Impact of Distributed Photovoltaic Systems on Zone Substation Peak Demand

Haghdadi, Navid; Bruce, Anna; MacGill, Iain; Passey, Robert

doi:10.1109/tste.2017.2751647

Cited by 47 publications

(18 citation statements)

References 10 publications

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“…There are a few case studies on how to approximate the supply capacity of renewable power resources [10,11]. Some studies used the capacity factor as a measure of how much energy is generated by power plants compared to their maximum output.…”

Section: Special Issue On Multidisciplinary Innovation In Engineeringmentioning

confidence: 99%

The Capacity Factor of Renewable Energy Power Plants During Electric Power Peak Times in Jeju Island

Otgongerel¹,

Lee²,

Baatarbileg³

2020

Adv. sci. technol. eng. syst. j.

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Jeju Island is the largest island of South Korea and has an independent electric supply system. The island has a special energy plan that realizes itself a carbon-free region by 2030. Wind and solar energy resources are considered as an important means. The introduction of weather-variable renewable energy generation sources into the electric power system makes it unstable. The main content of this study was to investigate the capacity factor of large scale renewable energy (solar, wind) power plants operated in Jeju Island during the electric power peak time. We can see their utilization rate or contribution rate during the power peak time by the value of their capacity factors. The largest 18 solar power plants (SPP) and 8 wind power plants (WPP) on the island were chosen and the 4, 12, 25, 50 and 100% electric power peak times were set for this study. There are two electric power peak seasons, summer and winter, in Jeju Island when the electric power demand arrives at peak. Generation of renewable energy plants depends on weather and the climates of the 5 regions of Jeju Island are a little different from one another. So, we investigated the difference between the average capacity factors of the 5 region's renewable plants. WPPs showed a high contribution to the power grid only during winter peak time and SPPs did high contribution to the power grid only during summer peak time, while WPPs showed low contribution to the power grid during summer peak time and SPPs did low contribution to power grid during winter peak time. WPPs and SPPs have intercompensation relation in the aspect to contribute to the power grid.

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Section: Special Issue On Multidisciplinary Innovation In Engineeringmentioning

confidence: 99%

The Capacity Factor of Renewable Energy Power Plants During Electric Power Peak Times in Jeju Island

Otgongerel¹,

Lee²,

Baatarbileg³

2020

Adv. sci. technol. eng. syst. j.

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“…However, distribution network is the terminal end of power system, with weak infrastructure and low reserve capacity. e increasing amount of highly intermittent and variant DPV generation will greatly affect the stability of power systems [3,4]. erefore, the accurate generation forecast of DPV is significant for the scheduling and stable operation of power systems.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

Jia

Hao

et al. 2021

Mathematical Problems in Engineering

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With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

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“…Photovoltaic (PV) generation in a distribution network plays a key role in promoting clean energy production. One of the well-recognized problems of PV generation is the increased power flow at the substation and in the distribution line under the substation [1]. The peak time and amount of power flow depend on the demand and PV generation in the network.…”

Section: Introductionmentioning

confidence: 99%

Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations

Kodaira¹,

Tsukazaki²,

Kure³

et al. 2021

Preprint

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Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs). However, several studies have dealt with geographically distributed PVs in a certain area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. In this case study, the proposed scheme was compared with conventional non-multistep forecasting. The proposed scheme improved the reliability of the PIs and deterministic PV forecasting results through 30 days of continuous operation with real data in Japan.

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Impact of Distributed Photovoltaic Systems on Zone Substation Peak Demand

Cited by 47 publications

References 10 publications

The Capacity Factor of Renewable Energy Power Plants During Electric Power Peak Times in Jeju Island

The Capacity Factor of Renewable Energy Power Plants During Electric Power Peak Times in Jeju Island

Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations

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