Online dictionary learning probability optimal power flow method under source-charge interaction electric power market

An optimal trend and dictionary learning technology, applied in complex mathematical operations, marketing, instruments, etc., can solve problems that are difficult to meet online calculations

Inactive Publication Date: 2018-12-07
CHINA SOUTHERN POWER GRID COMPANY
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Problems solved by technology

However, the traditional probabilistic optimal power flow method based on Mont

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  • Online dictionary learning probability optimal power flow method under source-charge interaction electric power market
  • Online dictionary learning probability optimal power flow method under source-charge interaction electric power market
  • Online dictionary learning probability optimal power flow method under source-charge interaction electric power market

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specific Embodiment

[0079] This embodiment is deployed based on the 24-hour real-time scheduling that is updated every 5 minutes. Select the net load curve updated every 5 minutes in California, USA, with an interval of 24 hours (288 points) as the expected net load curve (such as Image 6 shown). A typical uncertainty scenario for a day is described using this curve:

[0080] (1) During the period from 1 to 9 hours, the net load curve changes steadily, and the system uncertainty mainly comes from the prediction error of conventional load at this time;

[0081] (2) During the period of 12 to 17 hours, the net load curve changes steadily, and the new energy penetration rate reaches 90%. At this time, the new energy forecast error leads to a large degree of system uncertainty;

[0082] (3) During the time period of 9 to 12 hours and 17 to 24 hours, the change rate of the net load curve is relatively fast, and the uncertainty of generator output is relatively large due to the constraints of genera...

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Abstract

The invention provides an online dictionary learning probability optimal power flow method under a source-charge interaction electric power market. Trainings are performed in a lot of Monte-Carlo simulation sampling samples, a result with the highest occurrence frequency is obtained, so massive repeated calculation of the direct current optimal power flows is effectively avoided. By use of a dictionary learning theory which is widely applied to the signal processing technology, a dictionary is formed according to the theory based on an acquired basic signal group and plenty of signals are characterized by linear combinations of bytes in the dictionary. The objective of the invention is to pay attention to the fact how to acquire conditional probability distribution of system state quantities (such as line power flows, generator dispatching decisions, node boundary electricity price, and blocking states) in the future moment when prediction sequence samples of system parameter random variables such as new energy power generation and node load injection power are known. Compared with a traditional Monte-Carlo simulation-based probability optimal power flow method, online calculationrequirements can be met.

Description

technical field [0001] The invention proposes an online dictionary learning probability optimal power flow method under the source-load interactive power market, which belongs to the innovative technology of large-scale power system economic dispatching algorithm considering intermittent energy access. Background technique [0002] In the "source-load" interactive power market environment, there are many uncertain factors and random disturbances. The forecast errors of new energy and loads, the quotations of power generators and flexible load market participants, and changes in the network structure will cause transaction volume and Sharp fluctuations in real-time electricity prices. These factors make the traditional deterministic model unable to deal with the influence of random characteristics in the "source-load" interactive market environment on transactions. Taking into account the uncertainty characteristics of new energy output, load demand, generator quotation and ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q30/02G06Q50/06G06F17/18
CPCG06F17/18G06Q10/04G06Q30/0283G06Q50/06
Inventor 邓韦斯李鹏周毓敏李智勇林庆标王皓怀吴云亮梁彦杰
Owner CHINA SOUTHERN POWER GRID COMPANY
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