CN112615399A - Energy storage system participating power grid frequency modulation optimization control method and system and storage medium - Google Patents

Energy storage system participating power grid frequency modulation optimization control method and system and storage medium Download PDF

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CN112615399A
CN112615399A CN202011331911.4A CN202011331911A CN112615399A CN 112615399 A CN112615399 A CN 112615399A CN 202011331911 A CN202011331911 A CN 202011331911A CN 112615399 A CN112615399 A CN 112615399A
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frequency modulation
energy storage
agc
storage system
generating unit
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王红星
于国强
周挺
邹燕
张天海
罗凯明
顾文
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State Grid Jiangsu Electric Power Co Ltd
Jiangsu Fangtian Power Technology Co Ltd
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State Grid Jiangsu Electric Power Co Ltd
Jiangsu Fangtian Power Technology Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a method, a system and a storage medium for energy storage system participating in power grid frequency modulation optimization control, wherein the method comprises the following steps: (1) constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system; (2) calculating a weight coefficient based on the power change rate and the SOC deviation; (3) establishing an AGC frequency modulation control target optimization function; (4) establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system; (5) and establishing a model of energy storage auxiliary thermal power generating unit frequency modulation to obtain a frequency deviation value of a regional power grid at each moment, an energy storage system charge state, a fire storage frequency modulation instruction distribution result and an actual output value. The method of the invention can effectively reduce the unit loss and greatly improve the frequency modulation response rate and quality.

Description

Energy storage system participating power grid frequency modulation optimization control method and system and storage medium
Technical Field
The invention relates to an energy storage frequency modulation control method, an energy storage frequency modulation control system and a storage medium, in particular to a method for participating in power grid frequency modulation optimization control of an energy storage system based on a genetic algorithm.
Background
Frequency is one of important indexes for measuring the quality of electric energy in a power grid, and in order to maintain safe and stable operation of power generation equipment and power utilization equipment in a power system, the frequency of the power grid must be kept within a qualified range through various means.
At present, in a power system in China, a conventional thermal power generating unit is mainly used for bearing the basic load of a power grid, providing AGC frequency modulation service and maintaining the safety and stability of the power grid. However, the conventional thermal power generating unit has the problems of low frequency modulation response speed, energy waste caused by frequent start and stop, heavy self-borne tasks, incapability of fully playing the frequency modulation function and the like, and is difficult to meet the increasing frequency modulation requirement. The energy storage can adapt to different time dimensions to participate in flexible adjustment, and the safe and stable operation capability of the power system can be greatly improved when the energy storage is applied in a large scale and is cooperatively and optimally operated with 'sources, networks, loads' and other adjustment resources. The 10MW energy storage system can accurately adjust the frequency modulation task of up to 20MW within 200ms, and compared with the traditional thermal power generating unit which only needs 2-3 seconds of delay time, the precision and the response time performance of the energy storage frequency modulation technology are 50-100 times of those of the traditional thermal power generating unit. Therefore, the conventional thermal power generating unit is provided with an energy storage system with a certain proportion, the characteristic of quickly and flexibly adjusting large-capacity energy storage is fully exerted, the thermal power generating unit is assisted to participate in AGC frequency modulation service, the frequency modulation capability of a power grid can be greatly improved, the loss of the thermal power generating unit is reduced, and the safety and the stability of the power grid are maintained.
According to statistics of a global energy storage project database of the United states energy agency (DOE), electrochemical energy storage is the most frequently applied to frequency modulation service by high-capacity energy storage at present, and physical energy storage is less frequently applied. In the frequency modulation process, the AGC command adjustment frequency is high due to real-time fluctuation of the power grid, so that an energy storage system participating in AGC frequency modulation must have good cycle life and short-time power throughput capacity. Meanwhile, because an AGC command needs to be tracked rapidly during frequency modulation, the battery energy storage participating in frequency modulation also needs to have a high-rate characteristic. In addition, safety, power density, cost, charge and discharge efficiency, etc. are all factors to be considered in energy storage type selection. Because of the advantages of mature technology, continuous cost reduction in recent years and the like, the lithium ion battery is the most widely applied energy storage type in frequency modulation services at present. By the end of 2016, the lithium battery energy storage occupies 73% of the whole electrochemical energy storage frequency modulation project. The lithium battery with key performance advantages is still the first choice for frequency modulation application energy storage in the future. At present, nearly 20 countries in the world build or put into operation energy storage frequency modulation application projects with more than 160 megawatts, and the energy storage frequency modulation application projects relate to a transmitting end, a transmission and distribution link and a demand side. Typical demonstration projects of energy storage auxiliary thermal power generating units participating in frequency modulation at home and abroad mainly comprise that Kilroot coal-fired power plants in England are provided with 10MW/5MW & h lithium ion batteries to provide frequency modulation service for a power system of North Ireland; the German STEAG GmbH project is that a Lunen hot spot co-production coal-fired power plant with the installed capacity of 507MW is equipped with a 15MW/22.5MW & h lithium ion battery for joint frequency modulation; the Shanxi Tongda power plant in China is provided with a 9MW/4.478MW & h lithium ion battery to assist a thermal power generating unit to participate in AGC frequency modulation. However, the energy storage system participating in the grid frequency modulation needs further optimized control.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: and the energy storage system participates in the frequency modulation process of the power grid, and how to further realize optimization control.
The working principle of the invention is as follows: on the basis of a fire storage combined AGC frequency modulation principle, the influence of the frequency modulation output characteristics of a thermal power generating unit and an energy storage system, the operating characteristics and sustainability of the energy storage system, the fire storage (thermal power generating unit and energy storage system) combined frequency modulation operating cost and different types of load disturbance on an AGC frequency modulation responsibility distribution mode is comprehensively considered, and an energy storage system participation power grid frequency modulation optimization control strategy based on a genetic algorithm is provided.
A method for controlling an energy storage system to participate in power grid frequency modulation optimization comprises the following steps:
(1) constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
(2) constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
(3) establishing an AGC frequency modulation control target optimization function;
(4) establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
(5) according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model for assisting the thermal power generating unit in frequency modulation of the energy storage is established, and the model is solved to obtain the frequency deviation value of the regional power grid, the charge state of the energy storage system, the distribution result of the fire storage frequency modulation instruction and the actual output value at each moment in an ARR (Area adjustment Requirement) signal distribution mode.
Specifically, the step (1) comprises the following steps:
(11) determining the influence factors of the fire storage combined AGC frequency modulation control method: the influence of different types of load disturbance on an AGC frequency modulation responsibility distribution mode, the output characteristics of the energy storage system and the thermal power unit frequency modulation, the operating characteristics and sustainability of the energy storage system (with capacity limitation), and the operating cost of fire-storage combined frequency modulation;
(12) the operation cost and the output active power of the traditional thermal power generating unit are combined to form a quadratic function relation; constructing a function suitable for reflecting the frequency modulation operation cost, wherein the function reflecting the frequency modulation operation cost of the thermal power generating unit is as follows:
Figure BDA0002796048670000031
in the formula, Ai,kReflecting the frequency modulation operation cost of the ith thermal power generating unit at the moment k; pagc,i,kAn AGC instruction of an ith thermal power generating unit at the time k; pi,k-1The actual frequency modulation output of the ith thermal power generating unit at the moment (k-1) is obtained; m isi,1,kAnd mi,2,kThe weight coefficient is a first weight coefficient and a second weight coefficient;
the function reflecting the frequency modulation operation cost of the energy storage system is
Figure BDA0002796048670000032
In the formula, Aj,kReflecting the frequency modulation operation cost of the jth energy storage battery at the moment k; pagc,j,kAn AGC instruction of a jth energy storage battery at the moment k; SOCj,kThe theoretical state of charge of the jth energy storage battery at the moment k; SOCrefA reference state of charge for which maintenance of the energy storage battery is desired; n isj,1,kAnd nj,2,kIs weight coefficient three and weight coefficient four.
The step (2) comprises the following steps:
(21) establishing a dynamic function of the weight coefficient based on the AGC instruction power change rate, and updating the weight coefficient in real time according to the change characteristics of the AGC instruction:
Figure BDA0002796048670000041
in the formula, mi,1,0、mi,2,0、nj,1,0、nj,2,0Reference values of a weight coefficient I, a weight coefficient II, a weight coefficient III and a weight coefficient IV are respectively set; v. ofkThe AGC instruction power change rate at the k moment; v. ofrefA power change rate reference value used for dividing frequency modulation responsibility; alpha, beta and gamma are a first proportionality coefficient, a second proportionality coefficient and a third proportionality coefficient of the influence factors;
wherein, the AGC instruction power change rate at the time k can be expressed as:
Figure BDA0002796048670000042
in the formula, Pagc,kAnd the instruction is an AGC frequency modulation output instruction at the k moment. Δ t is the sampling interval time.
Further, the step (3) specifically includes the following steps:
(31) according to the thermal power generating unit and the energy storage frequency modulation operation cost function in the step (1), combining the weight coefficient setting mode in the step (2), establishing an AGC frequency modulation control objective function:
min(∑Ai,k+∑Aj,k)
the target function comprehensively considers various technical indexes of the thermal power generating unit and the energy storage system, fully considers the respective response capability of the thermal power generating unit and the energy storage system to different frequency modulation instructions, and achieves the purpose of sustainable and stable work of energy storage by managing the charge state of the energy storage in real time. And realizing an expected frequency modulation responsibility distribution mode by utilizing a strategy that the frequency modulation operation cost is low and is preferentially scheduled.
Further, in the step (4):
(41) for the frequency modulation requirement, the sum of frequency modulation active power instructions borne by the thermal power generating unit and the energy storage is equal to the total AGC instruction at the same moment, namely:
Pagc,k＝∑Pagc,i,k+∑Pagc,j,k
in the formula, Pagc,kAn AGC frequency modulation output instruction at the time k; pagc,i,kAn AGC instruction of an ith thermal power generating unit at the time k; pagc,j,kAnd carrying out AGC instruction on the j-th energy storage battery at the k moment.
(42) For the frequency modulation capability, the method mainly comprises the climbing rate and the load reserve capacity of the thermal power generating unit, the limitation of energy storage charging and discharging power and the variable range of the charge state, namely:
Figure BDA0002796048670000051
in the formula, viIs the ithThe ramp rate of the thermal power generating unit; pi,min、Pi,maxRespectively setting the upper limit and the lower limit of the frequency-modulated output of the ith thermal power generating unit; pj,kThe actual frequency modulation output of the jth energy storage system at the moment k is obtained; pj,min、Pj,maxRespectively setting the output upper limit and the output lower limit of the jth energy storage system; SOCj,min、SOCj,maxRespectively setting the upper limit and the lower limit of the charge state of the jth energy storage system;
(43) and establishing AGC frequency modulation control constraint conditions from two aspects of frequency modulation requirements and frequency modulation capacity.
The step (5) specifically comprises:
(51) establishing an energy storage system model containing SOC and used for researching energy storage auxiliary frequency modulation:
Figure BDA0002796048670000052
in the formula, TBIs the time constant of the energy storage system; kTCalculating a time constant for the integrated electrical quantity; eBThe rated capacity of the energy storage system; sSOC,in(s) is an initial value of the SOC of the energy storage system; pB,ref(s) is an active power target instruction of the energy storage system; pB(s) actual output active power of the energy storage system; sSOC(s) is the actual state of charge of the energy storage system; s is not a variable with actual physical significance, a signal which is related to a complex variable s and is converted to an s domain is obtained after a time domain signal related to time t is subjected to Laplace transform, the variables in the formula are signals on the s domain which are obtained after corresponding time domain signals are subjected to Laplace transform, and the signals are subjected to Laplace inverse transform and can be restored to obtain corresponding time domain signals.
The real-time state of charge (SOC) calculation formula of the energy storage system is as follows:
Figure BDA0002796048670000053
(53) the method comprises the following steps of constructing a regional power grid frequency modulation dynamic model containing energy storage auxiliary thermal power generating unit frequency modulation:
Figure BDA0002796048670000061
wherein Δ f(s) is the system frequency deviation; delta Pline(s) exchanging power for interconnected grid tie lines; kIIs the integral coefficient of the PI regulator; kkThe proportional coefficient of the PI regulator; b is a secondary frequency modulation frequency deviation coefficient; pGi(s) a secondary frequency modulation output instruction of the ith traditional thermal power generating unit; pBj(s) is a secondary frequency modulation output instruction of the jth energy storage system; pGi1(s) is the primary frequency modulation output of the ith traditional thermal power generating unit; p'Gi(s) is the actual output active power of the ith traditional thermal power generating unit; p'Bj(s) is the active power actually output by the jth energy storage system; pLd(s) is system net load fluctuation; t isBIs the time constant of the energy storage system; kTCalculating a time constant for the integrated electrical quantity; eBThe rated capacity of the energy storage system; sSOC,in(s) is an initial value of the SOC of the energy storage system; sSOC(s) is the actual state of charge of the energy storage system; t isgIs the governor time constant; t isrIs the reheat time constant; t istIs the generator time constant; r is a unit difference adjustment coefficient; krIs the reheat coefficient; kpIs the system gain; t ispIs the system time constant; pAGC(s) an AGC command issued by a dispatching center; ACE(s) are area control offset values.
An energy storage system participates in a power grid frequency modulation optimization control system, which comprises the following functional modules:
frequency modulation operation cost function module: constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
a weight coefficient module: constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
an objective optimization function module: establishing an AGC frequency modulation control target optimization function;
a function solving module: establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
a frequency modulation model module: according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model for assisting the thermal power generating unit in frequency modulation of the energy storage is established, and a frequency deviation value of the regional power grid at each moment, the charge state of the energy storage system, a distribution result of a fire storage frequency modulation instruction and an actual output value in an ARR (Area adjustment Requirement) signal distribution mode are obtained.
A storage medium for an energy storage system participating in a power grid frequency modulation optimization control system stores the following functional modules:
frequency modulation operation cost function module: constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
a weight coefficient module: constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
an objective optimization function module: establishing an AGC frequency modulation control target optimization function;
a function solving module: establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
a frequency modulation model module: according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model for assisting the thermal power generating unit in frequency modulation of the energy storage is established, and a frequency deviation value of the regional power grid at each moment, the charge state of the energy storage system, a distribution result of a fire storage frequency modulation instruction and an actual output value in an ARR (Area adjustment Requirement) signal distribution mode are obtained.
The invention achieves the following beneficial effects: compared with the prior art, the invention has the following remarkable progress: 1. the influence of the frequency modulation output characteristics of the thermal power generating unit and the energy storage system, the operating characteristics and sustainability of the energy storage system, the fire storage combined frequency modulation operating cost and different types of load disturbance on the AGC frequency modulation responsibility distribution mode is fully considered; 2. by adopting a weight coefficient control strategy based on the power change rate, different frequency modulation characteristics of the thermal power generating unit and the stored energy are fully exerted, the unit loss is effectively reduced, and the frequency modulation response rate and quality are greatly improved; 3. the weight coefficient based on the SOC deviation value is innovatively introduced, the SOC of the energy storage system is managed in real time, the energy storage can be subjected to adaptive charging and discharging adjustment in the frequency modulation process, the state of charge is controlled to be always kept near the optimal state, the continuous and efficient operation of energy storage frequency modulation is guaranteed, and the application value is high.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of an energy storage system model;
FIG. 3 is a schematic diagram of a frequency modulation dynamic model of a regional power grid participated by an energy storage auxiliary thermal power generating unit;
FIG. 4 is a dynamic load disturbance curve;
FIG. 5 is a frequency deviation response curve;
FIG. 6 shows the AGC frequency modulation output instruction distribution result;
FIG. 7 is a change curve of the state of charge of the energy storage battery;
FIG. 8 is a frequency modulated operating cost variation curve;
FIG. 9 is a graph showing the variation rate of the AGC command and the variation of the weighting factor;
FIG. 10 is a regional grid frequency deviation response curve under 3 frequency modulation modes;
FIG. 11 is a response curve of regional power grid frequency deviation for 3 frequency modulation control methods;
FIG. 12 is an active output variation curve for 3 frequency modulation control modes;
FIG. 13 is a change curve of the state of charge of the energy storage battery in 3 frequency modulation control modes;
fig. 14 is a variation curve of the cumulative fm operation cost for 3 fm control modes.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the specific embodiments.
As shown in fig. 1, an energy storage system based on a genetic algorithm participates in a grid frequency modulation optimization control strategy, which includes the following steps:
(1) constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
(2) constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
(3) establishing an AGC frequency modulation control target optimization function;
(4) establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
(5) according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model for assisting the thermal power generating unit in frequency modulation of the energy storage is established, and the model is solved to obtain the frequency deviation value of the regional power grid, the charge state of the energy storage system, the distribution result of the fire storage frequency modulation instruction and the actual output value at each moment in an ARR (Area adjustment Requirement) signal distribution mode.
Specifically, the step (1) comprises the following steps:
(11) determining the influence factors of the fire storage combined AGC frequency modulation control method: the influence of different types of load disturbance on an AGC frequency modulation responsibility distribution mode, the output characteristics of the energy storage system and the thermal power unit frequency modulation, the operating characteristics and sustainability of the energy storage system (with capacity limitation), and the operating cost of fire-storage combined frequency modulation;
(12) the operation cost and the output active power of the traditional thermal power generating unit are combined to form a quadratic function relation; constructing a function suitable for reflecting the frequency modulation operation cost, wherein the function reflecting the frequency modulation operation cost of the thermal power generating unit is as follows:
Figure BDA0002796048670000091
in the formula, Ai,kReflecting the frequency modulation operation cost of the ith thermal power generating unit at the moment k; pagc,i,kAn AGC instruction of an ith thermal power generating unit at the time k; pi,k-1The actual frequency modulation output of the ith thermal power generating unit at the moment (k-1) is obtained; m isi,1,kAnd mi,2,kThe weight coefficient is a first weight coefficient and a second weight coefficient;
the function reflecting the frequency modulation operation cost of the energy storage system is
Figure BDA0002796048670000092
In the formula, Aj,kReflecting the frequency modulation operation cost of the jth energy storage battery at the moment k; pagc,j,kAn AGC instruction of a jth energy storage battery at the moment k; SOCj,kThe theoretical state of charge of the jth energy storage battery at the moment k; SOCrefA reference state of charge for which maintenance of the energy storage battery is desired; n isj,1,kAnd nj,2,kIs weight coefficient three and weight coefficient four.
The thermal power generating unit has a primary frequency modulation control instruction besides secondary frequency modulation control, and the thermal power generating unit can better track the sum of the primary frequency modulation control instruction and the secondary frequency modulation control instruction by setting the formula (1). Meanwhile, the thermal power generating unit can generate higher running cost to a certain extent when bearing frequency modulation instructions with high fluctuation frequency and rapid amplitude change.
The theoretical value of the SOC of the energy storage battery at time k can also be expressed as a function of the AGC command component to which the energy storage battery is assigned, i.e.:
SOCj,k＝SOCj,k-1-Pagc,j,k·Δt/EB (3)
in the formula, SOCj,k-1Representing the actual state of charge of the jth energy storage battery at the moment (k-1); Δ t represents a sampling interval time; eBRepresenting the energy storage rated capacity.
The setting of the formula (2) fully considers the characteristics of the frequency modulation output characteristic of the energy storage system and the requirement of maintaining the state of charge within a certain sustainable working range. The larger the deviation of the energy storage SOC from the reference value is, the higher the correspondingly generated frequency modulation operation cost is, so that the distribution of frequency modulation responsibility in the same direction of the energy storage is reduced.
The step (2) comprises the following steps:
(21) establishing a dynamic function of the weight coefficient based on the AGC instruction power change rate, and updating the weight coefficient in real time according to the change characteristics of the AGC instruction:
Figure BDA0002796048670000101
in the formula, mi,1,0、mi,2,0、nj,1,0、nj,2,0Reference values of a weight coefficient I, a weight coefficient II, a weight coefficient III and a weight coefficient IV are respectively set; v. ofkThe AGC instruction power change rate at the k moment; v. ofrefA power change rate reference value used for dividing frequency modulation responsibility; alpha, beta and gamma are a first proportionality coefficient, a second proportionality coefficient and a third proportionality coefficient of the influence factors;
wherein, the AGC instruction power change rate at the time k can be further expressed as:
Figure BDA0002796048670000102
in the formula, Pagc,kAnd the instruction is an AGC frequency modulation output instruction at the k moment.
vrefThe value is taken as the climbing rate of the thermal power generating unit, and the setting of the formula (4) ensures that when the power change rate of the AGC instruction is smaller than the climbing rate of the thermal power generating unit, the weight coefficient of the thermal power generating unit is reduced, the energy storage weight coefficient is increased, the operating cost occupied by the thermal power generating unit is lower at the moment, and more AGC instructions are borne by the thermal power generating unit. Otherwise, when the AGC instruction power change rate is greater than the unit ramp rate, the weight coefficient is three nj,1,kBecomes smaller by a weight coefficient of one mi,1,kThe larger the energy storage system will assume more responsibility for frequency modulation. In addition, the distribution of frequency modulation responsibility is influenced to a greater extent by the current state of charge of the energy storage system, and the more the energy storage SOC deviates from the reference value, the weight coefficient is four nj,2,kThe larger the energy is storedFrequency modulation will result in more operating costs and thus reduced energy storage output.
Further, the step (3) specifically includes the following steps:
(31) according to the thermal power generating unit and the energy storage frequency modulation operation cost function provided in the step (1), combining the weight coefficient setting mode in the step (2), establishing an AGC frequency modulation control objective function:
min(∑Ai,k+∑Aj,k) (6)
the target function comprehensively considers various technical indexes of the thermal power generating unit and the energy storage system, fully considers the respective response capability of the thermal power generating unit and the energy storage system to different frequency modulation instructions, and achieves the purpose of sustainable and stable work of energy storage by managing the charge state of the energy storage in real time. And realizing an expected frequency modulation responsibility distribution mode by utilizing a strategy that the frequency modulation operation cost is low and is preferentially scheduled.
Further, in the step (4):
(41) for the frequency modulation requirement, the sum of the frequency modulation active power commands borne by the thermal power generating unit and the energy storage unit should be equal to the total AGC command at this moment, that is:
Pagc,k＝∑Pagc,i,k+∑Pagc,j,k (7)
in the formula, Pagc,kAn AGC frequency modulation output instruction at the time k; pagc,i,kAn AGC instruction of an ith thermal power generating unit at the time k; pagc,j,kAnd carrying out AGC instruction on the j-th energy storage battery at the k moment.
(42) For the frequency modulation capability, the method mainly comprises the climbing rate and the load reserve capacity of the thermal power generating unit, the limitation of energy storage charging and discharging power and the variable range of the charge state, namely:
Figure BDA0002796048670000111
in the formula, viThe slope climbing rate is the ith thermal power generating unit; pi,min、Pi,maxThe upper limit and the lower limit of the frequency modulation output of the ith thermal power generating unit are set; pj,kThe actual frequency modulation output of the jth energy storage system at the moment k is obtained; pj,min、Pj,maxIs jthThe upper and lower output limits of each energy storage system; SOCj,min、SOCj,maxAnd the upper and lower limits of the state of charge of the jth energy storage system.
(43) And establishing AGC frequency modulation control constraint conditions from two aspects of frequency modulation requirements and frequency modulation capacity.
If all thermal power generating units and energy storage systems are respectively regarded as a whole and the internal frequency modulation responsibility distribution mode of each thermal power generating unit and energy storage system is neglected, the AGC frequency modulation control can be converted into a one-dimensional optimization problem by combining equation constraint of formula (7), namely when the total AGC frequency modulation command at the moment k is known, P in an objective functionagc,i,kAnd Pagc,j,kEither of the two variables can be represented by the difference of the total AGC chirp command and the other variable, thereby converting the two-dimensional optimization problem into one-dimensional optimization.
The invention adopts Genetic Algorithm (GA) to solve the AGC frequency modulation control target optimization function.
The genetic algorithm is a global optimization algorithm, a natural evolution process is simulated, the global optimal solution in a solution space can be quickly searched by utilizing operators such as selection, intersection, mutation and the like, and compared with the traditional optimization algorithm (enumeration method and the like), the optimization calculation amount and time are greatly reduced.
The genetic algorithm optimization implementation process is that individuals are subjected to selection in a natural evolution manner according to a natural evolution principle of 'physical competition and survival of suitable persons' in a population, and the excellence and the disadvantage are eliminated according to the size of the individual fitness value, so that the evolution process is converged towards the optimal solution direction. The basic steps of a common genetic algorithm are mainly as follows:
the first step is as follows: selecting a proper encoding strategy, including floating point number encoding, binary encoding and the like;
the second step is that: selecting a proper fitness function;
the third step: setting a proper genetic strategy, including setting parameters such as the size of a population, the number of evolution iterations, the upper limit and the lower limit of a population optimization range, selection, crossing and variation methods, crossing probability, variation probability and the like;
the fourth step: randomly initializing a population;
the fifth step: calculating the fitness value of the decoded individual bit string in the population according to the fitness function;
and a sixth step: according to a set genetic strategy, evolving into a next generation population by acting on the population through selection, crossing and mutation operators;
the seventh step: judging whether the population performance meets the index requirement or the preset iteration number, if so, terminating the optimization calculation and outputting a global optimal solution; and if the termination requirement is not met, jumping back to the fifth step to continue the optimization calculation.
If the AGC frequency modulation command borne by the thermal power generating unit at each moment is used as a parameter needing to be optimized in the Genetic Algorithm, the implementation steps of performing frequency modulation responsibility allocation optimization at each moment by using the Genetic Algorithm (GA) are as follows:
the first step is as follows: acquiring an AGC frequency modulation instruction at a moment k, (k-1) an SOC of an energy storage system at a moment k, (k-1) an actual frequency modulation output value of a thermal power generating unit at a moment k, and (k-1) an AGC frequency modulation instruction of the thermal power generating unit at a moment k;
the second step is that: selecting a proper coding strategy to code the parameter set;
the third step: according to the objective function of the formula (6), and in combination with the equation constraint condition of the formula (7), the fitness function is constructed as follows:
f(x)＝1/{m1,kx2+m2,k(x-Pg,k-1)2+n1,k(Pagc,k-x)2+n2,k[SOCk-1-(Pagc,k-x)·Δt/EB-SOCref]2} (9)
in the formula, x represents an optimized parameter, and refers to an AGC frequency modulation instruction borne by a thermal power generating unit at the moment k; pg,k-1Representing the actual frequency modulation output value of the thermal power generating unit at the moment (k-1); pagc,kAn AGC frequency modulation instruction representing k time; SOCk-1Representing the state of charge of the energy storage system at the moment (k-1); Δ t represents a sampling interval time; eBRepresenting the rated capacity of the stored energy; SOCrefA reference state of charge for which maintenance of the energy storage battery is desired; m is1,k、m2,k、n1,k、n2,kIs respectively weight coefficient five, weight coefficient six and weightA coefficient seven and a weight coefficient eight;
the fourth step: setting a genetic strategy, including parameters such as population scale, maximum iteration times, selection, crossing and mutation methods, crossing probability, mutation probability and the like; setting upper and lower limits of a population optimizing range according to the frequency modulation output value and the climbing rate limit of the thermal power generating unit at the moment (k-1) and the upper and lower limits of the frequency modulation output of the thermal power generating unit;
the fifth step: randomly generating an initial population according to a set genetic strategy;
and a sixth step: calculating the individual fitness value of the population according to the fitness function;
the seventh step: according to a set genetic strategy, acting on the population through selection, crossing and mutation operators, and evolving to generate a next generation population;
eighth step: judging whether the population performance meets the index requirement or the evolution reaches the preset iteration times, and returning to the sixth step if the population performance does not meet the index requirement or the evolution reaches the preset iteration times; if the termination condition is met, finishing the optimization calculation;
the ninth step: decoding and outputting a global optimal solution, namely an AGC frequency modulation instruction distributed to the thermal power generating unit at the moment k;
the tenth step: and calculating the AGC frequency modulation instruction distributed to the energy storage system at the moment k according to the formula (7) and the AGC frequency modulation instruction optimization result born by the thermal power generating unit at the moment k based on the GA.
The flow of optimizing the distribution of frequency modulation responsibilities using genetic algorithm is shown in fig. 1.
The step (5) specifically comprises:
(51) an energy storage system model containing the SOC and used for researching the energy storage auxiliary frequency modulation is established as shown in the figure 2:
Figure BDA0002796048670000131
in the formula, TBIs the time constant of the energy storage system; kTCalculating a time constant for the integrated electrical quantity; eBThe rated capacity of the energy storage system; sSOC,in(s) is an initial value of the SOC of the energy storage system; pB,ref(s) is an active power target instruction of the energy storage system；PB(s) actual output active power of the energy storage system; sSOC(s) is the actual state of charge of the energy storage system.
The real-time state of charge (SOC) calculation formula of the energy storage system is as follows:
Figure BDA0002796048670000141
(52) the method comprises the following steps of (1) constructing a regional power grid frequency modulation dynamic model containing energy storage auxiliary thermal power generating unit frequency modulation as shown in figure 3:
Figure BDA0002796048670000142
wherein Δ f(s) is the system frequency deviation; delta Pline(s) exchanging power for interconnected grid tie lines; kIIs the integral coefficient of the PI regulator; kkThe proportional coefficient of the PI regulator; b is a secondary frequency modulation frequency deviation coefficient; pGi(s) a secondary frequency modulation output instruction of the ith traditional thermal power generating unit; pBj(s) is a secondary frequency modulation output instruction of the jth energy storage system; pGi1(s) is the primary frequency modulation output of the ith traditional thermal power generating unit; p'Gi(s) is the actual output active power of the ith traditional thermal power generating unit; p'Bj(s) is the active power actually output by the jth energy storage system; pLd(s) is system net load fluctuation; t isBIs the time constant of the energy storage system; kTCalculating a time constant for the integrated electrical quantity; eBThe rated capacity of the energy storage system; sSOC,in(s) is an initial value of the SOC of the energy storage system; sSOC(s) is the actual state of charge of the energy storage system; t isgIs the governor time constant; t isrIs the reheat time constant; t istIs the generator time constant; r is a unit difference adjustment coefficient; krIs the reheat coefficient; kpIs the system gain; t ispIs the system time constant; pAGC(s) an AGC command issued by a dispatching center; ACE(s) are area control offset values.
According to the established model for the frequency modulation of the energy storage auxiliary thermal power generating unit, the frequency deviation value of the regional power grid, the charge state of the energy storage system, the distribution result of the fire storage frequency modulation instruction and the actual output value at each moment in the ARR signal distribution mode can be obtained.
Example 1
Model parameters:
considering the technical characteristics, the output characteristics, the frequency modulation characteristics and the like of the thermal power generating unit and the energy storage system, setting simulation model parameters as follows:
TABLE 1 regional power grid frequency modulation dynamic simulation model parameters
Parameter(s) Numerical value
Tg/s 0.08
Tt/s 0.3
Tr/s 10
TB/s 0.05
Tp/s 10
Kr 0.05
Kp/Hz/p.u 50
KI -0.027
Kk -0.1
B 0.42
R 2.5
K T 1/3600
A simulation model is built by utilizing a Matlab/simulink platform and a Matlab Function module, the installed capacity of the system is 1000MW, the selected reference power is 1000MW, and the initial charge state of the energy storage is set to be 50%. The variable range of the energy storage SOC is controlled to be 10% -90%, the rated power of the energy storage is +/-30 MW, the rated capacity of the energy storage is 15MW & h, the optimal state of charge of the energy storage is 50%, the standby capacity of the thermal power unit is 40MW, and the climbing rate is 3%/min of the rated power.
According to data statistics, about 80% of AGC instruction values in actual engineering are within 3% of the total installed capacity, so that the load disturbance mode selected in the invention is as follows: the net load fluctuates 4000s within the range of +/-30 MW, including various typical working conditions such as continuous low frequency, continuous high frequency and step.
The dynamic load disturbance curve is shown in fig. 4.
And (3) an AGC frequency modulation control simulation result based on a genetic algorithm: the simulation results of the AGC frequency modulation control method participating in regional power grid frequency modulation are shown in figures 5-9.
As shown in fig. 5, the fire storage joint AGC frequency modulation control method can control the frequency deviation in a very small range for the slowly changing load disturbance energy; for step disturbance with large fluctuation, although the frequency deviation is obviously larger, the frequency can be quickly recovered in a very short time, and the frequency fluctuation is maintained in a small range.
As can be seen from FIG. 6, the output instruction allocated to the thermal power unit is smooth, the frequency modulation responsibility with low frequency and large amplitude is mainly assumed, and the abrasion of the thermal power unit is effectively reduced. Within 0-2500 s, the load disturbance changes slowly, and the output instruction of the energy storage system is extremely small; within 1000-1500 s, the output of the energy storage system is rapidly reduced to zero, and the thermal power generating unit bears all high-amplitude frequency modulation responsibilities, so that the energy storage SOC is effectively maintained; when the step load disturbance occurs in 2500s, the output of the energy storage system is rapidly responded, and in 2500-3500 s, a high-frequency AGC output instruction is mainly distributed to the energy storage system, so that the characteristics of high response speed and high adjustment precision of energy storage frequency modulation are fully exerted.
As can be seen from fig. 7, the charge state of the energy storage system is better maintained between 47% and 52%, so that the energy storage system can continuously work under better frequency modulation performance when assisting the AGC frequency modulation of the thermal power generating unit. Within 500-1000 s, the AGC frequency modulation instruction is increased, the SOC slowly decreases as the energy storage system bears part of smaller frequency modulation responsibility, and the SOC decreasing speed is gradually slowed down along with the increase of the weight coefficient n 2; within 1000-1500 s, the energy storage output instruction is rapidly reduced to zero, and the SOC is basically unchanged; within 1500-2500 s, the AGC frequency modulation output instruction is gradually reduced, the stored energy bears a small part of frequency modulation responsibility for charging, the SOC is gradually increased, and the SOC rising rate is increased and then decreased along with the change of the weight coefficient; within 2500-3500 s, the load disturbance is in a step-like high-frequency fluctuation state, the stored energy bears the main frequency modulation responsibility, the SOC is rapidly reduced within a short time, and the SOC reduction rate is slowed down along with the reduction of the fluctuation amplitude of the load disturbance; after 3500s, the load disturbance gradually decreases to zero, and the energy storage system performs charging and gradually recovers to the optimum state of charge.
Fig. 9 reflects the variation of the part weight coefficients in the AGC instruction variation rate and the frequency modulation operation cost function during the frequency modulation of the regional power grid. It can be seen that the weight coefficient n2 changes obviously with the change of the SOC, and the two influence each other, so that the energy storage battery is effectively managed to maintain a better state of charge. When the step load disturbance occurs at 2500s, the AGC command change rate is obviously increased, and then the weight coefficients m1 and n1 are obviously influenced, so that the high-frequency AGC output command is more assigned to the energy storage system to bear.
Comparison of frequency modulation modes: simulation comparison is carried out on the frequency modulation of the regional power grid under 3 different modes. The mode 1 is fire-storage combined AGC frequency modulation, the mode 2 is thermal power unit AGC frequency modulation (without energy storage), and the mode 3 is thermal power unit primary frequency modulation (without energy storage).
As can be seen from fig. 10, under the frequency modulation effect of the mode 3, since the primary frequency modulation is a difference frequency modulation, when the load disturbance exists in the regional power grid, the frequency deviation is large, within 1000 to 1500s, although the load disturbance is maintained unchanged, the frequency still has a stable deviation; for the mode 2, due to the effect of secondary frequency modulation of the thermal power generating unit, when the load disturbance changes slowly, the frequency deviation comparison mode 3 is obviously reduced, but due to the limitation of the climbing rate of the thermal power generating unit, the frequency deviation is larger in the face of the load disturbance of high-frequency fluctuation, and the requirement of the electric energy quality cannot be met; mode 1 adopts a fire-storage combined AGC frequency modulation control mode, fully utilizes the characteristics of rapid and accurate response of energy storage frequency modulation, can effectively control the frequency fluctuation within a small range for various load disturbances, and obviously improves the frequency modulation capability and quality.
And (3) comparing frequency modulation control methods: comparing the AGC frequency modulation control method of the present invention with two other common control methods, wherein method 1 uses a difference compensation method, method 2 uses a static proportion distribution method, and simulation results are shown in fig. 11-14.
Fig. 11 reflects the regional grid frequency deviation response under 3 frequency modulation control methods. It can be seen that, for load disturbance with slow change, the frequency offset can be controlled within a very small range by 3 methods, wherein the method and the method 1 of the present invention control the frequency offset change more smoothly. For high-frequency step load disturbance, the method has remarkable advantages in frequency modulation. In 2500-3500 s, the method of the invention achieves the maximum negative frequency deviation of-0.2237 Hz at 2505s and achieves the maximum positive frequency deviation of 0.074Hz at 2577 s. And method 1 reaches the maximum negative frequency deviation of-0.2360 Hz at 2506s and reaches the maximum positive frequency deviation of 0.08891Hz at 2530 s. Method 2 reached a maximum negative frequency offset of-0.4136 Hz at 2509s and a maximum positive frequency offset of 0.2762Hz at 2533 s. It can be seen that the maximum frequency offset is smaller by adopting the method of the invention, and the frequency offset fluctuation range is always obviously smaller than that of the other two methods in the whole time period, so that the frequency recovery speed is higher. In the method 2, the frequency modulation responsibility is distributed between the thermal power generating unit and the energy storage according to the static proportion, and the frequency modulation responsibility distributed to the thermal power generating unit obviously cannot be responded in time when step disturbance is faced, so that large frequency fluctuation is caused. The method 1 makes full use of the characteristic of rapid response of stored energy, and the frequency fluctuation is obviously smaller than that of the method 2.
Fig. 12 reflects the frequency modulation output conditions of the thermal power generating unit and the energy storage AGC under the 3 frequency modulation control methods. Compared with other two methods, the method has the advantages that the thermal power generating unit and the energy storage frequency modulation output are smoother, the fluctuation is less, the abrasion is reduced, and the operation reliability is improved.
Fig. 13 reflects the state of charge change of the energy storage battery under 3 frequency modulation control methods. It can be seen that by effectively managing the energy storage battery SOC in real time under the method of the invention, the state of charge of the energy storage battery is always maintained near the optimal state of charge, after bearing various frequency modulation tasks, the energy storage SOC can be gradually restored to the optimal state through self-adaptive charging and discharging regulation, the energy storage SOC is 49.52% at 4000s, and the energy storage SOC is restored to 50% at 4666s, so that sustainable and high-quality frequency modulation work is realized; after step-like high-frequency fluctuation load disturbance is added, the energy storage SOC under the method 1 is obviously deviated, the energy storage SOC is reduced to 44.13% at 3500s, and at the moment, the energy storage SOC under the method is 48.78%, the state of charge is maintained better, the energy storage SOC under the method 1 cannot be self-adaptively recovered to the optimal state of charge, the energy storage SOC is reduced to zero along with the load disturbance after 4000s, and the energy storage SOC is maintained at 43.58%; in the method 2, the energy storage output characteristic and the real-time state of charge are not considered, after a series of frequency modulation instructions, the energy storage SOC obviously deviates from the reference state of charge due to continuous output, the SOC is reduced to 20.51% at 4000s, the self-adaptive recovery cannot be realized, the frequency modulation is easily quitted due to too low electric quantity, and the continuous work cannot be realized.
Fig. 14 reflects the variation of the cumulative fm operation cost under 3 fm control methods. It can be seen that, with the excessive utilization of the energy storage battery, the frequency modulation operation cost of the method 2 is obviously higher than that of the other two methods; when the load disturbance shows low-frequency slow change, the frequency modulation operation cost of the method 1 is almost the same as that of the method 1, and along with the occurrence of high-frequency step load disturbance, the accumulated frequency modulation operation cost of the method 1 is gradually and obviously higher than that of the method. By the time of 4000s, the total accumulated total operating cost of frequency modulation under the method is 0.6276, the method 1 is 1.496, the method 2 is 33.650, and the method strategy of the invention also shows superiority in economic optimization.
In summary, on the basis of the principle of the fire storage combined AGC frequency modulation, the invention provides an energy storage system participation power grid frequency modulation optimization control strategy based on a genetic algorithm after comprehensively considering the frequency modulation output characteristics of the thermal power generating unit and the energy storage system, the operating characteristics and sustainability of the energy storage system, the fire storage combined frequency modulation operating cost and the influence of different types of load disturbance on the AGC frequency modulation responsibility distribution mode. The control strategy solves an optimization problem by establishing a mathematical model reflecting the frequency modulation operation cost, adopting a weight coefficient based on the power change rate and the SOC deviation and a genetic algorithm, and realizing an expected frequency modulation responsibility distribution mode by utilizing a strategy that the priority scheduling is carried out when the frequency modulation operation cost is low. According to the characteristics of a frequency modulation power supply and the technical characteristics of frequency modulation, a regional power grid dynamic frequency modulation model is built, and a simulation comparison experiment is carried out by utilizing a Matlab/simulink platform.
Simulation results show that the energy storage system provided by the invention is adopted to assist the thermal power generating unit to participate in a power grid frequency modulation optimization control strategy, and frequency modulation instructions with slow change, low frequency and high amplitude can be distributed to the thermal power generating unit, so that the output of the thermal power generating unit is smoother, and the unit loss is reduced; the energy storage mainly bears high-frequency and low-amplitude frequency modulation instructions, so that the frequency modulation advantage of the energy storage system in quick and accurate response is fully exerted, the frequency modulation speed and quality are greatly improved, and the frequency modulation requirement is met. Meanwhile, the energy storage SOC can be maintained to fluctuate slightly near the optimal state of charge through real-time management of the energy storage SOC, and continuous and efficient work of an energy storage system is guaranteed. In addition, the optimization control strategy of the invention also shows superiority for optimizing economy and reducing the frequency modulation operation cost.

Claims (8)

1. A method for controlling energy storage system participating in power grid frequency modulation optimization is characterized by comprising the following steps:
(1) constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
(2) constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
(3) establishing an AGC frequency modulation control target optimization function;
(4) establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
(5) according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model of energy storage auxiliary thermal power generating unit frequency modulation is established, and the model is solved to obtain the frequency deviation value of the regional power grid, the charge state of the energy storage system, the distribution result of the fire storage frequency modulation instruction and the actual output value at each moment in the distribution mode based on the regional regulation demand signal.
2. The method for participating in grid frequency modulation optimization control of the energy storage system according to claim 1, wherein the step (1) specifically comprises the steps of:
(11) determining the influence factors of the fire storage combined AGC frequency modulation control method: the influence of different types of load disturbance on an AGC frequency modulation responsibility distribution mode, the output characteristics of the energy storage system and the thermal power unit frequency modulation, the operating characteristics and sustainability of the energy storage system and the operating cost of the fire-storage combined frequency modulation;
(12) constructing a function suitable for reflecting the frequency modulation operation cost:
a. the function reflecting the frequency modulation operation cost of the thermal power generating unit is as follows:
Figure FDA0002796048660000011
in the formula, Ai,kReflecting the frequency modulation operation cost of the ith thermal power generating unit at the moment k; pagc,i,kAn AGC instruction of an ith thermal power generating unit at the time k; pi,k-1The actual frequency modulation output of the ith thermal power generating unit at the moment (k-1) is obtained;
mi,1,kand mi,2,kThe weight coefficient is a first weight coefficient and a second weight coefficient;
b. the function reflecting the frequency modulation operation cost of the energy storage system is
Figure FDA0002796048660000021
In the formula, Aj,kReflecting the frequency modulation operation cost of the jth energy storage battery at the moment k; pagc,j,kAn AGC instruction of a jth energy storage battery at the moment k; SOCj,kThe theoretical state of charge of the jth energy storage battery at the moment k; SOCrefA reference state of charge for which maintenance of the energy storage battery is desired; n isj,1,kAnd nj,2,kIs weight coefficient three and weight coefficient four.
3. The method for participating in grid frequency modulation optimization control of the energy storage system according to claim 2, wherein the step (2) specifically comprises the steps of:
establishing a dynamic function of the weight coefficient based on the AGC instruction power change rate, and updating the weight coefficient in real time according to the change characteristics of the AGC instruction:
Figure FDA0002796048660000022
in the formula, mi,1,0、mi,2,0、nj,1,0、nj,2,0Reference values of a weight coefficient I, a weight coefficient II, a weight coefficient III and a weight coefficient IV are respectively set; v. ofkThe AGC instruction power change rate at the k moment; v. ofrefA power change rate reference value used for dividing frequency modulation responsibility; alpha, beta and gamma are a first proportionality coefficient, a second proportionality coefficient and a third proportionality coefficient of the influence factors;
wherein, the AGC instruction power change rate at the k moment is expressed as:
Figure FDA0002796048660000023
in the formula, Pagc,kAnd delta t is an AGC frequency modulation output instruction at the moment k and is sampling interval time.
4. The method for participating in grid frequency modulation optimization control of the energy storage system according to claim 3, wherein in the step (3), the establishing of the AGC frequency modulation control objective function is:
min(∑Ai,k+∑Aj,k)。
5. the method for participating in grid frequency modulation optimization control of the energy storage system according to claim 4, wherein the step (4) specifically comprises the following steps:
(41) for the frequency modulation requirement, the sum of frequency modulation active power instructions borne by the thermal power generating unit and the energy storage is equal to the total AGC instruction at the same moment, namely:
Pagc,k＝∑Pagc,i,k+∑Pagc,j,k
in the formula, Pagc,kAn AGC frequency modulation output instruction at the time k; pagc,i,kAn AGC instruction of an ith thermal power generating unit at the time k; pagc,j,kAn AGC instruction of a jth energy storage battery at the moment k;
(42) for the frequency modulation capability, the ramp rate and the load reserve capacity of the thermal power generating unit, the energy storage charging and discharging power limitation and the charge state variable range are included, namely:
Figure FDA0002796048660000031
in the formula, viThe slope climbing rate is the ith thermal power generating unit; pi,min、Pi,maxRespectively setting the upper limit and the lower limit of the frequency-modulated output of the ith thermal power generating unit; pj,kThe actual frequency modulation output of the jth energy storage system at the moment k is obtained; pj,min、Pj,maxRespectively setting the output upper limit and the output lower limit of the jth energy storage system; SOCj,min、SOCj,maxRespectively setting the upper limit and the lower limit of the charge state of the jth energy storage system;
(43) and establishing AGC frequency modulation control constraint conditions from two aspects of frequency modulation requirements and frequency modulation capacity.
6. The method for participating in grid frequency modulation optimization control of the energy storage system according to claim 5, wherein the step (5) specifically comprises the following steps:
(51) establishing an energy storage system model containing SOC and used for researching energy storage auxiliary frequency modulation:
Figure FDA0002796048660000032
in the formula, TBIs the time constant of the energy storage system; kTCalculating a time constant for the integrated electrical quantity; eBThe rated capacity of the energy storage system; sSOC,in(s) is an initial value of the SOC of the energy storage system; pB,ref(s) is an active power target instruction of the energy storage system; pB(s) actual output active power of the energy storage system; sSOC(s) is the actual state of charge of the energy storage system.
The real-time SOC calculation formula of the energy storage system is as follows:
Figure FDA0002796048660000041
(52) the method comprises the following steps of constructing a regional power grid frequency modulation dynamic model containing energy storage auxiliary thermal power generating unit frequency modulation:
Figure FDA0002796048660000042
wherein Δ f(s) is the system frequency deviation; delta Pline(s) exchanging power for interconnected grid tie lines; kIIs the integral coefficient of the PI regulator; kkThe proportional coefficient of the PI regulator; b is a secondary frequency modulation frequency deviation coefficient; pGi(s) a secondary frequency modulation output instruction of the ith traditional thermal power generating unit; pBj(s) is a secondary frequency modulation output instruction of the jth energy storage system; pGi1(s) is the primary frequency modulation output of the ith traditional thermal power generating unit; p'Gi(s) is the actual output active power of the ith traditional thermal power generating unit; p'Bj(s) is the active power actually output by the jth energy storage system; pLd(s) is system net load fluctuation; t isBIs the time constant of the energy storage system; kTCalculating a time constant for the integrated electrical quantity; eBThe rated capacity of the energy storage system; sSOC,in(s) is an initial value of the SOC of the energy storage system; sSOC(s) is the actual state of charge of the energy storage system; t isgIs the governor time constant; t isrIs the reheat time constant; t istIs the generator time constant; r is a unit difference adjustment coefficient; krIs the reheat coefficient; kpIs the system gain; t ispIs the system time constant; pAGC(s) an AGC command issued by a dispatching center; ACE(s) are area control offset values.
7. The utility model provides an energy storage system participates in electric wire netting frequency modulation optimal control system which characterized in that: the system comprises the following functional modules:
frequency modulation operation cost function module: constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
a weight coefficient module: constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
an objective optimization function module: establishing an AGC frequency modulation control target optimization function;
a function solving module: establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
a frequency modulation model module: according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model for assisting the thermal power generating unit in frequency modulation of the energy storage is established, and the frequency deviation value of the regional power grid at each moment, the charge state of the energy storage system, the distribution result of the fire storage frequency modulation instruction and the actual output value in the distribution mode based on the regional adjustment demand signal are obtained.
8. A storage medium for an energy storage system participating in a power grid frequency modulation optimization control system is characterized in that: the following functional modules are stored:
frequency modulation operation cost function module: constructing a function suitable for reflecting the frequency modulation operation cost of the thermal power generating unit and the energy storage system;
a weight coefficient module: constructing a dynamic function of a weight coefficient in the frequency modulation operation cost function, and updating the weight coefficient value in real time based on the power change rate and the SOC deviation;
an objective optimization function module: establishing an AGC frequency modulation control target optimization function;
a function solving module: establishing an AGC frequency modulation control constraint condition, and solving the established AGC frequency modulation control target optimization function by using a genetic algorithm to obtain an instruction distribution result of an AGC instruction issued by a dispatching center at each moment between the thermal power generating unit and the energy storage system;
a frequency modulation model module: according to the output characteristics of the energy storage system and the thermal power generating unit and the frequency modulation mode of the regional power grid, a model for assisting the thermal power generating unit in frequency modulation of the energy storage is established, and the frequency deviation value of the regional power grid at each moment, the charge state of the energy storage system, the distribution result of the fire storage frequency modulation instruction and the actual output value in the distribution mode based on the regional adjustment demand signal are obtained.
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CN117713174A (en) * 2023-12-15 2024-03-15 国网青海省电力公司清洁能源发展研究院 Power adjusting method for energy storage system

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111697597A (en) * 2020-07-27 2020-09-22 国网江苏省电力有限公司 Fire storage combined AGC frequency modulation control method based on particle swarm optimization

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111697597A (en) * 2020-07-27 2020-09-22 国网江苏省电力有限公司 Fire storage combined AGC frequency modulation control method based on particle swarm optimization

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CN115313416A (en) * 2022-07-11 2022-11-08 华中科技大学 Multi-objective optimization control method suitable for auxiliary frequency modulation system of energy storage power station
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CN116404668B (en) * 2023-04-27 2024-01-23 华电国际电力股份有限公司朔州热电分公司 Flywheel energy storage auxiliary frequency modulation control method and system for improving AGC (automatic gain control) regulation rate
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