considerations of fairness and equity are also discussed. This paper seeks to advance scholarly understanding and foster progress toward achieving sustainable and carbon-free electric power systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingFairnessSimilar Papers 제목 키워드 기반
A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting
Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift …
Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction
Detailed scheduling has traditionally been optimized for the reduction of makespan and manufacturing costs. However, growing awareness of environmental concerns and increasingly stringent regulations are pushing manufact…
SchedulingJoint Trading and Scheduling among Coupled Carbon-Electricity-Heat-Gas Industrial Clusters
This paper presents a carbon-energy coupling management framework for an industrial park, where the carbon flow model accompanying multi-energy flows is adopted to track and suppress carbon emissions on the user side. To…
energy tradingManagementSchedulingDeep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change. Decarbonization of the electricity grid would allow for low-carbon heating, cooling …
Deep Reinforcement LearningGraph Neural Networks for Learning Real-Time Prices in Electricity Market
Solving the optimal power flow (OPF) problem in real-time electricity market improves the efficiency and reliability in the integration of low-carbon energy resources into the power grids. To address the scalability and …
Graph Neural Network