scholarly journals Multi-Objective Optimization of CO2 Sequestration in Heterogeneous Saline Aquifers under Geological Uncertainty

2021 ◽  
Vol 11 (20) ◽  
pp. 9759
Author(s):  
Changhyup Park ◽  
Jaehwan Oh ◽  
Suryeom Jo ◽  
Ilsik Jang ◽  
Kun Sang Lee
Keyword(s):  
Co2 Sequestration ◽  
Optimal Allocation ◽  
Volume Ratio ◽  
Saline Aquifers ◽  
Multi Objective Optimization ◽  
Geological Uncertainty ◽  
Injection Wells ◽  
Multi Objective ◽  
Shale Volume ◽  
Operation Pressure

This paper presents a Pareto-based multi-objective optimization for operating CO2 sequestration with a multi-well system under geological uncertainty; the optimal well allocation, i.e., the optimal allocation of CO2 rates at injection wells, is obtained when there is minimum operation pressure as well as maximum sequestration efficiency. The distance-based generalized sensitivity analysis evaluates the influence of geological uncertainty on the amount of CO2 sequestration through four injection wells at 3D heterogeneous saline aquifers. The spatial properties significantly influencing the trapping volume, in descending order of influence, are mean sandstone porosity, mean sandstone permeability, shale volume ratio, and the Dykstra–Parsons coefficient of permeability. This confirms the importance of storable capacity and heterogeneity in quantitatively analyzing the trapping mechanisms. Multi-objective optimization involves the use of two aquifer models relevant to heterogeneity; one is highly heterogeneous and the other is less so. The optimal well allocations converge to non-dominated solutions and result in a large injection through one specific well, which generates the wide spread of a highly mobile CO2 plume. As the aquifer becomes heterogeneous with a large shale volume and a high Dykstra–Parsons coefficient, the trapping performances of the combined structural and residual sequestration plateau relatively early. The results discuss the effects of spatial heterogeneity on achieving CO2 geological storage, and they provide an operation strategy including multi-objective optimization.

scholarly journals Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2961
Author(s):  
Anders Clausen ◽  
Aisha Umair ◽  
Yves Demazeau ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Resource Allocation ◽  
Social Welfare ◽  
Energy Demand ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Multi Objective Optimization ◽  
Multi Agent Systems ◽  
Multi Objective ◽  
Pareto Sets ◽  
The Impact

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

scholarly journals Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 558
Author(s):  
Taj-Aldeen Naser Abdali ◽  
Rosilah Hassan ◽  
Azana Hafizah Mohd Aman ◽  
Quang Ngoc Nguyen ◽  
Ahmed Salih Al-Khaleefa
Keyword(s):  
Energy Efficient ◽  
Optimal Allocation ◽  
Fog Computing ◽  
Cost Effective ◽  
Loop Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mathematical Problems ◽  
Metric Set ◽  
Network Manager

Fog computing is an emerging technology. It has the potential of enabling various wireless networks to offer computational services based on certain requirements given by the user. Typically, the users give their computing tasks to the network manager that has the responsibility of allocating needed fog nodes optimally for conducting the computation effectively. The optimal allocation of nodes with respect to various metrics is essential for fast execution and stable, energy-efficient, balanced, and cost-effective allocation. This article aims to optimize multiple objectives using fog computing by developing multi-objective optimization with high exploitive searching. The developed algorithm is an evolutionary genetic type designated as Hyper Angle Exploitative Searching (HAES). It uses hyper angle along with crowding distance for prioritizing solutions within the same rank and selecting the highest priority solutions. The approach was evaluated on multi-objective mathematical problems and its superiority was revealed by comparing its performance with benchmark approaches. A framework of multi-criteria optimization for fog computing was proposed, the Fog Computing Closed Loop Model (FCCL). Results have shown that HAES outperforms other relevant benchmarks in terms of non-domination and optimality metrics with over 70% confidence of the t-test for rejecting the null-hypothesis of non-superiority in terms of the domination metric set coverage.

scholarly journals Ensemble-Based Multi-Objective Optimization of On-Off Control Devices Under Geological Uncertainty

2015 ◽  
Author(s):  
R.M. Fonseca ◽  
O. Leeuwenburgh ◽  
E. Della Rossa ◽  
P.M.J. Van den Hof ◽  
J.D. Jansen
Keyword(s):  
Multi Objective Optimization ◽  
Geological Uncertainty ◽  
Multi Objective ◽  
Control Devices

scholarly journals A New Multi-Objective Optimization Model of Water Resources Considering Fairness and Water Shortage Risk

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2648
Author(s):  
Xiaoyu Tang ◽  
Ying He ◽  
Peng Qi ◽  
Zehua Chang ◽  
Ming Jiang ◽  
...  
Keyword(s):  
Water Resources ◽  
Optimization Model ◽  
Optimal Allocation ◽  
Risk Index ◽  
Water Shortage ◽  
Economic Benefits ◽  
Selection Strategy ◽  
Multi Objective Optimization ◽  
Structural Water ◽  
Multi Objective

Assessing the fairness of water resource allocation and structural water shortage risks is an urgent problem that needs to be solved for the optimal allocation of water resources. In this study, we established a new multi-objective optimization model of water resources based on structural water shortage risks and fairness. We propose an improved NSGA-III based on the reference point selection strategy (ARNSGA-III) to solve the optimization model. The superiority of this method was proven by comparing it with three other methods, namely, NSGA-III, MOSPO, and MOEA/D. The model was applied to optimize the allocation of water resources in Wusu City in China. The results show that the new multi-objective optimization model provides reasonable and feasible solutions for solving water conflicts. The convergence and stability of ARNSGA-III are better than those of the other three algorithms. Allocation schemes of water resources for Wusu City in normal years, dry years, and extremely dry years are proposed. In normal years, the structural water shortage risk index is reduced by 50.1%, economic benefits increased by 0.2%, and fairness is reduced by 60.5%. This study can provide new ideas for solving the multi-objective optimization of regional water resources.

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