Article, 2024

Optimal design and off-design performance improvement for power-to-methane system integrating solid oxide electrolysis cell with methanation reactor

Fuel, ISSN 0016-2361, 1873-7153, Volume 356, Page 129314, 10.1016/j.fuel.2023.129314

Contributors

Zhong, Like 0000-0002-9013-1818 [1] [2] Cui, Xiaoti 0000-0001-6514-0280 [1] Yao, Erren 0000-0002-1213-1628 (Corresponding author) [2] Xi, Guang (Corresponding author) [2] Zou, Hansen [2] Jensen, Søren Højgaard 0000-0001-8418-1408 [1] [3]

Affiliations

  1. [1] Aalborg University
  2. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  3. [2] Xi'an Jiaotong University
  4. [NORA names: China; Asia, East];
  5. [3] Dynelectro ApS, 4130, Viby Sjælland, Denmark
  6. [NORA names: Denmark; Europe, EU; Nordic; OECD]

Abstract

Power-to-methane (PtM) is a prospective solution to the mismatching between the supply and consumption of renewable energy resources (RES) by converting renewable power into methane. However, the continuous fluctuation of RES causes the PtM system to deviate from the design condition in the vast majority of cases, and thus it is significantly vital to study the operating characteristics of the PtM system under off-design conditions. This paper proposes a comprehensive investigation framework from design to off-design steps for the performance improvement of a PtM system combining solid oxide electrolysis cell with methanation reactor, and solar energy is selected as renewable energy input. Firstly, the system with the total exergy efficiency (η EX,tot) of 11.83% and levelized cost of exergy (LCOE) of 150.76 $/MWh is selected as the optimal design condition based on the homogeneous assessment from both thermodynamic and economic aspects, by means of Non-dominated sorting genetic algorithm-II. Then, based on the optimal design point, the off-design performances are quantitatively investigated under varying solar radiation and key operating parameters, in terms of synthetic natural gas (SNG) yield and η EX,tot. The results indicate that with the increment in solar radiation, the SNG yield rises, while the η EX,tot increases first and then decreases. Finally, the multi-objective optimization based on the Artificial Neural Network models is implemented for the system under off-design conditions to acquire the best trade-off between hourly SNG yield and η EX,tot. The off-design optimization solutions reveal that the hourly optimal SNG yield is located in the range of 275.06–946.53 kW, achieving a total annual SNG yield of 1697 MWh/y, and the hourly optimal η EX,tot mainly varies in the range of 10.40–11.40%.

Keywords

Artificial, E ex, EX, MWh/y, Non-dominated Sorting Genetic Algorithm II, PTM, PTM systems, Power-to-Methane, algorithm II, artificial neural network model, aspects, assessment, cases, cells, characteristics, conditions, consumption, consumption of renewable energy resources, continuous fluctuations, cost of exergy, design, design conditions, design point, economic aspects, efficiency, electrolysis cell, energy, energy input, energy resources, exergy, fluctuation of renewable energy resources, framework, gas, genetic algorithm II, homogeneity assessment, improvement, increment, input, investigation framework, levelized cost, levelized cost of exergy, methanation reactor, methane, mismatch, model, multi-objective optimization, natural gas, network model, neural network model, off-design conditions, off-design performance, operating characteristics, operating parameters, operation, optimal design, optimal design conditions, optimal design point, optimal solution, optimization, parameters, performance, performance improvement, point, prospective solution, radiation, range, reactor, renewable energy inputs, renewable energy resources, resources, results, solar energy, solar radiation, solid oxide electrolysis cells, solution, sorting genetic algorithm II, steps, supply, synthetic natural gas, synthetic natural gas yields, system, yield

Funders

  • National Natural Science Foundation of China
  • China Scholarship Council
  • Innovation Fund Denmark

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