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Minimizing voltage fluctuation in stand-alone microgrid system using a Kriging-based multi-objective stochastic optimization algorithm

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ELECTRICAL ENGINEERING 2024年第6期106卷 8017-8034页

作者： Evangeline, S. Ida Baskaran, K. Darwin, S. Govt Coll Engn Dept Elect & Elect Engn Tirunelveli 627007 Tamil Nadu India Alagappa Chettiar Govt Coll Engn & Technol Dept Elect & Elect Engn Karaikkudi 630003 Tamil Nadu India Dr Sivanthi Aditanar Coll Engn Dept Elect & Commun Engn Tiruchendur 628215 Tamil Nadu India

Ensuring sustainable access to electricity in regions with insufficient infrastructure, such as rural and hilly areas, can be effectively achieved through stand-alone microgrid systems utilizing renewable sources and storage systems. These systems face challenges due to the unpredictable behavior of users and the inherent uncertainties of renewable sources, which result in significant voltage fluctuations. This paper addresses these challenges by proposing a Kriging-based multi-objective stochastic optimization algorithm designed to minimize voltage fluctuations in photovoltaic and wind power integrated stand-alone microgrid systems. The algorithm optimizes the design of the microgrid by simultaneously minimizing the total cost, carbon emissions, system reliability, and voltage fluctuation. Numerical experiments demonstrate that the proposed algorithm outperforms existing methods, achieving lower costs, reduced emissions, and improved reliability while effectively mitigating voltage fluctuations. These results suggest that the proposed approach offers a robust solution for enhancing the performance and stability of stand-alone microgrids in areas lacking traditional electrical infrastructure.

关键词： Stand-alone microgrid Renewable energy Voltage fluctuation stochastic optimization algorithm Kriging

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A Personalized Urban Multicriteria Shortest Path stochastic optimization algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-8页

作者： Gong Bowen Lin Ciyun Jilin Univ State Key Lab Automobile Dynam Simulat Changchun 130022 Peoples R China Jilin Univ Coll Transportat Changchun 130022 Peoples R China

Drivers' route choice behavior is usually personalized and multicriteria in practice. Therefore, the urban shortest path problem is the personalized urban multicriteria shortest path (PUMSP) problem. However, the solutions of the PUMSP problem are difficult to meet the drivers' travel habits in the state of the art. To solve this problem, first, a new stochastic optimization algorithm based on the iterative calculation of a valid route set is proposed in this paper. The effective and reasonable path searching mechanism is designed based on drivers' route choice habits. Then, the evaluation method of calculation results is given. The comparative experimental results with the genetic algorithm show that the proposed algorithm has reached better results in the evaluation parameters and computing time. The experimental results also demonstrate that it is meaningful to consider drivers' travel law in the personalized urban multicriteria shortest path algorithm design for avoiding obtaining impractical routes solutions.

关键词： personalized urban multi-criteria shortest path problem drivers" travel route choice behavior valid route set stochastic optimization algorithm

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stochastic ISTA/FISTA Adaptive Step Search algorithms for Convex Composite optimization

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JOURNAL OF optimization THEORY AND APPLICATIONS 2025年第1期205卷 1-37页

作者： Nguyen, Lam M. Scheinberg, Katya Tran, Trang H. IBM Res Thomas J Watson Res Ctr Yorktown Hts NY USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA USA Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14850 USA

We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms (Beck and Teboulle in SIAM J Imag Sci 2(1):183-202, 2009;Scheinberg et al. in Found Comput Math 14(3):389-417, 2014) for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This work extends analysis of inexact fixed step ISTA/FISTA in Schmidt et al. (Convergence rates of inexact proximal-gradient methods for convex optimization, 2022. arXiv:1109.2415) to the case of stochastic gradient estimates and adaptive step-size parameter chosen by backtracking. It also extends the framework for analyzing stochastic line-search method in Cartis and Scheinberg (Math Program 169(2):337-375, 2018) to the proximal gradient framework as well as to the accelerated first order methods.

关键词： stochastic optimization algorithm Convex optimization

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stochastic Composition optimization of Functions Without Lipschitz Continuous Gradient

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JOURNAL OF optimization THEORY AND APPLICATIONS 2023年第1期198卷 239-289页

作者： Liu, Yin Tajbakhsh, Sam Davanloo Ohio State Univ Columbus OH 43210 USA

In this paper, we study stochastic optimization of two-level composition of functions without Lipschitz continuous gradient. The smoothness property is generalized by the notion of relative smoothness which provokes the Bregman gradient method. We propose three stochastic composition Bregman gradient algorithms for the three possible relatively smooth compositional scenarios and provide their sample complexities to achieve an epsilon-approximate stationary point. For the smooth of relatively smooth composition, the first algorithm requires O(epsilon(-2)) calls to the stochastic oracles of the inner function value and gradient as well as the outer function gradient. When both functions are relatively smooth, the second algorithm requires O(epsilon(-3)) calls to the inner function value stochastic oracle and O(epsilon(-2)) calls to the inner and outer functions gradients stochastic oracles. We further improve the second algorithm by variance reduction for the setting where just the inner function is smooth. The resulting algorithm requires O(epsilon(-5/2)) calls to the inner function value stochastic oracle, O(epsilon(-3/2)) calls to the inner function gradient, and O(epsilon(-2)) calls to the outer function gradient stochastic oracles. Finally, we numerically evaluate the performance of these three algorithms over two different examples.

关键词： Composition optimization stochastic optimization algorithm Bregman subproblem

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A hybrid stochastic optimization framework for composite nonconvex optimization

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MATHEMATICAL PROGRAMMING 2022年第2期191卷 1005-1071页

作者： Quoc Tran-Dinh Pham, Nhan H. Phan, Dzung T. Nguyen, Lam M. Univ North Carolina Dept Stat & Operat Res 318 Hanes Hall Chapel Hill NC 27599 USA IBM Res Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA

We introduce a new approach to develop stochastic optimization algorithms for a class of stochastic composite and possibly nonconvex optimization problems. The main idea is to combine a variance-reduced estimator and an unbiased stochastic one to create a new hybrid estimator which trades-off the variance and bias, and possesses useful properties for developing new algorithms. We first introduce our hybrid estimator and investigate its fundamental properties to form a foundational theory for algorithmic development. Next, we apply our new estimator to develop several variants of stochastic gradient method to solve both expectation and finite-sum composite optimization problems. Our first algorithm can be viewed as a variant of proximal stochastic gradient methods with a single loop and single sample, but can achieve the best-known oracle complexity bound as state-of-the-art double-loop algorithms in the literature. Then, we consider two different variants of our method: adaptive step-size and restarting schemes that have similar theoretical guarantees as in our first algorithm. We also study two mini-batch variants of the proposed methods. In all cases, we achieve the best-known complexity bounds under standard assumptions. We test our algorithms on several numerical examples with real datasets and compare them with many existing methods. Our numerical experiments show that the new algorithms are comparable and, in many cases, outperform their competitors.

关键词： Hybrid stochastic estimator stochastic optimization algorithm Oracle complexity Variance reduction Composite nonconvex optimization

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An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem

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SCIENTIFIC REPORTS 2025年第1期15卷 1-15页

作者： Zhang, Zhenzhong Zhang, Ling Li, Weichun Civil Aviat Flight Univ China CAAC Acad Guanghan 618307 Peoples R China Chongqing Univ Posts & Telecommun Sch Modern Posts Chongqing 400065 Peoples R China

In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving practical allocation problems. This study focuses on the problem of allocating orders to parallel machines with varying efficiencies under uncertain and high-dimensional conditions. To maximize the expected profit of order processing, a mathematical model for a high-dimensional stochastic optimization problem is developed, considering the uncertainty due to potential customer order cancellations in a real-world production. By integrating an intelligent optimization algorithm for the order assignment problem with a scenario generation approach, a novel framework for intelligent stochastic optimization is proposed. This framework employs an intelligent optimization algorithm suitable for the generalized assignment problem to search for improved solutions and utilizes the scenario generation method to produce the necessary scenarios for evaluating solutions in high-dimension. Experimental results demonstrate that the proposed approach effectively addresses the high-dimensional stochastic order allocation problem, outperforming the compared method in terms of efficiency and capability.

关键词： Order uncertainty Order allocation Scenario generation stochastic optimization algorithm

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A novel fitness-distance balanced artificial ecosystem approach for accurate solar PV parameter estimation

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第2期7卷

作者： Djeblahi, Zahia Mahdad, Belkacem Srairi, Kamel Univ Biskra Dept Elect Engn Lab Energy Syst Modeling LMSE BP 145 Biskra 07000 Algeria

Accurate parameter estimation of photovoltaic (PV) models is essential for enhancing the simulation and control of PV systems. This paper introduces a novel, robust stochastic algorithm that combines Fitness-Distance Balance (FDB) with Artificial Ecosystem optimization (AEO) and incorporates the Newton-Raphson method for estimating optimal PV parameters such as Photocurrent (Iph), Saturation Current (Io), Diode Ideality Factor (n), Series Resistance (Rs), and Shunt Resistance (Rsh). The main objective function considered it to minimize the difference between the estimated and measured I-V data pairs, quantified by the root mean square error (RMSE). The study involves four cases aimed at reducing the RMSE of PV models, comprising both single-diode (SDM) and double-diode (DDM) models of R.T.C France, and two PV panels: Photowatt-PWP201 and STM6-120/36 models. The simulation results confirm that the FDB-AEO algorithm outperforms other competing algorithms, achieving the best RMSE values for various models, including SDM, DDM, Photowatt-PWP201, and STM6-120/36 PV models are 7.7299e-04, 7.4584e-04, 2.3564e-03, and 1.7315e-03, respectively. Furthermore, the statistical analysis demonstrates that the proposed method requires a lower standard deviation (SD) to estimate the unknown electrical parameters for the mentioned models, with values of 6.1761e-06, 9.2956e-06, 4.8872e-05, and 6.1959e-05, respectively. This indicates its superiority over other powerful metaheuristic algorithms in terms of precision, solution quality, and convergence speed.

关键词： parameters estimation PV models stochastic optimization algorithm Fitness-Distance Balance (FDB) Artificial Ecosystem (AEO)

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Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

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ENGINEERING RESEARCH EXPRESS 2024年第3期6卷 035339-035339页

作者： Djeblahi, Zahia Mahdad, Belkacem Srairi, Kamel Biskra Univ Sci & Technol Coll Dept Elect Engn Lab Energy Syst Modeling LMSE Biskra Algeria

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes optimization algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plan

关键词： stochastic optimization algorithm modified IEEE-30 bus system fitness-distance balance-based (FDB-AOA) archimedes optimization algorithm optimal power flow (OPF) FACTS devices

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Novel integrative soft computing for daily pan evaporation modeling

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SMART STRUCTURES AND SYSTEMS 2022年第4期30卷 421-432页

作者： Zhang, Yu Liu, LiLi Zhu, Yongjun Wang, Peng Foong, Loke Kok Jiaying Univ Sch Geog Sci & Tourism Meizhou 514015 Guangdong Peoples R China Ordos Water Conservancy Dev Ctr Ordos 017000 Inner Mongolia Peoples R China Paotai Soil Improvement Expt Stn Shihezi 832000 Xinjiang Peoples R China Xinjiang Agr Univ Coll Hydraul & Civil Engn Urumqi 830052 Xinjiang Peoples R China Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neurometaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R-P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R-P = 0.9945), conventional MLPNN (MAE = 0.1062 and R-P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R-P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

关键词： indirect measurement neural network pan evaporation stochastic optimization algorithm

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optimization of maximum power density output for proton exchange membrane fuel cell based on a data-driven surrogate model

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APPLIED ENERGY 2022年 317卷

作者： Feng, ShengSen Huang, WenTao Huang, Zhe Jian, Qifei South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China

Operating conditions are of great significance for proton exchange membrane fuel cells (PEMFCs) and directly determine the output performance of PEMFC. In this paper, an optimization framework is proposed, which is combining a neural network data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variable global optimization, and optimizes the operating conditions for enhancing the power density of the PEMFC. A numerical model of the PEMFC is constructed as the source of the database, and the database is used to train a data-driven surrogate model based on radial basis functions (RBF), which is a typical feed-forward neural network. Then the surrogate model is fed into a particle swarm optimization (PSO) algorithm to obtain the optimal solution for the best combination of operating conditions. Results show that the surrogate model can accurately predict the output voltage of the PEMFC model, where the squared correction factor (R-square) and the mean percentage error of the test set are 0.99638 and 4.4686% respectively. And the optimization framework using a combination of data-driven surrogate model and stochastic optimization algorithm can obtain the maximum power density of 0.6097 W cm-2, with a relative error of only 1.5321% to the PEMFC model results. The result shows that the framework can effectively handle the multivariate optimization of complex systems.

关键词： PEMFC Operating conditions Data-driven surrogate model stochastic optimization algorithm

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