Reactive power optimization control for multi-energy system considering source-load uncertainty

Zuo, Hao; Xiao, Wanqiu; Ma, Shihui; Teng, Yun; Chen, Zhe

doi:10.1016/j.epsr.2023.110044

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…In addition to the uncertainty of load and unit active power, reactive power control is also a meaningful way to ensure the system's stable operation. Hao Zuo et al [16] determined the reactive power of a system based on the energy status of the system and the boundary of safe operation and established a reactive power optimization simulation model of a multi-energy system, which improved the voltage stability of the integrated energy system. Yujia Song et al [17] used an interval optimization model to measure the multiple uncertainties of a hydrogen-based integrated energy system to achieve the safe and stable operation of the system.…”

Section: Introductionmentioning

confidence: 99%

Source-Grid-Load Cross-Area Coordinated Optimization Model Based on IGDT and Wind-Photovoltaic-Photothermal System

Xu,

2024

Sustainability

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A source-grid-load cross-area coordinated optimal dispatch model based on IGDT and a wind-photovoltaic-photothermal system is suggested to handle the problem of renewable energy consumption under large-scale wind power and photovoltaic grid connections. Firstly, the peak flexibility of a wind-photovoltaic-photothermal co-generation system is investigated to improve the utilization rate of wind and solar resources. To increase the model’s efficiency and accuracy, the alternating direction multiplier method (ADMM) is used. Finally, arithmetic examples are utilized to examine and contrast how the system dispatch cost changed under risk-averse and risk-seeking strategies. It also examines how the installed ratio of concentrated solar power plants affects the overall cost of the system. The findings demonstrate that the suggested model may achieve a coordinated optimization of the source, grid and load while lowering system operation costs.

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Section: Introductionmentioning

confidence: 99%

Source-Grid-Load Cross-Area Coordinated Optimization Model Based on IGDT and Wind-Photovoltaic-Photothermal System

Xu,

2024

Sustainability

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Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm

Liu,

Hao,

et al. 2024

Energy Science & Engineering

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Distributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.

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Reactive power optimization control for multi-energy system considering source-load uncertainty

Cited by 2 publications

References 14 publications

Source-Grid-Load Cross-Area Coordinated Optimization Model Based on IGDT and Wind-Photovoltaic-Photothermal System

Source-Grid-Load Cross-Area Coordinated Optimization Model Based on IGDT and Wind-Photovoltaic-Photothermal System

Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm

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