Geothermal energy prospect for decarbonization, EWF nexus and energy poverty mitigation in East Africa; the role of hydrogen production

Bamisile, Olusola; Cai, Dongsheng; Adun, Humphrey; Taiwo, Michael; Li, Jian; Hu, Yihua; Huang, Qi

doi:10.1016/j.esr.2023.101157

Cited by 9 publications

(3 citation statements)

References 69 publications

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“…One WRM problem that is increasingly being studied by water modelers and managers is the optimal and cost-efficient water-energy resource allocation and production, under natural (hydrological) restrictions and sustainability-related goals, such as decarbonization [48,49].…”

Section: Problem Definitionmentioning

confidence: 99%

“…The OF (48) aims to minimize the deviations from the predefined targets ( 38)-( 47), thus minimizing the positive and negative deviation variables (d + i , d − i ), which will be weighted depending on their importance by weights w + i and w − i , respectively. As mentioned, these weights w + i and w − i are actually the penalties that the user defines for each deviation.…”

Section: Multi-objective Optimization-goal Programmingmentioning

confidence: 99%

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Optimization Examples for Water Allocation, Energy, Carbon Emissions, and Costs

Alamanos,

Garcia

2024

Encyclopedia

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The field of Water Resources Management (WRM) is becoming increasingly interdisciplinary, realizing its direct connections with energy, food, and social and economic sciences, among others. Computationally, this leads to more complex models, wherein the achievement of multiple goals is sought. Optimization processes have found various applications in such complex WRM problems. This entry considers the main factors involved in modern WRM, and puts them in a single optimization problem, including water allocation from different sources to different uses and non-renewable and renewable energy supplies, with their associated carbon emissions and costs. The entry explores the problem mathematically by presenting different optimization approaches, such as linear, fuzzy, dynamic, goal, and non-linear programming models. Furthermore, codes for each model are provided in Python, an open-source language. This entry has an educational character, and the examples presented are easily reproducible, so this is expected to be a useful resource for students, modelers, researchers, and water managers.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Multi-objective Optimization-goal Programmingmentioning

confidence: 99%

Optimization Examples for Water Allocation, Energy, Carbon Emissions, and Costs

Alamanos,

Garcia

2024

Encyclopedia

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“…Furthermore, due to the geothermal potential of 60 MW/m 2 of the globe, geothermal energy in decompression stations can be very favorable [9]. Geothermal energy can be used in various ways depending on its temperature [10][11][12]. However, the most appropriate resources to be implemented in natural gas decompression stations because the gas must not leak (closed-loop operation) are medium-temperature geothermal resources (80-150 • C), using devices commonly referred to as borehole heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

Energy, Exergy, Exergoeconomic Analysis, and Optimization in a Natural Gas Decompression Station with a Vortex Tube and Geothermal Preheating

Villalón-López,

Ambriz-Díaz,

Rubio-Maya

et al. 2024

Sustainability

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Natural gas stations require a preheating stage to prevent the formation of hydrates inside of them provoked by a sudden decompression process of the natural gas. The preheating process has been investigated to improve efficiency and to reduce costs as well. This work studies the behavior of a natural gas decompression station with a first-stage preheating process using a vortex tube and a geothermal heat exchanger, followed by a second stage involving a water bath heater (heating vat). An energetic, exergetic, and exergoeconomic study has been carried out based on a mathematical model and the theory of exergetic cost, obtaining key thermodynamic and thermoeconomic variables, including exergy flows and equipment costs. A heat flow of 26.41 kW was obtained in the geothermal preheating stage; meanwhile, a 60.43 kW heat flow was obtained in the heating vat. The results showed a saving in station fuel using only 2.046% of the natural gas in the system at the second preheating stage. Also, the system was optimized, obtaining a 15.73% reduction in the decompressed natural gas cost. These findings show the possibility of implementing these systems in zones with many geothermal resources to reach a constant, profitable natural gas supply in areas where a pipeline network does not exist.

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