Development and Operational Status of Wind Power Forecasting System

Aoki, Isao; Tanikawa, Ryoichi; Hayasaki, Nobuyuki; Matsumoto, Mitsuhiro; Enomoto, Shigero

doi:10.1541/ieejpes.133.366

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…In PSO, multiple search points called particles are generated in the search area (300 in this study), and the position and velocity vectors of each particle are repeatedly updated to search for the optimal solution. The position and velocity vectors of the i th particle are updated by (7) and (8), respectively, according to prior experience of the i th particle as well as the swarm of all particles.…”

Section: Overall Procedures Of Pso and Proposed Methodsmentioning

confidence: 99%

“…These upper and lower limits are set using ( 5) and ( 6). In ( 5) and ( 6), σ = 0.4, refers to the fact that the forecast error of wind power generation is generally within 40% of the rated output [8] Thus, the forecast errors exceeding 40% of the rated output are excluded from the search range.…”

Section: Pso Formulationmentioning

confidence: 99%

“…This is a shut-down spinning reserve power supply. 8 Under the start/stop conditions determined in 7 , OPF is obtained again with the minimization of the total fuel cost set as the objective function to determine the planned output of each spinning reserve power supply. 7 and 8 are repeated until the total up-spinning reserve value that can be activated exceeds 7% of the total load.…”

Section: A2 Gsfmentioning

confidence: 99%

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Estimation Method for Transmission Line Overloads Due to Renewable Energy Source (RES) Output Forecast Errors in Daily Supply and Demand Planning

Hibi

Kawabe

Masuta

2023

IEEJ Transactions Elec Engng

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We propose a method to estimate transmission‐line overloads due to forecast errors of power output of renewable energy sources. The proposed method involves particle swarm optimization and optimal power flow calculations. The effectiveness of the method is confirmed via numerical simulations using a modified IEEE RTS 24‐bus system model. The results indicate that the proposed method can determine the forecast errors of the renewable power output that causes thermal overload, and further predict the occurrences of transmission‐line overload under generator activation in supply and demand planning. Furthermore, we explain the occurrence mechanism of transmission‐line overload due to forecast errors, which may contribute to improve the daily supply and demand planning. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Overall Procedures Of Pso and Proposed Methodsmentioning

confidence: 99%

Section: Pso Formulationmentioning

confidence: 99%

Section: A2 Gsfmentioning

confidence: 99%

See 1 more Smart Citation

Estimation Method for Transmission Line Overloads Due to Renewable Energy Source (RES) Output Forecast Errors in Daily Supply and Demand Planning

Hibi

Kawabe

Masuta

2023

IEEJ Transactions Elec Engng

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“…The time resolutions of the forecast data and actual output data are 30 and 1 min, respectively. The forecast data are generated by a forecast system explained by Aoki et al [16]. The forecast data for the spot market are generated at 6:00 on each day, and the forecast horizon is 24 h (0:00--24:00).…”

Section: Data To Usementioning

confidence: 99%

Scheduling method of wind power generation for electricity market using state-of-charge transition and forecast error

Kikuchi

Ito

Hayashi

2019

Journal of International Council on Electrical Engineering

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This study focuses on the trade of power from wind power plants (WPPs) at an electricity market. To trade power, operators should produce generation schedules for bids and then supply the same amount of energy as the schedules. However, power from WPPs tends to fluctuate, which complicates scheduling. This paper proposes three scheduling methods with an energy storage system (ESS) to solve this problem. The first method considers state-of-charge (SOC) transition to maintain the appropriate SOC and minimize the imbalance between supplied and scheduled energy. The second and third methods consider SOC transition and forecast errors. The second method uses a linear regression model to estimate forecast errors. The third method adopts a bagged trees model, which is a machine learning method, to directly estimate the adjusted forecast data considering errors. Five patterns of the rated power of the ESS are assumed, and these three methods are simulated on each power. In comparison with the basic method, whose schedules are the same as the forecast, the third method can reduce 84% of the imbalance from the schedules when the rated power of the ESS is the minimum. The proposed methods help develop further correct and practical scheduling methods.

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“…It is still applicable for a longer horizon, for example, 1 week. Based on the scenario-generating method in Section 4.1, we use the historical data from [22,[25][26][27], containing samples at 1-min steps for 260 days and at 1-s steps for 3 days to create K = 6 scenarios for each hour.…”

Section: Data Processingmentioning

confidence: 99%

Spinning reserve quantification considering confidence levels of forecast in systems with high wind and solar power penetration

Nguyen

Yabe

Ito

et al. 2019

IEEJ Transactions Elec Engng

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The increasing penetration of renewable energy sources, featured by intermittent outputs, emphasizes the investigation of the potential effects on frequency deviation in the normal operation and solving the associated problems. This paper proposes a method for assessing the impacts of forecast errors in wind and solar generation outputs on a unit commitment problem. An iterative process is used across different spinning reserve requirements until the hourly average and intrahour variation forecast errors in demand and renewable energy generation are fully compensated at the lowest cost. The process, containing time series simulations, is applied to various confidence levels of the forecasted renewable energy generation so that violations of the frequency limit will not occur in the normal operation. The effectiveness and applicability of the proposed method are well illustrated through a case study on the 2030 power system of the island of Hokkaido in Japan. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Development and Operational Status of Wind Power Forecasting System

Cited by 7 publications

References 2 publications

Estimation Method for Transmission Line Overloads Due to Renewable Energy Source (RES) Output Forecast Errors in Daily Supply and Demand Planning

Estimation Method for Transmission Line Overloads Due to Renewable Energy Source (RES) Output Forecast Errors in Daily Supply and Demand Planning

Scheduling method of wind power generation for electricity market using state-of-charge transition and forecast error

Spinning reserve quantification considering confidence levels of forecast in systems with high wind and solar power penetration

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