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CN113452078B - AGC multi-target coordination optimization strategy based on new energy access and water, fire and electricity characteristics - Google Patents

AGC multi-target coordination optimization strategy based on new energy access and water, fire and electricity characteristics Download PDF

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CN113452078B
CN113452078B CN202110619380.7A CN202110619380A CN113452078B CN 113452078 B CN113452078 B CN 113452078B CN 202110619380 A CN202110619380 A CN 202110619380A CN 113452078 B CN113452078 B CN 113452078B
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吴军
韩锐
陈俊锋
邱睿
黄文鑫
郭子辉
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    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention relates to an AGC multi-target coordination optimization strategy based on new energy access and water, fire and electricity characteristics, which can effectively provide decision support for an AGC process of an electric power department. On the basis of analyzing large-scale new energy access power system AGC, three objective functions capable of comprehensively measuring system states are constructed based on the characteristics of each unit; the method adopts a multi-objective evolutionary algorithm based on a third generation non-dominated sorting genetic algorithm (NSGA-III) combined with Pareto theory to carry out optimization calculation on three targets, and solves the problems of conflict among a plurality of targets, non-uniform dimension and the like to a certain extent; the method provided by the invention combines the current actual state of the system, makes a centralized decision in the Pareto solution, is more flexible and is more suitable for a large-scale power system with new energy access.

Description

AGC multi-target coordination optimization strategy based on new energy access and water, fire and electricity characteristics
Technical Field
The invention constructs an AGC multi-target coordination optimization strategy based on new energy access and water, fire and electricity characteristics, and the strategy can effectively provide decision support for the AGC process of the power department.
Background
With the continuous consumption of non-renewable fossil fuels and the large emission of greenhouse gases, people pay more and more attention to how to efficiently utilize renewable clean energy. Numerous countries around the world have developed a great deal of research on new energy. According to the plan, the permeability of new energy in China in the power grid is more than 30% in 2050. Countries such as the european union in the united states also plan to further increase the proportion of new energy in the grid. With the development of new energy, the proportion of new energy in the power grid is continuously improved, and the traditional power grid faces new problems. At present, aiming at large-scale new energy grid connection, research for coordinating each unit to participate in system frequency adjustment is less by combining the characteristics of each unit. Most researches still focus on a new energy converter or a power plant level, and the frequency modulation proportion of each unit is less optimized from the system perspective; the existing research on the coordination problem of new energy in the traditional unit by using an AGC layer is only from the single angle of economy or stability, and the economy and the stability of a complex system comprising the new energy, hydropower and a thermal power unit are difficult to be considered simultaneously. Therefore, the method has very important significance on how to reasonably and comprehensively evaluate the system state on the AGC level, considering the requirements of system safety and economy and comprehensively planning the output of various types of units for a large-scale new energy power grid.
Disclosure of Invention
The invention aims to provide an AGC multi-target coordination optimization strategy based on new energy access and water-fire-electricity characteristics, and solves the problem of how to coordinate new energy and how to participate in system frequency adjustment by a traditional unit when large-scale new energy is connected to a grid.
In order to achieve the above object, the present invention provides an AGC multi-objective coordination optimization strategy based on new energy access and hydro-thermal-electrical characteristics, which is characterized by being based on the following objective functions:
an objective function I: the number of times of the hydroelectric generating set passing through the vibration area is minimum,
Figure BDA0003099143230000021
wherein, N is when the hydroelectric generating set k passes through the vibration regionkIs 1, otherwise is 0; lambda is a secondary frequency modulation scale factor participated by each unit; x is the prediction error percentage; delta PLxActive power corresponding to x% error; h is the number of the hydroelectric generating sets participating in AGC in the system;
the objective function II: the economic efficiency is optimal, and the method has the advantages that,
Figure BDA0003099143230000022
wherein, CiThe cost for the ith thermal power synchronous unit to participate in frequency modulation; cjWind abandon and light abandon punishment cost introduced for improving the utilization rate of new energy (light and wind); pjRReserving power for the new energy power station j, wherein 10% of a predicted value is generally taken, and M, N respectively represent the thermal power participating in AGC and the number of new energy machine sets in the system;
the objective function II I: the standby mode of the system is optimal,
Figure BDA0003099143230000023
the specific optimization method solves the three objective functions based on a multi-objective evolutionary algorithm of a third-generation non-dominated sorting genetic algorithm, and comprises the following specific steps of:
step 1, reading unit parameters, new energy and load prediction data;
step 2, randomly generating a population R0Initializing according to constraint conditions;
step 3, non-dominated sorting, and screening population individuals;
step 4, generating a progeny population through crossing and mutation;
and 5, repeating the initialized steps until the maximum iteration times are reached to obtain the Pareto front surfaces of the three targets.
And 6, according to the current state of the actual system, making a decision in a Pareto solution set to obtain AGC coordination factors of each unit.
In the above method, for the objective functions I and II, include
1) Frequency modulation cost of hydro-power generating unit
For a hydroelectric generating set, when the hydroelectric output is reduced, a water abandoning effect may exist, and the frequency modulation cost is not generally considered when the hydroelectric generating set is adjusted upwards to cope with the reduction of the system frequency. The text mainly considers the problem of how to coordinate the output of each unit to deal with the frequency reduction of the system.
2) Frequency modulation cost of thermal power generating unit
The thermal power cost mainly comprises the coal burning cost and the climbing cost without considering the start and stop of the unit.
Coal burning cost
The coal consumption and the output of the thermal power generating unit have a quadratic relation, as shown in formula (3):
Ci1＝aiPi 2+biPi+ci (1)
Pioutput active power, a, for a conventional unit ii、bi、ciAnd the coefficient is the energy consumption characteristic curve of the conventional unit i.
(ii) cost of climbing
Regarding the climbing cost, the cost function of the thermal power generating unit is related to the climbing speed between two adjacent moments, namely, the variation of the output force in unit time has a linear relation.
Figure BDA0003099143230000031
Wherein gamma is a ramp factor of the thermal power generating unit cost.
Finally, the cost of the secondary frequency modulation optimized medium-voltage generator is composed of the two parts, and can be expressed as follows:
Ci＝Ci1+Ci2 (3)。
in the above method, for the objective function II I, the Rkh、Rig、RjreRespectively as follows:
Rkh(λkhΔPLx)＝Kkh(PkHS-λkhΔPLx) (7)
wherein, PkHSReserve capacity for a hydroelectric generating set k; kkhThe k coefficient of the hydroelectric generating set is reduced along with the increase of the reserve capacity of the generating set;
Rig(λigΔPLx)＝Kig(PiGS-λigΔPLx) (8)
wherein, PiGSFor the standby of a thermal power generating unit iCapacity; kigThe coefficient is the i coefficient of the thermal power generating unit and is reduced along with the increase of the spare capacity of the unit;
Rjre(λjΔPLx)＝Kj1(PjreS-λjΔPLx)+Kj2(λjΔPLx) (9)
wherein, Kj1The coefficient of the new energy generator set j without considering volatility is the same as the thermal power stability coefficient and is reduced along with the increase of the spare capacity of the generator set; kj2The fluctuation coefficient brought by the output predicted value error of the new energy unit j is used for measuring the uncertainty of the new energy primary energy; pjreSThe capacity is reserved for the new energy unit j.
In the above method, the constraint condition includes:
system power balance constraint:
Figure BDA0003099143230000041
wherein, KsIs the system frequency deviation coefficient, and Δ f is the frequency deviation when the system spare capacity is insufficient. When the system spare capacity is sufficient, the following formula can be simplified:
Figure BDA0003099143230000042
output restraint of the thermal power generating unit:
PiGMin＜PiG＜PiGMax (6)
wherein, PiGMin、PiGMaxRespectively is the minimum output power and the maximum output power of the thermal power generating unit i without stopping.
In a high-proportion hydroelectric system, the operation of a hydropower station needs to consider various constraints such as a power grid, a reservoir, a unit and the like, and the output constraint of the hydroelectric unit. The output of each hydroelectric generating set in each time period needs to meet the requirements that the output does not exceed the maximum output value and is not less than the minimum output value of the motor:
PkHMin＜PkH＜PkHMax (7)
wherein, PkHMin、PkHMaxRespectively are the minimum output power and the maximum output power of the hydroelectric generating set k without stopping.
And (3) new energy output constraint:
0＜Pj＜PjMax (8)
wherein, PjMaxAnd the new energy output short-term predicted value is obtained.
And (3) the climbing rate of the thermal power generating unit is restrained:
DiG＜PiG(t+1)-PiG(t)＜UiG (9)
wherein D isiG、UiGThe maximum values of the downward climbing speed and the upward climbing speed of the thermal power generating unit i are respectively.
And (3) restricting the climbing rate of the hydroelectric generating set:
DkH＜PkH(t+1)-PkH(t)＜UkH (10)
wherein D iskH、UkHThe maximum values of the downward climbing speed and the upward climbing speed of the hydroelectric generating set k are respectively.
The output interval of the hydroelectric generating set is also restricted by a vibration area:
a＜PkH＜b or c＜PkH＜d (11)。
has the advantages that: 1. on the basis of analyzing large-scale new energy access power system AGC, three objective functions capable of comprehensively measuring system states are constructed based on the characteristics of each unit; 2. the method adopts a multi-objective evolutionary algorithm based on a third generation non-dominated sorting genetic algorithm (NSGA-III) combined with Pareto theory to carry out optimization calculation on three targets, and solves the problems of conflict among a plurality of targets, non-uniform dimension and the like to a certain extent; 3. the method provided by the invention combines the current actual state of the system, makes a decision in a Pareto solution set, is more flexible and is more suitable for a large-scale power system with new energy access.
Drawings
FIG. 1 is a flow chart of the multi-objective evolutionary algorithm based on the third generation non-dominated sorting genetic algorithm (NSGA-III) provided by the invention.
Fig. 2 is an IEEE 9 node system for new energy access according to an embodiment of the present invention.
Fig. 3 is a three-target Pareto frontier solved by the NSGA-III algorithm provided by the embodiment of the present invention.
Fig. 4 shows three illumination conditions provided by the embodiment of the present invention.
Fig. 5 shows system frequency conditions of three scenes under the photovoltaic participation frequency modulation provided by the embodiment of the invention.
FIG. 6 is a schematic flow chart of the method of the present invention.
Detailed Description
The following provides specific embodiments of the present invention, and further describes the technical scheme of the present invention in detail. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides an AGC unit coordination factor distribution strategy based on a multi-objective optimization method, which is used for carrying out optimization calculation on the AGC unit coordination factors of a system, weakening the adverse effect caused by large-scale new energy grid connection and being beneficial to the stability of a large-scale new energy grid connection system.
Aiming at the secondary frequency modulation process of a system comprising new energy and water and fire units, the characteristics of each unit in the system are comprehensively considered, three objective functions are provided, and the system state is comprehensively measured;
in the embodiment of the present invention, the objective function I: the hydroelectric generating set has the least times of crossing the vibration region:
Figure BDA0003099143230000061
wherein, N is when the hydroelectric generating set k passes through the vibration regionkIs 1, otherwise is 0; lambda is a secondary frequency modulation scale factor participated by each unit; x is the prediction error percentage; delta PLxActive power corresponding to x% error; h is the number of hydroelectric generating sets participating in AGC in the system;
in the embodiment of the present invention, the objective function ii: the economic efficiency is optimal:
Figure BDA0003099143230000062
wherein, CiThe cost for the ith thermal power synchronous unit to participate in frequency modulation; cjWind abandon and light abandon punishment cost introduced for improving the utilization rate of new energy (light and wind); pjRReserving power for the new energy power station j, wherein 10% of a predicted value is generally taken, and M, N respectively represent the thermal power participating in AGC and the number of new energy machine sets in the system;
1) frequency modulation cost of hydro-power generating unit
For the hydroelectric generating set, when the hydroelectric output is reduced, a water-abandoning effect may exist, and the frequency modulation cost is not generally considered when the hydroelectric generating set is adjusted upwards in response to the reduction of the system frequency. The text mainly considers the problem of how to coordinate the output of each unit to deal with the frequency reduction of the system.
2) Frequency modulation cost of thermal power generating unit
The thermal power cost mainly comprises the coal burning cost and the climbing cost without considering the start and stop of the unit.
Coal burning cost
The coal consumption and the output of the thermal power generating unit have a quadratic relation, as shown in formula (3):
Ci1＝aiPi 2+biPi+ci (12)
Piactive power is output for a conventional unit i, ai、bi、ciAnd the coefficient is the energy consumption characteristic curve of the conventional unit i.
(ii) cost of climbing
Regarding the climbing cost, the cost function of the thermal power generating unit is related to the climbing speed between two adjacent moments, namely, the variation of the output force in unit time has a linear relation.
Figure BDA0003099143230000071
Wherein gamma is a ramp factor of the thermal power generating unit cost.
Finally, the cost of the secondary frequency modulation optimized medium-voltage generator is composed of the two parts, and can be expressed as follows:
Ci＝Ci1+Ci2 (14)
3) new energy frequency modulation cost
The new energy frequency modulation cost is mainly caused by the fact that wind and light are abandoned due to the fact that frequency modulation is not reasonably arranged after the new energy is subjected to active load shedding. The difference value of the new energy load shedding power and the participating frequency modulation power is used for representing
In the embodiment of the present invention, the objective function ii: system standby mode optimization
Figure BDA0003099143230000072
Wherein R iskh、Rig、RjreRespectively representing related expressions of hydropower, thermal power and new energy power generation;
further, R iskh、Rig、RjreRespectively as follows:
Rkh(λkhΔPLx)＝Kkh(PkHS-λkhΔPLx) (7)
wherein, PkHSReserve capacity for hydroelectric generating set k; kkhThe k coefficient of the hydroelectric generating set is reduced along with the increase of the reserve capacity of the generating set;
Rig(λigΔPLx)＝Kig(PiGS-λigΔPLx) (8)
wherein, PiGSSpare capacity for a thermal power generating unit i; kigThe coefficient is the i coefficient of the thermal power generating unit and is reduced along with the increase of the spare capacity of the unit;
Rjre(λjΔPLx)＝Kj1(PjreS-λjΔPLx)+Kj2(λjΔPLx) (9)
wherein, Kj1The coefficient of the new energy generator set j without considering volatility is the same as the thermal power stability coefficient and is reduced along with the increase of the spare capacity of the generator set; kj2The fluctuation coefficient brought by the output predicted value error of the new energy unit j is used for measuring the uncertainty of the primary energy of the new energy; pjreSThe capacity is reserved for the new energy unit j.
On the basis of the three optimization targets, considering the constraint conditions of the system, further combining with a Pareto theory, and performing optimization calculation on the three objective functions through a multi-objective evolutionary algorithm based on a third generation non-dominated sorting genetic algorithm (NSGA-III);
further, the constraint conditions are as follows:
system power balance constraint:
Figure BDA0003099143230000081
wherein, KsIs the system frequency deviation coefficient, and Δ f is the frequency deviation when the system spare capacity is insufficient. When the system spare capacity is sufficient, the following formula can be simplified:
Figure BDA0003099143230000082
output restraint of the thermal power generating unit:
PiGMin＜PiG＜PiGMax (17)
wherein, PiGMin、PiGMaxRespectively a minimum output power and a maximum output power of the thermal power generating unit i without stopping.
In a high-proportion hydroelectric system, the operation of a hydropower station needs to consider various constraints such as a power grid, a reservoir, a unit and the like, and the output constraint of the hydroelectric unit. The output of each hydroelectric generating set in each time period needs to meet the requirement that the output is not more than the maximum output value and not less than the minimum output value of the motor:
PkHMin＜PkH＜PkHMax (18)
wherein, PkHMin、PkHMaxRespectively are the minimum output power and the maximum output power of the hydroelectric generating set k without stopping.
And (3) new energy output constraint:
0＜Pj＜PjMax (19)
wherein, PjMaxAnd the new energy output short-term predicted value is obtained.
And (3) the climbing rate of the thermal power generating unit is restrained:
DiG＜PiG(t+1)-PiG(t)＜UiG (20)
wherein D isiG、UiGThe maximum values of the downward climbing speed and the upward climbing speed of the thermal power generating unit i are respectively.
And (3) restricting the climbing rate of the hydroelectric generating set:
DkH＜PkH(t+1)-PkH(t)＜UkH (21)
wherein D iskH、UkHThe maximum values of the downward climbing speed and the upward climbing speed of the hydroelectric generating set k are respectively.
The output interval of the hydroelectric generating set is also restricted by a vibration area:
a＜PkH＜b or c＜PkH＜d (22)
if multiple vibration zones are present, the constraint equations are similar to those described above.
Further, the main steps of the third generation non-dominated sorting genetic algorithm are as follows:
reading unit parameters, new energy and load prediction data;
randomly generating a population R0Initializing according to the constraint condition;
non-dominant sorting, screening population individuals;
generating a progeny population through crossing and mutation;
repeating the initialized steps until the maximum iteration times is reached;
further, obtaining Pareto front surfaces of the three targets according to an iteration result;
further, according to the actual situation of the system, in the Pareto solution set, a desired solution is obtained.
And further, obtaining AGC coordination factors of each unit of the system.
In the established IEEE 9 node system, optimal calculation is carried out on AGC coordination factors, the calculation result is shown in figure 3, and decision is carried out by combining three conditions shown in figure 4, and the decision result is shown in a chart 1, a table 2 and a table 3.
Scene 1 illumination intensity variation trend: and (4) increasing. The optimization decision results are shown in table 1.
Figure BDA0003099143230000101
Table 1 scenario 1 optimization results
Scene 2 illumination intensity variation trend: and is not changed. The optimization decision results are shown in table 2.
Figure BDA0003099143230000102
Table 2 scenario 2 optimization results
Scene 3 illumination intensity variation trend: and decreases. The optimization decision results are shown in table 3.
Figure BDA0003099143230000103
Table 3 scenario 3 optimization results
Further, the optimization result is substituted into the simulation, and a system dynamic frequency diagram as shown in fig. 5 can be obtained;
further, the economics of Table 4 were obtained based on the optimization results.
Figure BDA0003099143230000104
TABLE 4 comparison of three scenarios
The method provided by the invention can be found to effectively coordinate the power distribution of new energy and traditional energy during secondary frequency modulation, and ensure the economy and reliability of the secondary frequency modulation stage. The method has certain guiding significance on how to coordinate new energy to participate in system frequency adjustment when large-scale new energy is connected to the grid.

Claims (3)

1. The AGC multi-objective coordination optimization method based on new energy access and water, fire and electricity characteristics is characterized by being based on the following objective functions:
the first objective function is as follows: the number of times of crossing the vibration area by the hydroelectric generating set is minimum,
Figure FDA0003585737820000011
wherein, N is when the hydroelectric generating set k passes through the vibration regionkIs 1, otherwise is 0; lambda is a secondary frequency modulation scale factor participated by each unit; x is the prediction error percentage; delta PLxActive power corresponding to x% error; h is the number of hydroelectric generating sets participating in AGC in the system;
and a second objective function: the economic efficiency is optimal, and the method has the advantages that,
Figure FDA0003585737820000012
wherein, CiThe cost for the ith thermal power synchronous unit to participate in frequency modulation; ci、CjWind and light abandon punishment cost introduced for improving the utilization rate of new energy; pjRReserving power for the new energy power station j, wherein 10% of a predicted value is generally taken, and M, N respectively represent the thermal power participating in AGC and the number of new energy machine sets in the system;
an objective function III: the standby mode of the system is optimal,
Figure FDA0003585737820000013
the specific optimization method solves the three objective functions based on a multi-objective evolutionary algorithm of a third-generation non-dominated sorting genetic algorithm, and comprises the following specific steps of:
step 1, reading unit parameters, new energy and load prediction data;
step 2, randomly generating a population R0Initializing according to the constraint condition;
step 3, non-dominant sorting, and screening population individuals;
step 4, generating a progeny population through crossing and mutation;
step 5, repeating the initialized steps until the maximum iteration times are reached to obtain a Pareto front surface of the three targets;
step 6, according to the current state of the actual system, making a decision in a Pareto solution set to obtain AGC coordination factors of each unit;
Rkh、Rig、Rjrerespectively as follows:
Rkh(λkhΔPLx)＝Kkh(PkHS-λkhΔPLx) (7)
wherein, PkHSReserve capacity for a hydroelectric generating set k; kkhThe k coefficient of the hydroelectric generating set is reduced along with the increase of the reserve capacity of the generating set;
Rig(λigΔPLx)＝Kig(PiGS-λigΔPLx) (8)
wherein, PiGSThe backup capacity is set for the thermal power generating unit i; kigThe coefficient is the i coefficient of the thermal power generating unit and is reduced along with the increase of the spare capacity of the unit;
Rjre(λjΔPLx)＝Kj1(PjreS-λjΔPLx)+Kj2(λjΔPLx) (9)
wherein, Kj1The coefficient of the new energy generator set j without considering volatility is the same as the thermal power stability coefficient and is reduced along with the increase of the spare capacity of the generator set; kj2The fluctuation coefficient brought by the output predicted value error of the new energy unit j is used for measuring the uncertainty of the primary energy of the new energy; pjreSThe capacity is reserved for the new energy unit j.
2. The method of claim 1, wherein: for the objective functions one and two, including
1) Frequency modulation cost of hydro-power generating unit
For a hydroelectric generating set, when the hydroelectric output is reduced, a water abandoning effect may exist, and the frequency modulation cost is usually not considered when the hydroelectric generating set is adjusted up to respond to the reduction of the system frequency; the problem of how to coordinate the output of each unit to cope with the system frequency reduction is mainly considered;
2) frequency modulation cost of thermal power generating unit
The starting and stopping of the unit are not considered, and the thermal power cost mainly comprises the coal burning cost and the climbing cost;
coal burning cost
The coal consumption and the output of the thermal power generating unit have a quadratic relation, as shown in formula (3):
Ci1＝aiPi 2+biPi+ci (1)
Pioutput active power, a, for a conventional unit ii、bi、ciI, the coefficient of the energy consumption characteristic curve of the conventional unit;
(ii) cost of climbing
Regarding the climbing cost, the cost function of the thermal power generating unit is related to the climbing speed between two adjacent moments, namely, the variable quantity of the output force in unit time has a linear relation;
Figure FDA0003585737820000021
wherein gamma is a ramp factor of the cost of the thermal power generating unit;
finally, the cost of the secondary frequency modulation optimized medium-voltage generator is composed of the two parts, and can be expressed as follows:
Ci＝Ci1+Ci2 (3)。
3. the method of claim 1, wherein: the constraint conditions include:
system power balance constraint:
Figure FDA0003585737820000022
wherein, KsIs the system frequency deviation coefficient, Δ f is the frequency deviation when the system spare capacity is insufficient; when the system spare capacity is sufficient, the following formula can be simplified:
Figure FDA0003585737820000031
output restraint of the thermal power generating unit:
PiGMin＜PiG＜PiGMax (6)
wherein, PiGMin、PiGMaxRespectively setting the minimum output power and the maximum output power of the thermal power generating unit i without stopping;
in a high-proportion hydroelectric system, the operation of a hydropower station needs to consider various constraints such as a power grid, a reservoir, a unit and the like, and the output constraint of the hydroelectric generating unit; the output of each hydroelectric generating set in each time period needs to meet the requirement that the output is not more than the maximum output value and not less than the minimum output value of the motor:
PkHMin＜PkH＜PkHMax (7)
wherein, PkHMin、PkHMaxRespectively obtaining the minimum output power and the maximum output power of the hydroelectric generating set k without stopping;
and (3) new energy output constraint:
0＜Pj＜PjMax (8)
wherein, PjMaxA short-term predicted value of new energy output is obtained;
and (3) the climbing rate of the thermal power generating unit is restrained:
DiG＜PiG(t+1)-PiG(t)＜UiG (9)
wherein D isiG、UiGMaximum values of downward climbing speed and upward climbing speed of the thermal power generating unit i are respectively set;
and (3) restricting the climbing rate of the hydroelectric generating set:
DkH＜PkH(t+1)-PkH(t)＜UkH (10)
wherein D iskH、UkHMaximum values of the downward climbing speed and the upward climbing speed of the hydroelectric generating set k are respectively;
the output interval of the hydroelectric generating set is also restricted by a vibration area:
a＜PkH＜b or c＜PkH＜d (11)。
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