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Multi-criteria optimal sizing and analysis of PV/wind/fuel cell/battery/diesel generator for rural electrification: A case study in Chad

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2024年第3期13卷 491-507页

作者： Abdoulaye, Mahamat Adoum Waita, Sebastian Wekesa, Cyrus Wabuge Mwabora, Julius Mwakondo Univ Nairobi Dept Phys POB 30197-00100 Nairobi Kenya Korea Inst Energy Res Jeju Global Res Ctr Distributed Energy Team 200 Haemajihaean Ro Jeju 63357 South Korea Univ Eldoret Sch Engn POB 1125-30100 Eldoret Kenya

. Access to sustainable, clean, affordable, and reliable electricity is crucial for social and economic development, yet Sub-Saharan Africa (SSA) struggles significantly in this context. In CHAD, only 11.3% of the population is able to access electricity, making it one of the least electrified countries in SSA with the lowest clean energy access. In rural areas, electricity access falls to just 1.3%. This research applies and executes a MultiObjective particle swarm optimization (MOPSO) algorithm using MATLAB R2023b to assess the techno-economic, environmental, and social impacts of a hybrid system based on optimal PV/Wind/Battery/Fuel Cell (FC)/Diesel generator (DG) sizing for rural electrification in CHAD. The proposed system's self-sufficiency index (SSSI) and the Annualized System Cost (ASC) were chosen as objective functions to guarantee the economic feasibility of the system, higher self-sufficiency, and lower dependence on external energy sources (DG). The simulation results show that the optimal size of the proposed system supplies the load demand by 100% of the renewable energy sources (RES) fraction, and the optimal capacities of the main components to supply the load demand are: Solar Power (493 KW), Wind Turbine (166 KW), Battery Energy Charge/Discharge (229180 kWh /221300 kWh), Hydrogen tank storage energy (83 874 kWh), Electrolyzer size (202 KW), Fuel cell size (144 KW). The evelized cost of electricity (LCOE) of 0.2982 $/kWh, which is 51.12% lower than the national unit production costs of electricity in rural areas of CHAD (0.61 $/kWh). This LCOE is also the lowest compared to previous works done using HOMER Pro for the country of CHAD. The results also give a levelized cost of hydrogen (LCOH) of 3.8563 US $/kg, lower than for all studies found in the literature for the country of Chad. The proposed system's yearly avoided greenhouse gas (GHG) emission is 374 640 kg. The proposed system will create five (5) new jobs (JCO) and improve the Human Develo

关键词： system particular Avoided greenhouse gas (GHG) social assessment Hybrid energy system Optimal sizing rural electrification particle swarm optimization algorithm zero-carbon electricity

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Design and key technology of the energy consumption management system for the liquid cooling data center

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ENERGY SCIENCE & ENGINEERING 2023年第3期11卷 1284-1293页

作者： Lai, Yibo Fang, Xiang Han, Rongjie Xuan, Yi Huang, Jiabin Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310009 Peoples R China

In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

关键词： energy consumption management system energy consumption sampling circuit liquid cooling data center particle swarm optimization algorithm resource configurator

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Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

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JOURNAL OF SUPERCOMPUTING 2023年第12期79卷 13889-13906页

作者： Zhou, Jian Wang, Weixin Lu, Jian Liu, Lingzhe Xian Polytech Univ Sch Elect & Informat Xian 710048 Shaanxi Peoples R China

The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particle swarm optimization least squares support vector machine (PSO-LSSVM) with a hybrid kernel function. The proposed method is based on a least square support vector machine and uses linear weighting of the polynomial kernel function (POLY) and Gaussian kernel function (RBF) to form a hybrid kernel function, and uses a particle swarm optimization algorithm to search for the optimal parameters. Finally, a mathematical model of the longitudinal and lateral passages of a SUH is established. According to the flight test data, the longitudinal and lateral channel models are trained and verified in the hover and low-speed forward flight states of a SUH. The experimental and comparison results demonstrate that the model established via this method has higher prediction accuracy and more accurate prediction results than a model established using a least squares support vector machine with a single kernel function. The identification accuracy of the SUH model is improved effectively.

关键词： Hybrid kernel function Least square support vector machines Mathematical modeling particle swarm optimization algorithm Small unmanned helicopter

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Estimation method of layered ground thermal conductivity for U-tube BHE based on the quasi-3D model

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RENEWABLE ENERGY 2023年 213卷 121-133页

作者： Deng, Zhenpeng Nian, Yongle Cheng, Wen-long Univ Sci & Technol China Dept Thermal Sci & Energy Engn Hefei 230027 Anhui Peoples R China

The distribution of ground thermal conductivity (As) is a significant consideration for the design of ground source heat pump systems. This paper proposed a new method to estimate the layered As for U-tube borehole heat exchanger (BHE) using particle swarm optimization (PSO) and distributed thermal response test (DTRT) data. Firstly, a quasi-3D heat transfer model considering geothermal gradient for U-tube BHE had been built to simulate fluid temperature profiles. And the experiment in DTRT was performed using a 45 m sandbox. Then, the layered As were estimated through PSO and DTRT data based on the established model. The results showed that the convergent As distribution could be reliably obtained by short-term DTRT. Furthermore, the estimation results are independent of the number of layers and data points. The mean difference in As distribution between 9 layers and 45 layers is 0.007 W/(m & BULL;K) and the layered As also could be obtained when the number of layers is more than the number of temperature data points, and the root mean square error between 30 and 220 points is less than 0.3 W/(m & BULL;K).

关键词： Ground source heat pump Layered ground thermal conductivity U-tube borehole heat exchanger Distributed thermal response test particle swarm optimization algorithm

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Investigating the Impact of Demand Management on the Microgrid with the Presence of Renewable Resources Uncertainty and Reliability

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2024年第3期8卷 889-903页

作者： Hong, Wei Tao, Zheng hua Yang, Bo Changzhou Univ Changzhou 213164 Peoples R China NARI Technol Dev Co Ltd Nanjing 211106 Peoples R China

The proliferation of renewable energy sources within distribution systems has given rise to a new structure known as microgrids. These microgrids are small power grids comprised of both controllable and uncontrollable loads. In the distribution system, microgrids can use renewable energy sources to be operated in far-away regions at lower investment costs. The energy industry faces numerous problems, including problems with energy efficiency, ensuring system confidence, and reducing the destructive environmental effects. Load management programs can help address the challenges confronting the energy industry. This essay proposes a method for evaluating the load responsiveness in microgrids. A combined algorithm, including a gravitational search algorithm as well as particle swarm optimization, to address a multi-objective optimization function. The Latin hypercube sampling method is adopted to create diverse scenarios, which are reduced by the K-means method. The objective function includes grid losses, generation costs, confidence index, and voltage stability. The suggested method is implemented on a modified 69-bus system consisting of wind turbines, solar power stations, and energy storage systems using the combined optimization algorithm in Matlab. The results show that increasing renewable energy production reduces power losses, power consumption, and cost per kilo and improves voltage deviation. Adding renewables and energy storage also reduces power fluctuations and grid losses. Adding renewable sources brings benefits such as reduced production costs, losses, and improved voltage profiles. From the simulation outcomes, it can be seen that the average voltage profile has been improved by about 4%. Also, increasing the responsive load has improved the mains voltage profile and increased by about 10%.

关键词： Energy Photovoltaic particle swarm optimization algorithm Wind energy Gravitational search optimization algorithm

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Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Li, Chengjie Wang, Yanglin Meng, Linghui Zhong, Wen Zhang, Chengfang Liu, Tao Southwest Minzu Univ Sch Comp & Artificial Intelligence Key Lab Comp Syst State Ethn Affairs Commiss Chengdu 610041 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Chengdu Third Peoples Hosp Dept Gen Med Chengdu 610014 Peoples R China Sichuan Police Coll Intelligent Policing & Natl Secur Risk Management Intelligent Policing Key Lab Sichuan Prov Luzhou Peoples R China

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability. The proposed method achieved remarkable accuracy rates of 93.22% and 95.20% on the UCI and Kaggle datasets, respectively, underscoring its exceptional performance even with small sample sizes. Ablation experiments further validated the efficacy of the data preprocessing and feature selection techniques employed. Notably, the EPSOSA algorithm surpassed classical optimization algorithms in terms of convergence speed, while also demonstrating improved sensitivity and specificity. This model holds significant potential for facilitating early identification of high-risk patients, which could ultimately save lives and optimize the utilization of medical resources. Despite implementation challenges, including technical integration and data standardization, the algorithm shows promise for use in emergency settings and community health services for regular cardiac risk monitoring.

关键词： particle swarm optimization algorithm Simulated annealing algorithm Principal component analysis Elimination strategy Feature engineering Cardiovascular disease (CVD)

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Quantitative determination of TVB-N content for different types of refrigerated grass carp fillets using near-infrared spectroscopy combined with machine learning

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 126卷

作者： Zhang, Qinyu Hu, Zhigang Xu, Zijian Zhang, Peilin Jiang, Yajun Fu, Dandan Chen, Yan Wuhan Polytech Univ Sch Mech Engn Wuhan 430023 Hubei Peoples R China Hubei Prov Cereal & Oil Machinery Engn Technol Res Wuhan 430023 Hubei Peoples R China Hubei Prov Aquat Prod Proc Equipment Engn Technol Wuhan 430023 Hubei Peoples R China

This research aims to accurately forecast the freshness indicators (TVB-N) of skinned and skinless grass carp fillets by integrating near-infrared spectroscopy (NIR) with machine learning algorithms. By comparing the predictive accuracy of machine learning models for the two types of grass carp fillets, the most effective modeling method is identified. Methodologically, the study first applies orthogonal signal correction (OSC) and the first derivative among other algorithms for spectral pre-processing. Subsequently, competitive adaptive reweighted sampling (CARS), moving window partial least squares (MWPLS), and random frog (RF) are used for the selection of variables. Lastly, partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural networks (BPNN), and particle swarm optimization-enhanced BP neural networks (PSO-BP) are employed to quantitatively analyze the NIR data. The most relevant results reveal that the (OSC+D1)-CARSPSO-BP model exhibits superior predictive capabilities. Especially when applied to skin-on fish fillets (R2P =0.988, RMSEP=0.092), this model surpasses that for skinless fish fillet data (R2P =0.987, RMSEP=0.096). Therefore, combining near-infrared with machine learning to predict the freshness (TVB-N) of grass carp fillets based on skin-on samples is a more effective non-destructive testing method.

关键词： Near -Infrared spectroscopy Rapid non-destructive detection Grass carp fillets Total volatile basic nitrogen Machine learning Orthogonal signal correction (OSC) particle swarm optimization algorithm optimized BP neural network (PSO-BP)

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Multi-UAV pursuit-evasion gaming based on PSO-M3DDPG schemes

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COMPLEX & INTELLIGENT SYSTEMS 2024年第5期10卷 6867-6883页

作者： Zhang, Yaozhong Ding, Meiyan Zhang, Jiandong Yang, Qiming Shi, Guoqing Lu, Meiqu Jiang, Frank Northern Polytech Univ Xian Peoples R China Guangxi Minzu Univ Nanning Peoples R China UTS Univ Technol Sydney Ultimo NSW Australia

The sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima. Mini-Max-Multi-agent Deep Deterministic Policy Gradient (M3DDPG) algorithm is a multi-agent reinforcement learning algorithm, which introduces the minimax theorem into Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It also has unstable convergence caused by sparse sample data and randomization. However, the particle swarm Optimisation (PSO) algorithm, unlike traditional reinforcement learning methods, involves the construction of independent populations of policy networks to generate sample data, followed by training the reinforcement learning algorithm. PSO optimizes and updates the policy population based on a fitness function, aiming to enhance the efficiency and convergence speed of the algorithm in learning from the sample data. In order to address the multi-agent pursuit-evasion problem, we propose the PSO-M3DDPG algorithm, which combines the PSO algorithm with the M3DDPG algorithm. Through experimental simulations, the improved algorithm demonstrates superior training results and faster convergence speeds, thus validating its effectiveness.

关键词： Pursuit-evasion game particle swarm optimization algorithm Reinforcement learning M3DDPG (mini-max-multi-agent deep deterministic policy gradient)

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Cross-social-network user alignment research based on multi-dimensional user features

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Zhao, Tao Gao, Heng Wang, Zecheng Bi, Dianjie Chen, Xuemin Anhui Univ Finance & Econ Dept Comp Sci & Technol Bengbu Peoples R China Texas Southern Univ Dept Engn Houston TX USA

Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate alignment accuracy. To address these challenges, we introduce a cross-social network user alignment model based on multi-dimensional user features (MDUF). First, inspired by the principles of entity recognition and Hartley's association method, we employed a block matrix association algorithm to project the original dataset into different high-dimensional spaces. Second, we proposed a new inertia weight calculation method to improve the convergence speed from linear to nonlinear transformations. This method improves the performance of traditional particle swarm optimization algorithms. Finally, we utilize improved particle swarm optimization and residual connection techniques to optimize bidirectional long short-term memory networks. The experimental results show that our proposed model significantly outperforms traditional alignment models in terms of alignment efficiency and accuracy, which is highly practical and has the potential to inspire further research on social network user alignment.

关键词： Social networks user alignment block matrix particle swarm optimization algorithm

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Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2024年第1期30卷 133-152页

作者： Kumaresan, Velmurugan Saravanasankar, S. Di Bona, Gianpaolo Kalasalingam Acad Res & Educ Krishnankoil India Univ Cassino & Lazio Meridionale Dept Civil & Mech Engn Cassino Italy

PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing ***/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service *** major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

关键词： Small and medium-sized enterprise Markov decision model particle swarm optimization algorithm Optimal decision-making process

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