Article
Thermodynamics
Shangling Chu, Yang Liu, Zipeng Xu, Heng Zhang, Haiping Chen, Dan Gao
Summary: This paper studies a distributed energy system integrated with solar and natural gas, analyzes the impact of different parameters on its energy utilization and emissions reduction, and obtains the optimal solution through an optimization algorithm. The results show that compared to traditional separation production systems, this integrated system achieves higher energy utilization and greater reduction in carbon emissions.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Computer Science, Artificial Intelligence
Linfei Yin, Zhixiang Sun
Summary: With the opening and development of power markets, large-scale multi-area interconnected power systems have become inevitable. This paper introduces a distributed concept to mitigate the drawbacks of traditional centralized economic dispatch optimization method and proposes a distributed MOGWO. Case studies show that the proposed method can effectively protect information privacy and achieve smaller objective values compared to centralized optimization.
APPLIED SOFT COMPUTING
(2022)
Article
Energy & Fuels
Yuwei Wang, Minghao Song, Mengyao Jia, Bingkang Li, Haoran Fei, Yiyue Zhang, Xuejie Wang
Summary: This paper proposes a multi-objective distributionally robust optimization (DRO) model for H-RE-CCHP planning. By considering economic and environmental objectives, as well as resisting uncertainties, a balance between economy and environment is achieved, promoting the transition towards low carbon energy.
Article
Computer Science, Hardware & Architecture
Rodney Martinez Alonso, David Plets, Margot Deruyck, Luc Martens, Glauco Guillen Nieto, Wout Joseph
Summary: This paper presents a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism, which significantly reduces network power consumption, exposure, and spectrum usage. Compared to traditional designs, the cloud-based architecture achieved better results even in worst-case scenarios.
Article
Green & Sustainable Science & Technology
Shirko Jafary, Shahram Khalilarya, Ali Shawabkeh, Makatar Wae-hayee, Mehran Hashemian
Summary: This study compares two solar powered trigeneration systems from energetic and exergetic viewpoints. The system with ORC-IHE configuration demonstrated higher energy and exergy efficiency compared to the RORC configuration, with energy efficiencies of 93.35% and 86.66%, and exergy efficiencies of 12.69% and 6.641%, respectively.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Thermodynamics
Yang Liu, Jitian Han, Huailiang You
Summary: A CCHP system combining SOFC/GT and transcritical CO2 power/refrigeration cycles is proposed to efficiently provide cooling, heating, and power. Energy, exergy, and exergoeconomic analysis is conducted, showing that the system achieves high performance with cooling, heating, and net electricity outputs of 48.37 kW, 240.65 kW, and 250.95 kW, and power generation and exergetic efficiencies of 62.65% and 62.27%. The system's capital, O&M, and fuel costs are also evaluated, along with its NPV and payback period. The impacts of key parameters on the system's performance are discussed, and an optimization process is applied to improve energy output and exergy efficiency.
APPLIED THERMAL ENGINEERING
(2023)
Article
Thermodynamics
Hanbing Wang, Zeting Yu, Daohan Wang, Guoxiang Li, Guoping Xu
Summary: The study proposes novel trigeneration systems based on atmospheric and pressurized SOFC, and conducts analyses on energetic, exergetic and economic aspects. Results show that the pressurized system outperforms the atmospheric system thermodynamically, but with higher economic costs. Multi-objective optimization is used to select the final optimal design point.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Operations Research & Management Science
Maude J. Blondin, Matthew Hale
Summary: In this study, a distributed optimization algorithm is proposed to explore Pareto optimal solutions for non-homogeneously weighted sums of objective functions. Agents initially disagree on the priorities of objective functions, but are gradually driven to reach a consensus as they optimize, ultimately reaching a common solution.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2021)
Article
Thermodynamics
Giuseppe Lucarelli, Matteo Genovese, Gaetano Florio, Petronilla Fragiacomo
Summary: The article discusses the use of a novel multi-objective optimization model for tri-generative energy systems, considering technical, economic, and environmental objectives. The model was employed to simulate the daily operation of various system configurations and evaluate the data. A comparison was made among 22 technology sets, resulting in the identification of an optimal tri-generation plant for a large industrial user.
Article
Green & Sustainable Science & Technology
Sven Schulz, Martin Schoenheit, Janis S. Neufeld
Summary: Reducing carbon emissions is crucial for achieving the goals of cleaner production, and the total amount of emissions depends not only on production technologies but also on electricity mix and product transportation. However, these factors have not been explicitly considered in distributed manufacturing. Therefore, this study addresses a multi-objective scheduling problem to minimize both makespan and carbon emission. By using mixed-integer programming and a novel multi-objective iterated greedy algorithm, the problem is effectively solved. A case study is conducted to analyze the impact of influencing factors such as product weights, energy consumption, and heterogeneity of production facilities.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Engineering, Multidisciplinary
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu
Summary: In this work, federated learning is formulated as multi-objective optimization and a new algorithm called FedMGDA+ is proposed, which guarantees fairness and robustness while maintaining individual performance for participating users.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Jiayin Song, Chao Lu, Qiang Ma, Hongwei Zhou, Qi Yue, Qinglin Zhu, Yue Zhao, Yiming Fan, Qiqi Huang
Summary: This paper proposes an integrated synthesis of adaptive multi-objective particle swarm optimization algorithm (ISAMOPSO) to solve the reactive power optimization problem in power systems. Through experiments, it is proven that the ISAMOPSO algorithm has stronger global search capability and better convergence. Moreover, the algorithm can be flexibly applied to different needs and achieve dynamic optimization.
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
TaiXiu Liu, ZhiMei Zheng, YuanLong Qin, Jun Sui, QiBin Liu
Summary: Multi-energy hybrid energy systems are crucial for achieving carbon neutrality by mitigating fluctuations in renewable energy supply. The utilization of solar-fuel thermochemical hybrid upgrades solar energy to chemical energy, improving solar energy utilization efficiency and reducing CO2 emissions. This study proposes a new operation strategy and optimization method for a mid-and-low temperature solar-fuel thermochemical hybrid CCHP system to enhance system performance.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Yushuang Hou, Hongfeng Wang, Yaping Fu, Kaizhou Gao, Hui Zhang
Summary: Currently, integrated production and distribution scheduling problems are receiving considerable attention due to their crucial roles in improving supply chain performance. This study investigates a multi-objective integrated distributed flow shop and distribution scheduling problem to maximize processing quality and minimize total weighted earliness and tardiness. A mathematical model is established and a multi-objective brain storm optimization algorithm is proposed to solve the NP-hard problem. Numerical experiments and statistical tests confirm the effectiveness of the proposed method.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)