Research on life cycle low carbon optimization method of multi-energy complementary distributed energy system: A review

Liu, Changrong; Wang, Hanqing; Wang, Zhiyong; Liu, Zhiqiang; Tang, Yifang; Yang, Sheng

doi:10.1016/j.jclepro.2022.130380

Cited by 48 publications

(14 citation statements)

References 58 publications

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“…The case analysis shown that the three objectives had been greatly reduced after optimization. Obviously, the comprehensive evaluation of multiple objectives often needs to be considered during the process of planning or scheduling optimization of MCS, which belongs to the multi-objective optimization problem [10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

Weight Analysis for Multi-objective Optimal Solution of Multi-energy Complementary System

Tang,

Wang,

Liu

et al. 2024

Preprint

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Multi-energy complementary system (MCS), integrating with renewable energy and new energy, is an effective way to promote low-carbon development and clean energy utilization. Reasonable system configurations and operation scheduling schemes play key roles in maintaining the long-term and efficient operations of the system. In general, multi-objective optimization of system integration is employed to achieve the optimal decision strategies for MCS operations. However, different weights used to determine the importance of each objective during the optimization may cause various optimization results. Thus, reasonable weight adopted is critical to the credibility of the optimization solutions and the resulting system efficiency. In this paper, a comprehensive weight analysis is implemented during the multi-objective optimization for the decision-making of the MCS. The weight determination of MCS design optimization objective is analyzed from three aspects namely subjective, objective, and the combination of subjective and objective. Based on a case analysis, it isfounded that when the weight is determined by subjective weighting method, the weight of economic index is the largest. When the objective weighting method is adopted, the environmental protection index is obviously greater than the economic index and the energy efficiency index. Due to the different information entropy carried by each target in each group of data, the weights obtained by objective weighting and subjective and objective comprehensive weighting are different. With different weight decision schemes, the capacity of each MCS equipment varies greatly. While using the intelligent optimization algorithm for multi-objective optimization, it is necessary to analyze the weight of each objective in the process of multiple independent experiments and make comprehensive decisions according to the objective preference of decision makers to determine the optimal solution. The results indicatethat the research provides an effective reference for the analysis of the weight of each objective and the decision of the optimal solution in the MCS optimization research process.

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Section: Introductionmentioning

confidence: 99%

Weight Analysis for Multi-objective Optimal Solution of Multi-energy Complementary System

Tang,

Wang,

Liu

et al. 2024

Preprint

Self Cite

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“…Furthermore, Sun et al analyzed the impact of typhoons on the carbon emissions of offshore wind farms and constructed a comprehensive lifecycle carbon emissions accounting model with typhoons as input conditions [17]. Moreover, Ogunjuyigbe et al, Yan et al, and Liu et al investigated the lifecycle carbon footprint of hybrid energy systems, distributed energy systems, and multi-energy complementary distributed energy systems [18][19][20]. In summary, LCA technology has been extensively applied in carbon emission studies within the renewable energy field.…”

Section: Introductionmentioning

confidence: 99%

Research on the Accounting and Prediction of Carbon Emission from Wave Energy Convertor Based on the Whole Lifecycle

Li,

Wang,

Wang

et al. 2024

Energies

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Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation (EFco2) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the EFco2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum).

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“…The operation optimization strategies used for multi-energy supply aim to solve singleor multi-objective models, using optimization algorithms to obtain optimal energy management strategies for energy systems. Researchers have designed various distributed CCHP systems based on multi-energy complementarity [28]. They have established mathematical models that include equipment energy systems, pressure networks, and terminal loads [29].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Energy Management and Case Study of Multi-Energy Coupled Supply for Green Ships

Wang

Sun

et al. 2023

JMSE

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The ship industry is currently facing numerous challenges, including rising fuel prices, limited fuel resources, and increasingly strict regulations related to energy efficiency and pollutant emissions. In this context, the adoption of green-ship wind–photovoltaic–electricity–fuel multi-energy supply systems has emerged as an efficient and clean technology that harnesses multiple energy sources. These systems have the potential to increase the utilization of renewable energy in ship operations while optimizing management practices in order to enhance overall energy efficiency. To address these challenges, this article presents a comprehensive energy supply system for ships that integrates multi-energy sources for cold–heat–electricity supply. The primary components of this system include fuel cells, photovoltaic equipment, wind turbines, electric heating pumps, electric refrigerators, thermal refrigerators, batteries, and heat storage tanks. By ensuring the safety of the system, our approach aims to minimize daily operating costs and optimize the performance of the multi-energy flow system by running scheduling models. To achieve this, our proposed system utilizes dynamic planning techniques combined with ship navigation conditions to establish an optimized management model. This model facilitates the coordinated distribution of green ship electricity, thermal energy, and cooling loads. The results of our study demonstrate that optimized management models significantly reduce economic costs and improve the stability of energy storage equipment. Specifically, through an analysis of the economic benefits of power storage and heat storage tanks, we highlight the potential for reducing fuel consumption by 6.0%, 1.5%, 1.4%, and 2.9% through the use of electric–thermal hybrid energy storage conditions.

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Research on life cycle low carbon optimization method of multi-energy complementary distributed energy system: A review

Cited by 48 publications

References 58 publications

Weight Analysis for Multi-objective Optimal Solution of Multi-energy Complementary System

Weight Analysis for Multi-objective Optimal Solution of Multi-energy Complementary System

Research on the Accounting and Prediction of Carbon Emission from Wave Energy Convertor Based on the Whole Lifecycle

Optimizing Energy Management and Case Study of Multi-Energy Coupled Supply for Green Ships

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