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Nearly-zero carbon optimal operation model of hybrid renewable power stations comprising multiple energy storage systems using the improved cso algorithm

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JOURNAL OF ENERGY STORAGE 2024年 79卷

作者： Gao, Jianwei Wu, Haoyu Gao, Fangjie North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

In light of the abundant renewable energy resources in Northwestern China, this study introduces a novel hybrid power plant structure known as the (Renewable energy-concentrating solar power-combined heat and power) RCC system. The RCC system integrates various energy sources, including the photovoltaic, the wind power plant, the concentrating solar power (CSP) plant, and the combined heat and power (CHP) plant. It also incorporates power-to-gas (P2G) technology to convert wind and solar power surpluses into methane. Moreover, carbon capture and storage (CCS) technology is applied to capture carbon dioxide emissions from the CHP plants, which serves as a raw material for the P2G process. To address the energy trilemma, we develop a nearly-zero carbon emission optimization model for the RCC system, considering different renewable energy source (RES) endowments. The fuzzy membership function method is employed to identify the optimum satisfaction target across multiple attributes. An enhanced cso algorithm is introduced and validated using a simulation study on a CSP plant in Dunhuang. The results show that compared with traditional power stations, the proposed RCC system can reduce the investment cost by 34.45 %, increase the operating income by 17.7 %, and reduce carbon emission by 3.2 %. At the same time, the methane stock in the system increased by 53.7 % compared with the traditional power station containing P2G equipment, which helps the hybrid power station to participate in the methane market and obtain more profits. In addition, the direct energy supply ratio of RES decreased by 83.16 %, reducing the risk of the system caused by the uncertainty of RES. To sum up, the proposed RCC system has a good development prospect.

关键词： Hybrid power Renewable energy Multiple energy storage systems Scheduling optimization cso algorithm

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Wind Farm Energy Storage System Based on Cat Swarm Optimization-Backpropagation Neural Network Wind Power Prediction

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Liu, Shu Wang, Lei Jiang, Hongliang Liu, Yan You, Hongyu Shenyang Inst Engn Shenyang Peoples R China Shenyang Faleo Technol Co Ltd Shenyang Peoples R China

To solve the instability problem of wind turbine power output, the wind power was predicted, and a wind power prediction algorithm optimized by the backpropagation neural network based on the cso (cat swarm optimization) algorithm was studied, and a wind farm energy storage system model was built on this basis. By collecting the wind power plant's historical wind speed, power, and other parameters, the short-term wind farm output power was predicted, and the operation of the wind farm energy storage system was controlled to suppress the output power of the wind farm when the wind farm was connected to the grid so as to improve the stability of the output power of the wind farm. At the same time, typical wind farm data were taken as an example to verify the feasibility of the proposed method.

关键词： cso algorithm wind power prediction BP neural network wind farm energy storage system suppress wind power fluctuations

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Optimal location and sizing of hybrid system by analytical crow search optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第5期30卷 1-20页

作者： Pandey, Anand Kumar Kirmani, Sheeraz Jamia Millia Islamia Dept Elect Engn New Delhi India

This study presents a strategy for selecting an optimal location and placing the optimal photovoltaic (PV) and energy storage system. The power loss, voltage stability of the system, and also sizes of PV and storage are the major objectives, which are obtained through analytical crow search optimization (cso) algorithm. Initially, the Newton-Raphson load flow analysis is performed;and voltage, and losses of active and reactive power are calculated. The dynamic modelling of the proposed system time interval varies between 1 and 24 hours. The injection of active and reactive power is based on the nondispatchable PV, which can work under the optimal load flow. The major calculation of this work includes the fill factor, load, maximum power, battery, energy loss, and voltage stability. The two test systems, IEEE 33 and IEEE 69 bus systems, have been utilized for validating the performance of a proposed strategy. Finally, the proposed strategy has been compared with some different methods such as improved analytical (IA), exhaustive load flow (ELF), analytical multiobjective index (AIMO), analytical particle swarm optimization (A-PSO), and fast IA (FIA). From the comparison results, the proposed strategy proves the efficient PV size selection for an optimal location to minimize power and voltage losses.

关键词： cso algorithm energy storage system optimal location optimal size power loss PV voltage

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Identification of Fractional Order Transfer Function Model Using Biologically Inspired algorithms

Identification of Fractional Order Transfer Function Model U...

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International Conference on Automation (AUTOMATION)

作者： Dziedzic, Klaudia AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9783030132736;9783030132729

This paper presents the identification of a non-integer order model for the heat transfer process using the particle swarm optimization algorithm (PSO), cockroach swarm optimization algorithm (cso), gray wolf optimizer algorithm (GWO) and fminsearch function. In the beginning, fractional order systems have been discussed. Then an overview of individual optimization methods was prepared. Simulations have been carried out for all used the algorithms.

关键词： Fractional order systems Identification PSO algorithm fminsearch cso algorithm GWO algorithm

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Tuning the Fractional Order PID Controller in the Forced Air Heating System Using Biologically Inspired algorithms

Tuning the Fractional Order PID Controller in the Forced Air...

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International Conference on Automation (AUTOMATION)

作者： Dziedzic, Klaudia AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9783030409715;9783030409708

The main objective of this research is to present the tuning of the fractional order PID controller for the forced air heating system using the particle swarm optimization algorithm (PSO), cockroach swarm optimization algorithm (cso), grey wolf optimizer algorithm (GWO). In preliminaries, fractional calculus is discussed. Then, all three biological algorithms are briefly presented. Obtained simulation results allow comparison of individual algorithms in terms of overshoot, settling time and performance criteria (IAE, ITAE).

关键词： Fractional order systems FOPID PSO algorithm cso algorithm GWO algorithm

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Comparison of Cat Swarm Optimization with Particle Swarm Optimization for IIR System Identification

Comparison of Cat Swarm Optimization with Particle Swarm Opt...

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47th Asilomar Conference on Signals, Systems, and Computers

作者： So, J. Jenkins, W. K. Penn State Univ Dept Elect Engn University Pk PA 16802 USA

ISBN: (纸本)9781479923908

Infinite impulse response (IIR) adaptive filters have been developed to identify IIIR systems, but system identification is challenging due to non-unimodality of the error surface and the non-linear relationship between the error signal and the system parameters. Cat Swarm Optimization (cso) was recently introduced to solve optimization problems with a new learning rule to achieve better performance than particle swarm optimization (PSO). Also, it has been used for IIR system identification. This paper examines the parameters of cso to optimize them for IIR system identification with a few benchmarked IIR plants. Results demonstrate better performance for the cso algorithm when compared to the inertia-weighted PSO algorithm.

关键词： IIR system identification PSO algorithm cso algorithm

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