CN115276055B - Energy storage configuration method and system based on power grid frequency spatial distribution characteristics - Google Patents
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Abstract
The invention discloses an energy storage configuration method and system based on power grid frequency spatial distribution characteristics, and belongs to the technical field of electrical engineering. The method comprises the following steps: the method comprises the steps of calculating the total power capacity of energy storage configuration and the system fault probability in the population iteration process based on a non-inferior ranking genetic algorithm to obtain the optimal total power capacity of energy storage configuration and the optimal system fault probability, wherein the total power capacity of energy storage configuration is used as an economic index for measuring the power system, the fault probability is used as a frequency stability index for measuring the power system, and an energy storage capacity configuration scheme which is most consistent with the expectation of an investor is obtained according to the economic performance of the energy storage configuration and the system frequency stability weight coefficient of the investor. The method can give consideration to both economy and power grid frequency stability, quickly realize the site selection and constant volume of the inertial support energy storage system, ensure the transient stability of the power grid frequency, and solve the problem that the existing energy storage configuration method is difficult to give consideration to both frequency space distribution characteristics and solving difficulty.
Description
Technical Field
The invention belongs to the technical field of electrical engineering, and particularly relates to an energy storage configuration method and system based on power grid frequency spatial distribution characteristics.
Background
In recent years, as new energy generating sets are continuously connected to a power system, the starting proportion of the traditional synchronous generating sets is continuously reduced, the inertia of the system is reduced, and the frequency stability faces a great challenge. And the energy storage can participate in the inertial support link of the frequency of the power system through a proper inertial support control mode by virtue of the bidirectional and quick adjusting characteristic of the energy storage, so that the frequency transient stability of the power system is improved. However, the price of energy storage is relatively high, and an energy storage configuration method for improving the inertia of the system is the key for ensuring the stability and the economy of the system.
The existing energy storage configuration method mainly takes the frequency of an inertia central point as the frequency of the whole network, neglects the influence of the power grid, and weights and equates all generators of the system into an equivalent unit according to the inertia size, so that the dynamic frequency change of the whole system can be well reflected, the calculation amount is small, the configuration is easy, but the model cannot reflect the dynamic frequency difference of different areas of the system, and when the energy storage inertia support is configured, the model can only analyze the influence of the energy storage capacity on the frequency support, cannot analyze the effect of the energy storage access point on the frequency support, and has certain limitation. In order to analyze the dynamic influence of the units in different areas, an all-state analysis method is mainly adopted at present, numerical iteration solution is carried out by establishing detailed mathematical models and network equations of the units, but the method has large calculation amount, the simulation mode adopted for optimal configuration has long time consumption, and the method is not suitable for being used as a frequency model for optimal configuration. In addition, the configuration methods in the prior art do not consider the economic efficiency and stability index of the power system at the same time.
Disclosure of Invention
Aiming at the defects and the improvement requirements of the prior art, the invention provides an energy storage configuration method and an energy storage configuration system based on the frequency-space distribution characteristics of a power grid, and aims to simultaneously consider the frequency-space distribution characteristics of energy storage and reduce the calculation amount of energy storage configuration.
In order to achieve the above object, according to an aspect of the present invention, there is provided an energy storage configuration method based on grid frequency spatial distribution characteristics, including:
s1, setting upper and lower limits of energy storage power capacity of a new energy source side and a network side and a node range set No accessed by a network side centralized energy storage power station e ；
S2, collecting No by using upper and lower limits of energy storage power capacity and node range e For input, a genetic algorithm is used to randomly generate N pop Configuring an initial population by each energy storage;
s3, calculating the total energy storage power capacity configured for each individual in the initial population; and calculating N-1 fault accident set A based on direct current power flow frequency response model N-1 The frequency change rate of the lower non-fault synchronous generator is limited according to the frequency change rate to obtain the fault switching rate of the system;
s4, according to the total energy storage power capacity and the fault tripping rate, performing non-dominated sorting and congestion degree sorting on the energy storage configuration population to obtain a Pareto grade and a congestion degree;
s5, selecting an energy storage configuration parent according to the Pareto grade and the crowding degree, generating energy storage configuration offspring through cross variation, calculating the total energy storage power capacity and the fault probability of individuals in the energy storage configuration offspring, and combining two indexes of the offspring and the parent to obtain the current total energy storage power capacity and the system fault probability; if the iteration times do not reach the preset value, the current total energy storage power capacity and the system fault probability are used as input, the step S4 is returned, otherwise, a set formed by individuals with Pareto grades of 1 in the current total energy storage power capacity and the system fault probability is used as a Pareto optimal solution set to be output;
s6, normalizing the Pareto optimal solution set and presetting a weight coefficient lambda 1 And λ 2 And obtaining an optimal energy storage configuration scheme.
Further, the total energy storage power capacity y configured for each individual in the population 1i Comprises the following steps:
wherein i is more than or equal to 1 and less than or equal to N pop When j is more than or equal to 1 and less than or equal to N e When x is ij The energy storage power capacity of the ith energy storage configuration result in the population configured on the j new energy sources is represented, and S is satisfied eminj ≤x ij ≤S emaxj (ii) a When j = N e At +1, x ij The energy storage power capacity of the ith energy storage configuration result in the population is centrally configured on the network side and meets the requirement of S eminj ≤x ij ≤S emaxj ；S emaxj Representing the upper limit of the energy storage power capacity of the new energy source side and the network side, S eminj Representing the new energy source side and the net side energy storage power capacity lower limit.
Further, calculating the probability of individual fault probability in the population, including:
under the constraint of upper and lower limits of energy storage power capacity, calculating a direct current power flow frequency response model N-1 fault accident set A N-1 Fault generating unbalanced power vector Δ P in (1) d Generating an unbalanced power vector Δ P based on the fault d And calculating the unbalanced electromagnetic power delta P born by the stored energy in the power system according to the satisfied relation with the unbalanced electromagnetic power E Unbalanced electromagnetic power Δ P borne by non-faulty synchronous generator nodes G ；
Judging unbalanced electromagnetic power delta P born by energy storage of all individuals in population E If the unbalanced power born by the mth stored energy exceeds the configured capacity limit, the mth stored energy is taken as a disturbance node and is merged into a disturbance and load node set d generated by a fault, and the unbalanced power delta P born by all the stored energy except the mth stored energy is recalculated E ' unbalanced power DeltaP ' borne by non-faulty synchronous generator ' G Again determine Δ P' E Whether the configured capacity limit is exceeded or not is judged until all the unbalanced power born by the stored energy meets the configured capacity limit, and the final unbalanced power born by the non-fault synchronous generator is obtained; wherein m is more than or equal to 1 and less than or equal to N e +1，N e The energy storage number of the inertial supports on the new energy side;
and calculating the frequency change rate of the non-fault synchronous generator according to the final unbalanced power born by the non-fault synchronous generator, and limiting according to the frequency change rate to obtain the fault switching rate.
Further, unbalanced electromagnetic power assumed by the energy storage and non-fault synchronous generator nodes in the power system and the fault-generated unbalanced power vector Δ P d Satisfies the following relation:
wherein, Δ P GE ＝[ΔP E ，ΔP G ] T ，
In order to only consider the power grid susceptance matrix of the power grid inductance, a subscript GE represents an energy storage and non-fault synchronous generator node set, and a subscript d represents disturbance generated by faults and a load node set; b is GE-GE 、B GE-d 、B d-GE And B d-d The block matrixes are all power grid susceptance matrixes;
Is B d-d The inverse matrix of (c).
Further, the frequency change rate of the non-fault synchronous generator and the unbalanced power borne by the non-fault synchronous generator meet the following conditions:
wherein, Δ P G Unbalanced electromagnetic power vector, Δ P, assumed for a non-faulty synchronous generator node G ＝[ΔP G1 ，ΔP G2 ，…，ΔP GNg ] T Ng is the number of non-fault synchronous generators in the power system; f. of 1 …f Ng For each non-faulted synchronous generator rotor frequency; j is a unit of 1 …J Ng Is the time constant of the inertia of the rotor,
representing the derivative operator.
Further, the probability of fail-over y 2i Comprises the following steps:
wherein N is AN-1 Indicates the number of N-1 faults, S a Capacity of the a-th non-failing synchronous generator, e ab Indicating whether the frequency change rate of the a-th non-fault generator exceeds the limit in the b-th fault scene, and if the frequency change rate of the generator exceeds the limit, e ab =1, otherwise, e ab ＝0。
Further, in step S6, the two normalized indexes of the Pareto optimal solution set are weighted and then the minimum value is obtained, so as to obtain the weight coefficient λ of the two indexes 1 And λ 2 。
According to another aspect of the present invention, there is provided an energy storage configuration system based on grid frequency spatial distribution characteristics, including:
a parameter setting module for setting the upper and lower limits of the energy storage power capacity of the new energy source side and the network side and the node range set No accessed by the network side centralized energy storage power station e ；
An initial population generation module for using the upper and lower limits of the energy storage power capacity and the node range set No e For input, an NSGA-II algorithm is adopted to randomly generate N pop Configuring an initial population by each energy storage;
the energy storage total power capacity and fault probability switching calculation module comprises an energy storage total power capacity calculation unit and a fault probability switching calculation unit, wherein the energy storage total power capacity calculation unit is used for calculating the energy storage total power capacity configured by each individual in the initial population; the fault probability switching calculation unit is used for calculating an N-1 fault accident set A based on a direct current power flow frequency response model N-1 The frequency change rate of the lower non-fault synchronous generator is limited according to the frequency change rate to obtain the fault switching rate of the system;
the sorting module is used for carrying out non-dominated sorting and congestion degree sorting on the energy storage configuration population according to two indexes of the total energy storage power capacity and the fault tripping rate to obtain a Pareto grade and a congestion degree;
the Pareto optimal solution set generation module is used for selecting an energy storage configuration parent according to the Pareto grade and the crowding degree, generating energy storage configuration offspring through cross variation, respectively calculating the energy storage total power capacity and the fault probability of an individual in the energy storage configuration offspring through an energy storage total power capacity calculation unit and a fault probability calculation unit, and combining two indexes of the offspring and the parent to obtain the current energy storage total power capacity and the current system fault probability; if the iteration times do not reach the preset value, taking the current total energy storage power capacity and the system failure rate as input, and returning to the step S4, otherwise, taking a set consisting of individuals with Pareto grades of 1 in the current total energy storage power capacity and the system failure rate as a Pareto optimal solution set to output;
an optimal energy storage configuration scheme generation module, configured to normalize the Pareto optimal solution set, and preset a weight coefficient λ 1 And λ 2 And obtaining an optimal energy storage configuration scheme.
Further, the failure tripping rate calculation unit includes:
the unbalanced electromagnetic power calculation node is used for calculating a direct current power flow frequency response model N-1 fault accident set A under the constraint of upper and lower limits of energy storage power capacity under the disregard of N-1 Fault generating unbalanced power vector Δ P in (1) d Generating an unbalanced power vector Δ P according to a fault d The relation satisfied between the electromagnetic power and the unbalanced electromagnetic power is calculated, and the unbalanced electromagnetic power delta P born by the stored energy in the power system is calculated E Unbalanced electromagnetic power Δ P borne by non-faulty synchronous generator nodes G ；
The unbalanced electromagnetic power updating node is used for judging the unbalanced electromagnetic power delta P borne by all individual stored energy in the population E If the unbalanced power born by the mth energy storage exceeds the configured capacity limit, the mth energy storage is taken as a disturbance node and is merged into a disturbance and load node set d generated by a fault, and the mth energy storage is recalculated and divided according to the unbalanced electromagnetic power calculation nodeUnbalanced power Δ P of all external energy storage loads E ' unbalanced power DeltaP ' borne by non-faulty synchronous generator ' G Again determine Δ P' E Whether the configured capacity limit is exceeded or not is judged until all the unbalanced power born by the stored energy meets the configured capacity limit, and the final unbalanced power born by the non-fault synchronous generator is obtained; wherein m is more than or equal to 1 and less than or equal to N e +1，N e The energy storage number of the inertial supports on the new energy side;
and the fault switching probability calculation node is used for calculating the frequency change rate of the non-fault synchronous generator according to the final unbalanced power born by the non-fault synchronous generator, and obtaining the fault switching probability according to the limitation of the frequency change rate.
According to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to any one of the first aspects.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
(1) The method is based on a genetic algorithm, the total power capacity of energy storage configuration and the system fault probability of switching are calculated in the population iteration process, the optimal total power capacity of energy storage configuration and the optimal system fault probability of switching are obtained, wherein the total power capacity of energy storage configuration is used as an economic index for measuring the power system, the fault switching rate is used as a frequency stability index for measuring the power system, and an energy storage capacity configuration scheme which is most consistent with the expectation of an investor is obtained according to the economic performance of the energy storage configuration and the frequency stability weight coefficient of the system of the investor.
(2) Further, when the system fault probability switching calculation is carried out, the frequency of the power grid has a spatial distribution characteristic when the system is disturbed, a frequency response analysis model based on direct current flow is adopted, unbalanced electromagnetic power borne by energy storage and non-fault synchronous generator nodes (namely, all inertial nodes) in the power system is calculated, whether unbalanced electromagnetic power borne by all individual energy storage in a population exceeds the configured energy storage power capacity (namely, whether the unbalanced electromagnetic power is saturated or not) is judged, the nodes with the saturated energy storage power capacity are merged into the non-inertial nodes, the unbalanced power of all the inertial nodes is recalculated until all the unbalanced power borne by the energy storage meets the configured capacity limit, the final unbalanced power of all the inertial nodes can be obtained, the frequency change rate of each unit under the non-fault condition is calculated based on the unbalanced power borne by the non-fault synchronous generator, the frequency change rate reflects the frequency dynamic difference of different areas of the system, namely, the spatial distribution characteristic of the power grid frequency is considered, and meanwhile, compared with the mode of a large number of full-state time domain analysis models in the prior art, the frequency response calculation process with the energy storage power system is simplified, the frequency change rate calculation process is calculated, and the time is saved effectively.
In summary, the energy storage configuration method and system based on the power grid frequency spatial distribution characteristics of the invention includes that an outer layer is an energy storage multi-objective optimization configuration calculation model based on a genetic algorithm, and the optimal position and capacity of the configured energy storage are searched and configured aiming at the set economic and stability targets; the inner layer is a frequency response model for improving the inertia of the power system by considering the energy storage of the frequency space distribution characteristic and is used for analyzing the frequency stability of the power system with the inertia support energy storage device under the fault condition; the method can give consideration to both economy and power grid frequency stability, quickly realize the site selection and constant volume of the inertial support energy storage system, ensure the transient stability of the power grid frequency, and solve the problem that the existing energy storage configuration method is difficult to give consideration to both frequency space distribution characteristics and solving difficulty.
Drawings
Fig. 1 is a flow chart of energy storage optimization configuration considering spatial distribution characteristics of grid frequency according to the present invention;
FIG. 2 is a schematic diagram of a four-machine two-zone power system according to an embodiment of the present invention;
fig. 3 is a Pareto optimal solution set diagram of an energy storage optimization configuration result provided by the embodiment of the present invention;
fig. 4 is a comparison diagram of simulation results of different schemes provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the present invention, the terms "first", "second", etc. in the present invention and the drawings are used for distinguishing similar objects and not necessarily for describing a particular sequential or chronological order.
As shown in fig. 1, the present invention provides an energy storage configuration method considering the spatial distribution characteristics of the frequency of the power grid, including the following steps:
s1, according to the number N of new energy power stations of the power system e Setting the energy storage number N of the new energy side inertial supports e And upper and lower limits S of energy storage power capacity at each location eminj 、S emaxj And setting a node range set No which can be accessed by the network side centralized energy storage power station according to the traditional generator set and the new energy access node e (ii) a The method comprises the following steps that the upper limit and the lower limit of the energy storage power capacity of a new energy source side and a network side are included;
s2, using the upper and lower limits S of the energy storage power capacity eminj 、S emaxj And access node range set No e For input, a genetic algorithm is adopted to randomly generate an energy storage configuration initial population X = [) 1 ,X 2 ,…,X Npop ] T (ii) a Wherein the initial population X comprises N pop Individual configuration results, N pop Is a predetermined population number, X i Configuring an ith configuration result individual in the initial population X for energy storage, wherein the expression is as follows:
X i ＝[x i1 x i2 …x i(Ne+2) ]
wherein, when j is more than or equal to 1 and less than or equal to N e When x is ij The energy storage power capacity of the ith energy storage configuration result in the initial population X configured on the side of the j new energy station is shown, and S is satisfied eminj ≤x ij ≤S emaxj ，S emaxj Representing the upper limit of the energy storage power capacity of the new energy source side and the network side, S eminj Representing the lower limit of the energy storage power capacity of the new energy source side and the network side; when j = N e At +1, x ij The energy storage power capacity of the ith energy storage configuration result in the initial population, which is configured in a centralized way at the network side, is shown, and S is satisfied eminj ≤x ij ≤S emaxj (ii) a When j = N e At +2, x ij The energy storage node position, x, of the ith energy storage configuration result in the initial population, which is configured in a network side set ij ∈No e ，No e And a node range set which can be accessed by the network side centralized energy storage power station.
In this embodiment, a non-inferiority ranking genetic algorithm (NSGA-II) is adopted, and in other embodiments, a genetic algorithm of other multi-objective optimization scenarios may also be adopted to generate the initial population for energy storage configuration.
S3, calculating each individual X in the randomly generated initial population X i Configured total energy storage power capacity y 1i (ii) a N-1 fault accident set A calculated based on direct current power flow frequency response model N-1 The frequency change rate of the lower non-fault synchronous generator set is limited according to the frequency change rate to obtain the system fault switching rate y 2i Wherein i is more than or equal to 1 and less than or equal to N pop ；
Specifically, each individual X i Configured total power capacity y of energy storage 1i Comprises the following steps:
wherein, the N-1 failure accident set A N-1 The method comprises the following steps: power generator tripping faults, load disconnection faults and transmission line disconnection faults.
The process of calculating the frequency change rate of the synchronous generator under the N-1 fault accident set based on the direct current power flow frequency response model comprises the following steps:
step S31, under the condition of not counting the limit of energy storage power capacity, calculating an N-1 fault accident set A N-1 Fault generating unbalanced power vector Δ P in (1) d Generating an unbalanced power vector Δ P based on the fault d ComputingUnbalanced electromagnetic power delta P borne by energy storage and non-fault synchronous generator nodes in power system GE Wherein the nodes of the energy storage and non-fault synchronous generator are inertia nodes, delta P GE Comprises the following steps:
in the formula (I), the compound is shown in the specification,
a power grid susceptance matrix which only considers power grid inductance; subscript GE represents the set of energy storage and non-fault synchronous generator nodes; subscript d represents the disturbance generated by the fault and the load node set; b is GE-GE 、B GE-d 、B d-GE And B d-d The block matrixes are all power grid susceptance matrixes;
Is B d-d The inverse matrix of (d); delta P d An unbalanced power vector is generated for the fault.
ΔP GE ＝[ΔP E ，ΔP G ] T ，ΔP E Unbalanced electromagnetic power vector, Δ P, to be absorbed for energy storage G Unbalanced electromagnetic power vectors borne by non-faulty synchronous generator nodes; wherein, Δ P E ＝[ΔP E1 ,ΔP E2 ,…,ΔP ENe ,ΔP E(Ne+1) ] T Ne +1 represents N e +1 stored energy; delta P G ＝[ΔP G1 ,ΔP G2 ,…,ΔP GNg ] T And Ng represents the number of non-faulty synchronous generators.
Step S32, judging the unbalanced power delta P born by the stored energy of each individual in the population E Whether the configured energy storage power capacity x is exceeded ij (j≤N e + 1), if m is m (1. Ltoreq. M. Ltoreq.N e + 1) unbalanced power Δ P borne by the stored energy Em Exceeding the corresponding capacity limit x im And merging the mth energy storage as a disturbance node into a set d, wherein the set d is a disturbance and load node generated by the faultA set of points, i.e. a set of non-inertial nodes; recalculating unbalanced power delta P 'borne by the remaining stored energy (i.e. all stored energy except the mth stored energy) and the non-faulty synchronous generator' GE Judging again the unbalanced power Delta P 'borne by the stored energy of each individual' E Whether the configured energy storage power capacity is exceeded until N e The unbalanced power born by +1 stored energy all meets the corresponding capacity limit to obtain N e +1 unbalanced powers of stored energy and unbalanced powers borne by all non-faulty synchronous generators;
wherein, delta P' GE Can be expressed as:
wherein sgn () is a sign function; the matrix variable denoted by the band' is the matrix variable after the node where the mth energy storage is located is merged into the node set d.
Step S33, calculating the frequency change rate of the non-fault synchronous generator according to the unbalanced power born by all the non-fault synchronous generators, wherein the expression is as follows:
ng is the number of non-fault synchronous generators in the power system; f. of 1 …f Ng For each non-faulted synchronous generator rotor frequency; j. the design is a square 1 …J Ng Is the time constant of the inertia of the rotor,
representing the derivative operator.
Step S33, according to the frequency change rate limitation, obtaining the fault switching rate y 2i ：
Wherein N is AN-1 Representing the number of N-1 faults; s a Capacity of the a-th non-fault synchronous generator; e.g. of the type ab Expressed as whether the frequency change rate of the a-th non-fault generator set exceeds the limit in the b-th fault scene, and when the frequency change rate of the generator set exceeds the limit, e ab And =1, otherwise 0.
S4, according to the configured total energy storage power capacity and the fault probability, performing non-dominated sorting and congestion degree sorting on the energy storage configuration population to obtain a Pareto grade and congestion degree;
s5, selecting an energy storage configuration parent by adopting a binary competitive bidding competition according to the sorted Pareto grades and the crowdedness, simulating binary intersection and polynomial variation according to a preset NSGA-II intersection probability and a variation probability, and generating energy storage configuration offspring; calculating the total energy storage capacity and the system fault switching probability of each individual configuration in the energy storage configuration filial generation, and combining the total energy storage capacity and the system fault switching probability of the energy storage configuration parent generation with the total energy storage capacity and the system fault switching probability of the filial generation to obtain the current total energy storage capacity and the system fault switching probability;
s6, performing iteration to the step S4 and the step S5, performing non-dominated sorting and congestion degree sorting again in the process of each iteration, performing energy storage configuration result elite preservation according to pareto grade and congestion degree, screening out a new energy storage configuration parent, combining the total energy storage power capacity configured by each individual in the new energy storage configuration parent and the total energy storage power capacity configured by each individual in filial generations generated by cross variation of the new energy storage configuration parent and the system fault tripping rate, and obtaining the total energy storage power capacity and the system fault tripping rate in the current iteration process;
circularly iterating until the preset iteration number G max Selecting a set consisting of individuals with Pareto grades of 1 in the total energy storage power capacity and the system fault probability as a Pareto optimal solution set for output, and recording to finally obtain the optimal total energy storage power capacity y in each individual 1i [1]And the system fail-over probability y 2i [1]；
S7, carrying out optimal energy storage total power capacity y in Pareto optimal solution set 1i [1]And the system fail-over probability y 2i [1]Normalizing and presetting corresponding weight coefficient lambda 1 And λ 2 Obtaining an energy storage inertial support power grid optimal configuration scheme;
specifically, the optimal energy storage total power capacity index is normalized as follows:
the optimal failover probability index is normalized to:
wherein, y 1i [1]，y 2i [1]Respectively representing the total energy storage power capacity and the fault probability of the ith energy storage configuration result in the energy storage configuration Pareto optimal solution set.
In this embodiment, two normalization indexes in the Pareto optimal solution set are weighted and then the minimum value is obtained to obtain the weight coefficient λ 1 And λ 2 And further obtaining an optimal energy storage configuration scheme, wherein the expression is as follows
Wherein λ is 1 、λ 2 And respectively obtaining a weight coefficient of the set energy storage total power capacity index and a weight coefficient of the fault switching probability index to obtain an energy storage inertial support power grid optimal configuration scheme.
The invention also provides an energy storage configuration system based on the spatial distribution characteristic of the power grid frequency, which comprises the following components:
a parameter setting module for setting the upper and lower limits of the energy storage power capacity of the new energy source side and the network side and the node range set No accessed by the network side centralized energy storage power station e ；
An initial population generation module for collecting No with the upper and lower limits of energy storage power capacity and node range e For input, N is randomly generated by adopting an NSGA-II algorithm pop Configuring an initial population by each energy storage;
the energy storage total power capacity and fault probability switching calculation module comprises an energy storage total power capacity calculation unit and a fault probability switching calculation unit, wherein the energy storage total power capacity calculation unit is used for calculating the energy storage total power capacity configured by each individual in the initial population; the fault probability switching calculation unit is used for calculating an N-1 fault accident set A based on a direct current power flow frequency response model N-1 The frequency change rate of the lower non-fault synchronous generator is limited according to the frequency change rate to obtain the fault switching rate of the system;
the sorting module is used for carrying out non-dominated sorting and congestion degree sorting on the energy storage configuration population according to two indexes of the total energy storage power capacity and the fault tripping rate to obtain a Pareto grade and a congestion degree;
the Pareto optimal solution set generation module is used for selecting an energy storage configuration parent according to the Pareto grade and the crowding degree, generating energy storage configuration offspring through cross variation, respectively calculating the energy storage total power capacity and the fault probability of an individual in the energy storage configuration offspring through an energy storage total power capacity calculation unit and a fault probability calculation unit, and combining two indexes of the offspring and the parent to obtain the current energy storage total power capacity and the current system fault probability; if the iteration times do not reach the preset value, the current total energy storage power capacity and the system fault probability are used as input, the step S4 is returned, otherwise, a set formed by individuals with Pareto grades of 1 in the current total energy storage power capacity and the system fault probability is used as a Pareto optimal solution set to be output;
an optimal energy storage configuration scheme generation module, configured to normalize the Pareto optimal solution set, and preset a weight coefficient λ 1 And λ 2 And obtaining an optimal energy storage configuration scheme.
Energy storage total power capacity y configured for each individual in population 1i Comprises the following steps:
wherein, i is more than or equal to 1 and less than or equal toN pop When j is more than or equal to 1 and less than or equal to N e When x ij The energy storage power capacity of the ith energy storage configuration result in the population configured on the j new energy sources is represented, and S is satisfied eminj ≤x ij ≤S emaxj (ii) a When j = N e At +1, x ij The energy storage power capacity of the ith energy storage configuration result in the population is centrally configured on the network side and meets the requirement of S eminj ≤x ij ≤S emaxj ；S emaxj Representing the upper limit of the energy storage power capacity of the new energy source side and the network side, S eminj Representing the new energy source side and the net side energy storage power capacity lower limit.
The failure tripping rate calculation unit comprises:
the unbalanced electromagnetic power calculation node is used for calculating a direct current power flow frequency response model N-1 fault accident set A under the constraint of upper and lower limits of energy storage power capacity under the disregard of N-1 Fault generating unbalanced power vector Δ P in (1) d Generating an unbalanced power vector Δ P based on the fault d The relation satisfied between the electromagnetic power and the unbalanced electromagnetic power is calculated, and the unbalanced electromagnetic power delta P born by the stored energy in the power system is calculated E Unbalanced electromagnetic power Δ P borne by non-faulty synchronous generator nodes G ；
The unbalanced electromagnetic power updating node is used for judging the unbalanced electromagnetic power delta P borne by all individual stored energy in the population E If the unbalanced power born by the mth stored energy exceeds the configured capacity limit, merging the mth stored energy into a disturbance and load node set d generated by the fault as a disturbance node, and recalculating the unbalanced power delta P 'born by all the stored energy except the mth stored energy according to the unbalanced electromagnetic power calculation node' E And unbalanced power delta P 'borne by non-faulty synchronous generator' G Again determine Δ P' E Whether the configured capacity limit is exceeded or not is judged until all the unbalanced power born by the stored energy meets the configured capacity limit, and the final unbalanced power born by the non-fault synchronous generator is obtained; wherein m is more than or equal to 1 and less than or equal to N e +1，N e The energy storage number of the inertial supports on the new energy side;
and the fault switching probability calculation node is used for calculating the frequency change rate of the non-fault synchronous generator according to the final unbalanced power born by the non-fault synchronous generator, and obtaining the fault switching probability according to the limitation of the frequency change rate.
Example 1:
in this embodiment, a typical two-zone four-machine system is taken as an example for explanation, as shown in fig. 2, in the typical two-zone four-machine system, 1 to 11 in the figure are different bus nodes in a power grid, a synchronous generator 2 is replaced by a wind farm, inertia constants of three equivalent units are respectively 50s, 4.2s and 3.58s, and capacity of each unit is 1000MVA. And selecting to configure an energy storage system on each of the wind power plant side and the power grid side to support the inertia of the power grid. The power capacities of the two energy storage systems and the access point of the energy storage system on the network side are variables to be optimized. The initial population size is set to 100, the number of iterations G max The setting is 1000, the cross probability is 0.9, the mutation probability is 1/3, the binary cross parameter is 10, and the mutation parameter is 10.
According to the provided optimal configuration method of the energy inertial support, a Pareto optimal solution set shown in fig. 3 can be obtained. The optimal solution set is an energy storage optimization configuration result which can be selected by an investor according to the attention degree of the investor on two indexes of the frequency stability of the power grid and the energy storage investment cost. The results of the partial configuration of the system are shown in table 1. Scheme 1 for investors to set a weighting factor lambda according to the method of the invention 1 ＝0.5，λ 2 The energy storage configuration result of =0.5 can greatly reduce the capacity ratio of the generator tripping with smaller energy storage investment cost; scheme 2 for investors to set a weighting factor lambda according to the method of the invention 1 ＝0，λ 2 The energy storage configuration result of =1 can minimize the tripping capacity ratio of the system under the existing constraint conditions. Fig. 4 is a comparison of the survival rates of the units without energy storage configuration and with two energy storage configuration schemes. As can be seen from FIG. 4, the two configuration schemes of scheme 1 and scheme 2 can both ensure that the frequency change rate of the system equivalent unit 1 is not out of limit under the condition of N-1 fault, and because the inertia constant of the synchronous generator 3 is large, when only one energy storage power station is configured on the network side, the synchronous generator 3 is preferentially ensured not to be switched off, and the survival rate of the synchronous generator 3 is higher than that of the synchronous generator 3The motor 4 is high. Because the fault scenes are few, the survival capacity of the unit can be greatly reduced by one fault. And because only one energy storage system is configured on the network side, the configuration scheme cannot ensure that the frequency change rate of all the units is within 0.5Hz/s in the N-1 fault transient process. If the survival rate of each unit in the system is further ensured, a proper amount of stored energy can be configured on the side of the weak-inertia synchronous generator, so that the inertia of the region is improved, and the generator tripping times of the unit under the fault condition are reduced. Table 2, table 3, and table 4 show the frequency change rate of each unit in the N-1 fault scenario after no energy storage and energy storage configuration according to the scheme 1 and the scheme 2, respectively. After energy storage is configured according to the scheme 1, the frequency change rate of the generator 1 in the fault scene 10 is reduced from 0.71Hz/s to 0.5Hz/s, so that the unit can tolerate the frequency change rate range. After energy storage is configured according to the scheme 2, the frequency change rate of the generator set 4 in the fault scene 1 is reduced from-0.57 Hz/s to-0.5 Hz/s in the scheme 2. The method can fully play the role of the energy storage inertia support and the reasonability of the energy storage inertia support configuration.
TABLE 1
TABLE 2
TABLE 3
TABLE 4
In general, based on a non-inferior ranking genetic algorithm, the energy storage optimal configuration Pareto optimal solution set considering both economy and frequency temporality can be solved according to the set economic index of the total energy storage configuration power capacity and the stability index of the fault probability; and simultaneously, per-unit combining the two indexes of the Pareto optimal solution set based on a normalization method, and obtaining an energy storage power capacity configuration scheme which best meets the expectation of an investor according to the energy storage configuration economy of the investor and a system frequency stability weight coefficient.
According to the method, when the system is disturbed, the frequency of the power grid has a spatial distribution characteristic, the frequency response analysis model based on the direct current load flow is adopted, the frequency change rate of different units can be rapidly calculated, compared with the mode of analyzing the model through a large number of full-state time domains in the prior art, the frequency response calculation process of the power system with the energy storage is simplified, the calculation solving time of an algorithm can be effectively saved, and the frequency response analysis model takes the frequency spatial distribution characteristic and the solving difficulty into consideration.
Furthermore, those skilled in the art will appreciate that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor 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 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.
The computer program 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 block or blocks and/or flowchart block or blocks.
These computer program 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 will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An energy storage configuration method based on grid frequency spatial distribution characteristics is characterized by comprising the following steps:
s1, setting upper and lower limits of energy storage power capacity of a new energy source side and a network side and setting node range set No accessed by a network side centralized energy storage power station e ；
S2, collecting No by upper and lower limits of energy storage power capacity and node range e For input, a genetic algorithm is used to randomly generate N pop Configuring an initial population by each energy storage;
s3, calculating the total energy storage power capacity configured by each individual in the initial population; and based on DC power flow frequency response modeType calculation N-1 failure accident set A N-1 The frequency change rate of the lower non-fault synchronous generator is limited according to the frequency change rate to obtain the fault switching probability of the system;
s4, according to the total energy storage power capacity and the fault tripping rate, performing non-dominated sorting and congestion degree sorting on the energy storage configuration population to obtain a Pareto grade and a congestion degree;
s5, selecting an energy storage configuration parent according to the Pareto grade and the crowding degree, generating energy storage configuration offspring through cross variation, calculating the total energy storage power capacity and the fault probability of individuals in the energy storage configuration offspring, and combining two indexes of the offspring and the parent to obtain the current total energy storage power capacity and the system fault probability; if the iteration times do not reach the preset value, the current total energy storage power capacity and the system fault probability are used as input, the step S4 is returned, otherwise, a set formed by individuals with Pareto grades of 1 in the current total energy storage power capacity and the system fault probability is used as a Pareto optimal solution set to be output;
s6, normalizing the Pareto optimal solution set and presetting a weight coefficient lambda 1 And λ 2 And obtaining an optimal energy storage configuration scheme.
2. The method of claim 1, wherein the total energy storage power capacity y configured for each individual in the population 1i Comprises the following steps:
wherein i is more than or equal to 1 and less than or equal to N pop When j is more than or equal to 1 and less than or equal to N e When x ij The energy storage power capacity of the ith energy storage configuration result in the population configured on the j new energy sources is represented, and S is satisfied eminj ≤x ij ≤S emaxj (ii) a When j = N e +1 time, x ij The energy storage power capacity of the ith energy storage configuration result in the population is centrally configured on the network side and meets the requirement of S eminj ≤x ij ≤S emaxj ；S emaxj Express new energyUpper limit of energy storage power capacity of source side and network side, S eminj Represents the lower limit of the energy storage power capacity of the new energy source side and the network side, N e The energy storage number of the inertial supports on the new energy side.
3. The method of claim 2, wherein calculating the probability of an individual failing in the population comprises:
under the constraint of upper and lower limits of energy storage power capacity, calculating a direct current power flow frequency response model N-1 fault accident set A N-1 Fault generating unbalanced power vector Δ P in (1) d Generating an unbalanced power vector Δ P based on the fault d The relation satisfied between the electromagnetic power and the unbalanced electromagnetic power is calculated, and the unbalanced electromagnetic power delta P born by the stored energy in the power system is calculated E Unbalanced electromagnetic power Δ P borne by non-faulty synchronous generator nodes G ；
Judging unbalanced electromagnetic power delta P born by energy storage of all individuals in population E If the unbalanced power born by the mth stored energy exceeds the configured capacity limit, the mth stored energy is taken as a disturbance node and is merged into a disturbance and load node set d generated by a fault, and the unbalanced power delta P born by all the stored energy except the mth stored energy is recalculated E ' unbalanced power DeltaP ' borne by non-faulty synchronous generator ' G Judging again the Δ P E Whether the unbalanced power born by the stored energy exceeds the configured capacity limit or not is judged until all the unbalanced power born by the stored energy meets the configured capacity limit, and the final unbalanced power born by the non-fault synchronous generator is obtained; wherein m is more than or equal to 1 and less than or equal to N e +1，N e The energy storage number of the inertial supports on the new energy side;
and calculating the frequency change rate of the non-fault synchronous generator according to the final unbalanced power born by the non-fault synchronous generator, and limiting according to the frequency change rate to obtain the fault switching rate.
4. The method of claim 3, wherein the unbalanced electromagnetic power assumed by the energy storage and non-faulted synchronous generator nodes in the power system is unbalanced from the faultPower vector Δ P d Satisfies the relationship:
wherein, Δ P GE ＝[ΔP E ，ΔP G ] T ，
In order to only consider the power grid susceptance matrix of the power grid inductance, a subscript GE represents an energy storage and non-fault synchronous generator node set, and a subscript d represents disturbance generated by faults and a load node set; b GE-GE 、B GE-d 、B d-GE And B d-d The block matrixes are all power grid susceptance matrixes;
Is B d-d The inverse matrix of (c).
5. The method of claim 4, wherein the ratio of frequency change of the non-faulty synchronous generator to the unbalanced power assumed by the non-faulty synchronous generator satisfies:
wherein, Δ P G Unbalanced electromagnetic power vector, Δ P, assumed for a non-faulty synchronous generator node G ＝[ΔP G1 ，ΔP G2 ，…，ΔP GNg ] T Ng is the number of non-fault synchronous generators in the power system; f. of 1 …f Ng For each non-faulted synchronous generator rotor frequency; j. the design is a square 1 …J Ng Is the time constant of the inertia of the rotor,
representing the derivative operator.
6. The method of claim 5, wherein the probability of fail-over y is 2i Comprises the following steps:
wherein, N AN-1 Indicates the number of N-1 faults, S a Capacity of the a-th non-failing synchronous generator, e ab Indicating whether the frequency change rate of the a-th non-fault generator exceeds the limit in the b-th fault scene, and if the frequency change rate of the generator exceeds the limit, e ab =1, otherwise, e ab ＝0。
7. The method according to any one of claims 1 to 6, wherein in step S6, the two normalized indexes of the Pareto optimal solution set are weighted and then the weighted two indexes are subjected to minimum value calculation to obtain the weight coefficient λ of the two indexes 1 And λ 2 。
8. An energy storage configuration system based on grid frequency spatial distribution characteristics, comprising:
a parameter setting module for setting the upper and lower limits of the energy storage power capacity of the new energy source side and the network side and the node range set No accessed by the network side centralized energy storage power station e ；
An initial population generation module for using the upper and lower limits of the energy storage power capacity and the node range set No e For input, an NSGA-II algorithm is adopted to randomly generate N pop Configuring an initial population by each energy storage;
the energy storage total power capacity and fault probability switching calculation module comprises an energy storage total power capacity calculation unit and a fault probability switching calculation unit, wherein the energy storage total power capacity calculation unit is used for calculating the energy storage total power capacity configured by each individual in the initial population; the fault switching probability calculation unit is used for calculating an N-1 fault accident set A based on a direct current power flow frequency response model N-1 Lower non-faulted synchronous generator frequency rate of change, according to frequency rate of changeLimiting to obtain the fault probability of the system;
the sorting module is used for carrying out non-dominated sorting and congestion degree sorting on the energy storage configuration population according to two indexes of the total energy storage power capacity and the fault tripping rate to obtain a Pareto grade and a congestion degree;
the Pareto optimal solution set generation module is used for selecting an energy storage configuration parent according to the Pareto grade and the crowding degree, generating energy storage configuration offspring through cross variation, respectively calculating the energy storage total power capacity and the fault probability of an individual in the energy storage configuration offspring through an energy storage total power capacity calculation unit and a fault probability calculation unit, and combining two indexes of the offspring and the parent to obtain the current energy storage total power capacity and the current system fault probability; if the iteration times do not reach the preset value, the current total energy storage power capacity and the system fault probability are used as input, the step S4 is returned, otherwise, a set formed by individuals with Pareto grades of 1 in the current total energy storage power capacity and the system fault probability is used as a Pareto optimal solution set to be output;
an optimal energy storage configuration scheme generation module, configured to normalize the Pareto optimal solution set, and preset a weight coefficient λ 1 And λ 2 And obtaining an optimal energy storage configuration scheme.
9. The system of claim 8, wherein the fault tripping rate calculation unit comprises:
the unbalanced electromagnetic power calculation node is used for calculating a direct current power flow frequency response model N-1 fault accident set A under the constraint of upper and lower limits of energy storage power capacity under the disregard of N-1 Fault generating unbalanced power vector Δ P in (1) d Generating an unbalanced power vector Δ P based on the fault d The relation satisfied between the electromagnetic power and the unbalanced electromagnetic power is calculated, and the unbalanced electromagnetic power delta P born by the stored energy in the power system is calculated E Unbalanced electromagnetic power Δ P borne by non-faulty synchronous generator nodes G ；
The unbalanced electromagnetic power updating node is used for judging the unbalanced electromagnetic power delta P borne by all individual stored energy in the population E Whether the configured stored energy work is exceededRate capacity, if the unbalanced power born by the mth energy storage exceeds the configured capacity limit, the mth energy storage is taken as a disturbance node and is merged into a disturbance and load node set d generated by a fault, and the unbalanced power delta P born by all the energy storages except the mth energy storage is recalculated according to an unbalanced electromagnetic power calculation node E ' unbalanced power DeltaP ' borne by non-faulty synchronous generator ' G Again, determine Δ P E Whether the unbalanced power born by all the stored energy meets the configured capacity limit or not is judged, and the unbalanced power born by the non-fault synchronous generator is finally obtained; wherein m is more than or equal to 1 and less than or equal to N e +1，N e The energy storage number of the inertial supports on the new energy side;
and the fault switching probability calculation node is used for calculating the frequency change rate of the non-fault synchronous generator according to the final unbalanced power born by the non-fault synchronous generator, and obtaining the fault switching probability according to the limitation of the frequency change rate.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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