Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures

Makarov, Yuri V.; Etingov, Pavel; Ma, Jian; Huang, Zhenyu; Subbarao, K.

doi:10.1109/tste.2011.2159254

Cited by 188 publications

(105 citation statements)

References 6 publications

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“…In the literature there is a conflict of selecting a distribution function that the historical error data fits [22,[30][31][32][33]. Our method uses the ECDF approach to model the historical data distribution.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, forecast error distribution is not as complicated as power distribution. Much of the literature makes the simplifying assumption that forecast errors follow a normal distribution [30][31][32], while actual studies [33,34] show that forecast errors do not follow a normal distribution. Tewari et al [32] concluded that the forecasting approach and site effects will change the shape of the distribution.…”

Section: Modeling Forecast Errorsmentioning

confidence: 99%

See 1 more Smart Citation

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Yıldız¹,

Tekin²,

Ganı³

et al. 2017

Sustainability

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During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality.

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Section: Discussionmentioning

confidence: 99%

Section: Modeling Forecast Errorsmentioning

confidence: 99%

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Yıldız¹,

Tekin²,

Ganı³

et al. 2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…Thermal production forecasting is characterized as net load [7], [8] and it is calculated by the differences between forecasted load and the sum of forecasted renewable production. These forecasts, further than spot values, incorporate uncertainty by probabilistic forecasting with the net load resulting from the convolution of the various forecasting probabilistic distributions.…”

Section: Forecasting Methodologymentioning

confidence: 99%

Student engagement with statistical design of experiments by active learning projects

Joao

Silva

2016

Proceedings of the 2nd International Conference on Higher Education Advances

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Abstract-This paper analyzes a real case study based on an islanding power grid, where there is wind power curtailment during the grid operation. This curtailment skews the wind power production database creating a huge challenge to the overall power production forecast. Thus, it is presented a solution which has allowed more accurate forecasts in order to improve the renewable production and reduce the fuel consumption in thermal power plants. The proposed filtering approach demonstrated to be a good solution allowing wind power forecasts with less error and net load forecasts with more accuracy.

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“…A methodology capable of evaluating the impact of wind generation and load uncertainties, as well as unexpected generation outages was developed [23], in which an EMS integration framework was proposed for power system operation, dispatch, and unit commitment. A hybrid power generation system consisting of PV arrays and fuel cells was studied by a model-based optimal approach [24], in which the power generation cost is minimized.…”

Section: Literature Reviewmentioning

confidence: 99%

“…DSM can be studied more in appliance scheduling of household [22] than in scheduling of small-scale hybrid system. Secondly, uncertainties within forecast errors of renewable energy and demand have been studied for large-scale integration of renewable energy [23], but uncertainties at the demand side are not well evaluated. Most related optimal scheduling methods cannot handle complicated cases when hybrid systems experience external disturbances; only a few closed-loop control methods have been designed [7,24].…”

Section: Introductionmentioning

confidence: 99%

Demand side management of photovoltaic-battery hybrid system

2015

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In the electricity market, customers have many choices to reduce electricity cost if they can economically schedule their power consumption. Renewable hybrid system, which can explore solar or wind sources at low cost, is a popular choice for this purpose nowadays. In this paper optimal energy management for a grid-connected photovoltaic-battery hybrid system is proposed to sufficiently explore solar energy and to benefit customers at demand side. The management of power flow aims to minimize electricity cost subject to a number of constraints, such as power balance, solar output and battery capacity. With respect to demand side management, an optimal control method (open loop) is developed to schedule the power flow of hybrid system over 24 hours, and model predictive control is used as a closed-loop method to dispatch the power flow in real-time when uncertain disturbances occur. In these two kinds of applications, optimal energy management solutions can be obtained with great cost savings and robust control performance.

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Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures

Cited by 188 publications

References 6 publications

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Student engagement with statistical design of experiments by active learning projects

Demand side management of photovoltaic-battery hybrid system

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