Short-term operation optimization method of electric power system including large-scale wind power

Patent metadata

Field	Value
Publication number	US-2016169202-A1
Application number	US-201414648663-A
Country	US
Kind code	A1
Filing date	Apr 2, 2014
Priority date	May 3, 2013
Publication date	Jun 16, 2016
Grant date	—

Abstract

The present invention discloses a short-term operation optimization method for a power system including large-scale wind power, comprising modeling the randomness of wind power output, modeling the randomness of the load of electric power system and modeling net load of electric power system. Net load refers that for probability distribution of net load that is too discretized, probability distribution curve of net load is divided into N intervals, the probabilities for each interval are obtained and probability distribution curve of net load is obtained through calculating and weighing each interval. Through calculating randomness of power wind output and standard deviation of load prediction error of the electric power system, net load prediction error of the electric power system is obtained and reasonable coordination is made on the electric power system according to prediction error and prediction amount to better regulate the correlations between randomness, volatility, regionalism, double-circuit peak shaving and load of wind power generation, so as to realize optimization operation of the electric power system.

First claim

1 . A short-term operation optimization method of an electric power system including large-scale wind power characterized in that, the method comprising the following steps:
modelling randomness of wind power output: as prediction error value of wind power output follows a zero-mean normal distribution, relation between standard deviation of prediction error of wind power output is expressed as σ wt =k w ×ŵ t +k 0 , wherein, σ wt is predicted standard deviation of wind power; ŵ t is predicted value of wind power; k w and k 0 refer to prediction error constant; calculating the wind power output according to the above predicted standard deviation of wind power, which is expressed as w t =ŵ t +θ wt , wherein, θ wt is random variable of prediction error of wind power; modeling randomness of the load of the electric power system: as load of the electric power system follows a normal distribution, standard deviation of prediction error of the load is in direct proportion to predicted value of the load, their relation is expressed as: σ dt =k d ×{circumflex over (d)} t , wherein, σ dt is standard deviation of prediction error random variable of the load; {circumflex over (d)}e t is prediction value of the load; k d is prediction error coefficient of the load; after modeling the above wind power output and the load, modeling net load of the electric power system, net load refers to the remaining load when wind power output is taken out from the load of the electric power system, relation of which is expressed as: n t =d t −w t , as wind power output and load of the electric power system are random variables of unrelated normal distribution, then net load follows normal distribution, standard deviation of the net load's prediction error is concluded from the following formula: σ nt =√{square root over (σ dt 2 +σ wt 2 )}, probability distribution of the net load is obtained; over-discretizing the probability distribution of the net load, wherein a net load probability distribution curve is divided into N intervals and the probabilities for each interval are obtained and probability distribution curve of net load is obtained through calculating and weighing each interval.

2 . The short-term operation optimization method of an electric power system including large-scale wind power according to claim 1 characterized in that a rational distribution is made on wind power and thermal power units in the electric power system according to the above probability distribution curve of the net load in the following steps:
step 1: based on statistical analysis of historical data of wind power, obtaining predicted data of wind power output for future 24 hours and obtaining prediction error value of wind power output according to current prediction mode of wind power output;
step 2: considering wind power output as negative load, obtaining the net load curve for one day after overlaying with the load; based on the net load curve, determining startup and shutdown periods of the thermal power unit for one day;
step 3: determining startup and shutdown periods of the thermal power unit for one day and completing seven representative plans according to the above net load probability distribution curve;
step 4: for each plan, dispatching the distribution of the net load in each thermal power unit;
step 5: analyzing dispatching results and obtaining the planned expected value with weighted summation of the results of each plan;
step 6: correcting the unit output plan of next period according to prediction value of the wind power output and the prediction error value of wind power output.

Assignees

• State Grid Corp China
• State Grid Gansu Electric Power Corp
• Gansu Electric Powr Corp Wind Power Technology Ct

Inventors

• Wang Ningbo
• Ma Ming
• Ma Yanhong
• Liu Guangtu
• Zhao Long
• Zhou Qiang
• Wang Dingmei
• Lu Liang
• Zhang Jianmei
• Lv Qingquan

Classifications

• F03D17/00

  Monitoring or testing of wind motors, e.g. diagnostics (testing during commissioning of wind motors F03D13/30) · CPC title

• F03D7/00

  Controlling wind motors  (supplying or distributing electrical power H02J, e.g. arrangements for adjusting, eliminating or compensating reactive power in networks H02J3/18; controlling electric generators H02P, e.g. arrangements for controlling electric generators for the purpose of obtaining a desired output H02P9/00) · CPC title

• F05B2270/1033

  Power (if explicitly mentioned) · CPC title

• F05B2260/821

  Parameter estimation or prediction · CPC title

• F03D9/255

  connected to electrical distribution networks; Arrangements therefor · CPC title