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Novel offshore wind plant reactive power optimization method based on mean value variance mapping

A mean-variance, wind farm technology, applied in the field of reactive power optimization of offshore wind farms, which can solve problems such as increasing the risk of premature convergence

Inactive Publication Date: 2020-01-21
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This is an advantage in terms of computational effort (i.e. less problem evaluation), but may increase the risk of premature convergence

Method used

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  • Novel offshore wind plant reactive power optimization method based on mean value variance mapping
  • Novel offshore wind plant reactive power optimization method based on mean value variance mapping
  • Novel offshore wind plant reactive power optimization method based on mean value variance mapping

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Embodiment 1

[0068] Embodiments of the present invention provide a new method for reactive power optimization of offshore wind farms based on mean-variance mapping, such as figure 1 shown, including:

[0069] Step 1: Perform optimization on a given scenario, which includes a set of future operating points within a 24-hour time frame. The predicted wind speed results for the considered time period directly form a neural network (NN) based wind speed forecast and are received as input by the optimization algorithm.

[0070] Step 2: Determine the system objective function, decision variables and related constraints to form the original global optimization problem.

[0071] Step 3: Initial setting of MOMV optimization algorithm, fitness evaluation, archiving of solutions and proposal of new mapping functions to form offspring.

[0072] Step 4: Execute the evolution loop until the specified termination criteria are met.

[0073] In an optional embodiment, the optimization is performed on a g...

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PUM

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Abstract

The invention provides a novel offshore wind plant reactive power optimization method based on mean value variance mapping. The method follows a predictive optimization scheme (i.e., day-ahead, intra-day applications). Predictive optimization is based on the principle of minimizing actual power loss and reducing the number of times of operation of an on-load tap changer (OLTC) in a daily time range (discretization is 24 hours). A new meta-heuristic algorithm mean-variance mapping optimization (MVMO) is utilized to solve the problems of mixed integer properties and limited calculation budget ofthe problem. By introducing a new mapping function, the evolutionary mechanism of the MVMO is enhanced, and the global search capability of the MVMO is improved. Practical investigation on an offshore wind power plant with HVDC connection proves that the MVMO is effective in finding a solution for ensuring minimum loss, minimum influence on OLTC life and optimal power grid specification conformance.

Description

technical field [0001] The invention relates to the technical field of power systems, focusing on offshore wind farms, and in particular to a new reactive power optimization method for offshore wind farms based on mean-variance mapping. Background technique [0002] Offshore wind energy is a competitive energy source and increasingly attractive, with various benefits for power generation. Europe is considered the leader in this field, with 3.02 GW of new offshore capacity connected to the European grid in 2015. According to the wind energy scenario in 2030, the installed capacity of offshore wind power is 66GW. However, the high penetration of wind energy in the energy system presents many technical / operational challenges. Offshore wind power plants are required to provide reactive power support in steady state as well as in AC fault conditions. [0003] Today, transmission system operators (TSOs) in every country have defined grid code requirements to ensure safe, reliab...

Claims

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Application Information

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IPC IPC(8): H02J3/18H02J3/38G06F17/10
CPCG06F17/10H02J3/1878Y02A30/00Y02E10/76Y02E40/30
Inventor 王士柏石岩李广磊辛征孙树敏腾玮程艳王楠王玥娇张兴友张惠张建华
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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