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crisscross optimization algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

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ENERGIES 2015年第12期8卷 13641-13659页

作者： Peng, Xiangang Lin, Lixiang Zheng, Weiqin Liu, Yi Guangdong Univ Technol Sch Automat Higher Educ Mega Ctr Guangzhou 510006 Guangdong Peoples R China

Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem.

关键词： distributed generation optimal allocation crisscross optimization algorithm Monte Carlo simulation uncertainties

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crisscross optimization algorithm and its application

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KNOWLEDGE-BASED SYSTEMS 2014年 67卷 218-229页

作者： Meng, An-bo Chen, Yu-cheng Yin, Hao Chen, Si-zhe Guangdong Univ Technol Fac Automat Guangzhou 510006 Peoples R China

How to improve the global search ability without significantly impairing the convergence speed is still a big challenge for most of the meta-heuristic optimization algorithms. In this paper, a concept for the optimization of continuous nonlinear functions applying crisscross optimization algorithm is introduced. The crisscross optimization algorithm is a new search algorithm inspired by Confucian doctrine of gold mean and the crossover operation in genetic algorithm, which has distinct advantages in solution accuracy as well as convergence rate compared to other complex optimization algorithms. The procedures and related concepts of the proposed algorithm are presented. On this basis, we discuss the behavior of the main search operators such as horizontal crossover and vertical crossover. It is just because of the perfect combination of both, leading to a magical effect on improving the convergence speed and solution accuracy when addressing complex optimization problems. Twelve benchmark functions, including unimodal, multimodal, shifted and rotated functions, are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that the crisscross optimization algorithm has an excellent performance on most of the test functions, compared to other heuristic algorithms. At the end, the crisscross optimization algorithm is successfully applied to the optimization of a large-scale economic dispatch problem in electric power system. It is concluded that the crisscross optimization algorithm is not only robust in solving continuous nonlinear functions, but also suitable for addressing the complex real-world engineering optimization problems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： crisscross optimization algorithm Economic dispatch optimization problem Horizontal crossover search Vertical crossover search

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crisscross optimization algorithm for large-scale dynamic economic dispatch problem with valve-point effects

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ENERGY 2015年第Part2期93卷 2175-2190页

作者： Meng, Anbo Hu, Hanwu Yin, Hao Peng, Xiangang Guo, Zhuangzhi Guangdong Univ Technol Fac Automat Guangzhou 510006 Guangdong Peoples R China

DED (Dynamic economic dispatch) considering valve-point effects is a complicated non-convex optimization problem in power system. For large-scale DED problem with hundreds of generators, how to avoid the curse of dimensionality remains a big challenge due to the exponential growth of search space. In allusion to this problem, this paper presents a promising heuristic approach named CSO (crisscross optimization) algorithm, which generates high quality solutions in large space by applying two interacting search operators, namely horizontal crossover and vertical crossover. The former has powerful global search ability while the latter can effectively alleviate the premature convergence problem. Their combination leads to a magical effect on improving solution quality and convergence rate especially for large-scale DED problems with valve-point effects. The feasibility and effectiveness of the proposed CSO algorithm is validated by seven test systems consisting of different numbers of generators. The results are compared with those of other heuristic methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions. To examine the availability of CSO in large power system, three new systems with 200-1000 generators are also tested, the obtained results confirm its suitability for large-scale DED problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： crisscross optimization algorithm Horizontal crossover Vertical crossover Dynamic economic dispatch Ramp-rate Valve-point effects

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crisscross optimization algorithm for solving combined heat and power economic dispatch problem

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ENERGY CONVERSION AND MANAGEMENT 2015年第Nov.期105卷 1303-1317页

作者： Meng, Anbo Mei, Peng Yin, Hao Peng, Xiangang Guo, Zhuangzhi Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

As cogeneration plays an increasingly important role in energy utilization, the combined heat and power economic dispatch (CHPED) becomes an important task in power system operation. In this paper, a novel crisscross optimization (CSO) algorithm is implemented to solve the large scale CHPED problem, which is a challenging non-convex optimization problem with a large number of local minima. The feature of applying CSO to address the CHPED problem lies in two interacting operators, namely horizontal crossover and vertical crossover. The horizontal crossover searches for the new solutions within a half population of hyper-cubes with a large probability while in their respective peripheries with a decreasing probability. The vertical crossover provides a effective mechanism for those stagnant dimensions of a population to escape from premature convergence. The combination of both gifts CSO with a powerful global search ability. The effectiveness of the proposed method is validated on six cogeneration systems with different characteristics. The numeric results demonstrates that the proposed CSO method achieves much better results than other methods reported in the literature. To investigate the robustness and applicability of CSO in large power system, two new systems with 96 and 192 units by duplicating the system of case 4 two times and four times are also studied. The results obtained substantiates the suitability of CSO for large-scale constrained CHPED problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Combined heat and power Economic dispatch crisscross optimization algorithm Large-scale optimization

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Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm

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ENERGY CONVERSION AND MANAGEMENT 2016年 114卷 75-88页

作者： Meng, Anbo Ge, Jiafei Yin, Hao Chen, Sizhe Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Wind speed forecasting is of great significance for wind farm management and safe integration into electric power grid. As wind speed is characterized by high autocorrelation and inherent volatility, it is difficult to predict with a single model. The aim of this study is to develop a new hybrid model for predicting the short wind speed at 1 h intervals up to 5 h based on wavelet packet decomposition, crisscross optimization algorithm and artificial neural networks. In the data pre-processing phase, the wavelet packet technique is used to decompose the original wind speed series into subseries. For each transformed components with different frequency sub-bands, the back-propagation neural network optimized by crisscross optimization algorithm is employed to predict the multi-step ahead wind speed. The eventual predicted results are obtained through aggregate calculation. To validate the effectiveness of the proposed approach, two wind speed series collected from a wind observation station located in the Netherlands are used to do the multi-step wind speed forecasting. To reduce the statistical errors, all forecasting methods are executed 50 times independently. The results of this study show that: (1) the proposed crisscross optimization algorithm has significant advantage over the back-propagation algorithm and particle swarm optimization in addressing the prematurity problems when applied to train the neural network. (2) Compared with the previous hybrid models used in this study, the proposed hybrid model consistently has the minimum mean absolute percentage error regardless of one-step, three step or five-step prediction. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Short-term wind speed forecasting crisscross optimization algorithm Wavelet packet decomposition Artificial neural network

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The application of crisscross optimization algorithm to dynamic traffic assignment problem 8

The application of crisscross optimization algorithm to dyna...

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8th International Conference on Information Technology in Medicine and Education (ITME)

作者： Wang, Jingying Qu, Jianhua Shandong Normal Univ Sch Management Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781509039067

Obtaining the optimal travel time on dynamic traffic assignment(DTA) is very important for intelligent traffic system (ITS). A lot of approaches have been applied for DTA to make the travel time costing least, but current algorithms cannot be solved to guarantee optimality. In this paper, a method based on crisscross optimization algorithm(CSO) is introduced to solve DTA problem. This novel algorithm adopts a double-crisscross search mechanism. It differs from algorithms given in introduction. It uses crisscross programming to enhance performance. This paper aims at time-varying flows in dynamic system optimal model and using CSO algorithm to solve it. At last, we obtain traffic flow distribution data by designing a numerical example and employing simulation. We realized this algorithm converges to the optimal final solution.

关键词： crisscross search dynamic traffic assignment crisscross optimization algorithm

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Location and Capacity Method of Electric Vehicle Charging Facilities Based on the crisscross optimization algorithm 4

Location and Capacity Method of Electric Vehicle Charging Fa...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Chen, Xiaohua Wang, Zhiping Chen, Shengyu Li, Jiaying Xu, Haiwen Li, Hongling Dongguan Univ Technol Sch Elect & Intelligentizat Dongguan Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Guangdong Polytech Normal Univ Sch Automat Guangzhou Peoples R China

ISBN: (纸本)9781665479141

The use of electric vehicles is increasing year by year, and a large number of them are connected to the distribution network, which has a negative impact on the normal operation of the power grid. By optimizing the configuration of charging facilities, the economy of the construction of charging facilities and the stability of the operation of the distribution network can be realized. This paper uses crisscross optimization algorithm (CSO) to select the location and capacity of the electric vehicle charging facilities. The upper layer considers its construction cost and operation and maintenance cost, and also considers the transportation cost from the users of each node to the nearest configured node;the lower layer will configure the location based on the configuration result of the upper layer. The stable and economic operation of the power grid is also introduced into the location and capacity of charging facilities, considering the influence of the power distribution network loss and voltage offset, and achieving the optimal configuration of charging facilities through the joint action of the upper and lower layers. Through the coupling of the 33-node system and the 25-node transportation system, the location and capacity of electric vehicle charging facilities are selected, which verifies the effectiveness and feasibility of the proposed method.

关键词： crisscross optimization algorithm double-layer optimization electric vehicle location and capacity

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An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects

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ENERGY 2016年第0期113卷 1147-1161页

作者： Meng, Anbo Li, Jinbei Yin, Hao Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

As one of important optimization problems in power system, economic dispatch (ED) with multiple fuel options is characterized by high non-convexity, non-linearity and discontinuity. The, combined action of multiple fuel options and valve-point effects increases the degree of difficulty to solve the ED problem. In this paper, a recently developed heuristic algorithm called crisscross optimization algorithm (CSO) is attempted to address the large-scale and non-convex ED problem with both multiple fuel options and valve-point effects taken into account. The proposed CSO method solves the ED problem through horizontal crossover and vertical crossover. The former searches for the new solutions within a half population of hyper-cubes by adopting a cross-border search approach while the latter provides a unique mechanism to prevent from the premature convergence problems based on the concept of dimensional local minimum. Both operators alternatively generate moderation solutions which are subsequently updated by an elite selection strategy. The proposed method is validated on six test systems consisting of 10-640 generating units and compared with other state-of-the-art methods in the literature. The results show that CSO yields higher quality solutions especially for solving large-scale ED problems with multiple fuel options. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch problem crisscross optimization algorithm Multiple fuel options Valve-point effects Large-scale optimization

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An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

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ENERGY CONVERSION AND MANAGEMENT 2017年 150卷 108-121页

作者： Yin, Hao Dong, Zhen Chen, Yunlong Ge, Jiafei Lai, Loi Lei Vaccaro, Alfredo Meng, Anbo Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Univ Sannio Engn Dept I-82100 Benevento Italy

Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.

关键词： Wind power forecasting Secondary hybrid decomposition Empirical mode decomposition Wavelet packet decomposition Extreme learning machine crisscross optimization algorithm

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A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization

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ENERGY 2022年第PartB期238卷 121795-121795页

作者： Meng, Anbo Chen, Shun Ou, Zuhong Ding, Weifeng Zhou, Huaming Fan, Jingmin Yin, Hao Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Guangdong Power Grid Co Ltd Machine Patrol Operat Ctr Guangzhou 510160 Guangdong Peoples R China

Accurate wind power forecasting is of great significance for power system operation. In this study, a triple-stage multi-step wind power forecasting approach is proposed by applying attention-based deep residual gated recurrent unit (GRU) network combined with ensemble empirical mode decomposition (EEMD) and crisscross optimization algorithm (CSO). In the data processing stage, the EEMD is used to decompose the wind power/speed time series and a bi-attention mechanism (BA) is applied to enhance the sensitivity of model to the important information from both time and feature dimension. In the prediction stage, a series-connected deep learning model called RGRU consisting of the residual network and GRU is proposed as the forecasting model, aiming to make full use of extracting the static and dy-namic coupling relationship among the input features. In the retraining-stage, a high-performance CSO algorithm is adopted to further optimize the fully-connected layer of RGRU model so as to improve the generalization ability of the model. The proposed method is validated on a wind farm located in Spain and the experimental results demonstrate that the proposed hybrid model has significant advantage over other state-of-the-art models involved in this study in terms of prediction accuracy and stability. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Bi-attention mechanism Ensemble empirical mode decomposition Wind power prediction Hybrid model crisscross optimization algorithm

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