Unscented Kalman filtering method for scheduling photovoltaic power generation system fluctuations

Li, Zheng; Zhang, Liping; Zhang, Rui; Wang, Xin; Xue, Zhang; Sun, Haiyan

doi:10.1177/0957650920950085

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…(2) DC bus topology Different power generation units are connected to the public bus in the form of DC, through which the bus supplies power to the DC load and ac users [11,12]. The topology of the DC bus is shown in Figure 3.…”

Section: Design Of Optimization Scheduling Methods Of Solar Photovolt...mentioning

confidence: 99%

Optimization Scheduling Method of Solar Photovoltaic and Fuel Cell Combined Power Generation System

Xue

Zheng

Duan³

2022

SPEE

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The performance of the battery used in the traditional solar photovoltaic power generation system is poor, and the solar energy has a certain volatility, which makes the performance of the solar photovoltaic power generation system decline significantly. In order to improve the performance of the solar photovoltaic power generation system, a solar photovoltaic fuel cell combined power generation system has been developed. However, there are some problems in the process of traditional combined generation system optimal scheduling, such as high investment cost and total cost, and short generation time of optimal scheduling scheme. This paper takes solving the problems of traditional system optimal scheduling as the research goal, a new optimization scheduling method of solar photovoltaic and fuel cell combined power generation system was designed. The composition and topological structure of the solar photovoltaic fuel cell combined power generation system are analyzed. The system is composed of photovoltaic array, fuel cell, electrolytic cell, short-term energy storage unit and energy control unit. It can mainly convert sunlight radiation into electric energy, and convert it into DC or AC used by people through multiple links, so as to ensure the stability and security of power supply in our country. A scheduling model is established according to the photovoltaic cell power generation model, the fuel cell power generation model, the electrolytic hydrogen production model, the battery model, the power conversion model and the combined power generation system model. And the multi-objective cuckoo algorithm is used to solve the model, the optimization scheduling results of solar photovoltaic fuel cell combined power generation system are obtained. The experimental results show that the total investment cost of this method is reduced by 123678.4 yuan and 175858.7 yuan compared with the experimental comparison method, and the total cost is reduced by 301195.5 yuan and 414991.8 yuan compared with the experimental comparison method. It shows that compared with the experimental comparison method, the total investment cost and total cost of this method are lower, and the generation time of the optimal scheduling scheme is between 0.19s and 0.25s, and the practical application effect is good. It fully solves the problems existing in the traditional methods and has certain application significance.

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Section: Design Of Optimization Scheduling Methods Of Solar Photovolt...mentioning

confidence: 99%

Optimization Scheduling Method of Solar Photovoltaic and Fuel Cell Combined Power Generation System

Xue

Zheng

Duan³

2022

SPEE

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“…The results showed that in a properly designed perovskite structure with multi-layer slender metal nanoparticles, the absorption of photons can be increased and the binding energy of excitons can be reduced at the same time. Thus, the efficiency can be improved through metallization, which cannot be achieved in traditional p-n junction batteries [8]. To achieve efficient photovoltaic power generation prediction in smart city energy management, Sun et al studied and designed a model based on a multi-scale short-term memory recurrent neural network that can predict very short-term photovoltaic power generation.…”

Section: Related Workmentioning

confidence: 99%

“…When C is 0, the switch is open; when C is 1, the switch is closed. D is calculated using Equation (8).…”

Section: Construction Of the Mppt Control Algorithm Based On Hqpsomentioning

confidence: 99%

Application of the MPPT Control Algorithm Based on Hybrid Quantum Particle Swarm Optimization in a Photovoltaic Power Generation System

Zhou

Wang

et al. 2023

Processes

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The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Roserock function and Rastigin function tests. The tracking error of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm was less than 1% under lighting conditions. The convergence time of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm in arbitrary shadow occlusion environments can reach a stable state within 0.1 s. In summary, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm proposed in the study has excellent performance and very wide applicability. To a certain extent, it improves the total power generation capacity of the photovoltaic power generation system and the power generation efficiency of the photovoltaic array.

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“…Hence, the most important challenge is making these microgrids efficiently cooperate and exchange power in case of system resilience [15]. Hence, this paper addresses this issue to smooth the progress of the DES' implementation [16], [17]. The principle of the multiagent system (MAS) has gained more use in recent years even for the management of centralized power systems or of the decentralized management and control strategy for distributed microgrids [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Multi-microgrids system’s resilience enhancement

Chalah,

Belaidi

2024

IJEECS

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Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.

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Unscented Kalman filtering method for scheduling photovoltaic power generation system fluctuations

Cited by 7 publications

References 22 publications

Optimization Scheduling Method of Solar Photovoltaic and Fuel Cell Combined Power Generation System

Optimization Scheduling Method of Solar Photovoltaic and Fuel Cell Combined Power Generation System

Application of the MPPT Control Algorithm Based on Hybrid Quantum Particle Swarm Optimization in a Photovoltaic Power Generation System

Multi-microgrids system’s resilience enhancement

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