Comprehensive evaluation of a gas turbine-based multi-generation system for power, heating, cooling, freshwater, hydrogen and ammonia: 4E assessment and multi-objective optimization with RSM desirability approach

IF 9.1 1区 工程技术 Q1 ENERGY & FUELS Renewable Energy Pub Date : 2025-03-14 DOI:10.1016/j.renene.2025.122900
Sadık Ata
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Abstract

In this study, the production of power, heating, cooling, freshwater, hydrogen and ammonia in a gas turbine cycle (GTC)-based multi-energy system is analysed. From parametric study with energy, exergy, economic, environmental and exergo-enviro analyses, regression models were created for five different responses depending on the decision parameters. These are exergy efficiency (ηex), dynamic payback period (DPP-year), CO2 footprint (kg/kWh), net present value (NPV-$) and levelized multi energy cost (LMEC-$/GJ). With these response values, bi-objective (BO), tri-objective (TO) and multi-objective optimization (MOO) studies including all responses were performed with Response Surface Method (RSM) and desirability function approach under various scenarios. In this context, RSM desirability plots and scores were generated by analyzing all binary (C(5,2)) and ternary C(5,3)) combinations of five different responses and MOO. As a result, a high desirability score of 0.8584 was obtained in MOO and an improvement of 2.19, 22.44, 1.37, 11.41 and 8.82 % was achieved for ηex, DPP-year, CO2 footprint-kg/kWh, NPV-$, and LMEC-$/GJ, respectively compared to the base case. Based on all response values pertaining to the energy, exergy, economic, environmental performance of the multi-energy system with RSM optimization, a performance enhancement of 9.25 % was determined.

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基于燃气轮机的电、热、冷、淡水、氢、氨多发电系统综合评价:基于RSM可取性方法的4E评价与多目标优化
本文分析了基于燃气轮机循环(GTC)的多能系统的发电、供热、制冷、淡水、氢气和氨的产生。通过对能源、能源、经济、环境和环境分析的参数化研究,根据决策参数建立了五种不同响应的回归模型。这些指标包括能源效率(ηex)、动态投资回收期(dpp年)、二氧化碳足迹(kg/kWh)、净现值(NPV-$)和平衡多能成本(LMEC-$/GJ)。根据这些响应值,采用响应面法(response Surface Method, RSM)和期望函数法(desirability function approach)对不同场景下的所有响应进行双目标(BO)、三目标(TO)和多目标优化(MOO)研究。在这种情况下,通过分析五种不同反应和MOO的所有二进制(C(5,2))和三元C(5,3))组合,生成RSM可取性图和分数。结果表明,MOO的理想分数为0.8584,ηex、dpp年、CO2足迹-kg/kWh、NPV-$和LMEC-$/GJ的理想分数分别比基本情况提高了2.19、22.44、1.37、11.41和8.82%。综合考虑RSM优化后的多能系统在能源、用能、经济、环境等方面的响应值,可获得9.25%的性能提升。
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来源期刊
Renewable Energy
Renewable Energy 工程技术-能源与燃料
CiteScore
18.40
自引率
9.20%
发文量
1955
审稿时长
6.6 months
期刊介绍: Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices. As an international, multidisciplinary journal in renewable energy engineering and research, we strive to be a premier peer-reviewed platform and a trusted source of original research and reviews in the field of renewable energy. Join us in our endeavor to drive innovation and progress in sustainable energy solutions.
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