Strategic bidding for wind power producers in electricity markets

Sharma, Kailash Chand; Bhakar, Rohit; Tiwari, Harpal

doi:10.1016/j.enconman.2014.05.002

Cited by 59 publications

(43 citation statements)

References 25 publications

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“…It has been seen that several studies [11][12][13][14][15][16][17][18] have used wind speed to generate wind power scenarios. These methods are not so suitable to generate wind power scenarios because it is well known that the wind to power conversion adds substantial errors to the results [19].…”

Section: Introductionmentioning

confidence: 99%

“…These methods are not so suitable to generate wind power scenarios because it is well known that the wind to power conversion adds substantial errors to the results [19]. Additionally, considerable amounts of studies [12][13][14][15][16][17][18] have used an autoregressive moving average model (ARMA) to generate wind power scenarios. The ARMA approach is actually for modeling stationary processes.…”

Section: Introductionmentioning

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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

“…Governments all over the world are trying to increase the use of renewable energy over the last few decades (Sharma et al, 2014). The sector's restructuring process brought out several challenges, requiring the transformation of the conceptual models that previously dominated the power sector (Sioshansi, 2013).…”

Section: Introductionmentioning

confidence: 99%

Enabling Communications in Heterogeneous Multi-Agent Systems: Electricity Markets Ontology

Santos

Pinto

Vale

et al. 2016

ADCAIJ

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“…The main drivers for this evolution are the EMs' restructuring and evolution into regional and continental scale markets, along with the constant changes brought about by the increasing necessity for an adequate integration of renewable energy sources [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Interoperable Approach for Energy Systems Simulation: Electricity Market Participation Ontologies

et al. 2016

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Electricity markets are complex environments with very particular characteristics. Some of the main ones for this complexity are the need for an adequate integration of renewable energy sources and the electricity markets' restructuring process. The growth of simulation tool usage is driven by the need to understand those mechanisms and how the involved players' interactions affect the markets' outcomes. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Although, they share a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow players from different systems to interact in common market environments. This paper proposes the use of ontologies for semantic interoperability between multi-agent platforms in the scope of electricity markets simulation. The achieved results allow the identification of the added value gained by using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from different systems.

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Strategic bidding for wind power producers in electricity markets

Cited by 59 publications

References 25 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

Enabling Communications in Heterogeneous Multi-Agent Systems: Electricity Markets Ontology

An Interoperable Approach for Energy Systems Simulation: Electricity Market Participation Ontologies

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