CN109830990B - Energy storage optimization configuration method based on wind and light access contained in output resistor plug - Google Patents
Energy storage optimization configuration method based on wind and light access contained in output resistor plug Download PDFInfo
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Abstract
An energy storage optimal configuration method based on wind and light access contained in an output resistor plug comprises the following steps: acquiring data of each unit; establishing an energy storage capacity two-stage optimization model; constructing a model by taking system operation economy under the condition of no energy storage as a target, selecting the power transmission line which is most likely to cause system blockage, and determining an installation node set to be selected; coding the energy storage position and capacity to randomly form a first generation population; taking the population as a known quantity, solving the output of the energy storage at each moment and the system operation cost G in the first-stage function by using a gradient method, and solving the system comprehensive operation cost F by taking the maximum output of the energy storage as the rated power of the energy storage; calculating the individual fitness of the population; whether the function is converged or whether the maximum iteration times is reached is judged, if not, the next step is carried out, and if not, an optimal result is output; and forming a new population and turning to the fifth step. The invention comprehensively considers the operation cost and the energy storage investment cost, and is beneficial to improving the effectiveness and the economy of energy storage and capacity configuration.
Description
Technical Field
The invention relates to the technical field of energy storage optimal configuration methods, in particular to the technical field of energy storage optimal configuration methods based on wind-light access of a power transmission resistor plug.
Background
The intermittency and randomness of wind power generation and photovoltaic power generation bring great challenges to the safe operation of a power system, the peak-to-valley difference of load is indirectly enlarged due to the peak-to-peak characteristic of wind power generation, the possibility of blockage of a power transmission line is increased, and the voltage fluctuation of the power system can be caused by uncertain output of photovoltaic power generation. The traditional scheduling method is to adjust by changing the generated energy of thermal power and hydropower according to a load prediction curve and a new energy generated energy prediction curve, but the thermal power adjustment range is quite limited, wind abandon is easily caused, the use maximization of clean energy is not facilitated, and the adjustment cost is high; although hydropower is flexible, it is greatly affected by regions. The method has the advantages that the method can prevent the circuit from being blocked while utilizing clean energy to the maximum extent, and can reduce the scheduling cost, so that the method has important significance in accessing the energy storage technology with fast response and high energy into the power grid.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides an energy storage optimal configuration method based on wind-light-contained access of an input resistor plug, which can reduce the wind abandon and the light abandon amount, increase the consumption rate of new energy and improve the economical efficiency of system operation.
In order to realize the purpose, the invention adopts the following technical scheme:
an energy storage optimal configuration method based on wind and light access contained in an output resistor plug comprises the following steps:
s1, acquiring the four-season typical output and the typical load curve of each node of the new energy unit, and acquiring data of each unit;
s2, establishing an energy storage capacity two-stage optimization model;
s3, constructing a model by taking system operation economy under the condition of no energy storage as a target, selecting the power transmission line which is most prone to system blocking, and determining an installation node set to be selected;
s4, coding the energy storage position and capacity based on the genetic algorithm, and forming a first generation population randomly;
s5, taking the population as a known quantity, solving the output of the energy storage at each moment and the system operation cost G in the first-stage function by using a gradient method, and solving the system comprehensive operation cost F by taking the maximum output of the energy storage as the rated power of the energy storage;
s6, calculating the individual fitness of the population;
s7, judging whether the function is converged or reaches the maximum iteration number, if not, turning to the step S8; if the result is the optimal result, the configuration is finished;
s8, selecting an operator by using the optimal individual storage strategy, performing copying, crossing and mutation operations to form a new population, and turning to the step S5.
According to the invention, the energy storage candidate node is determined by considering the energy output resistor plug, establishing a system operation model without energy storage, and calculating the line which is easy to generate the energy output resistor plug by taking the minimum system operation cost as a target. And then establishing a two-stage model with the aim of minimizing the sum of the system operation cost and the energy storage investment. By adding the energy storage node screening strategy into the model, the calculation convergence is better ensured, and the solving efficiency of the model is improved.
Has the advantages that: the invention can effectively deal with the output resistor plug under the condition that wind/light is connected into the power grid, better eliminate wind and light output and reduce the overall operation cost of the system. The first stage model of the invention adopts a gradient method to obtain the optimal output of each unit and the stored energy, thereby determining the maximum output of the stored energy, the second stage function model adopts a genetic algorithm to obtain the combination of nodes and capacity of the stored energy, and the optimal installation nodes and capacity of the stored energy are finally obtained through iterative solution of genetic operation.
Drawings
FIG. 1 is a general flow diagram of the present invention.
Fig. 2 is a flow chart of the present invention for determining nodes to be installed for energy storage.
Detailed Description
As shown in fig. 1 to 2, the technical solution of the present invention is described in further detail.
The general flow of the method of the present invention is described with reference to fig. 1, and the specific steps are as follows:
as shown in fig. 1, an energy storage optimization configuration method based on wind and light access of an output resistor plug includes the following steps:
s1, acquiring a four-season typical output and a typical load curve of each node of a new energy unit, acquiring data of each unit, and initializing each variable;
s2, establishing an energy storage capacity two-stage optimization model, which specifically comprises the following steps: constructing a first-stage optimization model by taking the system operation cost as a target, constructing a second-stage optimization model by taking the system comprehensive operation cost including system operation and energy storage investment as a target, and establishing an energy storage capacity two-stage optimization model; establishing equality or inequality constraints from the angles of power constraints, climbing constraints, transmission blocking constraints and the like;
s3, constructing a model by taking system operation economy under the condition of no energy storage as a target, selecting the power transmission line which is most prone to system blocking, and determining an installation node set to be selected;
s4, coding the energy storage position and capacity to randomly form a first generation population;
s5, taking the population as a known quantity, solving the output of the energy storage at each moment and the system operation cost G in the first-stage function by using a gradient method, and solving the system comprehensive operation cost F by taking the maximum output of the energy storage as the rated power of the energy storage; (ii) a
S6, calculating the individual fitness of the population;
s7, judging whether the function is converged or not or whether the maximum iteration number is reached or not, and if not, turning to the step S8; if the result is the optimal result, the configuration is finished;
and S8, selecting an operator by using the optimal individual storage strategy, performing copy, crossover and mutation genetic operations to form a new population, and turning to S5.
In step S2, the energy storage capacity two-stage optimization model is established as follows:
s21, constructing a first-stage optimization model by taking the system operation cost as a target:
G＝min(F 1 +F 2 +F 3 +F 4 ) (1)
s22, constructing a second-stage optimization model by taking the comprehensive operation cost of the system including system operation and energy storage investment as a target:
F＝min(F 1 +F 2 +F 3 +F 4 +F 5 ) (2)
wherein:
wherein:
F 1 for the fuel costs of conventional units, a dk 、b dk 、c dk Respectively representing the cost coefficient P of the normal operation of the unit k on the d-th operation day k (t) representing a predicted output value of the unit k before the day at the time t of the D-th operation day, wherein N represents the total number of conventional units except photovoltaic and wind power, and D is the total number of operation days in one year; f 2 Represents the income caused by peak clipping and valley filling of stored energy within one year, and is generally less than 0, P dis.d (t) and P ch.d (t) discharge and charge power stored at time t on the d-th operation day, respectively; xi shape dis.d (t) represents the discharge state of the stored energy at the t moment of the d-th operation day, and takes 0 or 1, the stored energy is not in the discharge state when taking 0, and the stored energy is in the discharge state when taking 1; xi ch.d (t) represents the charging state of the stored energy at the t moment of the d-th operation day, 0 or 1 is taken, the stored energy is not in the charging state when 0 is taken, the stored energy is in the charging state when 1 is taken, and m is d (t) represents the electricity price at time t on the d-th operation day; f 3 Penalty price, rho, of abandoning the wind and light for a new energy unit dro.n (t) is a wind curtailment penalty coefficient of the new energy unit n on the d-th operation day at the time t, P WP.dn (t) and P W.dn (t) maximum and actual outputs of the new energy unit at time t of the d-th operating day, N W The total number of the wind-solar new energy source units is; f 4 Denotes the load shedding penalty cost, λ dt Representing the load shedding penalty coefficient at the t moment of the d-th operation day; p LC.di (t) represents the load shedding amount at t moment at the node I, wherein I is the total number of the nodes; f 5 Annual average investment cost for energy storage, C E And C P Respectively representing the cost coefficient of energy storage unit capacity and the cost coefficient of energy storage unit power, E N And P N Respectively, the maximum output capacity and the maximum output power of the energy storage device, r is the depreciation rate, Y r Is the service life of the battery, Y a Design age for a project, λ is the annual maintenance cost coefficient of the energy storage mechanism, C rep The replacement cost of the energy storage battery within the project year, the size of the energy storage battery and the actual service life Y of the storage battery r In relation, the calculation formula is as follows:
wherein,
is the rated times of 100% charging and discharging of the storage battery, d is the charging and discharging depth,
the number of charge and discharge cycles in the case where the charge and discharge depth is d in one year,
k is the number of charge-discharge cycles equivalent to 100% of the charge-discharge depth in the case of d in the charge-discharge depth within one year p And the index coefficients of the cycle life of different types of energy storage batteries.
S23, establishing an equality constraint or an inequality constraint from the angles of power constraint, climbing constraint, transmission blocking constraint and the like as follows:
0≤P W.dn (t)≤P WP.dn (t) (13)
0≤|P dk (t)-P dk (t-1)|≤RD k (14)
0≤P ch.d (t)≤ξ ch.d (t)P N (16)
0≤P dis.d (t)≤ξ dis.d (t)P N (17)
ξ dis.d (t)+ξ ch.d (t)≤1 (18)
|P dl (t)|≤P l max, L are 1, 2, 3 … L. (19)
E d (t)＝E d (t-1)·(1-σ)+P ch.d (t)ξ ch.d (t)η ch -P dis.d (t)ξ dis.d (t)/η dis (20)
SOC min E N ≤E d (t)≤SOC max E N (21)
Wherein formula (11) is a power balance constraint, P LO.di (t) represents the load value of i-node at time t on the d-th operation day, SIG P loss.d (t) is the total power loss; equations (12), (13) are generator output limit constraints; formula (14) is generator ramp restriction, RD k Representing the maximum ramp rate allowed by the conventional generator set k; formula (15) for system backup rotational constraint, R u (t) rotating the load for standby; the energy storage charging and discharging functions of the formulas (16), (17) and (18)Rate constraint; formula (19) is the transmission line output resistor plug power constraint, P dl (t) represents the transmission power of the transmission line/at the moment t on the d-th day of operation, P l max Representing the maximum transmission power of the transmission line L, wherein L is the total number of the transmission lines; equations (20) and (21) are energy storage capacity constraints, and sigma is the self-discharge rate of the storage battery; equation (22) is the stored energy balance constraint.
Calculating the line power by adopting a direct current power flow method:
P dl (t)＝A l P in.d (t) (23)
wherein: a. the l A sensitivity coefficient vector matrix of the I branch is 1 (n-1); p in.d And (t) represents a node injection power vector matrix (n-1) multiplied by 1 except for the balance nodes at the t moment of the d-th operation day.
In step S3, the building of the model based on the system economy without energy storage is specifically as follows:
s31, selecting a new energy unit output curve in one year and a typical load curve of each node in each season;
s32, constructing a model by taking the system operation economy under the condition of no energy storage as a target:
C＝min(F 1 +F 3 +F 4 ) (24)
in the formula: c is the optimal operating cost of the system without energy storage, F 1 、F 3 、F 4 The same as the definitions of the formulas (3), (5) and (6).
And establishing an equality constraint or an inequality constraint from the angles of power constraint, climbing constraint, rotating standby constraint, power transmission blockage and the like:
0≤P W.dn (t)≤P WP.dn (t) (27)
0≤|P dk (t)-P dk (t-1)|≤RD k (28)
|P dl (t)|≤P l max (30)
wherein: equation (25) is a power balance constraint, equations (26) and (27) are output constraints of the conventional unit and the new energy unit, equation (28) is a climbing constraint of the conventional unit, equation (29) is a system rotation standby constraint, and equation (30) is a line output resistor plug constraint.
S33, calculating the transmission power of each branch circuit under the optimal economic operation condition by using a gradient method;
s34, defining the input resistor plug risk index as follows:
H l representing the degree of risk of line l becoming blocked, a smaller value indicates a greater susceptibility to line blocking. Taking nodes at two ends of the selected line as energy storage mounting nodes to be selected, and solving the risk value of the resistance plug of each power transmission line;
and S35, selecting a plurality of lines with the minimum risk indexes of the whole network transmission resistor plugs, and taking feasible nodes at two ends of the selected branch as energy storage installation nodes to be selected.
In step S33, the adam algorithm in the gradient descent method is used to solve the optimal output, and the method specifically includes the following steps:
for the minimization optimization problem of the objective function J (θ), the parameter θ only needs to be advanced by one step along the opposite direction of the gradient, i.e. the learning rate, so as to realize the reduction of the objective function. The parameter update formula is as follows:
wherein
Is the gradient of the parameter, η is the learning rate, the recommendation is set to 0.001, the hyper-parameter recommendation: beta is a 1 ＝0.9,β 2 ＝0.999,ε＝1e-8。
In step S4, the energy storage location and capacity are encoded based on a genetic algorithm, specifically as follows:
information pair (N) for information stored by adopting variable-length integer coding i ,E i ) Is represented by the formula, wherein N i Node position number indicating installation of stored energy, E i The number of the energy storage capacity is represented, and the position of the energy storage and the capacity scheme are respectively composed of a plurality of N which are in one-to-one correspondence i And E i The composition is shown as the following formula:
N＝{N 1 ,N 2 ,...,N n }
C＝{C 1 ,C 2 ,...,C n }
the coding length is related to the selection number of nodes for energy storage and the energy storage capacity.
In step S6, the population individual fitness is calculated as follows:
the fitness function is established as follows:
where f (x) is the objective function.
The convergence condition in step S7 of the present invention is specifically as follows:
the adaptability change rate of the optimal individual in the continuous two generations of populations in the genetic operation is in a convergence range, namely, the following conditions are met:
in the formula: c new The fitness of the newly generated population is the optimal individual fitness; c old And epsilon is a small positive value for the optimal individual fitness in the prior generation population.
In step S8, the invention selects an operator using the optimal individual preservation strategy, and performs the operations of replication, crossover and mutation to form a new population, which are as follows:
s81, selecting a genetic operator: the optimal storage strategy is adopted, namely, the individuals with the highest fitness in the current population directly replace the equivalent individuals with the lowest fitness in the current population, so that the fitness of the optimal individuals in the current population is not lower than that of the previous generation population, and the selected operators are copied to a new population;
s82, genetic operator intersection operation: the exchange is performed by means of a uniform crossover operator, e.g. by the presence of a ═ (N) i ,E i )、B＝(N j ,E j ) Two father individuals randomly generate a shielding word omega with the same length as the individual locus 1 ,ω 2 ,ω 3 ,...,ω 2n If ω is i When the gene value of the ith locus of the offspring individual A 'of the A inherits the gene value corresponding to the A, and the gene value of the ith locus of the offspring individual B' of the B inherits the gene value corresponding to the B; if omega i When 0, the value of the gene at the ith locus of a 'inherits the value of the gene corresponding to B, and the value of the gene at the ith locus of B' inherits the value of the gene corresponding to a.
S83, mutation operation of genetic operators: for an individual with an energy storage position and capacity scheme, if the randomly generated number between 0 and 1 does not exceed the variation probability, then for any two nodes N in the individual i 、N j Upper energy storage capacity E i 、E j And exchanging to obtain a next generation individual.
In order to more clearly illustrate the present invention, the following description will be made with reference to the accompanying drawings.
Model preprocessing method
And the convergence is increased by adopting the direct current power flow. The method has the advantages that the direct current power flow constraint is added to the main problem in the initial stage, the direct current power flow can be approximately seen as simplification of the alternating current power flow, the direct current power flow calculation is relatively simple, and on the premise that the constraint and the condition of the main problem are not added, compared with the method of directly solving and considering the network security power flow constraint problem, the solution which meets the security power flow constraint is easier to obtain;
(II) establishing energy storage two-stage optimization model based on system operation economy
Step 1, aiming at the system operation cost, constructing a first-stage optimization model:
G＝min(F 1 +F 2 +F 3 +F 4 )
and 2, constructing a second-stage optimization model by taking the comprehensive operation cost of the system including system operation and energy storage investment as a target:
F＝min(F 1 +F 2 +F 3 +F 4 +F 5 )
wherein:
wherein:
F 1 for the fuel cost of a conventional unit, a dk 、b dk 、c dk Respectively represents the cost coefficient, P, of the normal operation of the unit k on the d-th operation day k (t) representing a predicted output value of the unit k before the day at the time t of the D-th operation day, wherein N represents the total number of conventional units except photovoltaic and wind power, and D is the total number of operation days in one year; f 2 Represents the income caused by peak clipping and valley filling of stored energy within one year, and is generally less than 0, P dis.d (t) and P ch.d (t) discharge and charge power stored at time t on the d-th operation day, respectively; xi dis.d (t) represents the discharge state of the stored energy at the t moment of the d-th operation day, and takes 0 or 1, the stored energy is not in the discharge state when taking 0, and the stored energy is in the discharge state when taking 1; xi shape ch.d (t) represents the charging state of the stored energy at the t moment of the d-th operation day, 0 or 1 is taken, the stored energy is not in the charging state when 0 is taken, the stored energy is in the charging state when 1 is taken, and m is d (t) represents the electricity price at time t on the d-th operation day; f 3 Penalty price, rho, of abandoning the wind and light for a new energy unit dro.n (t) is a wind curtailment penalty coefficient of the new energy unit n on the d-th operation day at the time t, P WP.dn (t) and P W.dn (t) represents the maximum output and the actual output of the new energy unit at the time t of the d-th operating day, N W The total number of the wind-solar new energy source units is; f 4 Denotes the load shedding penalty cost, λ dt Representing the load shedding penalty coefficient at the t moment of the d-th operation day; p LC.di (t) represents the load shedding amount at t moment at the node I, wherein I is the total number of the nodes; f 5 Annual average investment cost for energy storage, C E And C P Respectively representing the cost coefficient of energy storage unit capacity and the cost coefficient of energy storage unit power, E N And P N Respectively, the maximum output capacity and the maximum output power of the energy storage device, r is the depreciation rate, Y r Is the battery life, Y a Design age for a project, λ is the annual maintenance cost coefficient of the energy storage mechanism, C rep The replacement cost of the energy storage battery within the project year, the size of the energy storage battery and the actual service life Y of the storage battery r In relation, the calculation formula is as follows:
wherein,
is the rated times of 100% charging and discharging of the storage battery, d is the charging and discharging depth,
the number of charge and discharge cycles in the case where the charge and discharge depth is d in one year,
k is the number of charge-discharge cycles equivalent to 100% of the charge-discharge depth in one year at d p And the index coefficients of the cycle life of different types of energy storage batteries.
Step 3, establishing an equality constraint or an inequality constraint from the angles of power constraint, climbing constraint, transmission blocking constraint and the like as follows:
0≤P W.dn (t)≤P WP.dn (t)
0≤|P dk (t)-P dk (t-1)|≤RD k
0≤P ch.d (t)≤ξ ch.d (t)P N
0≤P dis.d (t)≤ξ dis.d (t)P N
ξ dis.d (t)+ξ ch.d (t)≤1
P dl (t)≤P l max and L is 1, 2, 3 … L.
E d (t)＝E d (t-1)·(1-σ)+P ch.d (t)ξ ch.d (t)η ch -P dis.d (t)ξ dis.d (t)/η dis
SOC min E N ≤E d (t)≤SOC max E N
Wherein, P LO.di (t) represents the load value of i-node at time t on the d-th operation day, SIG P loss.d (t) is the total power loss, RD k Represents the maximum ramp rate, R, allowed by a conventional generator set k u (t) for load rotation standby, P dl (t) represents the transmission power of the transmission line l at the moment t on the d-th day of operation, P l max Represents the maximum transmission power of the transmission line L, and L is the total number of the transmission lines.
Calculating the line power by adopting a direct current power flow method:
P dl (t)＝A l P in.d (t)
wherein: a. the l A sensitivity coefficient vector matrix of the I branch is 1 (n-1); p in.d And (t) represents a node injection power vector matrix (n-1) multiplied by 1 except for a balance node at the t moment of the d operation day.
(III) selection method of energy storage node to be installed
As shown in fig. 2, for a complex power system, because the number of nodes of the system network is large, the calculation amount of the optimal configuration scheme is large by adopting an exhaustion method, the time consumption is long, a model is constructed by taking the system operation economy under the condition of no energy storage as a target, the power transmission line which is most likely to be blocked by the system is selected, and the set of installation nodes to be selected is determined as follows:
and step 1, taking the four-season typical output and the four-season typical load of the wind and the light in the energy storage optimization model as the four-season typical output and the four-season typical load of the wind and the light without an energy storage system.
Step 2, establishing an optimized operation model of the non-energy storage system:
C＝min(F 1 +F 3 +F 4 )
0≤P W.dn (t)≤P WP.dn (t)
0≤|P dk (t)-P dk (t-1)|≤RD k
|P dl (t)|≤P l max
step 3, calculating the transmission power of each branch under the optimal economic operation condition;
and step 4, defining the risk indexes of the output resistor plug as follows:
H l representing the degree of risk of line l becoming blocked, a smaller value indicates a greater susceptibility to line blocking. Taking nodes at two ends of the selected line as energy storage mounting nodes to be selected, and solving the risk value of the resistance plug of each power transmission line;
and 5, selecting a plurality of lines with the minimum risk indexes of the whole network transmission resistor plugs, and taking feasible nodes at two ends of the selected branch as energy storage installation nodes to be selected.
Solving method of optimization model of (IV) stored energy
The method adopts a gradient adam method to solve the first-stage model, and adopts a genetic algorithm to solve the second-stage model.
In the adam algorithm in the gradient descent method, for the minimization optimization problem of the objective function J (θ), the objective function can be descended only by advancing the parameter θ by one step along the direction opposite to the gradient, that is, by the learning rate. The parameter update formula is as follows:
wherein
Is the gradient of the parameter, η is the learning rate, the recommendation is set to 0.001, the hyper-parameter recommendation: beta is a 1 ＝0.9,β 2 ＝0.999,ε＝1e-8。
The genetic algorithm adopts variable-length integer coding, and the information of the stored energy is an information pair (N) i ,E i ) Is represented by the formula, wherein N i Node position number indicating installation of stored energy, E i The number of the energy storage capacity is represented, and the position of the energy storage and the capacity scheme are respectively composed of a plurality of N which are in one-to-one correspondence i And E i The composition is shown as the following formula:
N＝{N 1 ,N 2 ,...,N n }
C＝{C 1 ,C 2 ,...,C n }
the coding length is related to the selection number of nodes for energy storage and the energy storage capacity.
The fitness function, genetic operation and convergence condition of the genetic algorithm are specifically processed as follows:
1) the fitness function is established as follows:
where f (x) is the objective function.
2) Selection of genetic operators: the optimal storage strategy is adopted, namely, the individuals with the highest fitness in the current population directly replace the equivalent individuals with the lowest fitness in the current population, so that the fitness of the optimal individuals in the current population is not lower than that of the previous generation population;
3) and (3) genetic operator cross operation: the exchange takes place here by means of a uniform crossover operator, e.g. there is a ═ (N) i ,E i )、B＝(N j ,E j ) Two parents randomly generate screens with the same length as the individual lociWord covering omega 1 ,ω 2 ,ω 3 ,...,ω 2n If ω is i When the gene value of the ith locus of the offspring individual A 'of the A inherits the gene value corresponding to the A, and the gene value of the ith locus of the offspring individual B' of the B inherits the gene value corresponding to the B; if omega i When 0, the gene value at the ith locus of a 'inherits the gene value corresponding to B, and the gene value at the ith locus of B' inherits the gene value corresponding to a.
4) Genetic operator mutation operation: for an individual with an energy storage position and capacity scheme, if the randomly generated number between 0 and 1 does not exceed the variation probability, then for any two nodes N in the individual i 、N j Energy storage capacity E of i 、E j And exchanging to obtain a next generation individual.
5) The adaptability change rate of the optimal individual in the continuous two generations of populations in the genetic operation is in a convergence range, namely, the following conditions are met:
in the formula: c new The fitness of the newly generated population is the optimal individual fitness; c old And epsilon is a small positive value for the optimal individual fitness in the prior generation population.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (8)
1. An energy storage optimization configuration method based on wind and light access contained in an output resistor plug is characterized in that: the method comprises the following steps:
s1, acquiring the four-season typical output and the typical load curve of each node of the new energy unit, and acquiring data of each unit;
s2, establishing an energy storage capacity two-stage optimization model;
s3, constructing a model by taking system operation economy under the condition of no energy storage as a target, selecting the power transmission line which is most prone to system blocking, and determining an installation node set to be selected;
s4, coding the energy storage position and capacity based on the genetic algorithm, and forming a first generation population randomly;
s5, taking the population as a known quantity, solving the output of the energy storage at each moment and the system operation cost G in the first-stage function by using a gradient method, and solving the system comprehensive operation cost F by taking the maximum output of the energy storage as the rated power of the energy storage;
s6, calculating the individual fitness of the population;
s7, whether the function is converged or whether the maximum iteration number is reached is judged, and if not, the step is switched to S8; if the result is the optimal result, the configuration is finished;
s8, selecting an operator by using the optimal individual storage strategy, performing copying, crossing and mutation operations to form a new population, and turning to the step S5.
2. The energy storage optimization configuration method based on the wind and light access contained by the output resistor plug as claimed in claim 1, wherein: in step S2, the energy storage capacity two-stage optimization model process specifically includes:
s21, constructing a first-stage optimization model by taking the system operation cost as a target:
G＝min(F 1 +F 2 +F 3 +F 4 ) (1)
s22, constructing a second-stage optimization model by taking the comprehensive operation cost of the system including system operation and energy storage investment as a target:
F＝min(F 1 +F 2 +F 3 +F 4 +F 5 ) (2)
wherein:
wherein:
F 1 for the fuel cost of a conventional unit, a dk 、b dk 、c dk Respectively representing the cost coefficients of the normal operation of the unit k on the D-th operation day, wherein N represents the total number of conventional units except photovoltaic and wind power, and D is the total number of operation days in one year; f 2 Represents the income caused by peak clipping and valley filling of stored energy within one year, and is generally less than 0, P dis.d (t) is the discharge power stored at time t on the d-th operating day, P ch.d (t) the charging power stored at the time t of the d-th operation day; xi dis.d (t) represents the discharge state of the stored energy at the t moment of the d-th operation day, wherein 0 or 1 is taken, the stored energy is not in the discharge state when 0 is taken, and the stored energy is in the discharge state when 1 is taken; xi ch.d (t) represents the charging state of the stored energy at the t moment of the d-th operation day, 0 or 1 is taken, the stored energy is not in the charging state when 0 is taken, the stored energy is in the charging state when 1 is taken, and m is d (t) represents the electricity price at time t on the d-th operation day; f 3 Penalty price, rho, of abandoning wind and light for a new energy unit dro.n (t) is a wind curtailment penalty coefficient of the new energy unit n on the d-th operation day at the moment t, P WP.dn (t) and P W.dn (t) represents the time of the d-th day of operation tMaximum and actual output, N, of the new energy bank W The total number of the wind-solar new energy source units is; f 4 Denotes the load shedding penalty cost, λ dt Representing the load shedding penalty coefficient at the t moment of the d-th operation day; p LC.di (t) represents the load shedding amount at t moment at the node I, wherein I is the total number of the nodes; f 5 Annual average investment cost for energy storage, C E And C P Respectively representing the cost coefficient of energy storage unit capacity and the cost coefficient of energy storage unit power, E N And P N Respectively, the maximum output capacity and the maximum output power of the energy storage device, r is the depreciation rate, Y r Is the service life of the battery, Y a The design age for the project, λ is the annual maintenance cost coefficient of the energy storage mechanism, C rep The replacement cost of the energy storage battery within the project year, the size of the energy storage battery and the actual service life Y of the storage battery r In relation, the calculation formula is as follows:
wherein,
is the rated times of 100% charging and discharging of the storage battery, d is the charging and discharging depth,
the number of charge and discharge cycles in the case where the charge and discharge depth is d in one year,
k is the number of charge-discharge cycles equivalent to 100% of the charge-discharge depth in the case of d in the charge-discharge depth within one year p Index coefficients for cycle life of different types of energy storage batteries;
s23, establishing an equality or inequality constraint from the angles of power constraint, climbing constraint and output resistor plug constraint, wherein the equality or inequality constraint is as follows:
0≤P W.dn (t)≤P WP.dn (t) (13)
0≤|P dk (t)-P dk (t-1)|≤RD k (14)
0≤P ch.d (t)≤ξ ch.d (t)P N (16)
0≤P dis.d (t)≤ξ dis.d (t)P N (17)
ξ dis.d (t)+ξ ch.d (t)≤1 (18)
P dl (t)≤P l max l is 1, 2, 3 … L (19)
E d (t)＝E d (t-1)·(1-σ)+P ch.d (t)ξ ch.d (t)η ch -P dis.d (t)ξ dis.d (t)/η dis (20)
SOC min E N ≤E d (t)≤SOC max E N (21)
Where formula (11) is a power balance constraint, P LO.di (t) represents the load value of i-node at time t on the d-th operation day, SIG P loss.d (t) is powerTotal loss; equations (12), (13) are generator output limit constraints; formula (14) is generator ramp restriction, RD k Representing the maximum ramp rate allowed by the conventional generator set k; formula (15) is system alternate rotation constraint, R u (t) rotating the load for standby; the formulas (16), (17) and (18) are energy storage charging and discharging power constraints; formula (19) is the transmission line output resistor plug power constraint, P dl (t) represents the transmission power of the transmission line l at the moment t on the d-th day of operation, P l max Representing the maximum transmission power of the transmission line L, wherein L is the total number of the transmission lines; equations (20) and (21) are energy storage capacity constraints, and sigma is the self-discharge rate of the storage battery; equation (22) is the energy storage energy balance constraint;
calculating the line power by adopting a direct current load flow method:
P dl (t)＝A l P in.d (t) (23)
wherein: a. the l Is the sensitivity coefficient vector matrix of the l branch, 1 x (n-1); p in.d And (t) represents a node injection power vector matrix (n-1) multiplied by 1 except for a balance node at the t moment of the d operation day.
3. The energy storage optimization configuration method based on the wind and light access contained by the output resistor plug as claimed in claim 2, wherein: in the step S3, a model is constructed with the system operation economy under the condition of no energy storage as a target, the power transmission line which is most likely to be blocked by the system is selected, and the set of installation nodes to be selected is determined, wherein the specific process is as follows:
s31, selecting a new energy unit output curve in one year and a typical load curve of each node in each season;
s32, constructing a model by taking the system operation economy under the condition of no energy storage as a target:
C＝min(F 1 +F 3 +F 4 ) (24)
in the formula: c is the optimal operating cost of the system without energy storage, F 1 For the fuel cost of conventional units, F 3 Penalty price for abandoning wind and light for new energy plants, F 4 Penalizing costs for load shedding;
the constraints are specifically as follows:
0≤P W.dn (t)≤P WP.dn (t) (27)
0≤|P dk (t)-P dk (t-1)|≤RD k (28)
wherein: the formula (25) is power balance constraint, the formulas (26) and (27) are output constraint of a conventional unit and a new energy unit, the formula (28) is climbing constraint of the conventional unit, the formula (29) is system rotation standby constraint, and the formula (30) is line output resistor plug constraint;
s33, calculating the transmission power of each branch circuit under the optimal economic operation condition by using a gradient method;
s34, defining the input resistor plug risk index as follows:
H l representing the risk degree of the line l being blocked, and the smaller the value of the risk degree, the more easily the line is blocked; taking nodes at two ends of the selected line as energy storage mounting nodes to be selected, and solving the risk value of the resistance plug of each power transmission line;
and S35, selecting a plurality of lines with the minimum risk indexes of the whole network transmission resistor plugs, and taking feasible nodes at two ends of the selected branch as energy storage installation nodes to be selected.
4. The energy storage optimization configuration method based on the wind and light access contained by the output resistor plug as claimed in claim 3, wherein: in step S33, the transmission power of each branch under the optimal economic operation condition is calculated by a gradient method as follows:
for the minimization optimization problem of the objective function J (theta), advancing the parameter theta by one step along the direction opposite to the gradient, namely learning rate, and realizing the reduction of the objective function; the parameter update formula is as follows:
wherein
Is the gradient of the parameter, η is the learning rate, set to 0.001, the hyper-parameter is suggested: beta is a beta 1 ＝0.9,β 2 ＝0.999,ε＝1e-8。
5. The energy storage optimization configuration method based on the wind and light access contained by the output resistor plug as claimed in claim 1, wherein: in step S4, the energy storage location and capacity are encoded based on a genetic algorithm, which is specifically as follows:
information pair (N) for information stored by adopting variable-length integer coding i ,E i ) Is represented by the formula, wherein N i Node position number indicating installation of stored energy, E i The number of the energy storage capacity is represented, and the position of the energy storage and the capacity scheme are respectively composed of a plurality of N which are in one-to-one correspondence i And E i The composition is shown as the following formula:
N＝{N 1 ,N 2 ,...,N n }
C＝{C 1 ,C 2 ,...,C n }
the coding length is related to the selection number of nodes for energy storage and the energy storage capacity.
6. The energy storage optimization configuration method based on the wind and light access contained by the output resistor plug as claimed in claim 1, wherein: in the step S6, the population individual fitness is calculated as follows:
the fitness function is established as follows:
where f (x) is the objective function.
7. The energy storage optimization configuration method based on the wind-light-contained access of the power transmission resistor plug as claimed in claim 1, wherein the method comprises the following steps: the convergence condition in step S7 is specifically as follows:
the adaptability change rate of the optimal individual in the continuous two generations of populations in the genetic operation is in a convergence range, namely, the following conditions are met:
in the formula: c new Is newly generatedThe optimal individual fitness in the population; c old And epsilon is a small positive value for the optimal individual fitness in the prior generation population.
8. The energy storage optimization configuration method based on the wind and light access contained by the output resistor plug as claimed in claim 1, wherein: in step S8, the operator is selected by using the optimal individual preservation strategy, and the operations of copying, crossing, and mutating are performed to form a new population, which are as follows:
s81, selecting a genetic operator: adopting an optimal storage strategy, namely directly replacing equivalent individuals with the lowest fitness in the current population by a plurality of individuals with the highest fitness in the current population, so that the fitness of the optimal individuals in the current population is not lower than that of the previous generation population, and copying the selected operator to a new population;
s82, genetic operator crossover operation: exchanging by adopting a mode of uniform crossover operator, wherein A ═ N (N) exists i ,E i )、B＝(N j ,E j ) Two father individuals randomly generate a shielding word omega with the same length as the individual locus 1 ,ω 2 ,ω 3 ,...,ω 2n If ω is i When the gene value of the ith locus of the offspring individual A 'of the A inherits the gene value corresponding to the A, and the gene value of the ith locus of the offspring individual B' of the B inherits the gene value corresponding to the B; if omega i When the value is 0, the gene value of the ith locus of A 'inherits the gene value corresponding to B, and the gene value of the ith locus of B' inherits the gene value corresponding to A;
s83, mutation operation of genetic operators: for an individual with energy storage position and capacity scheme, if the randomly generated number between 0 and 1 does not exceed the variation probability, then for any two nodes N in the individual i 、N j Upper energy storage capacity E i 、E j And exchanging to obtain a next generation individual.
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