Continuous-Time Optimization Model for Source–Sink Matching in Carbon Capture and Storage Systems

Distributed Optimization for Resource Allocation Problem with Dynamic Event-Triggered Strategy

This study aims to unravel the resource allocation problem (RAP) by using a consensus-based distributed optimization algorithm under dynamic event-triggered (DET) strategies. Firstly, based on the multi-agent consensus approach, a novel one-to-all DET strategy is presented to solve the RAP. Secondly, the proposed one-to-all DET strategy is extended to a one-to-one DET strategy, where each agent transmits its state asynchronously to its neighbors. Furthermore, it is proven that the proposed two types of DET strategies do not have Zeno behavior. Finally, numerical simulations are provided to validate and illustrate the effectiveness of the theoretical results.

An interval chance-constrained programming-based optimization model for carbon capture, utilization, and storage system planning

Carbon capture, utilization, and storage (CCUS) are widely regarded as a crucial technological option for industrial large-scale carbon dioxide (CO₂) emissions reduction. However, high-cost and uncertainties hinder the widespread application of CCUS technology. In this study, an interval-chance-constrained programming-based optimization model was proposed to address random probability distributions, interval values, complex interactions, and the dynamics of capacity expansion issues. The model was applied to a CCUS project in China. A set of violation probability levels (0.01, 0.05, 0.1, and 0.2) were designed to reflect system costs and risk levels. And then the solutions for system costs, capacity expansion, and operating schemes under four violation probability levels (p_i') could be generated. The results revealed that the model could ensure the highest reliability and largest CO₂ storage under p_i' = 0.01. At this probability level, the amount of CO₂ storage would range from 4972.05-5429.75 kilotons per annum (ktpa), the CCUS system cost would be highest at $166.57 million, and the net system benefits would be slightly less at $105.91 million. If policymakers strive to achieve the net system benefits of the project, the highest net system benefits would be achieved under p_i' = 0.05. At this probability level, the net system benefits would increase to $135.45 million, the system cost would reduce to $138.62 million, but the total amount of CO₂ storage would decrease to between 4090.01 and 4653.24 ktpa, which would entail a high risk of system violation. These findings enable policymakers to determine the trade-offs among system reliability, CO₂ reduction, and the benefits of the project. The modeling approach can also address interactions among CCUS activities and the dynamics of facility expansion issues as well as help policymakers develop adaptive operational strategies. This study enriches CCUS research through an interval chance-constrained optimization modeling approach for CCUS system management under multiple uncertainties.

Could China meet its emission reduction goal by CO2-EOR

Purpose

This paper aims to discuss opportunities for pairing the carbon dioxide (CO2) points of supply from stationary sources such as power plants, steel and cement production, coal to liquid plants and refineries, with potential oil reservoirs in China.

Design/methodology/approach

This study builds a linear optimization model to analyze the tradeoffs in developing CO2-enhance oil recovery (EOR) projects in China for a range of policy options to match points of supply with the points of demand (oil fields). The model works on optimizing CO2 application costs by meeting four principal components; CO2 storage, CO2 capture, transport costs and additional oil recovery.

Findings

This study reveals new opportunities and economic sources to feed CO2-EOR applications and offers reasonable options to supply CO2 for potential points of demand. Furthermore, power plants and coal to liquid industries had the most significant and economic contributions to potential CO2-EOR projects in China. Total annual emission reduction is expected to be 10% (based on 10 Gton annual emissions). The emission reductions and potential CO2 storage from the different industries as follow; 94% from power plants, 4% from biofuel and 2% from coal to liquid plants.

Social implications

Carbon capture and storage (CCS) is one practice aiming to reduce the amounts of anthropogenic emissions of carbon dioxide emitted into the atmosphere and reduce the related social costs. However, given the relatively high cost associated with this practice, coupling it with EOR could offer a significant financial incentive to facilitate the development of CCS projects and meet climate change objectives.

Originality/value

The model used in this study can be straightforwardly adapted to any geographic location where industry and policymakers are looking to simultaneously reduce CO2 emissions while increasing hydrocarbon recovery. The model is highly adaptable to local values in the parameters considered and to include additional local considerations such as geographic variation in capture costs, taxes and premiums to be placed on CO2 capture in so-called “non-attainment zones” where pollution capture make could make a project politically and economically viable. Regardless of how and where this model is applied, it is apparent that CO2 from industrial sources has substantial potential value as a coproduct that offsets its sequestration costs using existing, commercially available CO2-EOR technology, once sources and sinks are optimally paired.