CN112101629A - Pumped storage operation state optimization method in multi-source power system dispatching containing wind storage - Google Patents

Pumped storage operation state optimization method in multi-source power system dispatching containing wind storage Download PDF

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CN112101629A
CN112101629A CN202010831819.8A CN202010831819A CN112101629A CN 112101629 A CN112101629 A CN 112101629A CN 202010831819 A CN202010831819 A CN 202010831819A CN 112101629 A CN112101629 A CN 112101629A
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storage
scheduling
net load
deviation
pumped storage
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王开艳
贾嵘
周承文
王娇
何伟同
颛孙旭
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Xian University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J15/00—Systems for storing electric energy
    • H02J15/003—Systems for storing electric energy in the form of hydraulic energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a pumped storage operation state optimization method in wind storage-containing multi-source power system scheduling, which is implemented according to the following steps: step 1, calculating net load values of the wind power after network access in each scheduling period t
Figure DDA0002638282570000011
Step 2, calculating the mean value of the net load of the whole dispatching cycle
Figure DDA0002638282570000012
Step 3, calculating the standard deviation of the net load of the whole dispatching cycle
Figure DDA0002638282570000013
Step 4, calculating the net load of each time interval
Figure DDA0002638282570000014
And mean value
Figure DDA0002638282570000015
Deviation of (2)
Figure DDA0002638282570000016
Step 5, defining a deviation threshold value according to the rated operation capacity of pumping and storing energy and the degree of deviation of the net load from the mean value; step 6, according to the net load of each time interval
Figure DDA0002638282570000017
Deviation from mean
Figure DDA0002638282570000018
And determining the operation state of the pumped storage in the corresponding time interval. The invention discloses a pumped storage running state optimization method in wind storage-containing multi-source power system scheduling, which solves the problems of pumped storage resource waste and difficulty in simultaneously solving discrete integer variables and continuous variables in a scheduling model in the prior art.

Description

Pumped storage operation state optimization method in multi-source power system dispatching containing wind storage
Technical Field
The invention belongs to the technical field of operation control methods of power systems, and relates to a pumped storage operation state optimization method in wind storage-containing multi-source power system scheduling.
Background
Under the background of current energy transformation, new energy power generation represented by wind power is rapidly developed, however, the wind power has the characteristics of random intermittency and reverse peak regulation, and new challenges are brought to safe and stable operation of a power grid. The energy storage of drawing water is honored as the best peak regulation power of wind-powered electricity generation, and the traditional running mode of energy storage of drawing water is "the peak clipping fills the millet", generates electricity in the peak period of load curve, and the low millet time period draws water, ensures the economic benefits of the energy storage of drawing water under the poor excitation mechanism of peak millet price. However, with the increase of the grid-connected scale of wind power and the existence of multi-source power in the system, the functions of pumping water and storing energy are diversified, and not only the load needs to be subjected to peak clipping and valley filling, but also the stabilizing effect on wind power fluctuation needs to be considered. Therefore, in a multi-source power system containing wind power, "peak load and low-valley load storage" is no longer the best operating state for pumped storage, and the efficiency thereof needs to be further developed.
At present, in the optimized scheduling of a power system with pumped storage, the pumped storage and power generation states of the pumped storage are usually represented by discrete integer variables-1 and 1, and the output of each power supply is usually a continuous variable, so the optimized scheduling of the power system with wind storage is a mixed integer programming problem, and the values of the integer variables and the continuous variables need to be optimized simultaneously, so that the difficulty of model solution is greatly increased.
Therefore, how to fully stimulate the exertion of the pumped storage function and improve the solving efficiency of the discrete integer variable of the pumped storage operation state is a technical problem to be solved urgently in the current multi-source power system scheduling containing wind power and pumped storage.
Disclosure of Invention
The invention aims to provide a pumped storage running state optimization method in multi-source power system scheduling containing wind storage, and solves the problems that pumped storage resources are wasted and the simultaneous solution of discrete integer variables and continuous variables in a scheduling model is difficult in the prior art.
The technical scheme adopted by the invention is that the method for optimizing the pumped storage running state in the multi-source power system scheduling containing wind storage is implemented according to the following steps:
step 1, calculating net load values of the wind power after network access in each scheduling period t
Figure BDA0002638282550000021
T is 1,2, …, T denotes the total number of scheduling periods of the entire scheduling cycle;
step 2, calculating the net load value of each scheduling time interval t according to the step 1
Figure BDA0002638282550000022
Calculating the mean of the payload of the entire scheduling period
Figure BDA0002638282550000023
Step 3, calculated according to step 1
Figure BDA0002638282550000024
And calculated in step 2
Figure BDA0002638282550000025
Calculating the standard deviation of the net load of the whole scheduling period
Figure BDA0002638282550000026
Step 4, calculating the net load of each time interval
Figure BDA0002638282550000027
And mean value
Figure BDA0002638282550000028
Deviation of (2)
Figure BDA0002638282550000029
Step 5, defining a deviation threshold value according to the rated operation capacity of pumping and storing energy and the degree of deviation of the net load from the mean value;
step 6, according to the net load of each time interval
Figure BDA00026382825500000210
Deviation from mean
Figure BDA00026382825500000211
And determining the operation state of the pumped storage in the corresponding time interval.
In step 1
Figure BDA00026382825500000212
The calculation method comprises the following steps:
Figure BDA00026382825500000213
wherein the content of the first and second substances,
Figure BDA00026382825500000214
a payload value representing a scheduling period T, T representing a sequence number of the scheduling period, the total number of scheduling periods of the entire scheduling cycle being denoted by T, T being 1,2, …, T, Pt LA load value representing a scheduling period t; pt WAnd representing the wind power predicted value of the scheduling time interval t.
In step 2
Figure BDA00026382825500000217
The calculation method comprises the following steps:
Figure BDA0002638282550000031
in step 3
Figure BDA0002638282550000032
The calculation method comprises the following steps:
Figure BDA0002638282550000033
net load per time interval in step 4
Figure BDA0002638282550000034
And mean value
Figure BDA0002638282550000035
Deviation of (2)
Figure BDA0002638282550000036
The calculation method comprises the following steps:
Figure BDA0002638282550000037
wherein the content of the first and second substances,
Figure BDA0002638282550000038
representing the deviation of the net load from the mean of the net loads over the t period.
The deviation threshold in step 5 is defined as follows:
Figure BDA0002638282550000039
wherein min (-) represents a function for solving the minimum value element in the vector,
Figure BDA00026382825500000310
for pumped storage rated power, λ is a variable that varies with system size, and λ represents a positive number less than 1.
The step 6 specifically comprises the following steps:
according to the degree of the net load deviation from the mean value in the t period, representing the integer variable u of the pumped storage running statekt，t＝1,2,…,T，uktThe assignment method is as follows:
if it is
Figure BDA00026382825500000311
Then u iskt1, representing that the pumped storage operates in a power generation state in a period t;
if it is
Figure BDA00026382825500000312
Then u isktWhen the time is equal to 0, the pumped storage is in an idle state, and neither power generation nor water pumping is performed in the period t;
if it is
Figure BDA00026382825500000313
Then u isktAnd the pumped storage is operated in a pumped state in the period t as 1.
The invention has the beneficial effects that:
according to the pumped storage running state optimization method in the multi-source power system scheduling containing wind storage, when the deviation of the net load and the mean value of the net load after wind power is connected into the network in a certain period is larger than the set deviation threshold value, pumped storage power generation is arranged, so that the net load in the period is reduced; when the deviation of the net load and the average value is smaller than a set deviation threshold value, arranging pumped storage to pump water, and filling the net load in the time period; when the deviation of the net load and the average value is within the deviation threshold range, the pumped storage is in an idle state, and neither pumping nor generating electricity is performed. The method is adopted to arrange the pumping and power generation running states of the pumped storage, on one hand, the variable assignment of the pumping storage running state is carried out in advance, the independent solution of the discrete integer variable in the dispatching model can be realized, the optimization process of the discrete integer variable is greatly simplified, and the method has universal applicability to the optimized dispatching problem of the power system containing the random intermittent power source and the pumped storage. On the other hand, the net load of each time interval after the wind power is connected to the network can be ensured to approach the average value as much as possible, and the optimization target of stabilizing wind power fluctuation and reducing the influence of randomness is realized. The method can fully excavate the efficiency of various energy storage devices including pumped storage in the dispatching of the power system, furthest reduce the uncertain influence of random intermittent power sources such as wind power or photovoltaic power and the like, provide guarantee for stable operation of stable load curve born by a conventional unit, and is simple and easy to implement.
Drawings
FIG. 1 is a flow chart of a method for optimizing pumped-storage operating conditions in wind-storage-containing multi-source power system scheduling according to the present invention;
FIG. 2 is a graph of load values, wind power predicted values and net loads after wind power is networked in each time period in daily scheduling in the method for optimizing the pumped storage operation state in the multi-source power system scheduling with wind storage according to the invention;
fig. 3 is a pumped storage operation state optimization result diagram based on a test system of the pumped storage operation state optimization method in the wind-storage-containing multi-source power system scheduling of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a pumped storage operation state optimization method in multi-source power system scheduling containing wind storage, the flow of which is shown in figure 1 and is implemented according to the following steps:
step 1, calculating net load after wind power is connected into the network in each scheduling period tLoad value
Figure BDA0002638282550000041
T is 1,2, …, T denotes the total number of scheduling periods of the entire scheduling cycle;
step 2, calculating the net load value of each scheduling time interval t according to the step 1
Figure BDA0002638282550000042
Calculating the mean of the payload of the entire scheduling period
Figure BDA0002638282550000051
Step 3, calculated according to step 1
Figure BDA0002638282550000052
And calculated in step 2
Figure BDA0002638282550000053
Calculating the standard deviation of the net load of the whole scheduling period
Figure BDA0002638282550000054
Step 4, calculating the net load of each time interval
Figure BDA0002638282550000055
And mean value
Figure BDA0002638282550000056
Deviation of (2)
Figure BDA0002638282550000057
Step 5, defining a deviation threshold value according to the rated operation capacity of pumping and storing energy and the degree of deviation of the net load from the mean value;
step 6, according to the net load of each time interval
Figure BDA0002638282550000058
Deviation from mean
Figure BDA0002638282550000059
And determining the operation state of the pumped storage in the corresponding time interval.
In step 1
Figure BDA00026382825500000510
The calculation method comprises the following steps:
Figure BDA00026382825500000511
wherein the content of the first and second substances,
Figure BDA00026382825500000512
the net load value of the scheduling period T is shown, T is the serial number of the scheduling period, the total number of the scheduling periods of the whole scheduling cycle is shown by T, T is 1,2, … and T, for the day scheduling of 24 hours a day, T is 24, P is obtainedt LA load value representing a scheduling period t, wherein in power system scheduling, the load value is usually predicted according to historical load data and serves as a known parameter of the system; pt WAnd representing the wind power predicted value of the scheduling time interval t.
In step 2
Figure BDA00026382825500000513
The calculation method comprises the following steps:
Figure BDA00026382825500000514
in step 3
Figure BDA00026382825500000515
The calculation method comprises the following steps:
Figure BDA00026382825500000516
net load per time interval in step 4
Figure BDA00026382825500000517
And mean value
Figure BDA00026382825500000518
Deviation of (2)
Figure BDA00026382825500000519
The calculation method comprises the following steps:
Figure BDA00026382825500000520
wherein the content of the first and second substances,
Figure BDA00026382825500000521
the deviation of the net load from the mean value of the net loads in the period t is represented, and the degree of deviation of the net loads from the mean value in each period is reflected.
The deviation threshold in step 5 is defined as follows:
Figure BDA0002638282550000061
wherein min (-) represents a function for solving the minimum value element in the vector,
Figure BDA0002638282550000062
and the value of the rated power for pumping and storing the energy is set by a scheduling decision maker according to the overall condition that the net load deviates from the mean value and the rated operation capacity of the pumping and storing the energy.
The step 6 specifically comprises the following steps:
according to the degree of the net load deviation from the mean value in the t period, representing the integer variable u of the pumped storage running statekt，t＝1,2,…,T，uktThe assignment method is as follows:
if it is
Figure BDA0002638282550000063
Then u iskt1, representing that the pumped storage operates in a power generation state in a period t;
if it is
Figure BDA0002638282550000064
Then u isktWhen the time is equal to 0, the pumped storage is in an idle state, and neither power generation nor water pumping is performed in the period t;
if it is
Figure BDA0002638282550000065
Then u isktAnd the pumped storage is operated in a pumped state in the period t as 1.
As shown in fig. 2, the load values, the wind power predicted values and the net load graph after the wind power is networked are obtained for 24 scheduling periods of 1 day. The wind power has the characteristic of reverse peak regulation, and after the wind power is connected into a network, the peak-valley time period of the load is shifted, and the load peak-valley difference is increased. As shown in fig. 3, for the pumped storage operation state value of 24 time intervals obtained by the present invention, the total number of pumped storage pumping time intervals and the total number of power generation time intervals are both 8, and power generation is performed not only in the peak load time interval 20 of the net load, but also in the power generation state in the time intervals 11, 12, 13, and 21 with higher net load; in the same way, the water pumping state is operated in the valley load period 24 of the net load and in the lower period 1-5 of the net load. The method for optimizing the pumped storage running state can fully play the roles of peak clipping and valley filling of pumped storage and stabilizing wind power fluctuation; meanwhile, the independent solution of discrete integer variables in the scheduling model is realized, and the method has good universality for the scheduling of the power system containing the intermittent power source and the energy storage and is simple and easy to realize.

Claims (7)

1. The pumped storage operation state optimization method in the multi-source power system scheduling containing wind storage is characterized by comprising the following steps:
step 1, calculating net load values of the wind power after network access in each scheduling period t
Figure FDA0002638282540000011
T represents the total number of scheduling periods of the whole scheduling cycle;
step 2, calculating the net load value of each scheduling time interval t according to the step 1
Figure FDA0002638282540000012
Calculating the mean of the payload of the entire scheduling period
Figure FDA0002638282540000013
Step 3, calculated according to step 1
Figure FDA0002638282540000014
And calculated in step 2
Figure FDA0002638282540000015
Calculating the standard deviation of the net load of the whole scheduling period
Figure FDA0002638282540000016
Step 4, calculating the net load of each time interval
Figure FDA0002638282540000017
And mean value
Figure FDA0002638282540000018
Deviation of (2)
Figure FDA0002638282540000019
Step 5, defining a deviation threshold value according to the rated operation capacity of pumping and storing energy and the degree of deviation of the net load from the mean value;
step 6, according to the net load of each time interval
Figure FDA00026382825400000110
Deviation from mean
Figure FDA00026382825400000111
And determining the operation state of the pumped storage in the corresponding time interval.
2. The method for optimizing the operation state of pumped storage in dispatching of multi-source power system with wind storage according to claim 1, wherein the step 1 is executed
Figure FDA00026382825400000112
The calculation method comprises the following steps:
Figure FDA00026382825400000113
wherein the content of the first and second substances,
Figure FDA00026382825400000114
a payload value representing a scheduling period T, T representing a sequence number of the scheduling period, the total number of scheduling periods of the entire scheduling cycle being denoted by T, T being 1,2, …, T, Pt LA load value representing a scheduling period t; pt WAnd representing the wind power predicted value of the scheduling time interval t.
3. The method for optimizing the operation state of pumped storage in dispatching of multi-source power system with wind storage according to claim 1, wherein the step 2 is performed
Figure FDA00026382825400000115
The calculation method comprises the following steps:
Figure FDA0002638282540000021
4. the method for optimizing the operation state of pumped storage in dispatching of multi-source power system with wind storage according to claim 1, wherein the step 3 is executed
Figure FDA0002638282540000022
The calculation method comprises the following steps:
Figure FDA0002638282540000023
5. the method for optimizing the operation state of pumped-storage in dispatching of multi-source power system with wind storage according to claim 1, wherein the net load of each time interval in step 4
Figure FDA0002638282540000024
And mean value
Figure FDA0002638282540000025
Deviation of (2)
Figure FDA0002638282540000026
The calculation method comprises the following steps:
Figure FDA0002638282540000027
wherein the content of the first and second substances,
Figure FDA0002638282540000028
representing the deviation of the net load from the mean of the net loads over the t period.
6. The method for optimizing the operating state of the pumped-storage system in the dispatching process of the multi-source power system with wind storage according to claim 1, wherein the deviation threshold in the step 5 is defined as follows:
Figure FDA0002638282540000029
wherein min (-) represents a function for solving the minimum value element in the vector,
Figure FDA00026382825400000210
rated power for pumped storage, λ being a system-dependent ruleA variable that varies modulo, λ represents a positive number less than 1.
7. The pumped storage operation state optimization method in the wind-storage-containing multi-source power system dispatching according to claim 1, wherein the step 6 specifically comprises:
according to the degree of the net load deviation from the mean value in the t period, representing the integer variable u of the pumped storage running statekt，t＝1,2,…,T，uktThe assignment method is as follows:
if it is
Figure FDA00026382825400000211
Then u iskt1, representing that the pumped storage operates in a power generation state in a period t;
if it is
Figure FDA00026382825400000212
Then u isktWhen the time is equal to 0, the pumped storage is in an idle state, and neither power generation nor water pumping is performed in the period t;
if it is
Figure FDA00026382825400000213
Then u isktAnd the pumped storage is operated in a pumped state in the period t as 1.
CN202010831819.8A 2020-08-18 2020-08-18 Pumped storage operation state optimization method in multi-source power system dispatching containing wind storage Pending CN112101629A (en)

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Publication number Priority date Publication date Assignee Title
CN106026184A (en) * 2016-07-31 2016-10-12 三峡大学 Pumped storage power station and wind power plant combination system for power grid peak regulation and optimized scheduling method thereof
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Patent Citations (2)

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Publication number Priority date Publication date Assignee Title
CN106026184A (en) * 2016-07-31 2016-10-12 三峡大学 Pumped storage power station and wind power plant combination system for power grid peak regulation and optimized scheduling method thereof
WO2018059096A1 (en) * 2016-09-30 2018-04-05 国电南瑞科技股份有限公司 Combined decision method for power generation plans of multiple power sources, and storage medium

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Title
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