Article
Operations Research & Management Science
Ellen H. Fukuda, L. M. Grana Drummond, Ariane M. Masuda
Summary: The proposed extension of the real-valued conjugate directions method is used for unconstrained quadratic multiobjective problems, aiming to find weak Pareto and Pareto optima through specific steps and calculations in each iteration.
Article
Energy & Fuels
Maximilian Hoffmann, Leander Kotzur, Detlef Stolten
Summary: This study introduces clustering techniques that can be applied to different energy system models to improve efficiency and accuracy, without requiring deep knowledge of the individual models.
Article
Green & Sustainable Science & Technology
Piotr Zebrowski, Ulf Dieckmann, Ake Braennstroem, Oskar Franklin, Elena Rovenskaya
Summary: Addressing climate change requires collective action and fair distribution of costs and benefits among multiple agents. However, existing integrated assessment models often overlook the distribution of costs and benefits, resulting in perceived unfairness in policy recommendations. This paper proposes adjusting the objectives within these models to derive policy recommendations that are perceived as fair by the agents involved.
Article
Computer Science, Hardware & Architecture
Derya Malak, Faruk Volkan Mutlu, Jinkun Zhang, Edmund M. Yeh
Summary: The paper addresses the issue of caching and power control in wireless HetNets. By formulating a joint optimization framework and using different optimization algorithms, the authors propose algorithms to reduce resource consumption and transmission delay. They obtain an approximation to the globally optimal solution and provide necessary conditions for the optimal solution.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Information Systems
Jinlong Zhou, Juan Zou, Shengxiang Yang, Jinhua Zheng, Dunwei Gong, Tingrui Pei
Summary: This paper proposes niche-based and angle-based selection strategies for many objective evolutionary optimization, which have been shown to be competitive and scalable to handle constrained many-objective optimization problems in experimental studies.
INFORMATION SCIENCES
(2021)
Article
Operations Research & Management Science
I. Kaliszewski, J. Miroforidis
Summary: In this study, the concept of lower shells and upper shells is used to provide information on the distance from the best feasible solution found before the optimization process has stopped to the true Pareto optimal solution in large-scale multiobjective optimization problems. The proposed approach is illustrated on biobjective multidimensional knapsack problems derived from single-objective multidimensional knapsack problems, without specific assumptions about the problems to be solved.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Computer Science, Artificial Intelligence
Amarjeet Prajapati
Summary: In this study, the performance of nine large-scale multi-objective optimization optimizers was evaluated and compared over five large-scale many-objective software clustering problems. The results showed that S3-CMA-ES and LMOSCO performed better in most cases, while H-RVEA was the worst performer.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mohammadali Saniee Monfared, Sayyed Ehsan Monabbati, Atefeh Rajabi Kafshgar
Summary: This paper discusses noncooperative multi-objective optimization problems where the objective holders are independent humans or human-based entities, suggesting a new solution concept of the Pareto-optimal Equilibrium point. The interplay between game problems and multi-objective optimization problems is investigated, with illustrative examples provided to deepen the understanding of when a POE solution is achievable.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Juan Zou, Jing Liu, Jinhua Zheng, Shengxiang Yang
Summary: This paper proposes a multi-objective optimization algorithm based on staged coordination selection, consisting of convergence and diversity stages. The algorithm aims to balance convergence and diversity in evolutionary algorithms, showing improved performance compared to existing algorithms on various benchmark instances.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Operations Research & Management Science
Dennis Adelhuette, Christian Biefel, Martina Kuchlbauer, Jan Rolfes
Summary: This paper introduces the application of Pareto efficiency to robust linear programming and generalizes this concept to robust optimization problems in Euclidean spaces with uncertainty. Additionally, it demonstrates the value of this approach through exemplary cases in the field of robust semidefinite programming. Furthermore, the paper modifies a famous algorithm to improve the approximation guarantee in non-worst-case scenarios for the robust max-cut problem.
OPTIMIZATION LETTERS
(2023)
Article
Thermodynamics
Zhengbiao Hu, Dongfeng He, Hongbo Zhao
Summary: The core objective of system energy conservation techniques in integrated steel works is to improve energy utilization and reduce energy costs. This study established a multi-objective optimization model for energy systems, aiming to minimize the energy cost and maximize the exergy efficiency. The results showed that the energy cost reduced by 22.81% and the exergy efficiency increased by 7.71% after the multi-objective optimization.
Article
Mathematics
Juan M. Benito-Ostolaza, Maria J. Campion, Asier Estevan
Summary: This work explores the role of social norms and presents a mathematical approach to solving agreement situations and regulation construction issues. By formalizing mathematically and defining the proximity between agreements or regulations, complex problems related to game theory or law can now be simplified into mathematical optimization problems.
Article
Mathematics
Nien-Che Yang, Chun-Wei Hsu, Abhilash Sen
Summary: With the growing attention to global ecological conservation, countries are shifting their energy policies towards renewable energy systems, mainly solar and wind. The WECC has developed a generic model as a simulation framework to analyze the response of actual equipment in a solar wind generation system. This paper proposes a parameter tuning process that combines Latin hypercube sampling and Pareto optimization to effectively match the output of an actual inverter device with the generic model.
Article
Green & Sustainable Science & Technology
Woo-sung Kim, Hyunsang Eom, Youngsung Kwon
Summary: This study proposes two Markov chain models, Embedded Markov and Absorbing Markov chain, to improve the reliability and sustainability of photovoltaic-coupled energy storage system. The models can help evaluate important measurements and determine the optimal system parameters combination.
Article
Operations Research & Management Science
P. B. Assuncao, O. P. Ferreira, L. F. Prudente
Summary: The paper analyzes the conditional gradient method, also known as the Frank-Wolfe method, for constrained multiobjective optimization. Different strategies for obtaining step sizes are considered, and asymptotic convergence properties and iteration-complexity bounds are established with and without convexity assumptions on the objective functions. Numerical experiments are provided to illustrate the effectiveness of the method and certify the obtained theoretical results.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Jan Priesmann, Lars Nolting, Christina Kockel, Aaron Praktiknjo
Summary: The analysis of future energy systems requires appropriate data, with a focus on energy consumption patterns for heat, cold, mechanical energy, information and communication, and light. The dataset provides comprehensive data on residential, industrial, commerce, and mobility consumers, aggregated and disaggregated to the NUTS2 level for validation and calculations. Multiple data sources are combined to enhance the scope, validity, and reproducibility of energy system modeling, particularly for scenarios involving renewable electricity replacing fossil fuels.
Article
Energy & Fuels
Christina Kockel, Lars Nolting, Jan Priesmann, Aaron Praktiknjo
Summary: While progress has been made in reducing greenhouse gas emissions in the power sector, sectors such as transportation and heating are lagging behind. Sector coupling is a strategy to extend emission reductions from the power sector to other sectors. This study analyzes the extent of energy demand matching renewable energy supply and explores the impacts of sector coupling pathways on future infrastructure requirements. Findings provide policy recommendations for integrating renewable energy sources into present energy systems effectively and efficiently.
Article
Energy & Fuels
Christina Kockel, Lars Nolting, Rafael Goldbeck, Christina Wulf, Rik W. De Doncker, Aaron Praktiknjo
Summary: This study aims to quantify the environmental impacts of DC and AC microgrids in building-level power distribution, demonstrating that operating a microgrid based on DC power distribution infrastructure can significantly reduce the environmental impacts of power electronic components used and lead to savings in climate change impact emissions. Additionally, the study suggests that current scaling rules of power electronics used in LCAs may lead to inaccurate results, and proposes a more technical approach for a detailed analysis of the environmental impacts of power electronic components at a system level.
Article
Energy & Fuels
Sinan Kufeoglu, Eray Acikgoz, Yunus Emre Tasci, Taha Yasin Arslan, Jan Priesmann, Aaron Praktiknjo
Summary: This paper discusses the development of energy data hubs and the trend towards decentralization. By designing a decentralized energy data hub's business ecosystem using blockchain technology, the transparency and flexibility of energy data are enhanced, leading to the emergence of new business models. The paper also compares centralized and decentralized methods, highlighting the advantages and disadvantages of both approaches.
Article
Thermodynamics
Lars Nolting, Aaron Praktiknjo
Summary: This study investigates the impact of increasing complexity in energy systems on decision-making processes and proposes a mathematical framework to determine the optimal level of model detail. The findings suggest that increasing model complexity does not necessarily improve accuracy, and high-complexity models suffer from uncertainties in input data and high costs for sensitivity analysis.
Article
Energy & Fuels
Clara Sophie Koehnen, Jan Priesmann, Lars Nolting, Leander Kotzur, Martin Robinius, Aaron Praktiknjo
Summary: Metamodeling is a suitable approach to complement traditional energy system modeling, as it can reduce complexity and enhance efficiency in certain applications. However, challenges such as accurate forecasting of interdependent variables and insufficient accuracy of the metamodel need to be addressed when utilizing metamodels in energy system analyses.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Correction
Energy & Fuels
Christina Kockel, Lars Nolting, Rafael Goldbeck, Christina Wulf, Rik W. De Doncker, Aaron Praktiknjo
Article
Environmental Studies
Jan Priesmann, Saskia Spiegelburg, Reinhard Madlener, Aaron Praktiknjo
Summary: This paper investigates the impacts of renewable energy support levies on income inequality and energy poverty and proposes three different reform options. The microsimulation results indicate that the levies do increase income inequality and energy poverty. The analysis for 2018 shows that all reform options would have led to lower income inequality and substantially decreased energy poverty.
ENERGY RESEARCH & SOCIAL SCIENCE
(2022)
Article
Energy & Fuels
Christina Kockel, Lars Nolting, Kevin Pacco, Carlo Schmitt, Albert Moser, Aaron Praktiknjo
Summary: The dependency of European power systems and economies on natural gas is assessed using simulation and optimization models. The analysis shows that reducing natural gas usage in the power sector by up to 30% has a moderate impact on electricity supply security. However, restrictions of 40% or more lead to significant reductions in electricity demand shortfall and incur economic costs exceeding EUR 77 billion. Close coordination of gas distribution at the European level is found to be crucial in mitigating negative economic consequences, while delaying planned power plant shutdowns can effectively compensate for reduced gas volumes in the electricity sector.
Article
Energy & Fuels
Leander Kotzur, Lars Nolting, Maximilian Hoffmann, Theresa Gross, Andreas Smolenko, Jan Priesmann, Henrik Buesing, Robin Beer, Felix Kullmann, Bismark Singh, Aaron Praktiknjo, Detlef Stolten, Martin Robinius
Summary: This article discusses how to deal with complexity issues in energy system model design, including how to avoid complexity factors, how to reduce systematic complexity, and provides guidance for energy system modelers to overcome computational limitations.
ADVANCES IN APPLIED ENERGY
(2021)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.