CN115882480B - Energy storage system optimization control method and system considering power grid frequency and voltage support - Google Patents

Energy storage system optimization control method and system considering power grid frequency and voltage support Download PDF

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CN115882480B
CN115882480B CN202310221747.9A CN202310221747A CN115882480B CN 115882480 B CN115882480 B CN 115882480B CN 202310221747 A CN202310221747 A CN 202310221747A CN 115882480 B CN115882480 B CN 115882480B
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energy storage
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storage system
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CN115882480A (en
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熊俊杰
赵伟哲
熊健豪
匡德兴
吴康
肖戎
周宇
支妍力
朱志杰
陈拓新
李侣
杨本星
马速良
蒋原
张远来
温志明
晏斐
晏欢
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Tellhow Software Co ltd
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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Abstract

The invention discloses an energy storage system optimization control method and system considering power grid frequency and voltage support, comprising the following steps: firstly, establishing a multi-objective function and constraint conditions under the participation of an energy storage system according to the technical requirements of power grid frequency and voltage and a regulation mechanism; then, inputting the predicted power change of the power system and the state of the energy storage system in a future period of time into the model; and finally, forming optimal active and reactive power output of the energy storage system by using an alternating iterative optimization idea, and finishing effective support of the frequency and the voltage of the power grid. By modeling and optimizing and solving the frequency and the voltage of the power grid actively supported by the energy storage system, the comprehensive requirements of the frequency and the voltage of the power grid are effectively considered under the condition of considering the actual state of the energy storage system, the comprehensive service capability of the energy storage system is exerted to the greatest extent, and the electric auxiliary service level of the energy storage system is improved.

Description

Energy storage system optimization control method and system considering power grid frequency and voltage support
Technical Field
The invention belongs to the technical field of energy storage systems, and particularly relates to an energy storage system optimization control method and system considering power grid frequency and voltage support.
Background
As new high-proportion new energy and high-proportion power electronic equipment in new power systems are increasingly deepened, the safety and reliability of grid operation face serious challenges. The problem of voltage fluctuation under the unbalance of active power of the power grid is more serious due to the randomness of the high-proportion new energy source and the unbalance of active power of the power grid. The energy storage system used as flexible and controllable resources has the functions of improving the electric energy quality, actively supporting tide, dynamically compensating reactive power and the like, and can reduce the influence by actively and effectively controlling, thereby greatly improving the operation safety of a power grid. How to mine the application potential of an energy storage system and reasonably maximize the service capability of the energy storage system has become a hot topic of application research of the energy storage system.
At present, expert scholars at home and abroad develop a great deal of researches aiming at providing specific single electric auxiliary service for an energy storage system at a power supply side, a power grid side and a user side, the energy storage system control and planning technology surrounding auxiliary service requirements such as new energy consumption, electric power peak regulation, voltage regulation, frequency modulation and the like is mature day by day, and the control problem of a multi-target energy storage system facing to a plurality of electric power service main bodies and a plurality of electric power service requirements is to be perfected. Meanwhile, along with the construction of a novel power system, new energy and an energy storage system start to develop from the traditional grid following type to the grid constructing type. This means that energy storage system control needs to be further advanced towards power service initiative and power service compatibility.
Disclosure of Invention
The invention provides an energy storage system optimization control method and system considering power grid frequency and voltage support, which are used for solving the technical problem that high-proportion new energy and distributed power supply access affect power grid frequency and voltage fluctuation.
In a first aspect, the present invention provides an energy storage system optimization control method taking into account grid frequency and voltage support, comprising: according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed; acquiring a predicted power value on each node of the power system in a future T period; initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period; inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value; judging whether the current iteration number reaches the maximum iteration number or not; if not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value; and if the active power output sequence error between two adjacent iterations is not greater than the first set threshold value and the reactive power output sequence error is not greater than the second set threshold value, acquiring the active power and the reactive power of the energy storage system optimal control at the current moment, and entering the optimal control at the next moment.
In a second aspect, the present invention provides an energy storage system optimization control system accounting for grid frequency and voltage support, comprising: the construction module is configured to construct a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system; the power system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is configured to acquire a predicted power value on each node of the power system in a future T period; the initialization module is configured to initialize a reactive power output sequence and a maximum iteration number of the energy storage system in a future T period; the calculation module is configured to input the reactive power output sequence into the first model, calculate and obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, input the optimal output active power sequence into the second model, and calculate and obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value; the first judging module is configured to judge whether the current iteration number reaches the maximum iteration number or not; the second judging module is configured to judge whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value if the active power output sequence error is not reached; and the control module is configured to acquire the active power and the reactive power of the energy storage system optimal control at the current moment and enter the optimal control at the next moment if the active power output sequence error between two adjacent iterations is not greater than a first set threshold value and the reactive power output sequence error is not greater than a second set threshold value.
In a third aspect, there is provided an electronic device, comprising: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the grid frequency and voltage support taking into account the energy storage system optimization control method of any of the embodiments of the present invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, causes the processor to perform the steps of the grid frequency and voltage support taking into account the energy storage system optimization control method of any of the embodiments of the present invention.
The energy storage system optimization control method and system considering the power grid frequency and voltage support has the following advantages:
the unified optimization model for power grid frequency modulation and voltage regulation is formed through the simultaneous energy storage system frequency modulation and voltage regulation mathematical model, an optimization basis is provided for energy storage system control, the capability of the energy storage system for simultaneously outputting active power and reactive power is reflected, the model prediction control thought is combined, and the power auxiliary service capability of the energy storage system can be exerted to the greatest extent in an optimal control process considering future power supply power change and energy storage system state constraint is formed; the coupling effect of active output in the frequency modulation process and reactive output in the voltage regulation process of the energy storage system is fully considered, the optimization process of alternating and iterating active output power and reactive output power is utilized, the optimal control of the energy storage system which takes the electric power frequency modulation and voltage regulation requirements into consideration is effectively formed, the complexity of model optimization is reduced, the optimal control efficiency is improved, and the method has important significance in actively supporting the frequency and voltage service of the power grid of the energy storage system.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a 33 node power system according to one embodiment of the present invention;
FIG. 2 is a flow chart of an energy storage system optimization control method taking into account grid frequency and voltage support according to an embodiment of the present invention;
FIG. 3 is a block diagram of an energy storage system optimization control system that accounts for grid frequency and voltage support according to one embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Taking the 33-node power network structure shown in fig. 1 as an example, the heat-engine plant is configured at a node 1, 2 photovoltaic power stations are connected at a node 9 and a node 16, 2 wind power stations are connected at a node 22 and a node 33, 1 energy storage system is connected at a node 6, a load user is at each node (such as a node 4 and a node 11), and the photovoltaic power stations and the wind power stations only output active power and do not perform virtual inertia control. The iterative optimization solving flow for realizing the energy storage system taking the frequency and the voltage support of the power grid into consideration by applying the technology is shown in figure 2. The energy storage system optimization control method considering the power grid frequency and voltage support specifically comprises the following steps:
step S101, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system.
In this embodiment, the first model includes an active power constraint condition of a power system frequency modulation control process and a frequency modulation control objective function under the active power constraint condition, and the second model includes an operation constraint condition of the power system voltage modulation control process and a voltage modulation control objective function under the operation constraint condition.
The active power constraint conditions include:
differential equation of system frequency change rate and active power change quantity, system equivalent inertia constraint, thermal power unit active power change quantity constraint, photovoltaic power station active power change quantity constraint, wind power plant active power change quantity constraint, energy storage system active power change quantity constraint, energy storage energy state constraint, frequency maximum deviation, frequency deviation change rate constraint, power supply rated power constraint, thermal power unit climbing rate constraint and energy storage energy state limit constraint, wherein the specific calculation formula is as follows:
Figure SMS_1
，
in the method, in the process of the invention,
Figure SMS_3
rated frequency for the power grid, < >>
Figure SMS_10
For the system equivalent inertia->
Figure SMS_11
For system capacity>
Figure SMS_6
For the variation of the system frequency at time t, < >>
Figure SMS_7
Is the equivalent damping coefficient of the system->
Figure SMS_8
For the active power variable quantity of the thermal power unit of the ith power system node at the moment t,/>
Figure SMS_9
The active power variation of the photovoltaic power station at the moment t is the ith power system node,
Figure SMS_2
for the active power variation of the wind power plant of the ith power system node at the moment t, +.>
Figure SMS_4
For the active power variation of the energy storage system of the ith power system node at the time t,/for the power system node>
Figure SMS_5
The active power variation of the load user at the moment t is the ith power system node;
Figure SMS_12
，
In the method, in the process of the invention,
Figure SMS_14
is inertia coefficient>
Figure SMS_17
Inertia coefficient of thermal power generating unit on ith power system node, < ->
Figure SMS_18
Rated power of thermal power generating unit is added to ith power system node,/->
Figure SMS_15
The inertia coefficient of the photovoltaic power station on the ith power system node,
Figure SMS_19
rated power of photovoltaic power station on ith power system node, < >>
Figure SMS_20
For the inertia coefficient of the wind farm at the ith power system node, +.>
Figure SMS_21
Rated power of wind farm on ith power system node, +.>
Figure SMS_13
For the inertia coefficient of the energy storage system at the ith power system node,/>
Figure SMS_16
Rated power of the energy storage system on the ith power system node;
Figure SMS_22
，
in the method, in the process of the invention,
Figure SMS_24
for the system frequency at time t +.>
Figure SMS_31
For the system frequency at the initial moment +.>
Figure SMS_32
For the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure SMS_25
For the active power of the thermal power unit of the ith power system node at the initial moment, +.>
Figure SMS_26
Light at time t for the ith power system nodeActive power of a photovoltaic power station, +.>
Figure SMS_28
For the active power of the photovoltaic power station of the ith power system node at the initial moment, +.>
Figure SMS_30
For the active power of the wind farm at time t of the ith power system node, +.>
Figure SMS_23
For the active power of the wind farm at the initial moment of the ith power system node, +. >
Figure SMS_27
Active power of energy storage system at t moment for ith power system node, +.>
Figure SMS_29
The active power of the energy storage system at the initial moment is the ith power system node;
Figure SMS_33
，
in the method, in the process of the invention,
Figure SMS_34
energy storage system on ith power system node>
Figure SMS_35
The state of energy at the moment in time,
Figure SMS_36
for the energy state of the energy storage system t moment on the ith power system node, +.>
Figure SMS_37
Charging efficiency for energy storage system->
Figure SMS_38
For time interval +.>
Figure SMS_39
For the capacity of the energy storage system on the ith power system node, for>
Figure SMS_40
Discharging efficiency of the energy storage system;
Figure SMS_41
，
in the method, in the process of the invention,
Figure SMS_42
allow threshold for frequency deviation, +.>
Figure SMS_43
Allow threshold for rate of frequency change,>
Figure SMS_44
maximum output active power of photovoltaic power station at t moment on ith power system node,/>
Figure SMS_45
The maximum output active power of the wind farm at the time t on the ith power system node, and (2)>
Figure SMS_46
Reactive power of the energy storage system at the t moment on the ith power system node;
Figure SMS_47
，
in the method, in the process of the invention,
Figure SMS_48
is the ith power system node is->
Figure SMS_49
Active power of thermal power generating unit at moment +.>
Figure SMS_50
A climbing threshold of the thermal power generating unit is set for the ith power system node;
Figure SMS_51
，
in the method, in the process of the invention,
Figure SMS_52
is the lower limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure SMS_53
The upper limit value of the energy state of the energy storage system on the ith power system node;
The frequency modulation control objective function under the active power constraint condition specifically comprises the following steps: the active power of the thermal power generating unit is minimized, the active power output of new energy is maximized, and the expression is:
Figure SMS_54
，
in the method, in the process of the invention,
Figure SMS_55
for the number of nodes of the power system, < > for>
Figure SMS_56
For prediction step size +.>
Figure SMS_57
Is a time interval.
Wherein the operating constraints include:
active power constraint of a power system, reactive power balance constraint of the power system, node voltage and power constraint, rated power constraint of a thermal power generating unit, maximum active adjustment constraint, maximum reactive adjustment constraint, climbing speed constraint, active power output constraint of a photovoltaic power station, active power output constraint of a wind power plant, energy storage energy balance constraint, energy state limit and rated power constraint;
Figure SMS_58
，
in the method, in the process of the invention,
Figure SMS_60
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure SMS_62
Active power of energy storage system at t moment for ith power system node, +.>
Figure SMS_64
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure SMS_66
For the active power of the wind farm at time t of the ith power system node, +.>
Figure SMS_68
For the ith power system node to load the active power of the user at time t,/for the power system node>
Figure SMS_69
For the active power output by the ith power system node at time t to the jth power system node,/ >
Figure SMS_71
For the current output by the ith power system node at time t to the jth power system node,/>
Figure SMS_61
For the resistance between node j and node i, +.>
Figure SMS_63
Reactive power of thermal power unit at t moment for ith power system node, +.>
Figure SMS_65
Reactive power of the energy storage system at time t for the ith power system node, < >>
Figure SMS_67
Reactive power for the i-th power system node to load the subscriber at time t,/for the i-th power system node>
Figure SMS_59
Reactive power output by the ith power system node to the jth power system node at time t,/>
Figure SMS_70
Impedance between node j and node i;
Figure SMS_72
，
in the method, in the process of the invention,
Figure SMS_73
for node i voltage at time t +.>
Figure SMS_74
The voltage of the node j at the moment t;
Figure SMS_75
，
in the method, in the process of the invention,
Figure SMS_77
energy storage system on ith power system node>
Figure SMS_79
Energy state at time->
Figure SMS_81
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure SMS_78
Charging efficiency for energy storage system->
Figure SMS_80
Active power of energy storage system at t moment for ith power system node, +.>
Figure SMS_82
For time interval +.>
Figure SMS_83
For the capacity of the energy storage system on the ith power system node, for>
Figure SMS_76
Discharging efficiency of the energy storage system;
Figure SMS_84
，
in the method, in the process of the invention,
Figure SMS_93
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure SMS_87
Reactive power of thermal power unit at t moment for ith power system node, +. >
Figure SMS_90
The i power system node is powered on with the rated power of the thermal power generating unit,
Figure SMS_98
minimum output active power of thermal power generating unit is applied to ith power system node, +.>
Figure SMS_100
Maximum output active power of thermal power unit on ith power system node, < >>
Figure SMS_101
Is at +.>
Figure SMS_103
Active power of thermal power generating unit at moment +.>
Figure SMS_95
Climbing threshold value of thermal power generating unit is set for ith power system node, < ->
Figure SMS_96
Minimum output reactive power of thermal power generating unit is applied to ith power system node, +.>
Figure SMS_85
Maximum output reactive power of thermal power unit on ith power system node, < >>
Figure SMS_91
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure SMS_88
Maximum output active power of photovoltaic power station at t moment on ith power system node,/>
Figure SMS_94
For the active power of the wind farm at time t of the ith power system node, +.>
Figure SMS_102
Maximum output active power of wind farm at t moment on ith power system node, +.>
Figure SMS_104
For the lower voltage limit of node i,
Figure SMS_89
for node i voltage upper limit, < >>
Figure SMS_92
For maximum current through the branch, < > for>
Figure SMS_97
Is the lower limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure SMS_99
Is the upper limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure SMS_86
Rated power of the energy storage system on the ith power system node;
The voltage regulation control objective function under the operation constraint condition is specifically: and the square sum of the active power and the reactive power output by the energy storage system is minimized in the prediction period, namely the output of the energy storage system is minimized, and the expression is:
Figure SMS_105
，
in the method, in the process of the invention,
Figure SMS_106
for the number of nodes of the power system, < > for>
Figure SMS_107
For prediction step size +.>
Figure SMS_108
For time interval +.>
Figure SMS_109
For the energy storage system on the ith power system node at (t 0 +kΔt) active power output at the moment, +.>
Figure SMS_110
For the energy storage system on the ith power system node at (t 0 Reactive power output at +kΔt), t 0 Indicating the current time.
Step S102, obtaining the predicted power value of each node of the power system in the future T period.
In the embodiment, according to a large amount of historical data of a load user and a large amount of historical data of a photovoltaic power station and a wind power station, a regression model such as a neural network, a support vector machine and the like is utilized to establish an active and reactive short-term prediction model of the load user and an active power short-term prediction model of the photovoltaic power station and the wind power station;
based on the data of the load user node at the current t moment, the data of the photovoltaic power station and the data of the wind power plant node, obtaining according to a short-term prediction modelActive power of load user in future T period
Figure SMS_111
Reactive power->
Figure SMS_112
Maximum active power of photovoltaic power station
Figure SMS_113
Maximum active power of wind farm
Figure SMS_114
。
Step S103, initializing a reactive power output sequence and a maximum iteration number of the energy storage system in a future T period.
Step S104, the reactive power output sequence is input into the first model, an optimal output active power sequence of the energy storage system for frequency modulation is calculated based on the predicted power value, the optimal output active power sequence is input into the second model, and an optimal output reactive power sequence of the energy storage system for voltage regulation is calculated based on the predicted power value.
In this embodiment, substituting the reactive power output sequence of the energy storage system in the future T period into the first model, so as to update the active power constraint in the first model, where the expression of the updated active power constraint is:
Figure SMS_115
，
in the method, in the process of the invention,
Figure SMS_116
active power of energy storage system at t moment for ith power system node, +.>
Figure SMS_117
Reactive power of energy storage system at t moment for ith power system node in the nth iteration,/>
Figure SMS_118
Rated power of the energy storage system on the ith power system node;
optimizing the first model based on a preset classical optimization method to obtain an optimal output active power sequence of the energy storage system for frequency modulation, namely
Figure SMS_119
. The classical optimization method is Lagrange optimization, gradient descent, simplex method and the like.
Further, substituting an optimal output active power sequence of the energy storage system in a future T period into the second model, so as to update the operation constraint condition in the second model and the voltage regulation control objective function under the operation constraint condition, wherein the expression of the updated operation constraint condition is as follows:
Figure SMS_120
，
in the method, in the process of the invention,
Figure SMS_121
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure SMS_123
For the active power of the energy storage system at t moment of the ith power system node in the nth iteration, +.>
Figure SMS_124
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure SMS_122
For the active power of the wind farm at time t of the ith power system node, +.>
Figure SMS_126
For the ith power system node to load the active power of the user at time t,/for the power system node>
Figure SMS_127
For the active power output by the ith power system node at time t to the jth power system node,/>
Figure SMS_128
For the current output by the ith power system node at time t to the jth power system node,/>
Figure SMS_125
The resistance between the node j and the node i;
Figure SMS_129
，
in the method, in the process of the invention,
Figure SMS_130
energy storage system on ith power system node>
Figure SMS_131
Energy state at time- >
Figure SMS_132
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure SMS_133
Charging efficiency for energy storage system->
Figure SMS_134
For time interval +.>
Figure SMS_135
For the capacity of the energy storage system on the ith power system node, for>
Figure SMS_136
Discharging efficiency of the energy storage system;
Figure SMS_137
，
in the method, in the process of the invention,
Figure SMS_138
for the reactive power of the energy storage system at the t moment of the ith power system node in the r-th iteration,
Figure SMS_139
rated power of the energy storage system on the ith power system node;
the updated expression of the voltage regulation control objective function is:
Figure SMS_140
，
in the method, in the process of the invention,
Figure SMS_141
the energy storage system on the ith power system node is within the scope of%t 0 +kΔt) Active power output at the moment, +.>
Figure SMS_142
The energy storage system on the ith power system node is within the scope of%t 0 +kΔt) The reactive power output at the moment of time,t 0 indicating the current moment +.>
Figure SMS_143
For the number of nodes of the power system, < > for>
Figure SMS_144
For prediction step size +.>
Figure SMS_145
Is a time interval.
Step S105, judging whether the current iteration number reaches the maximum iteration number.
In this embodiment, the iteration number R is determined to be greater than or equal to the maximum iteration number R, and if yes, the current iteration number is obtained
Figure SMS_146
Energy storage system optimization control capable of taking power grid frequency and voltage into consideration at any time>
Figure SMS_147
，/>
Figure SMS_148
The ith power system node at +.1 for the R-1 iteration>
Figure SMS_149
Active power of the time energy storage system, +.>
Figure SMS_150
For the ith power system node at the R-th iteration +. >
Figure SMS_151
Reactive power of the energy storage system at the moment enters the optimal control of the energy storage system at the next moment.
And step S106, if not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value.
And step S107, if the active power output sequence error between two adjacent iterations is not greater than a first set threshold value and the reactive power output sequence error is not greater than a second set threshold value, acquiring the active power and the reactive power of the energy storage system optimization control at the current moment, and entering the optimization control at the next moment.
In this embodiment, it is determined that the active power output sequence error is not greater than the first set threshold
Figure SMS_152
And the reactive power output sequence error is not greater than a second set threshold +.>
Figure SMS_153
If the following conditions are satisfied:
Figure SMS_154
then get the current->
Figure SMS_155
Energy storage system optimization control capable of taking power grid frequency and voltage into consideration at any time>
Figure SMS_156
Entering the next time to optimally control the energy storage system; if not, continuing to calculate to obtain the optimal output active power sequence of the energy storage system for frequency modulation.
In summary, the method mainly comprises the energy storage system participating in the modeling of the power frequency modulation and voltage regulation service and the energy storage frequency modulation and voltage regulation control optimization method under the prediction of the power information input. According to the technical requirements of the power grid frequency and voltage and a regulation mechanism, establishing a multi-objective function and constraint conditions under the participation of an energy storage system; then, inputting the predicted power change of the power system and the state of the energy storage system in a future period of time into the model; and finally, forming optimal active and reactive power output of the energy storage system by using an alternating iterative optimization idea, and finishing effective support of the frequency and the voltage of the power grid. By modeling and optimizing and solving the frequency and the voltage of the power grid actively supported by the energy storage system, the comprehensive requirements of the frequency and the voltage of the power grid are effectively considered under the condition of considering the actual state of the energy storage system, the comprehensive service capability of the energy storage system is exerted to the greatest extent, and the electric auxiliary service level of the energy storage system is improved.
Referring to fig. 3, a block diagram of an energy storage system optimization control system that accounts for grid frequency and voltage support is shown.
As shown in fig. 3, the optimization control system 200 includes a construction module 210, an acquisition module 220, an initialization module 230, a calculation module 240, a first judgment module 250, a second judgment module 260, and a control module 270.
The construction module 210 is configured to construct a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system; an acquisition module 220 configured to acquire predicted power values at each node of the power system during a future T period; an initialization module 230 configured to initialize a reactive power output sequence and a maximum number of iterations of the energy storage system during a future T period; the calculation module 240 is configured to input the reactive power output sequence into the first model, calculate an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, input the optimal output active power sequence into the second model, and calculate an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value; a first judging module 250 configured to judge whether the current iteration number reaches the maximum iteration number; a second judging module 260 configured to judge whether the active power output sequence error between two adjacent iterations is greater than a first set threshold and whether the reactive power output sequence error is greater than a second set threshold if not; the control module 270 is configured to obtain the active power and the reactive power of the energy storage system optimization control at the current moment and enter the optimization control at the next moment if the active power output sequence error between two adjacent iterations is not greater than the first set threshold and the reactive power output sequence error is not greater than the second set threshold.
It should be understood that the modules depicted in fig. 3 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 3, and are not described here again.
In other embodiments, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program, which when executed by a processor, causes the processor to perform the method of optimizing control of an energy storage system taking into account grid frequency and voltage support in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed;
acquiring a predicted power value on each node of the power system in a future T period;
initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period;
Inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
judging whether the current iteration number reaches the maximum iteration number or not;
if not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value;
and if the active power output sequence error between two adjacent iterations is not greater than the first set threshold value and the reactive power output sequence error is not greater than the second set threshold value, acquiring the active power and the reactive power of the energy storage system optimal control at the current moment, and entering the optimal control at the next moment.
The computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of an energy storage system optimization control system that accounts for grid frequency and voltage support, and the like. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely located with respect to the processor, which may be connected via a network to an energy storage system optimization control system that accounts for grid frequency and voltage support. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 4, where the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 4. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running non-volatile software programs, instructions and modules stored in the memory 320, i.e., implementing the energy storage system optimization control method described above in connection with the method embodiments described above, taking into account grid frequency and voltage support. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the energy storage system optimization control system that accounts for grid frequency and voltage support. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
As an embodiment, the electronic device is applied to an energy storage system optimization control system considering grid frequency and voltage support, and is used for a client, and the electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed;
acquiring a predicted power value on each node of the power system in a future T period;
initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period;
inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
Judging whether the current iteration number reaches the maximum iteration number or not;
if not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value;
and if the active power output sequence error between two adjacent iterations is not greater than the first set threshold value and the reactive power output sequence error is not greater than the second set threshold value, acquiring the active power and the reactive power of the energy storage system optimal control at the current moment, and entering the optimal control at the next moment.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1. An energy storage system optimization control method considering power grid frequency and voltage support is characterized by comprising the following steps:
according to the acquired power network structure information, power supply information, load user information and physical information of an energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed, wherein the first model comprises an active power constraint condition of a power system frequency modulation control process and a frequency modulation control objective function under the active power constraint condition, and the second model comprises an operation constraint condition of the power system voltage modulation control process and a voltage modulation control objective function under the operation constraint condition;
Wherein the active power constraint condition includes:
differential equation of system frequency change rate and active power change quantity, system equivalent inertia constraint, thermal power unit active power change quantity constraint, photovoltaic power station active power change quantity constraint, wind power plant active power change quantity constraint, energy storage system active power change quantity constraint, energy storage energy state constraint, frequency maximum deviation, frequency deviation change rate constraint, power supply rated power constraint, thermal power unit climbing rate constraint and energy storage energy state limit constraint, wherein the specific calculation formula is as follows:
Figure QLYQS_1
，
in the method, in the process of the invention,
Figure QLYQS_3
rated frequency for the power grid, < >>
Figure QLYQS_6
For the system equivalent inertia->
Figure QLYQS_9
For system capacity>
Figure QLYQS_4
For the variation of the system frequency at time t, < >>
Figure QLYQS_5
Is the equivalent damping coefficient of the system->
Figure QLYQS_8
For the active power variable quantity of the thermal power unit of the ith power system node at the moment t,/>
Figure QLYQS_11
Active power variation of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_2
For the active power variation of the wind power plant of the ith power system node at the moment t, +.>
Figure QLYQS_7
For the active power variation of the energy storage system of the ith power system node at the time t,/for the power system node>
Figure QLYQS_10
The active power variation of the load user at the moment t is the ith power system node;
Figure QLYQS_12
，
In the method, in the process of the invention,
Figure QLYQS_15
is inertia coefficient>
Figure QLYQS_17
Inertia coefficient of thermal power generating unit on ith power system node, < ->
Figure QLYQS_19
Rated power of thermal power generating unit is added to ith power system node,/->
Figure QLYQS_14
The inertia coefficient of the photovoltaic power station on the ith power system node,
Figure QLYQS_18
rated power of photovoltaic power station on ith power system node, < >>
Figure QLYQS_20
For the inertia coefficient of the wind farm at the ith power system node, +.>
Figure QLYQS_21
Rated power of wind farm on ith power system node, +.>
Figure QLYQS_13
For the inertia coefficient of the energy storage system at the ith power system node,/>
Figure QLYQS_16
Rated power of the energy storage system on the ith power system node;
Figure QLYQS_22
，
in the method, in the process of the invention,
Figure QLYQS_24
for the system frequency at time t +.>
Figure QLYQS_27
To be at the beginningSystem frequency of etching->
Figure QLYQS_29
For the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_25
For the active power of the thermal power unit of the ith power system node at the initial moment, +.>
Figure QLYQS_28
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_30
For the active power of the photovoltaic power station of the ith power system node at the initial moment, +.>
Figure QLYQS_32
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_23
For the active power of the wind farm at the initial moment of the ith power system node, +. >
Figure QLYQS_26
Active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_31
The active power of the energy storage system at the initial moment is the ith power system node;
Figure QLYQS_33
，
in the method, in the process of the invention,
Figure QLYQS_34
energy storage system on ith power system node/>
Figure QLYQS_35
Energy state at time->
Figure QLYQS_36
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure QLYQS_37
Charging efficiency for energy storage system->
Figure QLYQS_38
In order to provide for the time interval of time,
Figure QLYQS_39
for the capacity of the energy storage system on the ith power system node, for>
Figure QLYQS_40
Discharging efficiency of the energy storage system;
Figure QLYQS_41
，
in the method, in the process of the invention,
Figure QLYQS_42
allow threshold for frequency deviation, +.>
Figure QLYQS_43
Allow threshold for rate of frequency change,>
Figure QLYQS_44
maximum output active power of photovoltaic power station at t moment on ith power system node,/>
Figure QLYQS_45
Maximum output active power of wind farm at t moment on ith power system node, +.>
Figure QLYQS_46
Reactive power of the energy storage system at the t moment on the ith power system node;
Figure QLYQS_47
，
in the method, in the process of the invention,
Figure QLYQS_48
is at +.>
Figure QLYQS_49
Active power of thermal power generating unit at moment +.>
Figure QLYQS_50
A climbing threshold of the thermal power generating unit is set for the ith power system node;
Figure QLYQS_51
，
in the method, in the process of the invention,
Figure QLYQS_52
is the lower limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure QLYQS_53
The upper limit value of the energy state of the energy storage system on the ith power system node;
The frequency modulation control objective function under the active power constraint condition specifically comprises the following steps: the active power of the thermal power generating unit is minimized, the active power output of new energy is maximized, and the expression is:
Figure QLYQS_54
，
in the method, in the process of the invention,
Figure QLYQS_55
for the number of nodes of the power system, < > for>
Figure QLYQS_56
For prediction step size +.>
Figure QLYQS_57
Is a time interval;
wherein the operating constraints include:
active power constraint of a power system, reactive power balance constraint of the power system, node voltage and power constraint, rated power constraint of a thermal power generating unit, maximum active adjustment constraint, maximum reactive adjustment constraint, climbing speed constraint, active power output constraint of a photovoltaic power station, active power output constraint of a wind power plant, energy storage energy balance constraint, energy state limit and rated power constraint;
Figure QLYQS_58
，
in the method, in the process of the invention,
Figure QLYQS_60
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_66
Active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_69
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_61
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_63
Is the ith power system nodeActive power of the load subscriber at time t, < >>
Figure QLYQS_68
For the active power output by the ith power system node at time t to the jth power system node,/ >
Figure QLYQS_71
For the current output by the ith power system node at time t to the jth power system node,/>
Figure QLYQS_59
For the resistance between node j and node i, +.>
Figure QLYQS_64
Reactive power of thermal power unit at t moment for ith power system node, +.>
Figure QLYQS_67
Reactive power of the energy storage system at time t for the ith power system node, < >>
Figure QLYQS_70
Reactive power for the i-th power system node to load the subscriber at time t,/for the i-th power system node>
Figure QLYQS_62
Reactive power output by the ith power system node to the jth power system node at time t,/>
Figure QLYQS_65
Impedance between node j and node i;
Figure QLYQS_72
，
in the method, in the process of the invention,
Figure QLYQS_73
for node i voltage at time t +.>
Figure QLYQS_74
The voltage of the node j at the moment t;
Figure QLYQS_75
，
in the method, in the process of the invention,
Figure QLYQS_76
energy storage system on ith power system node>
Figure QLYQS_80
Energy state at time->
Figure QLYQS_81
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure QLYQS_77
Charging efficiency for energy storage system->
Figure QLYQS_79
Active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_82
For time interval +.>
Figure QLYQS_83
For the capacity of the energy storage system on the ith power system node, for>
Figure QLYQS_78
Discharging efficiency of the energy storage system;
Figure QLYQS_84
，
in the method, in the process of the invention,
Figure QLYQS_94
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_87
Reactive power of thermal power unit at t moment for ith power system node, +. >
Figure QLYQS_89
Rated power of thermal power generating unit is added to ith power system node,/->
Figure QLYQS_88
Minimum output active power of thermal power generating unit is applied to ith power system node, +.>
Figure QLYQS_92
Maximum output active power of thermal power unit on ith power system node, < >>
Figure QLYQS_95
Is at +.>
Figure QLYQS_99
Active power of thermal power generating unit at moment +.>
Figure QLYQS_98
Climbing threshold value of thermal power generating unit is set for ith power system node, < ->
Figure QLYQS_102
Minimum output reactive power of thermal power generating unit is applied to ith power system node, +.>
Figure QLYQS_85
Maximum output reactive power of thermal power unit on ith power system node, < >>
Figure QLYQS_91
Is the ith power systemActive power of photovoltaic power station at t moment of system node,/->
Figure QLYQS_97
Maximum output active power of photovoltaic power station at t moment on ith power system node,/>
Figure QLYQS_101
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_103
Maximum output active power of wind farm at t moment on ith power system node, +.>
Figure QLYQS_104
For node i voltage lower limit,/-, for>
Figure QLYQS_90
For node i voltage upper limit, < >>
Figure QLYQS_93
For maximum current through the branch, < > for>
Figure QLYQS_96
Is the lower limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure QLYQS_100
Is the upper limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure QLYQS_86
Rated power of the energy storage system on the ith power system node;
The voltage regulation control objective function under the operation constraint condition is specifically: and the square sum of the active power and the reactive power output by the energy storage system is minimized in the prediction period, namely the output of the energy storage system is minimized, and the expression is:
Figure QLYQS_105
，
in the method, in the process of the invention,
Figure QLYQS_106
for the number of nodes of the power system, < > for>
Figure QLYQS_107
For prediction step size +.>
Figure QLYQS_108
For time interval +.>
Figure QLYQS_109
The energy storage system on the ith power system node is within the scope of%t 0 +kΔt) Active power output at the moment, +.>
Figure QLYQS_110
The energy storage system on the ith power system node is within the scope of%t 0 +kΔt) The reactive power output at the moment of time,t 0 representing the current time;
acquiring a predicted power value on each node of the power system in a future T period;
initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period;
inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
judging whether the current iteration number reaches the maximum iteration number or not;
If not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value;
and if the active power output sequence error between two adjacent iterations is not greater than the first set threshold value and the reactive power output sequence error is not greater than the second set threshold value, acquiring the active power and the reactive power of the energy storage system optimal control at the current moment, and entering the optimal control at the next moment.
2. The energy storage system optimization control method considering power grid frequency and voltage support according to claim 1, wherein each node of the power system is a load user node, a photovoltaic power station node and a wind power plant node; the obtaining the predicted power value of each node of the power system in the future T period comprises the following steps:
acquiring historical data of load user nodes, historical data of photovoltaic power station nodes and historical data of wind farm station nodes, and establishing a short-term prediction model based on a preset regression model, wherein the regression model is a neural network regression model or a support vector machine model, and the short-term prediction model comprises an active and reactive short-term prediction model of the load user nodes, an active power short-term prediction model of the photovoltaic power station nodes and an active power short-term prediction model of the wind farm nodes;
Based on the data of the load user node at the current T moment, the data of the photovoltaic power station node and the data of the wind power station node, obtaining the active power of the load user node in the future T period according to the short-term prediction model
Figure QLYQS_111
Reactive power->
Figure QLYQS_112
Maximum active power of photovoltaic power station node +.>
Figure QLYQS_113
Maximum active power of wind farm node
Figure QLYQS_114
。
3. The method according to claim 1, wherein the step of inputting the reactive power output sequence into the first model and calculating an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value comprises:
substituting a reactive power output sequence of the energy storage system in a future T period into the first model to update active power constraint in the first model, wherein the updated active power constraint has the following expression:
Figure QLYQS_115
，
in the method, in the process of the invention,
Figure QLYQS_116
active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_117
Reactive power of energy storage system at t moment for ith power system node in the nth iteration,/>
Figure QLYQS_118
Rated power of the energy storage system on the ith power system node;
Optimizing the first model based on a preset classical optimization method to obtain an optimal output active power sequence of the energy storage system for frequency modulation, namely
Figure QLYQS_119
。
4. The method for optimizing control of an energy storage system according to claim 1, wherein the step of inputting the optimal output active power sequence into the second model and calculating an optimal output reactive power sequence for voltage regulation of the energy storage system based on the predicted power values comprises:
substituting an optimal output active power sequence of the energy storage system in a future T period into the second model to update an operation constraint condition in the second model and a voltage regulation control objective function under the operation constraint condition, wherein the updated operation constraint condition has the expression:
Figure QLYQS_120
，
in the method, in the process of the invention,
Figure QLYQS_122
reactive power of thermal power unit at t moment for ith power system node, +.>
Figure QLYQS_126
Reactive power of the energy storage system at time t for the ith power system node, < >>
Figure QLYQS_129
Reactive power for the i-th power system node to load the subscriber at time t,/for the i-th power system node>
Figure QLYQS_123
For the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_125
For the active power of the energy storage system at t moment of the ith power system node in the nth iteration, +. >
Figure QLYQS_128
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_130
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_121
For the ith power system node to load the active power of the user at time t,/for the power system node>
Figure QLYQS_124
For the active power output by the ith power system node at time t to the jth power system node,/>
Figure QLYQS_127
The current output by the ith power system node to the jth power system node at the moment t,
Figure QLYQS_131
the resistance between the node j and the node i;
Figure QLYQS_132
，
in the method, in the process of the invention,
Figure QLYQS_133
energy storage system on ith power system node>
Figure QLYQS_134
Energy state at time->
Figure QLYQS_135
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure QLYQS_136
Charging efficiency for energy storage system->
Figure QLYQS_137
In order to provide for the time interval of time,
Figure QLYQS_138
for the capacity of the energy storage system on the ith power system node, for>
Figure QLYQS_139
Discharging efficiency of the energy storage system;
Figure QLYQS_140
，
in the method, in the process of the invention,
Figure QLYQS_141
reactive power of energy storage system at t moment for ith power system node in the nth iteration,/>
Figure QLYQS_142
Rated power of the energy storage system on the ith power system node;
the updated expression of the voltage regulation control objective function is:
Figure QLYQS_143
，
in the method, in the process of the invention,
Figure QLYQS_144
the energy storage system on the ith power system node is within the scope of%t 0 +kΔt) Active power output at the moment, +.>
Figure QLYQS_145
The energy storage system on the ith power system node is within the scope of% t 0 +kΔt) The reactive power output at the moment of time,t 0 indicating the current moment +.>
Figure QLYQS_146
For the number of nodes of the power system, < > for>
Figure QLYQS_147
For prediction step size +.>
Figure QLYQS_148
Is a time interval.
5. An energy storage system optimization control system that accounts for grid frequency and voltage support, comprising:
the power grid control system comprises a construction module, a load user module and an energy storage system, wherein the construction module is configured to construct a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation according to acquired power network structure information, power supply information, load user information and physical information of the energy storage system, the first model comprises an active power constraint condition of a power system frequency modulation control process and a frequency modulation control objective function under the active power constraint condition, and the second model comprises an operation constraint condition of the power system voltage modulation control process and the voltage modulation control objective function under the operation constraint condition;
wherein the active power constraint condition includes:
differential equation of system frequency change rate and active power change quantity, system equivalent inertia constraint, thermal power unit active power change quantity constraint, photovoltaic power station active power change quantity constraint, wind power plant active power change quantity constraint, energy storage system active power change quantity constraint, energy storage energy state constraint, frequency maximum deviation, frequency deviation change rate constraint, power supply rated power constraint, thermal power unit climbing rate constraint and energy storage energy state limit constraint, wherein the specific calculation formula is as follows:
Figure QLYQS_149
，
In the method, in the process of the invention,
Figure QLYQS_151
rated frequency for the power grid, < >>
Figure QLYQS_155
Is a systemEquivalent inertia, & gt>
Figure QLYQS_156
For system capacity>
Figure QLYQS_152
For the variation of the system frequency at time t, < >>
Figure QLYQS_154
Is the equivalent damping coefficient of the system->
Figure QLYQS_158
For the active power variable quantity of the thermal power unit of the ith power system node at the moment t,/>
Figure QLYQS_159
Active power variation of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_150
For the active power variation of the wind power plant of the ith power system node at the moment t, +.>
Figure QLYQS_153
For the active power variation of the energy storage system of the ith power system node at the time t,/for the power system node>
Figure QLYQS_157
The active power variation of the load user at the moment t is the ith power system node;
Figure QLYQS_160
，
in the method, in the process of the invention,
Figure QLYQS_162
is inertia coefficient>
Figure QLYQS_165
Inertia coefficient of thermal power generating unit on ith power system node, < ->
Figure QLYQS_168
Rated power of thermal power generating unit is added to ith power system node,/->
Figure QLYQS_163
The inertia coefficient of the photovoltaic power station on the ith power system node,
Figure QLYQS_164
rated power of photovoltaic power station on ith power system node, < >>
Figure QLYQS_167
For the inertia coefficient of the wind farm at the ith power system node, +.>
Figure QLYQS_169
Rated power of wind farm on ith power system node, +.>
Figure QLYQS_161
For the inertia coefficient of the energy storage system at the ith power system node,/>
Figure QLYQS_166
Rated power of the energy storage system on the ith power system node;
Figure QLYQS_170
，
In the method, in the process of the invention,
Figure QLYQS_173
for the system frequency at time t +.>
Figure QLYQS_176
For the system frequency at the initial moment +.>
Figure QLYQS_179
For the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_172
For the active power of the thermal power unit of the ith power system node at the initial moment, +.>
Figure QLYQS_175
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_177
For the active power of the photovoltaic power station of the ith power system node at the initial moment, +.>
Figure QLYQS_180
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_171
For the active power of the wind farm at the initial moment of the ith power system node, +.>
Figure QLYQS_174
Active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_178
The active power of the energy storage system at the initial moment is the ith power system node;
Figure QLYQS_181
，
in the method, in the process of the invention,
Figure QLYQS_182
energy storage system on ith power system node>
Figure QLYQS_183
Energy state at time->
Figure QLYQS_184
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure QLYQS_185
Charging efficiency for energy storage system->
Figure QLYQS_186
In order to provide for the time interval of time,
Figure QLYQS_187
for the capacity of the energy storage system on the ith power system node, for>
Figure QLYQS_188
Discharging efficiency of the energy storage system;
Figure QLYQS_189
，
in the method, in the process of the invention,
Figure QLYQS_190
allow threshold for frequency deviation, +.>
Figure QLYQS_191
Allow threshold for rate of frequency change,>
Figure QLYQS_192
maximum output active power of photovoltaic power station at t moment on ith power system node,/ >
Figure QLYQS_193
Maximum output active power of wind farm at t moment on ith power system node, +.>
Figure QLYQS_194
Reactive power of the energy storage system at the t moment on the ith power system node;
Figure QLYQS_195
，
in the method, in the process of the invention,
Figure QLYQS_196
is at +.>
Figure QLYQS_197
Active power of thermal power generating unit at moment +.>
Figure QLYQS_198
A climbing threshold of the thermal power generating unit is set for the ith power system node;
Figure QLYQS_199
，
in the method, in the process of the invention,
Figure QLYQS_200
is the lower limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure QLYQS_201
The upper limit value of the energy state of the energy storage system on the ith power system node;
the frequency modulation control objective function under the active power constraint condition specifically comprises the following steps: the active power of the thermal power generating unit is minimized, the active power output of new energy is maximized, and the expression is:
Figure QLYQS_202
，
in the method, in the process of the invention,
Figure QLYQS_203
for the number of nodes of the power system, < > for>
Figure QLYQS_204
For prediction step size +.>
Figure QLYQS_205
Is a time interval;
wherein the operating constraints include:
active power constraint of a power system, reactive power balance constraint of the power system, node voltage and power constraint, rated power constraint of a thermal power generating unit, maximum active adjustment constraint, maximum reactive adjustment constraint, climbing speed constraint, active power output constraint of a photovoltaic power station, active power output constraint of a wind power plant, energy storage energy balance constraint, energy state limit and rated power constraint;
Figure QLYQS_206
，
In the method, in the process of the invention,
Figure QLYQS_208
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_214
Active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_216
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_210
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_211
At time t for the ith power system nodeActive power of load subscriber, +.>
Figure QLYQS_215
For the active power output by the ith power system node at time t to the jth power system node,/>
Figure QLYQS_218
For the current output by the ith power system node at time t to the jth power system node,/>
Figure QLYQS_207
For the resistance between node j and node i, +.>
Figure QLYQS_213
Reactive power of thermal power unit at t moment for ith power system node, +.>
Figure QLYQS_217
Reactive power of the energy storage system at time t for the ith power system node, < >>
Figure QLYQS_219
Reactive power for the i-th power system node to load the subscriber at time t,/for the i-th power system node>
Figure QLYQS_209
Reactive power output by the ith power system node to the jth power system node at time t,/>
Figure QLYQS_212
Impedance between node j and node i;
Figure QLYQS_220
，
in the method, in the process of the invention,
Figure QLYQS_221
for node i voltage at time t +.>
Figure QLYQS_222
The voltage of the node j at the moment t;
Figure QLYQS_223
，
in the method, in the process of the invention,
Figure QLYQS_225
energy storage system on ith power system node >
Figure QLYQS_228
The state of energy at the moment in time, and (2)>
Figure QLYQS_230
For the energy state of the energy storage system t moment on the ith power system node, +.>
Figure QLYQS_226
Charging efficiency for energy storage system->
Figure QLYQS_227
Active power of energy storage system at t moment for ith power system node, +.>
Figure QLYQS_229
For time interval +.>
Figure QLYQS_231
For the capacity of the energy storage system on the ith power system node, for>
Figure QLYQS_224
Discharging efficiency of the energy storage system;
Figure QLYQS_232
，
in the method, in the process of the invention,
Figure QLYQS_242
for the active power of the thermal power unit of the ith power system node at the moment t, +.>
Figure QLYQS_240
Reactive power of thermal power unit at t moment for ith power system node, +.>
Figure QLYQS_241
Rated power of thermal power generating unit is added to ith power system node,/->
Figure QLYQS_243
Minimum output active power of thermal power generating unit is applied to ith power system node, +.>
Figure QLYQS_244
Maximum output active power of thermal power unit on ith power system node, < >>
Figure QLYQS_246
Is at +.>
Figure QLYQS_251
Active power of thermal power generating unit at moment +.>
Figure QLYQS_245
Climbing threshold value of thermal power generating unit is set for ith power system node, < ->
Figure QLYQS_250
Minimum output reactive power of thermal power generating unit is applied to ith power system node, +.>
Figure QLYQS_233
Maximum output reactive power of thermal power unit on ith power system node, < >>
Figure QLYQS_234
Active power of photovoltaic power station at time t for ith power system node, +.>
Figure QLYQS_247
Maximum output active power of photovoltaic power station at t moment on ith power system node,/ >
Figure QLYQS_249
For the active power of the wind farm at time t of the ith power system node, +.>
Figure QLYQS_248
Maximum output active power of wind farm at t moment on ith power system node, +.>
Figure QLYQS_252
For node i voltage lower limit,/-, for>
Figure QLYQS_236
For node i voltage upper limit, < >>
Figure QLYQS_237
For maximum current through the branch, < > for>
Figure QLYQS_238
Is the lower limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure QLYQS_239
Is the upper limit value of the energy state of the energy storage system on the ith power system node, +.>
Figure QLYQS_235
Rated power of the energy storage system on the ith power system node;
the voltage regulation control objective function under the operation constraint condition is specifically: and the square sum of the active power and the reactive power output by the energy storage system is minimized in the prediction period, namely the output of the energy storage system is minimized, and the expression is:
Figure QLYQS_253
，
in the method, in the process of the invention,
Figure QLYQS_254
for the number of nodes of the power system, < > for>
Figure QLYQS_255
For prediction step size +.>
Figure QLYQS_256
For time interval +.>
Figure QLYQS_257
The energy storage system on the ith power system node is within the scope of%t 0 +kΔt) Active power output at the moment, +.>
Figure QLYQS_258
The energy storage system on the ith power system node is within the scope of%t 0 +kΔt) The reactive power output at the moment of time,t 0 representing the current time;
the power system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is configured to acquire a predicted power value on each node of the power system in a future T period;
the initialization module is configured to initialize a reactive power output sequence and a maximum iteration number of the energy storage system in a future T period;
The calculation module is configured to input the reactive power output sequence into the first model, calculate and obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, input the optimal output active power sequence into the second model, and calculate and obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
the first judging module is configured to judge whether the current iteration number reaches the maximum iteration number or not;
the second judging module is configured to judge whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value if the active power output sequence error is not reached;
and the control module is configured to acquire the active power and the reactive power of the energy storage system optimal control at the current moment and enter the optimal control at the next moment if the active power output sequence error between two adjacent iterations is not greater than a first set threshold value and the reactive power output sequence error is not greater than a second set threshold value.
6. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any of claims 1 to 4.
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