CN117543615A - Auxiliary thermal power unit peak regulation and frequency modulation method and system - Google Patents
Auxiliary thermal power unit peak regulation and frequency modulation method and system Download PDFInfo
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Abstract
The invention provides a method and a system for assisting in peak regulation and frequency modulation of a thermal power unit, and belongs to the technical field of battery energy storage systems. The method comprises the following steps: collecting operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data; taking the output scheme of the battery energy storage system which is communicated with the photovoltaic power station circuit at each moment in the whole day as a model variable, and constructing an optimal scheduling model of the battery energy storage system by taking the maximum net income of single-day operation as an optimization target; solving an optimal scheduling model of the battery energy storage system based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain a power scheme of the battery energy storage system; and adjusting the output power of the battery energy storage system based on the output scheme of the battery energy storage system. By the method and the system, when the net income of single-day operation is maximum, the operation state of the battery energy storage system at each moment can be obtained, so that the performance advantage of the battery energy storage system is brought into play, the operation cost is reduced, and the operation profitability of the battery energy storage system is further improved.
Description
Technical Field
The invention relates to the technical field of battery energy storage systems, in particular to an auxiliary thermal power unit peak regulation and frequency modulation method and an auxiliary thermal power unit peak regulation and frequency modulation system.
Background
The high-quality frequency modulation and peak shaving service is a scarce resource which is needed by the current power grid, and the battery energy storage system has the advantages of quick response and accurate instruction tracking, has the capability of carrying out space-time translation on energy, and has wide application prospect when participating in auxiliary service of the power market.
However, the capability of the battery energy storage system to participate in the auxiliary service of the electric power market still cannot overcome the intensive initial cost, and the performance advantage of the battery energy storage system cannot be effectively exerted in a single scene operation mode, and the auxiliary service with the most profitable capability cannot be captured in real time, so that the operation cost is reduced.
Therefore, how to perform coordinated optimization control on the battery energy storage system, and reduce the operation cost while playing the performance advantage of the battery energy storage system is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a peak regulation and frequency modulation method and system for assisting a thermal power generating unit, which at least solve the problems that the capacity of a battery energy storage system in participating in auxiliary service of an electric power market is still difficult to overcome the intensive initial cost, the performance advantage of the battery energy storage system cannot be effectively exerted in a single scene operation mode, and the auxiliary service with the most profitable capacity cannot be captured in real time so as to reduce the operation cost.
In order to achieve the above object, a first aspect of the present invention provides a method for assisting peak shaving and frequency modulation of a thermal power generating unit, comprising:
collecting operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data;
taking the output scheme of the battery energy storage system which is communicated with the photovoltaic power station circuit at each moment in the whole day as a model variable, and constructing an optimal scheduling model of the battery energy storage system by taking the maximum net income of single-day operation as an optimization target;
solving an optimal scheduling model of the battery energy storage system based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain a power scheme of the battery energy storage system;
and adjusting the output power of the battery energy storage system based on the output scheme of the battery energy storage system.
Optionally, the construction rule of the battery energy storage system optimization scheduling model is as follows:
collecting frequency modulation AGC data of a power grid dispatching center and operation data of a battery energy storage system, and establishing an optimal dispatching model of the battery energy storage system; the frequency modulation AGC data of the power grid dispatching center comprises expected output power P of frequency modulation AGC instructions at each moment of the power grid dispatching center ref And the continuous output time T, and the battery energy storage system operation data comprise output power of the battery energy storage system at each moment, power of the battery energy storage system which is put into operation in different peak regulation and frequency modulation application scenes at the same moment, battery energy storage system operation constraint conditions, battery energy storage system operation cost and electricity selling conditions of the battery energy storage system which participate in electricity selling reservation peak regulation space of an energy market.
Optionally, the formula of the battery energy storage system optimization scheduling model is as follows:
I＝S-C＝(S 1 +S 2 +S 3 )-(C 1 +C 2 +C 3 )；
wherein I is the net benefit of the operation of the battery energy storage system, S is the total benefit, S 1 Frequency modulation gain for battery energy storage system S 2 Peak shaving gain for battery energy storage system, S 3 For energy market benefit, C is the total cost, C 1 C is the loss cost of the battery energy storage system 2 Punishment cost for unfinished frequency modulation instruction of battery energy storage system, C 3 Penalty costs incurred for battery energy storage systems not having completed peak shaving instructions.
Optionally, a calculation formula of the frequency modulation gain obtained by the battery energy storage system is as follows:
wherein P is fr Output power for frequency modulation action, B AGC The compensation unit price of the battery energy storage system participating in frequency modulation is represented by D, wherein D is the compensation depth, i represents an ith period frequency modulation AGC instruction, and j represents a jth frequency modulation AGC instruction in the ith period frequency modulation AGC instruction;
k 1 、k 2 and k 3 The regulation effect indexes of the battery energy storage system tracking frequency modulation AGC instruction are all shown; wherein,
wherein,P S output power P when receiving frequency modulation AGC instruction for battery energy storage system E For the effective threshold range of the FM AGC command, P E >0 is the discharge of the battery energy storage system, P E <0 is the battery energy storage system charge, v N To adjust the rate to the standard, P fr Output power of frequency modulation action, deltaP N To adjust the allowable deviation amount, t st For standard response time, Δt is the output power of the battery energy storage system from P S Change to P E The time required.
Optionally, a calculation formula of peak regulation income obtained by the battery energy storage system is as follows:
wherein B is pr The compensation unit price of the battery energy storage system participating in peak regulation, P pr And the output power of the frequency modulation action is output time, and i represents an i-th period frequency modulation AGC instruction.
Optionally, the calculation formula of the energy market benefit is as follows:
wherein B is em Time-of-use electricity price, P, for participation of battery energy storage system in energy market electricity selling em And the output power of the energy market action is output time, i represents an ith period frequency modulation AGC instruction.
Optionally, the calculation formula of the loss cost of the battery energy storage system is as follows:
wherein C is p For unit power cost of battery energy storage system, P p C is the maximum charge and discharge power of the battery energy storage system E For unit capacity cost of battery energy storage system, E b For rated capacity of battery energy storage system, N by The average service life of the battery energy storage system is prolonged.
Optionally, a calculation formula of penalty cost of the battery energy storage system for not finishing the frequency modulation instruction is as follows:
C 2 ＝∑B pfr P ref (fail)；
Wherein B is pfr Punishment unit price of frequency modulation action which does not reach standard, P ref (fail) is the expected output of a frequency modulation command that does not meet the standard.
Optionally, a calculation formula of penalty cost of the battery energy storage system for not completing the peak shaving instruction is as follows:
C 3 ＝∑B ppr P rw (fail)t；
wherein B is ppr Punishment unit price of peak regulation action which does not reach standard, P rw (fail) is the expected output power of the peak shaving instruction which does not reach the standard, and t is the output time.
Optionally, the battery energy storage system operation constraint conditions include an output power constraint condition, a first constraint condition, a frequency modulation AGC instruction effective threshold range constraint condition and a battery energy storage system charge state constraint condition; wherein,
the output power constraint conditions are as follows:
P pr (i)∈{0,λ 2 P rw (i)}0<λ 2 <1；
P em (i)∈{0,λ 3 P max }0<λ 3 <1；
wherein,output power of frequency modulation action for j-th instruction in n frequency modulation AGC instructions in i-th period, +.>Representing the expected output power of the j-th command of the n frequency modulation AGC commands in the i-th period, P rw (i) Output power representing ith period peak shaving instruction, P pr (i) Output power of frequency modulation action in ith period, P em (i) Output power for the ith period of energy market action, P max Represents the maximum output power lambda of the battery energy storage system 1 Representing the frequency modulation output power coefficient lambda 2 Represents the peak-regulating output power coefficient lambda 3 Representing an energy market output power coefficient;
wherein,representing a lower limit on the operating power of the battery energy storage system, < + >>Representing an upper limit on the operating power of the battery energy storage system; and->P pr (i)P em (i)＝0；
The first constraint is:
0<Δt≤T-2；
wherein, deltat is battery energy storageSystem output power from P S Change to P E Time required, T re The electricity selling time length of the battery energy storage system participating in electricity selling reservation peak shaving space of the energy market is represented, i represents an ith period frequency modulation AGC instruction, and t is output time;
the constraint condition of the effective threshold range of the frequency modulation AGC instruction is as follows:
P ref -ΔP N ≤P E ≤P ref +ΔP N ；
wherein P is ref ΔP is the desired output power of the battery energy storage system N To adjust the allowable deviation amount, P E An effective threshold range for the frequency modulation AGC command;
the battery energy storage system state of charge constraint conditions are:
SOC min ≤SOC(i)≤SOC max ；
wherein SOC is min SOC is the lower limit of the state of charge of a battery energy storage system max As the upper limit of the charge state of the battery energy storage system, SOC (i) is the charge state of the battery energy storage system at the ith moment, SOC (i+1) is the charge state of the battery energy storage system at the (i+1) th moment,output power T when the battery energy storage system receives the j command in the n frequency modulation AGC commands in the i time period j (i) For the duration of the j-th of the n FM AGC commands in the i-th period, Q N Is the capacity of the battery energy storage system.
Optionally, the operation data of each moment of the photovoltaic power station includes load power P of each moment of the distributed photovoltaic power station Y (t) and actual output power P of each moment of the distributed photovoltaic power station W (t)；
The peak regulation and frequency modulation basic data comprise the abandoned light power P of the distributed photovoltaic power station at each moment rw (t) and Battery energy storage System parametersElectricity selling time length T of reserved peak regulation space with electricity selling of energy market re ；
Collecting operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data, wherein the method comprises the following steps:
according to the load power P of each moment of the distributed photovoltaic power station Y (t) and actual output power P of each moment of the distributed photovoltaic power station W (t) using the formula P rw (t)＝P Y (t)-P W (t) calculating to obtain the abandoned light power P of the distributed photovoltaic power station at each moment rw (t)；
According to the light discarding power P of each moment of the distributed photovoltaic power station rw (t) determining a peak period of light loss;
according to the peak time of the solar waste, the formula is utilizedCalculating and obtaining electricity selling time length T of the battery energy storage system participating in electricity selling reservation peak regulation space of energy market re The method comprises the steps of carrying out a first treatment on the surface of the Wherein t is h Indicating peak period of light rejection, P rw (t h ) Representing the light discarding power, P of a distributed photovoltaic power station at each moment of the light discarding peak time max Indicating the maximum power output of the battery energy storage system.
Optionally, the above-mentioned discarding power P according to each moment of the distributed photovoltaic power station rw (t) determining a peak solar disposal time period comprising:
if the light rejection power P of the distributed photovoltaic power station at any moment t rw (t) satisfyThen the time t is marked as a light-discarding peak time; wherein, beta is more than or equal to 2.5 and less than or equal to 3.5;
and when the continuous preset number of moments are all the light-discarding peak moments, recording the continuous preset number of moments as the light-discarding peak time.
Optionally, the peak-shaving frequency modulation basic data includes optical power P discarded at each moment of the distributed photovoltaic power station rw (T) and the electricity selling time length T of the reserved peak regulation space of the electricity selling of the energy market participated by the battery energy storage system re ；
Based on peak shaving frequency modulation basic data and a self-adaptive greedy search algorithm, solving the battery energy storage system optimization scheduling model comprises the following steps:
according to the light discarding power P of each moment of the distributed photovoltaic power station rw (t) expected output power P of frequency modulation AGC instruction at each moment of power grid dispatching center ref And the continuous power output time T, calculating the benefit value of the battery energy storage system in each time period of the whole day, and the electricity selling time length T with the lowest benefit value re As an initial solution Ω for participating in energy market electricity selling;
pre-running according to initial solution omega and battery energy storage system state of charge constraint conditions to obtain a battery energy storage system SOC running curve, and recording the time when the battery energy storage system continuously discharges to cause the state of charge to be lower than the lower limit of the state of charge of the battery energy storage system and participate in energy market electricity selling failure And the moment of failure of participating in peak shaving service due to the state of charge of the battery energy storage system being higher than the upper limit of the state of charge of the battery energy storage system as a result of continuous discharge of the battery energy storage system +.>
Time of failure in selling electricity to participate in energy marketRemoving from the initial solution omega to obtain a damaged solution set omega D ；
According to the time of failure of electricity selling in the energy marketAnd the moment of failure to participate in peak shaving service ∈>Determining iteration parameters of a reconstruction stage;
based on the reconstruction phase iteration parameters, the solution set omega' of the battery energy storage system participating in the reconstruction phase of electricity selling of the energy market is calculated in an iterative mode.
Optionally, the above-mentioned iteration parameters of the reconfiguration stage include the number of times N of participation of the battery energy storage system of the reconfiguration stage in the energy market and the adaptive interval τ R ；
Based on the reconstruction phase iteration parameters, iteratively calculating a reconstruction phase solution set Ω' of the battery energy storage system participating in energy market electricity selling, including:
selecting an adaptive interval τ R The moment with the lowest profit value is taken as a reconstruction stage solution set omega ', the pre-operation is carried out according to the reconstruction stage solution set omega' and the state of charge constraint condition of the battery energy storage system, and the SOC operation curve of the battery energy storage system is obtained again;
if the obtained battery energy storage system SOC running curve meets the number of times N of the battery energy storage system participating in the energy market in the reconstruction stage, updating the damage solution set omega by using the solution set omega' in the corresponding reconstruction stage D If the obtained SOC running curve of the battery energy storage system does not meet the number of times N of the battery energy storage system participating in the energy market in the reconstruction stage, the battery energy storage system is in the self-adaptive interval tau R And (3) re-selecting the solution set omega' in the reconstruction stage until the number of times N of participation of the battery energy storage system in the reconstruction stage in the energy market is met.
The second aspect of the invention provides an auxiliary thermal power unit peak regulation and frequency modulation system, which comprises:
the peak regulation and frequency modulation basic data acquisition module is used for acquiring operation data of the photovoltaic power station at each moment to acquire peak regulation and frequency modulation basic data;
the battery energy storage system optimization scheduling model construction module is used for constructing a battery energy storage system optimization scheduling model by taking an output scheme of the battery energy storage system which is communicated with a photovoltaic power station circuit at each moment in the whole day as a model variable and taking the maximum net income of single-day operation as an optimization target;
the battery energy storage system optimization scheduling model solving module is used for solving the battery energy storage system optimization scheduling model based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain an output scheme of the battery energy storage system;
and the output power adjusting module is used for adjusting the output power of the battery energy storage system based on the output power scheme of the battery energy storage system.
In a third aspect the invention provides a machine readable storage medium having stored thereon instructions which, when executed by a processor, cause the processor to be configured to perform the auxiliary thermal power plant peaking and frequency modulation method described above.
In a fourth aspect of the present invention, an electronic device is provided, where the electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the peak shaving and frequency modulation method of the auxiliary thermal power unit when executing the computer program.
According to the technical scheme, the peak regulation and frequency modulation method and the system for assisting the thermal power unit are provided, and the electricity selling duration of the battery energy storage system participating in electricity selling reservation peak regulation space of the energy market is calculated by acquiring operation data of the distributed photovoltaic power station at each moment; the method comprises the steps of obtaining expected output power and continuous output time of a frequency modulation AGC instruction at each moment of a power grid dispatching center, taking an output scheme of a battery energy storage system which is communicated with a photovoltaic power station circuit at each moment of the whole day as a model variable, taking the maximum net income of single-day operation as an optimization target, and establishing a battery energy storage system optimization dispatching model; and then solving the battery energy storage system optimization scheduling model by using a self-adaptive greedy search algorithm to obtain the output scheme of the battery energy storage system at each moment in the electricity selling time. And adjusting the output power of the battery energy storage system according to the output scheme. Therefore, when the net income of single-day operation is maximum, the operation state of the battery energy storage system at each moment can be obtained through the method and the system, and the operation cost is reduced and the operation profitability of the battery energy storage system is further improved while the performance advantage of the battery energy storage system is brought into play.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
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The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for assisting in peak regulation and frequency modulation of a thermal power unit according to an embodiment of the invention;
FIG. 2 is a flow chart of another method for assisting in peak shaving and frequency modulation of a thermal power unit according to an embodiment of the present invention;
FIG. 3 is a flow chart of a solution using an adaptive greedy search algorithm provided by one embodiment of the invention;
FIG. 4 is a block diagram of an auxiliary thermal power unit peak shaving and frequency modulation system provided by an embodiment of the invention;
fig. 5 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention.
Description of the reference numerals
10-electronic device, 100-processor, 101-memory, 102-computer program.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Fig. 1 is a flowchart of a method for assisting in peak regulation and frequency modulation of a thermal power unit according to an embodiment of the present invention, and fig. 2 is a flowchart of another method for assisting in peak regulation and frequency modulation of a thermal power unit according to an embodiment of the present invention. As shown in fig. 1 and fig. 2, an embodiment of the present invention provides a method for assisting peak shaving and frequency modulation of a thermal power generating unit, including:
s110: collecting operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data;
the photovoltaic power station can be a distributed photovoltaic power station, a centralized photovoltaic power station or a building integrated photovoltaic power station.
In some implementations of this embodiment, the operating data of the photovoltaic power plant at each time includes a distributed photovoltaic power plant load power P Y (t) and actual output power of each moment of distributed photovoltaic power stationP W (t) peak shaving frequency modulation basic data comprise various moments of light discarding power P of distributed photovoltaic power station rw (T) and the electricity selling time length T of the reserved peak regulation space of the electricity selling of the energy market participated by the battery energy storage system re The method comprises the steps of carrying out a first treatment on the surface of the Collecting operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data, wherein the method comprises the following steps:
according to the load power P of each moment of the distributed photovoltaic power station Y (t) and actual output power P of each moment of the distributed photovoltaic power station W (t) using the formula P rw (t)＝P Y (t)-P W (t) calculating to obtain the abandoned light power P of the distributed photovoltaic power station at each moment rw (t)；
According to the light discarding power P of each moment of the distributed photovoltaic power station rw (t) determining a peak period of light loss;
wherein, the light discarding power P is used according to each moment of the distributed photovoltaic power station rw (t) determining a peak solar disposal time period comprising: if the light rejection power P of the distributed photovoltaic power station at any moment t rw (t) satisfy Then the time t is marked as a light-discarding peak time; wherein, beta is more than or equal to 2.5 and less than or equal to 3.5; and when the continuous preset number of moments are all the light-discarding peak moments, recording the continuous preset number of moments as the light-discarding peak time.
Illustratively, when 12 or more consecutive times are peak light-off times, the time period is referred to as peak light-off time period.
According to the peak time of the solar waste, the formula is utilizedCalculating and obtaining electricity selling time length T of the battery energy storage system participating in electricity selling reservation peak regulation space of energy market re The method comprises the steps of carrying out a first treatment on the surface of the Wherein t is h Indicating peak period of light rejection, P rw (t h ) Representing the light discarding power, P of a distributed photovoltaic power station at each moment of the light discarding peak time max Indicating the maximum power output of the battery energy storage system.
S120: taking the output scheme of the battery energy storage system which is communicated with the photovoltaic power station circuit at each moment in the whole day as a model variable, and constructing an optimal scheduling model of the battery energy storage system by taking the maximum net income of single-day operation as an optimization target;
In some implementations of this embodiment, the rule for constructing the battery energy storage system optimization scheduling model is as follows: collecting frequency modulation AGC data of a power grid dispatching center and operation data of a battery energy storage system, and establishing an optimal dispatching model of the battery energy storage system; the frequency modulation AGC data of the power grid dispatching center comprises expected output power P of frequency modulation AGC instructions at each moment of the power grid dispatching center ref And the continuous output time T, and the battery energy storage system operation data comprise output power of the battery energy storage system at each moment, power of the battery energy storage system which is put into operation in different peak regulation and frequency modulation application scenes at the same moment, battery energy storage system operation constraint conditions, battery energy storage system operation cost and electricity selling conditions of the battery energy storage system which participate in electricity selling reservation peak regulation space of an energy market.
Specifically, a frequency modulation AGC command issued by a power grid dispatching center every 15 minutes is obtained during operation, and the expected output power P of the frequency modulation AGC command is obtained ref And the continuous power output time T takes the output of the battery energy storage system at each moment in the whole day as a decision variable, takes the power of the battery energy storage system which is put into each peak regulation and frequency modulation application scene to operate at the same moment as a participant strategy, and establishes a battery energy storage system optimization scheduling model by taking the maximum net income of single-day operation as an optimization target under the condition of taking various operation constraint conditions, income and cost of the battery energy storage system and the electricity selling condition of the reserved peak regulation space of the electricity selling of the energy market into consideration.
The formula of the optimal scheduling model of the battery energy storage system is as follows: i=s—c= (S 1 +S 2 +S 3 )-(C 1 +C 2 +C 3 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein I is the net benefit of the operation of the battery energy storage system, S is the total benefit, S 1 Frequency modulation gain for battery energy storage system S 2 Peak shaving gain for battery energy storage system, S 3 Is energyMarket benefit, C is total cost, C 1 C is the loss cost of the battery energy storage system 2 Punishment cost for unfinished frequency modulation instruction of battery energy storage system, C 3 Penalty costs incurred for battery energy storage systems not having completed peak shaving instructions.
In some implementations of this embodiment, the calculation formula of the frequency modulation gain obtained by the battery energy storage system is as follows:wherein P is fr Output power for frequency modulation action, B AGC The battery energy storage system participates in the frequency modulation compensation unit price, D is the compensation depth, and when the compensation depth of the frequency modulation power supply reaches the instruction effective threshold range P E And the value of the prescribed AGC command is +.> Expected output power P of frequency modulation AGC command for the i-th period ref The expected output power of the j-th instruction in the n AGC frequency modulation instructions, i represents the i-th period frequency modulation AGC instruction, and j represents the j-th frequency modulation AGC instruction in the i-th period frequency modulation AGC instruction;
k 1 、k 2 And k 3 The regulation effect indexes of the battery energy storage system tracking frequency modulation AGC instruction are all shown; wherein,
wherein P is S Output power P when receiving frequency modulation AGC instruction for battery energy storage system E For the effective threshold range of the FM AGC command, P E >0 is the discharge of the battery energy storage system, P E <0 is the battery energy storage system charge, v N To adjust the rate to the standard, P fr Output power of frequency modulation action, deltaP N To adjust the allowable deviation amount, t st For standard response time, Δt is the output power of the battery energy storage system from P S Change to P E The time required.
In some implementations of this embodiment, the calculation formula of the peak shaving benefit obtained by the battery energy storage system is as follows:wherein B is pr The compensation unit price of the battery energy storage system participating in peak regulation, P pr And the output power of the frequency modulation action is output time, and i represents an i-th period frequency modulation AGC instruction.
The peak shaving and energy metrics were calculated at one time (one time every 15 minutes), and the output was considered constant within 15 minutes.
In some implementations of this embodiment, the energy market benefit is calculated as follows:wherein B is em Time-of-use electricity price, P, for participation of battery energy storage system in energy market electricity selling em And the output power of the energy market action is output time, i represents an ith period frequency modulation AGC instruction.
In some implementations of this embodiment, the calculation formula of the loss cost of the battery energy storage system is as follows:wherein C is p For unit power cost of battery energy storage system, P b C is the maximum charge and discharge power of the battery energy storage system E For unit capacity cost of battery energy storage system, E b For rated capacity of battery energy storage system, N by The average service life of the battery energy storage system is prolonged.
In some implementations of this embodiment, the calculation formula of the penalty cost for the battery energy storage system to complete the frequency modulation command is as follows: c (C) 2 ＝∑B pfr P ref (fail); wherein B is pfr Punishment unit price of frequency modulation action which does not reach standard, P ref (fail) is the expected output of a frequency modulation command that does not meet the standard.
In some implementations of this embodiment, the calculation formula of the penalty cost suffered by the battery energy storage system that does not complete the peak shaving instruction is as follows: c (C) 3 ＝∑B ppr P rw (fail) t; wherein B is ppr Punishment unit price of peak regulation action which does not reach standard, P rw (fail) is the expected output power of the peak shaving instruction which does not reach the standard, and t is the output time.
It should be noted that parameters in the established battery energy storage system optimization scheduling model need to meet the battery energy storage system operation constraint conditions.
In some implementations of this embodiment, the battery energy storage system operating constraints include an output power constraint, a first constraint, a frequency modulation AGC instruction effective threshold range constraint, and a battery energy storage system state of charge constraint; wherein,
the output power constraint conditions are as follows:
P pr (i)∈{0,λ 2 P rw (i)}0<λ 2 <1；
P em (i)∈{0,λ 3 P max }0<λ 3 <1；
wherein,for n FM AGC commands in the ith periodForce power of frequency modulation action of jth instruction, < ->Representing the expected output power of the j-th command of the n frequency modulation AGC commands in the i-th period, P rw (i) Output power representing ith period peak shaving instruction, P pr (i) Output power of frequency modulation action in ith period, P em (i) Output power for the ith period of energy market action, P max Represents the maximum output power lambda of the battery energy storage system 1 Representing the frequency modulation output power coefficient lambda 2 Represents the peak-regulating output power coefficient lambda 3 Representing an energy market output power coefficient;
wherein,representing a lower limit on the operating power of the battery energy storage system, < + >>Representing an upper limit on the operating power of the battery energy storage system; and->P pr (i)P em (i)＝0；
The first constraint is:
0<Δt≤T-2；
wherein, delta t is the output power of the battery energy storage system from P S Change to P E Time required, T re The electricity selling time length of the battery energy storage system participating in electricity selling reservation peak shaving space of the energy market is represented, i represents an ith period frequency modulation AGC instruction, and t is output time;
The constraint condition of the effective threshold range of the frequency modulation AGC instruction is as follows:
P ref -ΔP N ≤P E ≤P ref +ΔP N ；
wherein P is ref ΔP is the desired output power of the battery energy storage system N To adjust the allowable deviation amount, P E An effective threshold range for the frequency modulation AGC command;
the battery energy storage system state of charge constraint conditions are:
SOC min ≤SOC(i)≤SOC max ；
wherein SOC is min SOC is the lower limit of the state of charge of a battery energy storage system max As the upper limit of the charge state of the battery energy storage system, SOC (i) is the charge state of the battery energy storage system at the ith moment, SOC (i+1) is the charge state of the battery energy storage system at the (i+1) th moment,output power T when the battery energy storage system receives the j command in the n frequency modulation AGC commands in the i time period j (i) For the duration of the j-th of the n FM AGC commands in the i-th period, Q N Is the capacity of the battery energy storage system.
S130: solving an optimal scheduling model of the battery energy storage system based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain a power scheme of the battery energy storage system;
specifically, based on peak regulation and frequency modulation basic data, solving a battery energy storage system optimization scheduling model by using a self-adaptive greedy search algorithm to obtain electricity selling duration T of the battery energy storage system participating in energy market electricity selling reservation peak regulation space re And (3) the output scheme of the battery energy storage system at each moment.
Referring to fig. 3, fig. 3 is a flowchart of a solution using an adaptive greedy search algorithm according to an embodiment of the present invention. In some implementations of this embodiment, the peak-shaving fm base data includes a discard power P at each moment of the distributed photovoltaic power station rw (T) and the electricity selling time length T of the reserved peak regulation space of the electricity selling of the energy market participated by the battery energy storage system re The method comprises the steps of carrying out a first treatment on the surface of the The solution to the battery energy storage system optimization scheduling model based on the peak shaving frequency modulation basic data and the self-adaptive greedy search algorithm comprises the following steps: according to the light discarding power P of each moment of the distributed photovoltaic power station rw (t) expected output power P of frequency modulation AGC instruction at each moment of power grid dispatching center ref And the continuous power output time T, calculating the benefit value of the battery energy storage system in each time period of the whole day, and the electricity selling time length T with the lowest benefit value re As an initial solution Ω for participating in energy market electricity selling; pre-running according to initial solution omega and battery energy storage system state of charge constraint conditions to obtain a battery energy storage system SOC running curve, and recording the time when the battery energy storage system continuously discharges to cause the state of charge to be lower than the lower limit of the state of charge of the battery energy storage system and participate in energy market electricity selling failure And the moment of failure of participating in peak shaving service due to the state of charge of the battery energy storage system being higher than the upper limit of the state of charge of the battery energy storage system as a result of continuous discharge of the battery energy storage system +.>Time of failure in selling electricity to participate in energy market +.>Removing from the initial solution omega to obtain a damaged solution set omega D The method comprises the steps of carrying out a first treatment on the surface of the According to the time of failure of selling electricity in the energy market +.>And the moment of failure to participate in peak shaving service ∈>Determining iteration parameters of a reconstruction stage; based on the reconstruction phase iteration parameters, the solution set omega' of the battery energy storage system participating in the reconstruction phase of electricity selling of the energy market is calculated in an iterative mode.
The above-mentioned iteration parameters of the reconstruction stage include the number of times N of participation of the battery energy storage system in the reconstruction stage in the energy market and the self-adaptive interval τ R The method comprises the steps of carrying out a first treatment on the surface of the The above-mentioned based on the iterative parameter of the reconstruction phase, calculate the solution set Ω' of the reconstruction phase that the battery energy storage system participates in the electricity selling of energy market iteratively, including: selecting an adaptive interval τ R The moment with the lowest profit value is taken as a reconstruction stage solution set omega ', the pre-operation is carried out according to the reconstruction stage solution set omega' and the state of charge constraint condition of the battery energy storage system, and the SOC operation curve of the battery energy storage system is obtained again; if the obtained battery energy storage system SOC running curve meets the number of times N of the battery energy storage system participating in the energy market in the reconstruction stage, updating the damage solution set omega by using the solution set omega' in the corresponding reconstruction stage D If the obtained SOC running curve of the battery energy storage system does not meet the number of times N of the battery energy storage system participating in the energy market in the reconstruction stage, the battery energy storage system is in the self-adaptive interval tau R And (3) re-selecting the solution set omega' in the reconstruction stage until the number of times N of participation of the battery energy storage system in the reconstruction stage in the energy market is met.
Specifically, the self-adaptive greedy search algorithm is utilized to solve the optimal scheduling model of the battery energy storage system, and the optimal scheduling model is divided into three stages of initialization, destruction and reconstruction. The first stage is an initialization stage, and the optical power P is abandoned according to each moment of the distributed photovoltaic power station rw (t) expected output power P of frequency modulation AGC instruction at each moment of power grid dispatching center ref Calculating the benefit value of the battery energy storage system in each period of the whole day by the duration of the output time T, and enabling the benefit value to be the lowest T re Time of day as participation energy marketAn initial solution Ω of electricity. The second stage is a destruction stage, pre-running is carried out according to the initial solution omega by considering the energy storage SOC limit, an SOC running curve of the energy storage system is calculated, and the time when the SOC is lower than a specified minimum value and participates in the electricity selling failure of the energy market due to continuous discharging is recordedAnd the time of participation in peak shaving service failure due to SOC higher than the prescribed maximum value as a result of continued charging +. >Will belong toPeriod culling from Ω results in a corrupted solution set Ω D . The third phase is the reconstruction phase, according to +.>And +.>Determining the number of times N of electricity selling in the energy market and the adaptive interval tau in the reconstruction stage R . Successive iterations, selecting τ in each iteration process R The moment with the lowest benefit is taken as a reconstruction stage solution set omega ', an SOC curve is calculated, and if the number of electricity selling times in the reconstruction stage is N, omega ' is updated by omega ' D Otherwise, at τ again R And omega' is selected until the electricity selling times in the reconstruction stage reach N. Wherein (1)>
S140: and adjusting the output power of the battery energy storage system based on the output scheme of the battery energy storage system.
In the implementation process, the method calculates and obtains the electricity selling time length of the battery energy storage system participating in the electricity selling reservation peak regulation space of the energy market by acquiring the operation data of the distributed photovoltaic power station at each moment; the method comprises the steps of obtaining expected output power and continuous output time of a frequency modulation AGC instruction at each moment of a power grid dispatching center, taking an output scheme of a battery energy storage system which is communicated with a photovoltaic power station circuit at each moment of the whole day as a model variable, taking the maximum net income of single-day operation as an optimization target, and establishing a battery energy storage system optimization dispatching model; and then solving the battery energy storage system optimization scheduling model by using a self-adaptive greedy search algorithm to obtain the output scheme of the battery energy storage system at each moment in the electricity selling time. And adjusting the output power of the battery energy storage system according to the output scheme. Therefore, when the net income of single-day operation is maximum, the operation state of the battery energy storage system at each moment can be obtained by the method, so that the performance advantage of the battery energy storage system is brought into play, the operation cost is reduced, and the operation profitability of the battery energy storage system is further improved.
Fig. 4 is a block diagram of an auxiliary peak shaving and frequency modulation system for a thermal power unit according to an embodiment of the present invention. As shown in fig. 4, an embodiment of the present invention provides an auxiliary peak shaving and frequency modulation system for a thermal power generating unit, including:
the peak regulation and frequency modulation basic data acquisition module is used for acquiring operation data of the photovoltaic power station at each moment to acquire peak regulation and frequency modulation basic data;
the battery energy storage system optimization scheduling model construction module is used for constructing a battery energy storage system optimization scheduling model by taking an output scheme of the battery energy storage system which is communicated with a photovoltaic power station circuit at each moment in the whole day as a model variable and taking the maximum net income of single-day operation as an optimization target;
the battery energy storage system optimization scheduling model solving module is used for solving the battery energy storage system optimization scheduling model based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain an output scheme of the battery energy storage system;
and the output power adjusting module is used for adjusting the output power of the battery energy storage system based on the output power scheme of the battery energy storage system.
Specifically, the system calculates and obtains the electricity selling time length of the battery energy storage system participating in the electricity selling reservation peak shaving space of the energy market by acquiring the operation data of the distributed photovoltaic power station at each moment; the method comprises the steps of obtaining expected output power and continuous output time of a frequency modulation AGC instruction at each moment of a power grid dispatching center, taking an output scheme of a battery energy storage system which is communicated with a photovoltaic power station circuit at each moment of the whole day as a model variable, taking the maximum net income of single-day operation as an optimization target, and establishing a battery energy storage system optimization dispatching model; and then solving the battery energy storage system optimization scheduling model by using a self-adaptive greedy search algorithm to obtain the output scheme of the battery energy storage system at each moment in the electricity selling time. And adjusting the output power of the battery energy storage system according to the output scheme. Therefore, when the net income of single-day operation is maximum, the operation state of the battery energy storage system at each moment can be obtained through the system, and the performance advantage of the battery energy storage system is brought into play, meanwhile, the operation cost is reduced, and the operation profitability of the battery energy storage system is further improved.
Embodiments of the present invention also provide a machine-readable storage medium having instructions stored thereon that, when executed by the processor 100, cause the processor 100 to be configured to perform the auxiliary thermal power unit peak shaving and frequency modulation method described above.
Machine-readable storage media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
The embodiment of the invention also provides an electronic device 10, the electronic device 10 comprises a memory 101, a processor 100 and a computer program 102 stored in the memory 101 and capable of running on the processor 100, and the processor 100 implements the auxiliary thermal power unit peak regulation and frequency modulation method when executing the computer program 102.
Fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 5, the electronic device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. The steps of the method embodiments described above are implemented by the processor 100 when executing the computer program 102. Alternatively, the processor 100, when executing the computer program 102, performs the functions of the modules/units of the apparatus embodiments described above.
By way of example, computer program 102 may be partitioned into one or more modules/units that are stored in memory 101 and executed by processor 100 to accomplish the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program 102 in the electronic device 10. For example, the computer program 102 may be partitioned into a peak shaving and frequency modulation base data acquisition module, a battery energy storage system optimization scheduling model construction module, a battery energy storage system optimization scheduling model solution module, and an output power adjustment module.
The electronic device 10 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 10 may include, but is not limited to, a processor 100, a memory 101. It will be appreciated by those skilled in the art that fig. 5 is merely an example of the electronic device 10 and is not intended to limit the electronic device 10, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The processor 100 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or a memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 10. Further, the memory 101 may also include both internal storage units and external storage devices of the electronic device 10. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program 102 product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program 102 product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program 102 products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program 102 instructions. These computer program 102 instructions may be provided to a processor 100 of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor 100 of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program 102 instructions may also be stored in a computer-readable memory 101 that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory 101 produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program 102 instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (17)
1. The peak regulation and frequency modulation method for the auxiliary thermal power unit is characterized by comprising the following steps of:
collecting operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data;
taking the output scheme of the battery energy storage system which is communicated with the photovoltaic power station circuit at each moment in the whole day as a model variable, and constructing an optimal scheduling model of the battery energy storage system by taking the maximum net income of single-day operation as an optimization target;
solving an optimal scheduling model of the battery energy storage system based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain a power scheme of the battery energy storage system;
and adjusting the output power of the battery energy storage system based on the output scheme of the battery energy storage system.
2. The auxiliary thermal power unit peak regulation and frequency modulation method according to claim 1, wherein the construction rule of the battery energy storage system optimization scheduling model is as follows:
collecting frequency modulation AGC data of a power grid dispatching center and operation data of a battery energy storage system, and establishing an optimal dispatching model of the battery energy storage system; wherein the frequency modulation AGC data of the power grid dispatching center comprises power grid dispatchingExpected output power P of central frequency modulation AGC command ref And the continuous output time T, wherein the battery energy storage system operation data comprise output power of the battery energy storage system at each moment, power of the battery energy storage system which is put into operation in different peak regulation and frequency modulation application scenes at the same moment, battery energy storage system operation constraint conditions, battery energy storage system operation cost and electricity selling conditions of the battery energy storage system which participates in the electricity selling reservation peak regulation space of the energy market.
3. The auxiliary thermal power unit peak regulation and frequency modulation method according to claim 2, wherein the formula of the battery energy storage system optimization scheduling model is as follows:
I＝S-C＝(S 1 +S 2 +S 3 )-(C 1 +C 2 +C 3 )；
wherein I is the net benefit of the operation of the battery energy storage system, S is the total benefit, S 1 Frequency modulation gain for battery energy storage system S 2 Peak shaving gain for battery energy storage system, S 3 For energy market benefit, C is the total cost, C 1 C is the loss cost of the battery energy storage system 2 Punishment cost for unfinished frequency modulation instruction of battery energy storage system, C 3 Penalty costs incurred for battery energy storage systems not having completed peak shaving instructions.
4. The method for assisting peak regulation and frequency modulation of a thermal power generating unit according to claim 3, wherein the calculation formula of the frequency modulation gain obtained by the battery energy storage system is as follows:
wherein P is fr Output power for frequency modulation action, B AGC The compensation unit price of the battery energy storage system participating in frequency modulation is represented by D, wherein D is the compensation depth, i represents an ith period frequency modulation AGC instruction, and j represents a jth frequency modulation AGC instruction in the ith period frequency modulation AGC instruction;
k 1 、k 2 and k 3 The regulation effect indexes of the battery energy storage system tracking frequency modulation AGC instruction are all shown; wherein,
wherein P is S Output power P when receiving frequency modulation AGC instruction for battery energy storage system E For the effective threshold range of the FM AGC command, P E >0 is the discharge of the battery energy storage system, P E <0 is the battery energy storage system charge, v N To adjust the rate to the standard, P fr Output power of frequency modulation action, deltaP N To adjust the allowable deviation amount, t st For standard response time, Δt is the output power of the battery energy storage system from P S Change to P E The time required.
5. The method for assisting peak regulation and frequency modulation of a thermal power generating unit according to claim 3, wherein the calculation formula of the peak regulation gain obtained by the battery energy storage system is as follows:
wherein B is pr The compensation unit price of the battery energy storage system participating in peak regulation, P pr And the output power of the frequency modulation action is output time, and i represents an i-th period frequency modulation AGC instruction.
6. The auxiliary thermal power generating unit peak regulation and frequency modulation method according to claim 3, wherein the calculation formula of the energy market benefit is as follows:
wherein B is em Time-of-use electricity price, P, for participation of battery energy storage system in energy market electricity selling em And the output power of the energy market action is output time, i represents an ith period frequency modulation AGC instruction.
7. The auxiliary thermal power unit peak shaving and frequency modulation method according to claim 3, wherein the loss cost of the battery energy storage system is calculated as follows:
Wherein C is p For unit power cost of battery energy storage system, P b C is the maximum charge and discharge power of the battery energy storage system E For unit capacity cost of battery energy storage system, E b For rated capacity of battery energy storage system, N by The average service life of the battery energy storage system is prolonged.
8. The method for assisting thermal power generating unit in peak regulation and frequency modulation according to claim 3, wherein the penalty cost of the battery energy storage system for not finishing the frequency modulation instruction is calculated as follows:
C 2 ＝∑B pfr P ref (fail)；
wherein B is pfr Punishment unit price of frequency modulation action which does not reach standard, P ref (fail) is the expected output of a frequency modulation command that does not meet the standard.
9. The auxiliary thermal power generating unit peak regulation and frequency modulation method according to claim 3, wherein a calculation formula of penalty cost suffered by the battery energy storage system when the peak regulation instruction is not completed is as follows:
C 3 ＝∑B ppr P rw (fail)t；
wherein B is ppr Punishment unit price of peak regulation action which does not reach standard, P rw (fail) is the expected output power of the peak shaving instruction which does not reach the standard, and t is the output time.
10. The auxiliary thermal power unit peak shaving and frequency modulation method according to claim 2, wherein the battery energy storage system operation constraint conditions comprise an output power constraint condition, a first constraint condition, a frequency modulation AGC instruction effective threshold range constraint condition and a battery energy storage system state of charge constraint condition; wherein,
The output power constraint conditions are as follows:
P pr (i)∈{0，λ 2 P rw (i)}0＜λ 2 ＜1；
P em (i)∈{0，λ 3 P max }0＜λ 3 ＜1；
wherein,the output power of the frequency modulation action for the j-th instruction in the n frequency modulation AGC instructions in the i-th period,representing the expected output power of the j-th command of the n frequency modulation AGC commands in the i-th period, P rw (i) Output power representing ith period peak shaving instruction, P pr (i) Output power of frequency modulation action in ith period, P em (i) Output power for the ith period of energy market action, P max Represents the maximum output power lambda of the battery energy storage system 1 Representing the frequency modulation output power coefficient lambda 2 Representing peak-shaving output power coefficient，λ 3 Representing an energy market output power coefficient;
wherein,representing a lower limit on the operating power of the battery energy storage system, < + >>Representing an upper limit on the operating power of the battery energy storage system; and->P pr (i)P em (i)＝0；
The first constraint condition is:
0＜Δt≤T-2；
wherein, delta t is the output power of the battery energy storage system from P S Change to P E Time required, T re The electricity selling time length of the battery energy storage system participating in electricity selling reservation peak shaving space of the energy market is represented, i represents an ith period frequency modulation AGC instruction, and t is output time;
the constraint condition of the effective threshold range of the frequency modulation AGC instruction is as follows:
P ref -ΔP N ≤P E ≤P ref +ΔP N ；
wherein P is ref ΔP is the desired output power of the battery energy storage system N To adjust the allowable deviation amount, P E An effective threshold range for the frequency modulation AGC command;
the battery energy storage system state of charge constraint conditions are:
SOC min ≤SOC(i)≤SOC max ；
wherein SOC is min SOC is the lower limit of the state of charge of a battery energy storage system max As the upper limit of the charge state of the battery energy storage system, SOC (i) is the charge state of the battery energy storage system at the ith moment, SOC (i+1) is the charge state of the battery energy storage system at the (i+1) th moment,output power T when the battery energy storage system receives the j command in the n frequency modulation AGC commands in the i time period j (i) For the duration of the j-th of the n FM AGC commands in the i-th period, Q N Is the capacity of the battery energy storage system.
11. The auxiliary thermal power unit peak regulation and frequency modulation method according to claim 1, wherein the operation data of each moment of the photovoltaic power station comprises load power P of each moment of the distributed photovoltaic power station Y (t) and actual output power P of each moment of the distributed photovoltaic power station W (t)；
The peak regulation and frequency modulation basic data comprise the light discarding power P of the distributed photovoltaic power station at each moment rw (T) and the electricity selling time length T of the reserved peak regulation space of the electricity selling of the energy market participated by the battery energy storage system re ；
The method for acquiring the operation data of the photovoltaic power station at each moment to obtain peak regulation and frequency modulation basic data comprises the following steps:
According to the load power P of each moment of the distributed photovoltaic power station Y (t) and actual output power P of each moment of the distributed photovoltaic power station W (t) using the formula P rw (t)＝P Y (t)-P W (t) calculating to obtain the abandoned light power P of the distributed photovoltaic power station at each moment rw (t)；
According to the light discarding power P of each moment of the distributed photovoltaic power station rw (t) determining a peak period of light loss;
according to the peak time of the abandoned light, the formula is utilizedCalculating and obtaining electricity selling time length T of the battery energy storage system participating in electricity selling reservation peak regulation space of energy market re The method comprises the steps of carrying out a first treatment on the surface of the Wherein t is h Indicating peak period of light rejection, P rw (t h ) Representing the light discarding power, P of a distributed photovoltaic power station at each moment of the light discarding peak time max Indicating the maximum power output of the battery energy storage system.
12. The method for peak regulation and frequency modulation of auxiliary thermal power unit according to claim 11, wherein the light power P is abandoned at each moment according to the distributed photovoltaic power station rw (t) determining a peak solar disposal time period comprising:
if the light rejection power P of the distributed photovoltaic power station at any moment t rw (t) satisfyThen the time t is marked as a light-discarding peak time; wherein, beta is more than or equal to 2.5 and less than or equal to 3.5;
and when the continuous preset number of moments are all the light-discarding peak moments, marking the continuous preset number of moments as the light-discarding peak moments.
13. The auxiliary thermal power unit peak regulation and frequency modulation method according to claim 1, wherein the peak regulation and frequency modulation basic data comprise the light rejection power P at each moment of the distributed photovoltaic power station rw (t) and battery energy storage systemElectricity selling time length T of reserved peak regulation space for participating in electricity selling in energy market re ；
The method for solving the optimal scheduling model of the battery energy storage system based on the peak shaving frequency modulation basic data and the self-adaptive greedy search algorithm comprises the following steps:
according to the light discarding power P of each moment of the distributed photovoltaic power station rw (t) expected output power P of frequency modulation AGC instruction at each moment of power grid dispatching center ref And the continuous power output time T, calculating the benefit value of the battery energy storage system in each time period of the whole day, and the electricity selling time length T with the lowest benefit value re As an initial solution Ω for participating in energy market electricity selling;
pre-running according to initial solution omega and battery energy storage system state of charge constraint conditions to obtain a battery energy storage system SOC running curve, and recording the time when the battery energy storage system continuously discharges to cause the state of charge to be lower than the lower limit of the state of charge of the battery energy storage system and participate in energy market electricity selling failureAnd the moment of failure of participating in peak shaving service due to the state of charge of the battery energy storage system being higher than the upper limit of the state of charge of the battery energy storage system as a result of continuous discharge of the battery energy storage system +. >
Time of failure in selling electricity to participate in energy marketRemoving from the initial solution Q to obtain a corrupted solution set omega D ；
According to the time of failure of electricity selling in the energy marketAnd the moment of failure to participate in peak shaving service ∈>Determining a reconstruction stageIteration parameters;
based on the reconstruction phase iteration parameters, a reconstruction phase solution set Q' of the battery energy storage system participating in energy market electricity selling is calculated in an iteration mode.
14. The method for assisting thermal power generating unit in peak regulation and frequency modulation according to claim 13, wherein the iteration parameters of the reconstruction stage comprise the number of times N of participation of a battery energy storage system of the reconstruction stage in energy market electricity selling and an adaptive interval tau R ；
The iterative calculation of the reconstruction phase solution set Q' of the battery energy storage system participating in the energy market electricity selling based on the reconstruction phase iteration parameters comprises the following steps:
selecting an adaptive interval τ R Taking the moment with the lowest profit value as a reconstruction stage solution set Q ', and pre-running according to the reconstruction stage solution set Q' and the state of charge constraint condition of the battery energy storage system to obtain a battery energy storage system SOC running curve again;
if the obtained battery energy storage system SOC running curve meets the number of times N of the battery energy storage system participating in the energy market in the reconstruction stage, updating the damage solution set Q by using the solution set Q' in the corresponding reconstruction stage D If the obtained SOC running curve of the battery energy storage system does not meet the number of times N of the battery energy storage system participating in the energy market in the reconstruction stage, the battery energy storage system is in the self-adaptive interval tau R And (3) re-selecting the solution set Q' in the reconstruction stage until the number of times N of participation of the battery energy storage system in the reconstruction stage in the energy market is met.
15. The utility model provides an supplementary thermal power generating unit peak regulation frequency modulation system which characterized in that includes:
the peak regulation and frequency modulation basic data acquisition module is used for acquiring operation data of the photovoltaic power station at each moment to acquire peak regulation and frequency modulation basic data;
the battery energy storage system optimization scheduling model construction module is used for constructing a battery energy storage system optimization scheduling model by taking a power output scheme of the battery energy storage system which is communicated with the photovoltaic power station circuit at each moment in the whole day as a model variable and taking the maximum net income of single-day operation as an optimization target;
the battery energy storage system optimization scheduling model solving module is used for solving the battery energy storage system optimization scheduling model based on peak regulation and frequency modulation basic data and a self-adaptive greedy search algorithm to obtain an output scheme of the battery energy storage system;
and the output power adjusting module is used for adjusting the output power of the battery energy storage system based on the output power scheme of the battery energy storage system.
16. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to be configured to perform the auxiliary thermal power unit peak shaving and frequency modulation method of any one of claims 1 to 14.
17. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the auxiliary thermal power unit peak shaving and frequency modulation method of any one of claims 1 to 14 when the computer program is executed by the processor.
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