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Multi-objective online driving strategy optimization for energy storage tram considering battery life

JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Wang, Xiaoxu Wu, Jian Zhang, Chi Zhang, Yanru Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing 100044 Peoples R China

Compared with the traditional overhead contact grid or third-rail power supply, energy storage trams equipped with lithium batteries have been developed rapidly because of their advantages of flexible railway laying and high regenerative braking energy utilization. However, trams may face expensive battery replacement costs due to battery degradation. Therefore, this paper proposes a multi-objective optimization method for the tram's driving strategy to reduce operational energy consumption and extend battery life. The method describes the optimization problem as second-order cone programming (SOCP). It uses the interior point algorithm for fast solving, thus obtaining the theoretical optimal speed trajectory in real-time. This feature enables the method to be applied online. To verify the effectiveness and robustness of the proposed method, we conducted simulations under actual operating conditions. The simulation results show that the proposed multi-objective optimization method can extend the battery life by 40 % compared to the single-objective optimization method for energy saving. As a result, the total cost of the whole life cycle is reduced by 6.54 %. Furthermore, this online optimization driving strategy method can effectively deal with unexpected conditions during the actual operation of the tram to ensure the secure operation, punctual arrival, and precise parking of trams. Finally, the effectiveness of the proposed method is further verified by a hardware-in-the-loop (HIL) test.

关键词： Energy storage tram Driving strategy Multi-objective optimization Convex optimization second-order cone programming

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Distribution-free Bayesian regularized learning framework for semi-supervised learning

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NEURAL NETWORKS 2024年 174卷 106262页

作者： Ma, Jun Yu, Guolin North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China

In machine learning it is often necessary to assume or know the distribution of the data, however it is difficult to do so in practical applications. Aiming to this problem, this work, we propose a novel distribution-free Bayesian regularized learning framework for semi-supervised learning, which is called Hessian regularized twin minimax probability extreme learning machine (HRTMPELM). In this framework, we attempt to construct two non-parallel hyperplanes by introducing the high separation probability assumption, such that each hyperplane separates samples from one class with maximum probability while moving away from samples from the other class. Subsidiently, the framework can be utilized to construct reasonable semi-supervised classifiers by using the information of the inherent geometric distribution of the samples through the Hessian regularization term. Additionally, the proposed framework controls the misclassification error of samples by minimizing the upper limit of the worst-case misclassification probability, and improves the generalization performance of the model by introducing the idea of regularization to avoid the occurrence of ill-posedness and overfitting problems. More importantly, the framework has no hyperparameters, making the learning process very simplified and efficient. Finally, a simple and reliable algorithm with globally optimal solutions via multivariate Chebyshev inequalities is designed for solving the proposed learning framework. Experiments on multiple datasets demonstrate the reliability and effectiveness of the proposed learning framework compared to other methods. Especially, we applied the framework to Ningxia wolfberry quality detection, which greatly enriches and facilitates the application of machine learning algorithms in the agricultural field.

关键词： Distribution-free Bayes optimal classifier Semi-supervised learning Multivariate Chebyshev inequality Non-parallel hyperplane second-order cone programming

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A SOCP-Based ACOPF for Operational Scheduling of Three-Phase Unbalanced Distribution Systems and Coordination of PV Smart Inverters

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第1期39卷 229-244页

作者： He, Mingyue Soltani, Zahra Ghaljehei, Mohammad Esmaili, Masoud Ma, Shanshan Chen, Mengxi Khorsand, Mojdeh Ayyanar, Raja Vittal, Vijay Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85281 USA

The proliferation of distributed energy resources (DERs) imposes new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution networks, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. Moreover, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effectiveness of the proposed ACOPF for unbalanced systems in providing an optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simulations show that the optimal settings cause no stability issue for the distribution system.

关键词： Reactive power Indexes Voltage IEEE Standards Computational modeling Load flow Distribution networks AC optimal power flow distributed energy resources second-order cone programming three-phase unbalanced distribution system Volt-Var controller

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A cell-based smoothed radial point interpolation method applied to lower bound limit analysis of thin plates

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2025年 172卷

作者： Chen, Shenshen Dong, Hao Wei, Xing Liu, Fengtao East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Peoples R China East China Jiaotong Univ Jiangxi Key Lab Disaster Prevent Mitigat & Emergen Nanchang 330013 Peoples R China Guilin Univ Technol Guangxi Key Lab Geotech Mech & Engn Guilin 541004 Peoples R China

This paper proposes a novel numerical method based on the cell-based smoothed radial point interpolation method (CS-RPIM) combined with second-order cone programming to perform lower bound limit analysis of elastic-perfectly-plastic thin plates, using only deflection as nodal variable. The problem domain is initially discretized using a simple triangular background mesh, where each triangular cell is subsequently subdivided into multiple smoothing domains. Shape functions are formulated using the radial point interpolation method, allowing direct imposition of essential boundary conditions for deflection. Rotational constraints are conveniently handled through the construction of smoothed curvatures. By utilizing a generalized gradient smoothing technique, complex domain integrals are simplified into boundary integrals over the smoothing domains, thus eliminating the need to compute second-order derivatives of the shape functions. The virtual work principle is employed to enforce the equilibrium conditions for the self-equilibrated residual moment field in a weak sense. The von Mises yield conditions are expressed as conic constraints and the resulting optimization problems are solved using highly efficient primal-dual interior point solvers. Numerical examples demonstrate that it is feasible and effective to conduct lower bound limit analysis of thin plates using the proposed CS-RPIM and second-order cone programming.

关键词： Lower bound limit analysis Radial point interpolation method Thin plates Generalized gradient smoothing technique second-order cone programming

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Robust routing for a mixed fleet of heavy-duty trucks with pickup and delivery under energy consumption uncertainty

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APPLIED ENERGY 2024年 368卷

作者： Wang, Ruiting Keyantuo, Patrick Zeng, Teng Sandoval, Jairo Vishwanath, Aashrith Borhan, Hoseinali Moura, Scott Univ Calif Berkeley Berkeley CA 94720 USA Cummins Inc Columbus IN 47201 USA

Electrification of the truck fleet has the potential to reduce the "harder-to-abate"emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the energy- efficient routing of a mixed fleet of conventional and electric heavy-duty trucks with pickup and delivery under energy consumption uncertainty. We propose an energy consumption model that accounts for realistic driving dynamics, road conditions, weight, and distances. Integrating this model into the routing problem, we address energy consumption uncertainty using second-order cone mixed-integer programming. A quantitative case study is then performed on the operating costs and CO2 2 emissions benefits of electrifying heavy-duty trucks, which demonstrates improved fleet performance with optimal operating results. Scenarios with different parameter settings are tested to compare different performance metrics and provide practical insights. We evaluate routing decisions to demonstrate that stochastic optimization is necessary for reliable truck routing and produces robust results that significantly reduce capacity violations in route execution.

关键词： Vehicle routing problem Heavy-duty trucks electrification Mix-integer programming second-order cone programming

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Current Reference Optimization for Inverter-Based Resources to Mitigate Grid Voltage Unbalance Factor

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 6103-6106页

作者： Zhao, Chen Sun, Dan Duan, Xiaohan Nian, Heng Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

Modern grid codes require inverter-based resources (IBRs) to inject reactive current in proportion to voltage drop into the power system to support voltage recovery under voltage faults. Yet the current reference calculated as per the well-established grid code cannot maximize the dynamic voltage support capability of IBRs. In this letter, a novel current reference optimization method that has a better voltage support effect than conventional current reference is proposed. The current constraint of each phase of the IBR is added to the model of optimal attenuation of voltage unbalance factor (OAVUF). By changing the voltage orientation and replacing variables, the OAVUF model is recast as a second-order cone problem. Case studies based on MATLAB/Simulink are carried out to demonstrate the effectiveness of the proposed optimization method.

关键词： Voltage Power system stability Codes Synchronization Stability analysis Circuit faults Attenuation Voltage unbalance factor inverter-based resource voltage support second-order cone programming

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Robust contingency-constrained load restoration method for transmission systems considering modified SOCP formulation of power flow

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 163卷

作者： Sun, Lei Fang, Weikang Wei, Youwang Lin, Zhenzhi Jin, Xudong Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China Anhui Prov Lab Renewable Energy Utilizat & Energy Hefei 230009 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

A proper load restoration method can speed up the load restoration phase and reduce the outage losses of power systems after natural disasters. Existing research assumes that transmission lines would not trip again in the load restoration phase, which is not always acceptable, especially in the outage scenario due to natural disasters. Besides, the feasibility of the load restoration scheme relies on an exact mathematical model of the load restoration problem, while the existing second-order cone programming (SOCP) based model is not suitable for transmission power systems due to the lack of voltage angle related arctangent constraints. To address these two main drawbacks in existing research on load restoration, a robust contingency-constrained load restoration model (CCLRM) for transmission systems considering the modified SOCP formulation of power flow is proposed with the objective of reducing the outage losses taking the worst contingency into account. To identify the worst contingency for the CCLRM, a bi-level worst contingency identification model (WOCIM) is proposed and reformulated into a single-level model by employing the duality theory to make the WOCIM solvable. The nonlinear voltage angle related arctangent constraints in the modified SOCP based power flow expression in both models are studied and reformulated into the convex ones based on the convex envelope theory, thereby enhancing the accuracy of the presented models. Case studies on the IEEE 30-bus power system and IEEE 118-bus power system are conducted to illustrate the effectiveness of the proposed method.

关键词： Load restoration Worst contingencies second-order cone programming Convex envelope

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Barrier methods based on Jordan-Hilbert algebras for stochastic optimization in spin factors

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RAIRO-OPERATIONS RESEARCH 2024年第1期58卷 1011-1044页

作者： Alzalg, Baha Univ Jordan Dept Math Amman 11942 Jordan

Infinite-dimensional stochastic second-order cone programming involves minimizing linear functions over intersections of affine linear manifolds with infinite-dimensional second-order cones. However, even though there is a legitimate necessity to explore these methods in general spaces, there is an absence of infinite-dimensional counterparts for these methods. In this paper, we present decomposition logarithmic-barrier interior-point methods based on unital Jordan-Hilbert algebras for this class of optimization problems in the infinite-dimensional setting. The results show that the iteration complexity of the proposed algorithms is independent on the choice of Hilbert spaces from which the underlying spin factors are formed, and so it coincides with the best-known complexity obtained by such methods for the finite-dimensional setting. We apply our results to an important problem in stochastic control, namely the two-stage stochastic multi-criteria design problem. We show that the corresponding infinite-dimensional system in this case is a matrix differential Ricatti equation plus a finite-dimensional system, and hence, it can be solved efficiently to find the search direction.

关键词： Jordan-Hilbert algebras second-order cone programming programming in abstract spaces stochastic programming interior-point methods stochastic control

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Optimized Distribution Systems Reconfiguration Considering Protection Devices Constraints

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IEEE Transactions on Power Delivery 2025年第3期40卷 1267-1278页

作者： Kahnamouei, Ali Shakeri Lotfifard, Saeed Rostami, Mohammadali Washington State University School of Electrical Engineering and Computer Science PullmanWA99164 United States

This paper presents a method for the optimized reconfiguration of radial distribution systems that explicitly considers the protection systems constraints. A fully automated method based on graph analysis is proposed to automatically identify the primary-backup relation between each pair of protection devices in different network topologies. Accordingly, protection systems constraints are derived automatically and incorporated into the optimization problem. The linearized and convexified nonlinear power flow equations, along with protection system coordination constraints, are solved using the second-order cone programming (SOCP) that assures the feasibility of the results while accelerating the convergence rate. The effectiveness of the proposed methodology is demonstrated through the simulation of the 33-node IEEE test system. © 1986-2012 IEEE.

关键词： second-order cone programming

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TOPOLOGY OPTIMIZATION OF LATTICED SHELLS SOLVED AS DC programming PROBLEM

Journal of Structural and Construction Engineering

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Journal of Structural and Construction Engineering 2024年第817期89卷 286-295页

作者： Okuzono, Naoto Fujita, Shinnosuke Dept. of Architecture Faculty of Environmental Eng. The University of Kitakyushu Japan

It is known that the topology optimization problem of frame structures can be formulated as mixed-integer second-order cone programming problem (MISOCP). Although MISOCP can obtain the global optimal solution, it is difficult to apply to large-scale frame structures due to its high computational cost. In this paper, such topology optimization problem is solved as a DC (Difference of Convex Functions) programming problem to reduce computational cost. The effectiveness of proposed approach is confirmed thorough numerical examples while comparing with the global optimal solution obtained by solving MISOCP. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： second-order cone programming

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