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Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a multi-objective Approach

IFAC-PapersOnLine 2024年第13期58卷 380-385页

作者： Meriem Belaid Said El Beid Anas Hatim CISIEV Team Cadi Ayyad University Marrakech 40160 Morocco TIM team Cadi Ayyad University Marrakech 40160 Morocco

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi-objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, the algorithm strategically selects the optimal procurement strategy, either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

关键词： Energy management multi-objective optimization algorithm Smart grid Charging infrastructure Electric vehicle Python PYPSA PV power

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A novel probabilistic forecasting system based on quantile combination in electricity price

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Xu, Yan Li, Jing Wang, Honglu Du, Pei Ocean Univ China Qingdao Peoples R China Ningbo Cigarette Factory Zhejiang China Tobacco Ind Co Ltd Ningbo Peoples R China Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Qingdao Financial Res Inst Qingdao Peoples R China

In the electricity market, the accuracy of electricity price forecasting is significant for real-time control;however, the complexity and volatility of electricity prices make this a challenge. Existing forecasting models focus on deterministic forecasting and rarely address the uncertainty in electricity price forecasting. Therefore, this study fills this knowledge gap by introducing a novel combined probability forecasting system (CPFS) and creatively incorporating probability density estimation based on kernel functions in a multi-objective optimization algorithm. In addition, to effectively integrate the forecasting components, the tuna optimization algorithm was enhanced to overcome the limitations of traditional multi-objective optimization algorithms. Finally, the validity of the CPFS is confirmed through two electricity price cases, considering three equally important aspects: reliability, resolution, and sharpness. From a comprehensive perspective, CPFS outperformed the most advanced benchmark by more than 5.66% and 38.93% in AIS and by more than 13.41% and 3.55% in quantile loss on the NSW and Singapore datasets, respectively. The experimental results demonstrate that the CPFS provides an effective range for electricity price fluctuations. Furthermore, given that probabilistic forecasting is essential for risk management, it offers important implications for the electricity market.

关键词： Probabilistic forecasting Deep learning multi-objective optimization algorithm Probability density forecasting

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Intermittent solar power hybrid forecasting system based on pattern recognition and feature extraction

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期277卷

作者： Yu, Yue Niu, Tong Wang, Jianzhou Jiang, He Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Zhengzhou Univ Ctr Energy Environm & Econ Res Sch Management Zhengzhou 450001 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang 330013 Peoples R China

Solar energy, with its abundance and accessibility, occupies an irreplaceable position in the shift in global energy consumption patterns. The difficulties of managing solar energy on the grid, caused by its highly volatile and intermittent nature, necessitate an accurate and stable forecasting system. However, existing studies have focused more on the accuracy of prediction without considering the reactive power caused by intermittency, whose prevalence leads to the solar power series having both discrete and continuous characteristics, making the prediction problem more challenging. To fill this gap, a multistep ahead hybrid forecasting system has been constructed in this study that contains feature extraction, pattern recognition, forecasting, and integrated opti-mization modules. The system integrates pattern recognition algorithms into the prediction models to appro-priately deal with the inconsistent data features caused by intermittency and introduces a data decomposition strategy to achieve feature extraction, such that it could be adapted to forecast situations where relevant weather information is not available. Finally, the multi-objective optimization algorithm allows selecting, weighting, and integrating all submodels and yields a hybrid model with excellent accuracy and stability. The results showed that the hybrid forecasting system achieved the best performance in all aspects of the four forecast scenarios and also performed well in the multistep advance forecast. Taking Site 1 as an example, the mean absolute scale errors of the one-step, two-step, and three-step advance forecasts are 2.9953%, 3.7095%, and 3.4945%, respectively;and the standard deviations of the errors are 3.1086, 3.6210, and 3.2133, respectively. Further-more, the hybrid forecasting system achieves a performance improvement in accuracy and stability of more than 90%, and this performance improvement is significant at the 10% significance level.

关键词： Solar power forecasting Feature extraction Patten identification multi-objective optimization algorithm Integrated Learning

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Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression

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RENEWABLE ENERGY 2021年 179卷 1246-1261页

作者： Wang, Jianzhou Wang, Shuai Li, Zhiwu Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

As a renewable, clean and economical energy source, wind energy has rapidly infiltrated into the modern power grid system. Wind speed forecasting, the crucial technology of wind power grid connection, has attracted large amounts of scholars for research and modeling. However, a large number of models only focus on the point forecasts, which are far from meeting the requirements of risk control and evaluation of power system. To fill the gap, a novel forecasting model which combined the modified multi-objective tunicate algorithm, benchmark models, and Quantile regression is proposed for deterministic and probabilistic interval forecasts. Theoretical proof demonstrates that the proposed modified algorithm can combine the merits of all benchmark models and better solve the nonlinear characteristics of wind speed. Comparative experiments which include sixteen relevant models are performed on three datasets to validate the performance of the proposed model. Simulation results show that the proposed model is the most accurate in all datasets, and can also get the interval forecast results with relatively high coverage and the narrowest width. Therefore, this model can provide accurate point forecasting results and uncertainty information, which is beneficial to the real-time control of wind turbine and power grid dispatching. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind speed forecasts Deep learning model Forecast uncertainty Combination forecasting strategy multi-objective optimization algorithm

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Comprehensive dispatch model of agricultural water resources based on multi-objective quantum genetic algorithm

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DESALINATION AND WATER TREATMENT 2021年 239卷 192-201页

作者： Yan, Yaning Xian Peihua Univ Sch Intelligent Sci & Informat Engn Xian 710125 Peoples R China

In order to solve the problems of slow convergence speed, poor optimization effect, the large deviation between irrigation area and yield, and low water resource utilization rate, the integrated scheduling model of agricultural water resources based on a multi-objective quantum genetic algorithm is proposed. Taking the largest fully irrigated area and the largest crop yield as the optimization goals, construct a comprehensive scheduling model of agricultural water resources. On the basis of quantum genetic algorithm and multi-objective optimization algorithm, multi-objective quantum genetic algorithm is adopted, combined with real number coding of qubits, and quantum state interference characteristics are used to carry out probability crossover. According to the non-dominant sorting group classification mechanism and the non-inferior solution level sorting population classification, multi-objective optimization strategies such as elite retention and hierarchical clustering are used to solve the comprehensive scheduling model of agricultural water resources and realize the comprehensive scheduling of agricultural water resources. The experimental results show that the deviation of irrigation area proportion and irrigation yield of the proposed algorithm is small, and the optimization effect is good, which can effectively improve the utilization rate of water resources and accelerate the

关键词： multi-objective quantum genetic algorithm Quantum genetic algorithm multi-objective optimization algorithm Quantum bit coding

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A rapid diagnosis technology of short circuit fault in DC microgrid

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 147卷

作者： Wan, Qingzhu Zheng, Shuai Shi, Chenlu North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China

With the introduction of new energy sources, the structure of DC microgrid is becoming more complex, and short circuit fault diagnosis is inefficient. A rapid diagnosis technology of short circuit fault in DC microgrid is pro-posed, which consists of two parts: fault classification and fault location. Firstly, the law of transient current change at the beginning and end of a line is analyzed when different faults occur in the line and the converter, and a classification criterion is constructed to judge the fault type by using the rate of change of transient current at the head and end of the line, and a converter fault location criterion is constructed to judge the location of fault converter. Then for line fault location, the mathematical model is established according to the specific type of line fault, the analytical expression of line parameters is given, multiple objective functions are constructed, and the parameters of the fault line are identified by the multi-objective optimization algorithm based on genetic algorithm, and fault location is carried out by using line parameters. Finally, a simulation model of the DC microgrid is built in Matlab/Simulink to prove the validity and practicability of the scheme.

关键词： New energy sources DC microgrid Short circuit fault Transient current change rate multi-objective optimization algorithm

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Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence optimization algorithm for Wind Speed Forecasting

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IEEE ACCESS 2020年 8卷 51482-51499页

作者： He, Xiaohui Nie, Ying Guo, Hengliang Wang, Jianzhou Zhengzhou Univ Sch Geosci & Technol Zhengzhou 450001 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Wind speed forecasting takes a significant place in electric system owing to the fact that it has significant influence on operation efficiency and economic benefits. Aimming at improving forecast performance, a substantial number of wind speed prediction models have been proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this study, a novel forecasting system is proposed consisting of three modules: data preprocessing module, individual forecasting module and weight optimization module, which effectively achieve better forecasting ability. For data preprocessing and individual forecasting module, more regular sequences are obtained by decomposition technology, and association features are extracted by deep learning algorithm in prediction module. In the weight optimized module, the combination method base on the multi-objective optimization algorithm and nonnegative constraint theory are used to improve the prediction effectiveness. The combination model successfully exceeds the limits of individual predicton models and comparatively improves prediction accuracy. The effectiveness of the developed combination system is evaluated by 10-min wind speed in Penglai, China. The experiment results indicate that proposed forecasting system is better than other traditional forecasting models on three real wind speed datasets indeed.

关键词： Wind speed forecasting deep learning multi-objective optimization algorithm combination system

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multi-objective Structural optimization of a Wind Turbine Tower

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Journal of Shanghai Jiaotong university(Science) 2020年第4期25卷 538-544页

作者： ZHENG Yuqiao ZHANG Lu PAN Yongxiang HE Zhe School of Mechanical and Electrical Engineering Lanzhou University of TechnologyLanzhou 730050China

The 2MW wind turbine tower is considered as the baseline configuration for structural *** design variables consist of the thickness and height located at the top tower *** relationships between the design variables and the optimization objectives(mass,equivalent stress,top displacement and fatigue life)are mapped on the basis of uniform design and regression ***,five solutions are developed by an algorithm,*** to their efficiency and applicability,the most suitable solution is *** approach yields a decrease of 0.48%in the mass,a decrease of 54.48%in the equivalent stress and an increase of 8.14%in fatigue life,as compared with existing tower *** improved wind turbine tower is obtained for this practice.

关键词： structural optimization wind turbine tower multi-objective optimization algorithm uniform design

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Using Hybrid Artificial Intelligence and Evolutionary optimization algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices

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REMOTE SENSING 2021年第13期13卷 2555-2555页

作者： Yoosefzadeh-Najafabadi, Mohsen Tulpan, Dan Eskandari, Milad Univ Guelph Dept Plant Agr Guelph ON N1G 2W1 Canada Univ Guelph Dept Anim Biosci Guelph ON N1G 2W1 Canada

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R-2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.

关键词： high-throughput phenotyping machine learning multi-objective optimization algorithm radial basis function random forest support vector regression SPEA2

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Adaptive differential privacy preserving based on multi-objective optimization in deep neural networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第20期33卷 e6367-e6367页

作者： Fan, Tian Cui, Zhihua Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 003024 Peoples R China

Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks. ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi-objective optimization model (DPMOM). DPMOM adopts multi-objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA-II algorithm as the basic framework, a multi-objective optimization algorithm based on differential privacy protection (DPPMOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA-II, IBEA, PESA-II, and AGE-II, DPPMOA is proved that the solution set of this algorithm is better.

关键词： deep neural networks differential privacy protection multi-objective optimization algorithm multi-objective optimization model

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