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A stochastic programming model for an energy planning problem: formulation, solution method and application

ANNALS OF OPERATIONS RESEARCH 2022年第2期311卷 695-730页

作者： Irawan, Chandra Ade Hofman, Peter S. Chan, Hing Kai Paulraj, Antony Univ Nottingham Ningbo China Nottingham Univ Business Sch China 199 Taikang East Rd Ningbo 315100 Peoples R China

The paper investigates national/regional power generation expansion planning for medium/ long-term analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants. Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the epsilon-constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments showthat the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future.

关键词： Energy planning Stochastic programming multi-objective optimization

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Single and multi-objective optimization of steel moment-resisting frame buildings under vehicle impact using evolutionary algorithms

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Journal of Building Pathology and Rehabilitation 2021年第1期6卷 1-13页

作者： Sadeghi, Abbasali Kazemi, Hamid Samadi, Maysam Department of Civil Engineering Mashhad Branch Islamic Azad University Mashhad Iran

The evaluation of optimization approaches such as single and multi-objective optimization is important for determining the optimal design of structures against abnormal loadings. In this study, the optimal design of steel moment-resisting frames (SMRFs) with 5 and 12-story is investigated and compared by using the mentioned optimization approaches under light vehicle impact loading. Then, the single and multi-objective optimization methodologies are discussed. Next, for single-objective optimization approach, three evolutionary algorithms such as a particle swarm optimization (PSO), a genetic algorithm (GA) and a whale optimization algorithm (WOA) are considered and then for another approach, a multi-objective optimization cuckoo search algorithm (MOCSA) is noticed by linking the nonlinear dynamic analysis responses of OpenSees software to MATLAB software for weight optimization of SMRFs. Finally, the effectiveness and efficiency of these optimization algorithms are evaluated and compared with each other. The results indicated that in comparison to single-objective method, the MOCSA was able to present a solution leading to a more practical and economic selection of minimum total weights and drift. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Light vehicle impact multi-objective optimization algorithm Single-objective Steel moment-resisting frame

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A comprehensive review of soft computing algorithms for optimal generation scheduling

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第2期45卷 1170-1189页

作者： Lolla, Phani Raghav Rangu, Seshu Kumar Dhenuvakonda, Koteswara Raju Singh, Arvind R. Natl Inst Technol Dept Elect Engn Silchar India Shandong Univ Sch Elect Engn Jinan Peoples R China

Strategic planning for optimal operation of modern power systems has always been a significant task to satisfy the techno-social aspects in the sustainable energy scenario. In the light of restructured power systems and decentralized generation, the economic dispatch (ED) problem has remained the utmost important economic assignment for the network operators. The conventional techniques have failed to address the nonlinear characteristics of generators for solving the practical ED problem. The complications identified in the literature are valve point loading, prohibited operating zones, multiple fuels, ramp rate limits, and spinning reserve. Therefore, a diverse range of traditional and modern optimization techniques have been adopted to tackle those complexities and nonlinearities. With the recent advancements in centralized optimization techniques, several research works were reported in the past decade to accomplish better results. This review paper endeavors to cover the pioneering research on centralized approaches as well as present-day research trends on decentralized and distributed approaches by focusing on the economic aspects. First, a detailed survey of several deterministic, stochastic, nature-inspired, and meta-heuristic based centralized approaches for solving the ED problem is reviewed. A brief analysis based on the performance evaluation of centralized algorithms on six standard test systems is presented. Then, the recent literature on decentralized and distributed optimization approach and the consensus protocols with respect to various network topologies is briefly reviewed. The prime objective of this review is to summarize the centralized, decentralized, and distributed approaches to solve the classic dispatch, dynamic dispatch, economic emission dispatch, and multi-area ED problems.

关键词： centralized optimization decentralized optimization distributed optimization dynamic economic dispatch (DED) economic dispatch (ED) economic emission dispatch (EED) multi-area economic dispatch (MAED) multi agent system

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multi-level Design optimization of an IPMSM Drive System Considering an Improved Loss Model 32

Multi-level Design Optimization of an IPMSM Drive System Con...

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32nd Australasian Universities Power Engineering Conference (AUPEC)

作者： Liu, Lin Guo, Youguang Lei, Gang Zhu, Jianguo Univ Technol Sydney Sch Elect & Data Engn Sydney Australia

ISBN: (纸本)9798350339567

The worsening in oil resource crisis and climate change has contributed to the widespread investigations and applications of electric vehicles (EVs). Meanwhile, the performance of electrical drive systems, as the most important power unit, corresponds directly to the operational stability, reliability and economy of EVs. This paper proposes a system-level design optimization approach for the EV drive system with an interior permanent magnet synchronous motor (IPMSM), targeting improved system efficiency, reduced torque ripple and minimal costs. Specifically, an improved system-level loss model, considering both the motor and control system losses, is presented for objective efficiency calculation. Moreover, to handle the high dimensional and nonlinear problem, the multi-level optimization strategy is employed, in which four subspaces containing parameters in both motor and control levels are defined based on the sensitivity to the objectives. The approximate models and optimization algorithms are utilized to reduce the computation cost of finite element analysis in each level. Finally, the proposed approaches are investigated with a practical IPMSM drive system used in EVs. Results illustrate the effectiveness and show that the proposed multi-level design optimization method with the improved system-level loss model can obtain parametric optimums, while maintaining better steady-state and dynamic system performances.

关键词： Electric vehicle (EV) electrical drive system interior permanent magnet synchronous motor (IPMSM) system loss model multi-level design optimization

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multi-objective evolutionary optimization of extreme gradient boosting regression models of the internal turning of PEEK tubes

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Vieira, Jessica Tito Pereira, Robson Bruno Dutra Lauro, Carlos Henrique Brandao, Lincoln Cardoso Ferreira, Joao Roberto Univ Fed Itajuba Inst Ind Engn & Management Itajuba MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Sustainable CIMS Dept Mech & Ind Engn Sao Joao Del Rei MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn Sao Joao Del Rei MG Brazil

Internal turning or boring is an attractive machining process for hole enlarging as it presents low costs, good flexibility, and adaptability. The process presents some instability problems due to the high relation length/diameter ratio of the boring bar. To achieve the best process conditions, predictive models must be estimated and optimization must be conducted. This work presents a statistical learning approach for modeling and optimization of the internal turning process in PEEK tubes. Cutting speed, feed rate, and fixture position were considered input parameters. Cross-validation is used for learning and model selection, including k-fold and bootstrap approaches. The results pointed out that the extreme gradient boosting model was the best for all predictors. For R-a the final prediction metrics results were RMSE = 0.1395, MEA = 0.1126, and R-2 = 1.0031, for F-c, RMSE = 1.8609, MAE = 0.9311, and R-2 = 0.9280, and for Ron(t), RMSE = 21.3084, MAE = 17.8053, and R-2 = 0.6562. These results were much superior to the other methods. multi-objective evolutionary optimization was performed considering the extreme gradient boosting models for roughness, total roundness, and cutting force, besides the deterministic model of the material removal rate. The NSGA-II method was selected considering the hypervolume metric. The pseudo-weight approach is used for select high trade-off solutions to be used in practical production scenarios.

关键词： Internal turning Polyetheretherketone Statistical learning Machine learning Extreme gradient boosting multi-objective evolutionary optimization

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Beyond Diagonal RIS for multi-Band multi-Cell MIMO Networks: A practical Frequency-Dependent Model and Performance Analysis

arXiv

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arXiv 2024年

作者： de Sena, Arthur Sousa Rasti, Mehdi Mahmood, Nurul H. Latva-Aho, Matti University of Oulu Oulu Finland

This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs. © 2024, CC BY.

关键词： multiobjective optimization

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Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Xu, Lei Rongke Digital Energy Informat Technol Beijing Co Beijing Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

关键词： Energy hub systems Renewable energy integration multi-objective optimization Particle swarm optimization algorithm System reliability

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SharpSeq: Empowering Continual Event Detection through Sharpness-Aware Sequential-task Learning

SharpSeq: Empowering Continual Event Detection through Sharp...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Le, Thanh-Thien Dao, Viet Van Nguyen, Linh Nguyen, Thi-Nhung Van Linh, Ngo Nguyen, Thien Huu VinAI Research Viet Nam Hanoi University of Science and Technology Viet Nam University of Oregon United States

ISBN: (纸本)9798891761148

Continual event detection is a cornerstone in uncovering valuable patterns in many dynamic practical applications, where novel events emerge daily. Existing state-of-the-art approaches with replay buffers still suffer from catastrophic forgetting, partially due to overly simplistic objective aggregation. This oversight disregards complex trade-offs and leads to sub-optimal gradient updates, resulting in performance deterioration across objectives. While there are successful, widely cited multi-objective optimization frameworks for multitask learning, they lack mechanisms to address data imbalance and evaluate whether a Pareto-optimal solution can effectively mitigate catastrophic forgetting, rendering them unsuitable for direct application to continual learning. To address these challenges, we propose SharpSeq, a novel continual learning paradigm leveraging sharpness-aware minimization combined with a generative model to balance training data distribution. Through extensive experiments on multiple real-world datasets, we demonstrate the superior performance of SharpSeq in continual event detection, proving the importance of our approach in mitigating catastrophic forgetting in continual event detection. ©2024 Association for Computational Linguistics.

关键词： multiobjective optimization

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Automatic clustering and feature selection using multi-objective crow search algorithm

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APPLIED SOFT COMPUTING 2023年 142卷

作者： Ranjan, Rajesh Chhabra, Jitender Kumar Natl Inst Technol Comp Engn Dept Kurukshetra 136119 India

Today's real-world data is frequently significant in size, with many redundant, missing, and noise -based features and data instances must be addressed before applying various data-mining-based algorithms for further knowledge discovery. Excessive dimensionality may be mitigated by carefully excluding unnecessary characteristics and selecting a reasonable subset of features. When presented as an optimization issue, choosing the best clusters using the most suitable subset of attributes is a challenge that may be handled using practical meta-heuristic approaches. Besides this, the automatic finding of the appropriate cluster number is another challenging task for the real-world dataset in the unsupervised machine-learning study. The present work proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism for a better exploration phase of the crow and further making it suitable for multi-objective optimization problems. MO-CSACFS addresses both issues using the three objective functions to find appropriate cluster numbers and features. MO-CSACFS is implemented over several real-life and synthetic datasets with varying instances, features, and cluster numbers to assess the algorithm's performance;apart from that, the present work is also applied over several gene-expression datasets. MO-CSACFS is compared with two similar recently proposed multi-objective optimization processes used over an automatic, unsupervised machine learning task. The results show that the MO-CSACFS has produced a compact and robust cluster comparable to other similar works from the literature. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： multi -objective optimization Data clustering Feature selection Crow search algorithm

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Maximizing data gathering and energy efficiency in UAV-assisted IoT: A multi-objective optimization approach

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COMPUTER NETWORKS 2023年第1期235卷

作者： Liu, Lingling Wang, Aimin Sun, Geng Li, Jiahui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

The data volume and energy consumption are important design aspects of data collection in the Internet of Things (IoT). In this paper, the unmanned aerial vehicles (UAVs)-assisted data gathering (DG) in IoT is investigated, in which multiple rotary-wing UAVs are dispatched to gather data from multiple terrestrial IoT devices within specified coverage range. In order to ensure the freshness of the collected data, we must consider that each UAV should spend as little time as possible to collect data. Moreover, it is assumed that UAVs collect data from the devices only when they are hovering, and the transmission distance and on-board energy of the UAVs are limited. Under these practical hypotheses, we formulate a UAV-assisted data gathering multi objective optimization problem (UAVDGMOP) to simultaneously maximize the total data volume collected by UAVs, minimize the maximum-time for UAVs collecting data and approaching the optimal positions, and minimize the total energy consumptions of UAVs via jointly optimizing the locations of UAVs, the transmission power of UAVs, and the scheduling of UAV-device pairs. Moreover, the formulated UAVDGMOP consists of the hybrid and complex solutions, which is difficult to be solved by using the conventional methods, and thus we solve it by proposing a hybrid update multi-objective evolutionary algorithm based on decomposition (HUMOEA/D). Simulation results demonstrate that the proposed approach achieves higher data amount and energy efficiency of the network, and it is more suitable for solving the formulated UAVDGMOP comparing to other comparative approaches, including the random, uniform and layering deployment approaches, as well as other multi-objective evolutionary algorithms (MOEAs). Moreover, the proposed HUMOEA/D still achieves the overall excellent performance in more use cases.

关键词： Unmanned aerial vehicles Internet of things Data gathering multi-objective optimization Deployment optimization Scheduling optimization

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