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39th Chinese Control Conference (CCC)

作者： Gao Long Yang Yi Ren Xiaolin Gu Haiqin Han Qingqing Yu Jingya Nantong Univ Coll Elect Engn Nantong 226019 Jiangsu Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

The air source heat pump heat collection control system is a system with strong nonlinearity and large time delay. It is difficult to achieve the expected control effect on the outlet water temperature by using conventional control algorithms. In view of this problem, this paper makes up for the shortcomings of fuzzy control and PID control, and proposes an improved particle swarm optimization (IPSO) fuzzy PID control strategy to control the temperature of the effluent of the air source heat pump heat collection control system. First of all, the particle swarm optimization algorithm is easy to fall into the problem of local optimization. By increasing the vector, the optimal position of individual particles is stored to increase the range of particle search. Then use the improved particle swarm algorithm to optimize the fuzzy PID scale factor and quantization factor to modify the PID parameters in real time to improve the control accuracy of the heat pump system. Finally, a MATLAB / Simulink simulation model for the heat collection temperature control of the heat pump is established and an improved particle swarm optimization algorithm program is written, and compared with the fuzzy PID and conventional PID control algorithms. Simulation results show that the heat-collection control system based on IPSO has strong adaptability, robustness and anti-interference.

关键词： Air source heat pump fuzzy PID improved particle swarm algorithm temperature control

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Impact of Energy System Optimization Based on Different Ground Source Heat Pump Models

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ENERGIES 2024年第23期17卷 6023页

作者： Lai, Yingjun Gao, Yan Gao, Yaping Beijing Univ Civil Engn & Architecture Beijing Key Lab Heating Gas Supply Ventilating & A Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Sch Architecture & Urban Planning Beijing 100044 Peoples R China

With rapidly developing new energy technologies, rational energy planning has become an important area of research. Ground source heat pumps (GSHPs) have shown themselves to be highly efficient. effective in reducing building or district energy consumption and operating costs. However, when optimizing integrated energy systems, most studies simplify the GSHP model by using the rated coefficient of performance (COP) of the GSHP unit, neglecting factors such as soil, buried piping, and actual operating conditions. This simplification leads to a deviation from the actual operation of GSHPs, creating a gap between the derived operational guidelines and real-world performance. Therefore, this paper examines a hotel equipped with photovoltaic panels, a GSHP, and a hybrid energy storage unit. By constructing models of the underground pipes, GSHP units, and pumps, this paper considers the thermal exchanger between the underground pipes and the soil, the thermal pump, and the operating status of the unit. The purpose is to optimize the running expenses using an enhanced mote swarm optimization (PSO) algorithm to calculate the optimal operating strategy of system equipment. Compared to the simplified energy system optimization model, the detailed GSHP unit model shows a 21.36% increase in energy consumption, a 13.64% decrease in the mean COP of the GSHP unit, and a 44.4% rise in system running expenses. The differences in the GSHP model affect the energy consumption results of the unit by changing the relationship between the power consumption of the PV system and the GSHP at different times, which in turn affects the operation of the energy storage unit. The final discussion highlights significant differences in the calculated system operating results derived from the two models, suggesting that these may profoundly affect the architectural and enhancement processes of more complex GSHP configurations.

关键词： ground source heat pump improved particle swarm algorithm day-ahead optimization buried pipe

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Configuration optimization of energy storage and economic improvement for household photovoltaic system considering multiple scenarios

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JOURNAL OF ENERGY STORAGE 2023年 67卷

作者： Wang, Weijun Kang, Keyi Sun, Gang Xiao, Limin North China Elect Power Univ Dept Econ Management 689 Huadian Rd Baoding 071000 Peoples R China Guangdong Power Grid Co Ltd Grid Planning & Res Ctr 757 Dongfeng East Rd Guangzhou 510620 Peoples R China

Household photovoltaic (PV) is booming in China. In 2021, household PV contributed 21.6 GW of new installed capacity, accounting for 73.8 % of the new installed capacity of distributed PV. However, due to the randomness and intermittency of PV power generation, large-scale household PV grid connection has a serious impact on the safe and stable operation of the distribution network. Based on this background, this paper considers different application scenarios of household PV, and constructs the optimization model of energy storage configuration of household PV with the annual net profit as the optimization goal. Taking a natural village in China as an example, the improved particle swarm optimization algorithm is used to solve the optimal energy storage ca-pacity, power and typical daily energy storage output. According to the optimization results, the operation effects and economic benefit indicators of the household PV system and the household PV storage system in different scenarios are compared and analyzed. The results show that the configuration of energy storage for household PV can significantly reduce PV grid-connected power, improve the local consumption of PV power, promote the safe and stable operation of the power grid, reduce carbon emissions, and achieve appreciable economic benefits. Finally, some suggestions are put forward to further promote the development of household PV storage system. The research results can provide reference for promoting the sustainable development of household PV, ensuring the smooth implementation of distributed PV development pilot project in China, and accelerating the appli-cation of household PV storage system.

关键词： Household photovoltaic Energy storage optimization configuration improved particle swarm algorithm Local consumption of photovoltaic power Economic benefits

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Research on Multi-Objective Energy Management of Renewable Energy Power Plant with Electrolytic Hydrogen Production

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PROCESSES 2024年第3期12卷 541页

作者： Shi, Tao Gu, Libo Xu, Zeyan Sheng, Jialin Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Carbon Neutral Nanjing 210023 Peoples R China

This study focuses on a renewable energy power plant equipped with electrolytic hydrogen production system, aiming to optimize energy management to smooth renewable energy generation fluctuations, participate in peak shaving auxiliary services, and increase the absorption space for renewable energy. A multi-objective energy management model and corresponding algorithms were developed, incorporating considerations of cost, pricing, and the operational constraints of a renewable energy generating unit and electrolytic hydrogen production system. By introducing uncertain programming, the uncertainty issues associated with renewable energy output were successfully addressed and an improved particle swarm optimization algorithm was employed for solving. A simulation system established on the Matlab platform verified the effectiveness of the model and algorithms, demonstrating that this approach can effectively meet the demands of the electricity market while enhancing the utilization rate of renewable energies.

关键词： electrolytic hydrogen power fluctuation smoothing peak shaving auxiliary services fuzzy chance constraints improved particle swarm algorithm

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An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 144卷

作者： Zhang, Liang Liu, Dongyuan Cai, Guowei Lyu, Ling Koh, Leong Hai Wang, Tianshuo Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin Peoples R China Natl Metrol Ctr Singapore 138635 Singapore

The grid connection of large-scale clean energy provides the possibility for the establishment of a clean energy system. The urgent problem that needs to be solved is how to improve the utilization efficiency of clean energy to reduce carbon emissions. First, the virtual power plant (VPP) operation mode under the carbon trading and green certificate trading mechanism is analyzed. Secondly, this paper incorporates carbon trading mechanism and green certificate trading mechanism into the optimal dispatch model of VPP including wind power generation, photovoltaic power generation, gas turbines and energy storage device. Taking the net profit of VPP as the optimization goal, which can take into account both economic and environmental protection. Based on whether VPP participates in carbon trading and green certificate trading, three schemes are established and compared and analyzed. In addition, to cope with the volatility of renewable energy, the utilization rates of the renewable energy output scenarios for four typical days under the three schemes were compared and analyzed. To solve this problem, this paper proposes the Self-Conclusion and Variational particle swarm Optimization (SCV-PSO) algorithm. The simulation results show that the VPP optimal scheduling model and solving algorithm proposed can effectively improve the utilization rate of renewable energy and reduce the carbon emission under the premise of ensuring economic efficiency. It can provide a useful reference for the low-carbon economic operation of the power system in the future.

关键词： Virtual power plant Optimal dispatch Carbon trading Green certificate trading improved particle swarm algorithm

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Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management

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ENERGY 2024年 311卷

作者： Wang, Haifeng Liao, Yate Zhang, Jiarui Cai, Ziwen Zhao, Yun Wang, Weijun North China Elect Power Univ Dept Econ & Management Baoding 071003 Hebei Peoples R China China Southern Power Grid Co Ltd Guangzhou 510663 Guangdong Peoples R China

Distributed renewable energy is more abundant in rural areas, and a large amount of distributed photovoltaic grid-connected power brings challenges to the stable of the power grid. How to promote the self-generation and self-consumption of distributed renewable energy has become an urgent problem. In this paper, a village-level distributed photovoltaic power generation system including energy storage and electric vehicles is constructed. With the goal of minimizing the photovoltaic grid-connected power and maximizing the annual comprehensive revenue, the planning model of energy storage capacity allocation for village-level distributed power generation system is constructed. Considering the charging management for different numbers of electric vehicles, the optimal energy storage capacity allocation strategy is solved using the improved particle swarm *** scenarios are set up as examples to be *** conclusions are:(1)After the configuration of a reasonable energy storage,the grid-connected generation of the system is significantly reduced,and the annual comprehensive revenue of the system is significantly increased.(2)After considering charging management for different numbers of electric vehicles, the capacity and power of the energy storage configuration can be significantly reduced, thus reducing the energy storage investment cost and operation and maintenance cost, and further significantly improving the annual comprehensive revenue of this system.

关键词： Village-level distributed photovoltaic systems Energy storage configuration planning improved particle swarm algorithm Electric vehicle Charge management Nomenclature

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Development and Optimization of a Novel Soft Sensor Modeling Method for Fermentation Process of Pichia pastoris

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SENSORS 2023年第13期23卷 6014-6014页

作者： Wang, Bo Liu, Jun Yu, Ameng Wang, Haibo Jiangsu Univ Sch Elect & Informat Engn Key Lab Agr Measurement & Control Technol & Equipm Zhenjiang 212013 Peoples R China

This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM designed to address the issue of model failure caused by varying fermentation data distributions resulting from different operating conditions during the fermentation of different batches of Pichia pastoris. First, the problem of significant differences in data distribution among different batches of the fermentation process is addressed by adopting the balanced distribution adaptation (BDA) method from transfer learning. This method reduces the data distribution differences among batches of the fermentation process, while the fuzzy set concept is employed to improve the BDA method by transforming the classification problem into a regression prediction problem for the fermentation process. Second, the soft sensor model for the fermentation process is developed using the least squares support vector machine (LSSVM). The model parameters are optimized by an improved particle swarm optimization (IPSO) algorithm based on individual differences. Finally, the data obtained from the Pichia pastoris fermentation experiment are used for simulation, and the developed soft sensor model is applied to predict the cell concentration and product concentration during the fermentation process of Pichia pastoris. Simulation results demonstrate that the IPSO algorithm has good convergence performance and optimization performance compared with other algorithms. The improved BDA algorithm can make the soft sensor model adapt to different operating conditions, and the proposed soft sensor method outperforms existing methods, exhibiting higher prediction accuracy and the ability to accurately predict the fermentation process of Pichia pastoris under different operating conditions.

关键词： soft sensor improved particle swarm algorithm least squares support vector machine transfer learning Pichia pastoris

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A Rotating Tidal Current Controller and Energy Router Siting and Capacitation Method Considering Spatio-Temporal Distribution

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ENERGIES 2024年第23期17卷 5919页

作者： Jia, Junqing Zhou, Jia Gao, Yuan Shao, Chen Lu, Junda Jia, Jiaoxin Key Lab Intelligent Power Grid Simulat Enterprise Hohhot 010020 Peoples R China Inner Mongolia Elect Power Grp Co Ltd Inner Mongolia Elect Power Res Inst Hohhot 010020 Peoples R China Inner Mongolia Power Grp Co Ltd Nav Inspect Branch Hohhot 010020 Peoples R China Inner Mongolia Elect Power Grp Co Ltd Ordos Power Supply Branch Ordos 017000 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microgr Baoding 071003 Peoples R China

As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost.

关键词： active distribution network expansion planning rotating current controller energy router improved particle swarm algorithm second order cone planning

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Adaptive Fractional Order PID Controller for Angle Tracking In Steer-By-Wire System

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中国机械工程学刊 2024年第2期45卷 177-186页

作者： Cong-Cong Guo Chang-Shui Wu De-Guan He

To ensure fast and accurate angle tracking during car steering, reduce delay time, and improve the tracking performance of the Steer-by-Wire (SBW) system, a fractional order PID (FOPID) controller with feedforward compensation is designed. The PID controller is approximated using the Oustaloup approximation method, reducing the tuning dimension of FOPID. An improved particle swarm optimization (PSO) algorithm is utilized to dynamically adjust the four parameters of FOPID, thereby decreasing the optimization time of the algorithm. Finally, the Adaptive Inertia Weight particle swarm Optimization algorithm (AW-PSO), based on global search pattern, the Fixed Inertia Weight particle swarm Optimization algorithm (F-PSO), based on variable neighborhood search pattern, and the Compressed Factor particle swarm Optimization algorithm (CF-PSO) are used as comparisons for simulation verification and bench test. The effectiveness of the proposed method is verified. The comparative results show that, in terms of computation time and accuracy, the optimization capability of the proposed method is significantly superior to the AW-PSO, F-PSO, and CF-PSO algorithms. Hence, the algorithm's computation accuracy and stability fully meet the requirements. The bench test results reveal that under step input, the delay time is about 0.2s, with a maximum overshoot of 1.57deg. Under sinusoidal input, the delay time is approximately 0.02s, the maximum steering speed is 0.70rad/s, and the maximum output torque of the steering motor is 2.27Nm. Based on experimental verification, it can be possible to conclude that this method enables better completion of the angle tracking control task for the SBW system and exhibits good robustness and tracking control performance.

关键词： SBW technology angle-tracking-control improved particle swarm algorithm FOPID controller

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A developed model updating method based on extended frequency response functions and its application study of offshore structures

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APPLIED OCEAN RESEARCH 2023年第1期135卷

作者： Liu, Fushun Li, Xingguo Song, Hong Liu, Dianzi Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Ocean Univ China Shandong Prov Key Lab Ocean Engn Qingdao 266100 Peoples R China Univ East Anglia Sch Engn Norwich NR14 7TH England

Accurate finite element (FE) models are extremely vital to the analysis of the dynamic characteristics of engineering structures. However, it is challenging for the frequency response function (FRF)-based model updating method to obtain reliable numerical results as the limited number of FRFs at the selected peak positions usually leads to an unacceptable or failure of model updating. Moreover, the increased number of updating coefficients in complex engineering problems generates nonlinear optimization models with the local solutions by traditional optimization techniques. To tackle these two issues, a new model updating method is proposed to make full use of FRFs extracted from measured field data of structures and the improved particle swarm optimization (PSO) technique for accurately estimating physical parameters of structures. The novelties of this research include: (1) The signature assurance criterion (SAC)-based FRF is evaluated to eliminate the influence of limited frequency points on the accuracy of the updated model using the normalized acceleration component;(2) The enhanced (PSO) is developed to realize the adaptive selection of inertia factors for the better diversity by introducing an average value of the fitness function, and then accurate predictions of updating coefficients within a smaller number of iterations are achieved using the developed constraint factor. The effectiveness of the proposed method is verified by mathematical model of a jacket platform. Results show that the proposed method can accurately obtain the updating coefficients under spatial incomplete condition, and the maximum error of natural frequencies is 0.779% using the accelerations containing 5% noise. To prove the robustness of the proposed method, experimental studies of monopile offshore wind turbine are also conducted and the maximum error of natural frequencies is 1.887% under the consideration of spatial incompleteness represented by the structural stiffness degradatio

关键词： Model updating Frequency response function Signature assurance criterion Normalized acceleration components improved particle swarm algorithm

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