CN113972694B - Investment decision-making method for distributed photovoltaic and energy storage power station of power distribution network - Google Patents
Investment decision-making method for distributed photovoltaic and energy storage power station of power distribution network Download PDFInfo
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- 238000010248 power generation Methods 0.000 claims abstract description 49
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- 238000009434 installation Methods 0.000 claims description 41
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- 230000035772 mutation Effects 0.000 claims description 5
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- 210000000349 chromosome Anatomy 0.000 claims description 4
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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Abstract
The invention discloses a decision-making method for investment of a distributed photovoltaic and energy storage power station of a power distribution network, which belongs to the field of distributed power planning of power distribution networks and specifically comprises the following steps: step 1: inputting a historical power generation power data set and a historical power load data set of the photovoltaic power station, and extracting a photovoltaic output typical scene and a power load typical scene by adopting an improved K center point clustering algorithm; step 2: then constructing a photovoltaic and load combined time sequence scene according to the photovoltaic output time sequence characteristic of the photovoltaic output typical scene and the power consumption load time sequence characteristic of the power consumption load typical scene; the invention provides a decision-making method for investment of distributed photovoltaic and energy storage power stations of a power distribution network, which not only reflects the fluctuation of the time sequence of distributed photovoltaic power generation, but also realizes the maximization of the investment benefit of the photovoltaic power stations, and simultaneously ensures the safe and stable operation of the power grid.
Description
Technical Field
The invention belongs to the field of distributed power supply planning of power distribution networks, and particularly relates to a power distribution network distributed photovoltaic and energy storage power station investment decision method based on an improved genetic simulated annealing algorithm.
Background
With the implementation of the national 'double carbon' proposal, the installed capacity and the generated energy of the photovoltaic power generation are continuously increased. The distributed photovoltaic output and the conventional load sizes of different types have obvious time sequence characteristics, the distributed photovoltaic site selection and installation capacity planning of the power distribution network at the present stage generally adopt a constant power model and a probability model, the time sequence fluctuation cannot be accurately reflected, and the actual situation is not met. Meanwhile, the distributed photovoltaic output has obvious fluctuation, so that the safe and stable operation of the power distribution network is influenced, and the distributed photovoltaic site selection and installation capacity planning of the power distribution network at the present stage are not considered, so that the safe and stable operation of the power distribution network is influenced.
Disclosure of Invention
The invention aims to provide a distributed photovoltaic and energy storage power station investment decision method for a power distribution network, which not only reflects the fluctuation of the distributed photovoltaic power generation time sequence, but also realizes the maximization of the investment benefit of the photovoltaic power station, and simultaneously ensures the safe and stable operation of a power grid.
The technical solution for realizing the purpose of the invention is as follows: the investment decision-making method for the distributed photovoltaic and energy storage power station of the power distribution network specifically comprises the following steps:
step 1: inputting a historical power generation power data set and a historical power load data set of the photovoltaic power station, and extracting a photovoltaic output typical scene and a power load typical scene by adopting an improved K center point clustering algorithm;
step 2: then constructing a photovoltaic and load combined time sequence scene according to the photovoltaic output time sequence characteristic of the photovoltaic output typical scene and the power consumption load time sequence characteristic of the power consumption load typical scene;
step 3: from the perspective of occurrence probability of a photovoltaic and load combined time sequence scene, determining the installation position and capacity of the distributed photovoltaic by adopting an improved genetic simulated annealing algorithm with the aim of maximizing the economic benefit of the photovoltaic power station;
step 4: and adopting the super capacitor to stabilize the fluctuation characteristic of the output of the distributed photovoltaic power generation, and determining the installation position and the capacity of the energy storage power station by taking the minimum capacity as a target.
Furthermore, the improved K-center clustering algorithm in the step 1 extracts a photovoltaic output typical scene and an electrical load typical scene, which are specifically described as follows:
(1) Calculating the distance between different date power generation curves of the distributed photovoltaic power station by adopting the dynamic time bending distance, and constructing a distance matrix between the different date power generation curves of the distributed photovoltaic power station;
(2) Carrying out clustering analysis on power generation power curves of the distributed photovoltaic power station at different dates by adopting a K central point clustering algorithm with the optimal profile coefficient as a target, and selecting central points of each cluster as a photovoltaic output typical scene;
(3) Calculating the distance between load curves of different days of the power consumption load by adopting the dynamic time bending distance, and constructing a distance matrix between the load curves of different days;
(4) And carrying out clustering analysis on load curves of different dates of various types of loads by adopting a K central point clustering algorithm with the optimal profile coefficient as a target, and selecting the central points of all clusters as typical scenes of the power loads.
Further, the dynamic time warping distance calculation formula is as follows:
assume that there are two time sequences a= { a 1 ,…,a l ,…,a m Sum b= { B 1 ,…,b j ,…,b n First, a matrix M of M x n is constructed, and the element M (i, j) is a i And b j The distance between the two sequences is found, and then a curved path which minimizes the accumulated distance between the two sequences is found in the matrix; the curved path w= { W 1 ,…,w l ,…,w K A matrix M is a set of consecutive elements, K is the number of curved path elements, and satisfies the following constraint:
1) Bounded restraint: max (m, n) is more than or equal to K and less than or equal to m+n-1.
2) Boundary constraint: element w 1 =m (1, 1) and w K =m (M, n) is the start and end of the curved path, respectively;
3) Continuity constraint: given element w l =m (i, j), its adjacent element w l-1 M (i ', j') is required to satisfy i-i '1, j-j' 1, i.e., the curved path elements are adjacent;
4) Monotone ofSex constraint: given element w l =m (i, j), its adjacent element w l-1 =m (i ', j') needs to satisfy i-i '. Gtoreq.0, j-j'. Gtoreq.0;
there are a plurality of curved paths in the matrix M that satisfy the above constraint, and the dynamic time-curved distance of the time series A and B is the smallest curved path;
the curved path is solved by adopting a dynamic programming algorithm, and the optimal solution substructure is as follows:
d(i,j)＝M(i,j)+min{d(i-1,j-1),d(i-1,j),d(i,j-1)}
where i=1, 2, …, m, j=1, 2, …, n, d (0, 0) =0, d (i, 0) =d (0, j-j+++;
the above time sequence a= { a 1 ,…,a i ,…,a m Sum b= { B 1 ,…,b j ,…,b n Dynamic time warping distance of D dtw (A,B)＝d(m,n)。
Further, the contour coefficients are defined as follows:
for a data set D of n objects, suppose D is divided into k clusters C 1 ，...，C k The method comprises the steps of carrying out a first treatment on the surface of the For each object o e D, calculating an average distance a (o) between o and other objects o 'of the cluster to which o belongs and a minimum average distance b (o) from o to all cluster objects o' not belonging to o;
suppose o ε C i And i is more than or equal to 1 and less than or equal to k, then
The contour coefficient of object o is defined as
Wherein dist (o, o ') is the distance between objects o and o';
dist (o, o ") is the distance between objects o and o';
the value of the profile factor is between-1 and 1.
Further, the specific steps of constructing the photovoltaic and load combined time sequence scene are as follows:
(1) The occurrence probability of each photovoltaic output typical scene and each electricity load typical scene is calculated according to the following specific formula:
wherein P is gi Probability of occurrence for the ith photovoltaic output typical scene, |C gi I is the number of samples of the ith cluster of the power generation power curve of the distributed photovoltaic power station, N g Generating power curve quantity for the distributed photovoltaic power station; p (P) lj For the j-th power load typical scene occurrence probability, |C lj I is the number of samples of the jth cluster of the load curve, N l The number of the load curves;
(2) Building a photovoltaic and load joint time sequence scene, wherein the occurrence probability of each scene is as follows:
P gi×li ＝P gi ×P lj
P gi×lj
and the generation probability of the joint time sequence scene of the ith photovoltaic output typical scene and the j power consumption load typical scenes is shown in the formula.
Further, the specific steps of the step 3 are as follows:
(1) The distributed photovoltaic installation location and capacity optimization targets are as follows:
wherein: p (P) tol Is the total income of photovoltaic power generation, S eg Selling electricity benefits for photovoltaic power generation; c (C) pv Investment and installation cost for the distributed photovoltaic equipment; c (C) yun Delta C, the operation and maintenance cost of distributed photovoltaic loss Energy conservation and loss reduction benefits for photovoltaic installation power generation; p (P) i Is the ith photovoltaic negativeThe occurrence probability of the load joint time sequence scene is m, which is the number of the photovoltaic load joint time sequence scenes;
the investment and installation cost of the distributed photovoltaic equipment is as follows:
wherein: n is the number of nodes of the photovoltaic access power grid;photovoltaic installation capacity for the inode; c (C) PVe The equipment cost per unit capacity of photovoltaic; c (C) PVk Installation cost per unit capacity of photovoltaic;
the operation and maintenance cost of the distributed photovoltaic equipment is as follows:
wherein: c (C) PVy The operation and maintenance cost of photovoltaic with unit capacity per year; m is m PV The investment recovery period of the photovoltaic; r is the discount rate;
the electricity generation and electricity selling benefits in the photovoltaic equipment investment recovery period are as follows:
wherein: j (J) i The electricity selling price of the node i; p (P) ij Node i, jth annual energy production;
the energy-saving and loss-reducing benefits of the photovoltaic power generation grid are as follows:
wherein: j (J) buy The electricity purchasing price is the online electricity price; p (P) loss The power distribution network loses each year before photovoltaic installation,the power grid loss after photovoltaic installation;
(2) The improved genetic simulated annealing algorithm is adopted to develop and determine the distributed photovoltaic capacity, and the specific contents are as follows:
1) Since the goal of distributed photovoltaic planning is to maximize the photovoltaic power plant investment economy, the fitness function is constructed as:
wherein F (x) is an objective function of an individual, namely the investment economic benefit of the photovoltaic power station; f (x) is an fitness function of the individual; t (T) 0 Initial temperature to simulate annealing problems; n (N) max K is the current iteration number, and alpha is the expansion coefficient;
2) The distributed photovoltaic installation position and capacity belong to discrete variables, and the chromosome adopts decimal integer coding as follows:
X＝[x 1 ,x 2 ,…,x n ]
in which x is i =0, indicating that position i does not mount a photovoltaic; x is x i M, i is m photovoltaic units, i is greater than or equal to 1 and n;
3) Randomly selecting two individuals to compare the fitness of the two individuals, and storing the individuals with higher fitness; directly copying the individuals with the highest fitness in the current population to the next generation;
4) Adaptive crossover and mutation operations: increasing crossover probability p when a population has a tendency to fall into a locally optimal solution c Probability of variation p m Corresponding decrease in p when populations diverge in solution space c And p m ，p c And p m The following formula is shown:
in which k is 0.ltoreq.k i ≤1(i＝1,2,3,4)，k i Self-adaptive constant, f max The method is the maximum adaptability in the current generation of the group; f (f) avg Is the average fitness in the current generation of the population; f is the greater fitness used to cross two individuals; f' is the fitness of the individual to be mutated;
5) Acceptance of the annealing process for new individuals: a group of new individuals generated by the genetic algorithm, wherein two genes of each individual are independently and randomly selected as disturbance points, if the individual fitness is increased, the new individuals are accepted, otherwise, the new individuals are accepted according to the probability of the expression;
T k+1 ＝α·T k
wherein f k+1 And f k The adaptation values of the new individual and the old individual respectively; p (T) k+1 ) At T k+1 Probability of acceptance at temperature; alpha is the cooling coefficient.
Furthermore, in the step 4, the super capacitor is adopted to stabilize the fluctuation of the output of the distributed photovoltaic power generation, and the installation position and the capacity of the energy storage power station are determined by taking the minimum capacity as the target:
and calculating 360-day charge and discharge power of the energy storage system according to the photovoltaic power data with the time span of 360 days and an energy storage system control strategy, and calculating the energy storage capacity required by the j th day.
Wherein Cap (j) is the energy storage capacity configured on the j th day; p (P) ji The charging and discharging power value at the j th day i moment; Δt is the sampling intervalA partition; 1 to m 1 、m 2 ～m 3 、m j ～m s And the time of data sampling is the time of uninterrupted charge and discharge of the stored energy.
Compared with the prior art, the invention has the remarkable advantages that:
with the implementation of the national 'double carbon' proposal, the installed capacity and the generated energy of the photovoltaic power generation are continuously increased. The photovoltaic power generation output has obvious fluctuation, and the safe and stable operation of the power distribution network is influenced. The invention provides a decision-making method for investment of distributed photovoltaic and energy storage power stations of a power distribution network, which not only reflects the fluctuation of the time sequence of distributed photovoltaic power generation, but also realizes the maximization of the investment benefit of the photovoltaic power stations, and simultaneously ensures the safe and stable operation of the power grid.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram of an IEEE-33 power distribution system of the present invention;
FIG. 3 is a schematic diagram of a typical scenario of the distributed photovoltaic output of the present invention;
FIG. 4 is a typical scenario diagram of the user load of the present invention;
FIG. 5 is a representative scene graph of a commercial load for the present invention;
FIG. 6 is a graph of a 32 node 24-period load simulation of the present invention;
FIG. 7 is a graph of an algorithm fitness curve according to the present invention;
fig. 8 is a charge-discharge power diagram of the energy storage device of the present invention.
Detailed Description
For a clearer description of the idea of the invention, technical solutions and advantages, specific embodiments are shown by examples and figures. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue creative effort, are within the scope of protection of the present invention based on the embodiments in the present invention.
Examples
As shown in fig. 1
The investment decision-making method for the distributed photovoltaic and energy storage power station of the power distribution network specifically comprises the following steps:
step 1: inputting a historical power generation power data set and a historical power load data set of the photovoltaic power station, and extracting a photovoltaic output typical scene and a power load typical scene by adopting an improved K center point clustering algorithm;
the improved K center point clustering algorithm is used for extracting a photovoltaic output typical scene and an electricity load typical scene, and the specific description is as follows:
(1) Calculating the distance between different date power generation curves of the distributed photovoltaic power station by adopting the dynamic time bending distance, and constructing a distance matrix between the different date power generation curves of the distributed photovoltaic power station;
(2) Carrying out clustering analysis on power generation power curves of the distributed photovoltaic power station at different dates by adopting a K central point clustering algorithm with the optimal profile coefficient as a target, and selecting central points of each cluster as a photovoltaic output typical scene;
(3) Calculating the distance between load curves of different days of the power consumption load by adopting the dynamic time bending distance, and constructing a distance matrix between the load curves of different days;
(4) And carrying out clustering analysis on load curves of different dates of various types of loads by adopting a K central point clustering algorithm with the optimal profile coefficient as a target, and selecting the central points of all clusters as typical scenes of the power loads.
The dynamic time bending distance calculation formula is as follows:
assume that there are two time sequences a= { a 1 ,…,a l ,…,a m Sum b= { B 1 ,…,b j ,…,b n First, a matrix M of M x n is constructed, and the element M (i, j) is a i And b j The distance between the two sequences is found, and then a curved path which minimizes the accumulated distance between the two sequences is found in the matrix; the curved path w= { W 1 ,…,w l ,…,w K A matrix M is a set of consecutive elements, K is the number of curved path elements, and satisfies the following constraint:
1) Bounded restraint: max (m, n) is more than or equal to K and less than or equal to m+n-1.
2) Boundary constraint: element w 1 =m (1, 1) and w K =m (M, n) is the start and end of the curved path, respectively;
3) Continuity constraint: given element w l =m (i, j), its adjacent element w l-1 M (i ', j') is required to satisfy i-i '1, j-j' 1, i.e., the curved path elements are adjacent;
4) Monotonicity constraint: given element w l =m (i, j), its adjacent element w l-1 =m (i ', j') needs to satisfy i-i '. Gtoreq.0, j-j'. Gtoreq.0;
there are a plurality of curved paths in the matrix M that satisfy the above constraint, and the dynamic time-curved distance of the time series A and B is the smallest curved path;
the curved path is solved by adopting a dynamic programming algorithm, and the optimal solution substructure is as follows:
d(i,j)＝M(i,j)+min{d(i-1,j-1),d(i-1,j),d(i,j-1)}
where i=1, 2, …, m, j=1, 2, …, n, d (0, 0) =0, d (i, 0) =d (0, j-j+++;
the above time sequence a= { a 1 ,…,a i ,…,a m Sum b= { B 1 ,…,b j ,…,b n Dynamic time warping distance of D dtw (A,B)＝d(m,n)。
The profile factor is defined as follows:
for a data set D of n objects, suppose D is divided into k clusters C 1 ，...，C k The method comprises the steps of carrying out a first treatment on the surface of the For each object o e D, calculating an average distance a (o) between o and other objects o 'of the cluster to which o belongs and a minimum average distance b (o) from o to all cluster objects o' not belonging to o;
suppose o ε C i And i is more than or equal to 1 and less than or equal to k, then
The contour coefficient of object o is defined as
Wherein dist (o, o ') is the distance between objects o and o';
dist (o, o ") is the distance between objects o and o';
the value of the profile factor is between-1 and 1.
Step 2: then constructing a photovoltaic and load combined time sequence scene according to the photovoltaic output time sequence characteristic of the photovoltaic output typical scene and the power consumption load time sequence characteristic of the power consumption load typical scene;
the specific steps for constructing the photovoltaic and load combined time sequence scene are as follows:
(1) The occurrence probability of each photovoltaic output typical scene and each electricity load typical scene is calculated according to the following specific formula:
wherein P is gi Probability of occurrence for the ith photovoltaic output typical scene, |C gi I is the number of samples of the ith cluster of the power generation power curve of the distributed photovoltaic power station, N g Generating power curve quantity for the distributed photovoltaic power station; p (P) lj For the j-th power load typical scene occurrence probability, |C lj I is the number of samples of the jth cluster of the load curve, N l The number of the load curves;
(2) Building a photovoltaic and load joint time sequence scene, wherein the occurrence probability of each scene is as follows:
P gi×li ＝P gi ×P lj
P gi×lj
and the generation probability of the joint time sequence scene of the ith photovoltaic output typical scene and the j power consumption load typical scenes is shown in the formula.
Step 3: from the perspective of occurrence probability of a photovoltaic and load combined time sequence scene, determining the installation position and capacity of the distributed photovoltaic by adopting an improved genetic simulated annealing algorithm with the aim of maximizing the economic benefit of the photovoltaic power station;
the method comprises the following specific steps:
(1) The distributed photovoltaic installation location and capacity optimization targets are as follows:
wherein: p (P) tol Is the total income of photovoltaic power generation, S eg Selling electricity benefits for photovoltaic power generation; c (C) pv Investment and installation cost for the distributed photovoltaic equipment; c (C) yun Delta C, the operation and maintenance cost of distributed photovoltaic loss Energy conservation and loss reduction benefits for photovoltaic installation power generation; p (P) i The occurrence probability of the ith photovoltaic load joint time sequence scene is represented by m, which is the number of the photovoltaic load joint time sequence scenes;
the investment and installation cost of the distributed photovoltaic equipment is as follows:
wherein: n is the number of nodes of the photovoltaic access power grid;photovoltaic installation capacity for the inode; c (C) PVe The equipment cost per unit capacity of photovoltaic; c (C) PVk Installation cost per unit capacity of photovoltaic;
the operation and maintenance cost of the distributed photovoltaic equipment is as follows:
wherein: c (C) PVy The operation and maintenance cost of photovoltaic with unit capacity per year; m is m PV The investment recovery period of the photovoltaic; r is the discount rate;
the electricity generation and electricity selling benefits in the photovoltaic equipment investment recovery period are as follows:
wherein: j (J) i The electricity selling price of the node i; p (P) ij Node i, jth annual energy production;
the energy-saving and loss-reducing benefits of the photovoltaic power generation grid are as follows:
wherein: j (J) buy The electricity purchasing price is the online electricity price; p (P) loss The power distribution network loses each year before photovoltaic installation,the power grid loss after photovoltaic installation;
(2) The improved genetic simulated annealing algorithm is adopted to develop and determine the distributed photovoltaic capacity, and the specific contents are as follows:
1) Since the goal of distributed photovoltaic planning is to maximize the photovoltaic power plant investment economy, the fitness function is constructed as:
wherein F (x) is an objective function of an individual, namely the investment economic benefit of the photovoltaic power station; f (x) is an fitness function of the individual; t (T) 0 Initial temperature to simulate annealing problems; n (N) max K is the current iteration number, and alpha is the expansion coefficient;
2) The distributed photovoltaic installation position and capacity belong to discrete variables, and the chromosome adopts decimal integer coding as follows:
X＝[x 1 ,x 2 ,…,x n ]
in which x is i =0, indicating that position i does not mount a photovoltaic; x is x i M, i is m photovoltaic units, i is greater than or equal to 1 and n;
3) Randomly selecting two individuals to compare the fitness of the two individuals, and storing the individuals with higher fitness; directly copying the individuals with the highest fitness in the current population to the next generation;
4) Adaptive crossover and mutation operations: increasing crossover probability p when a population has a tendency to fall into a locally optimal solution c Probability of variation p m Corresponding decrease in p when populations diverge in solution space c And p m ，p c And p m The following formula is shown:
in which k is 0.ltoreq.k i ≤1(i＝1,2,3,4)，k i Self-adaptive constant, f max The method is the maximum adaptability in the current generation of the group; f (f) avg Is the average fitness in the current generation of the population; f is the greater fitness used to cross two individuals; f' is the fitness of the individual to be mutated;
5) Acceptance of the annealing process for new individuals: a group of new individuals generated by the genetic algorithm, wherein two genes of each individual are independently and randomly selected as disturbance points, if the individual fitness is increased, the new individuals are accepted, otherwise, the new individuals are accepted according to the probability of the expression;
T k+1 ＝α·T k
wherein f k+1 And f k The adaptation values of the new individual and the old individual respectively; p (T) k+1 ) At T k+1 Temperature (temperature)The probability of acceptance; alpha is the cooling coefficient.
Step 4: and adopting the super capacitor to stabilize the fluctuation characteristic of the output of the distributed photovoltaic power generation, and determining the installation position and the capacity of the energy storage power station by taking the minimum capacity as a target.
And calculating 360-day charge and discharge power of the energy storage system according to the photovoltaic power data with the time span of 360 days and an energy storage system control strategy, and calculating the energy storage capacity required by the j th day.
Wherein Cap (j) is the energy storage capacity configured on the j th day; p (P) ji The charging and discharging power value at the j th day i moment; Δt is the sampling interval; 1 to m 1 、m 2 ～m 3 、m j ～m s And the time of data sampling is the time of uninterrupted charge and discharge of the stored energy.
Specific cases:
(1) An IEEE33 node power distribution system is selected as shown in fig. 2, for example. And selecting photovoltaic power generation historical data of the whole year in 2020 of a certain city as a distributed photovoltaic power generation output reference, and carrying out distributed photovoltaic planning analysis on a 33-node standard power distribution system.
(2) Based on a historical power generation power data set and a historical power load data set of the photovoltaic power station, an improved K center point clustering algorithm is adopted to extract a photovoltaic output typical scene and a power load typical scene; a typical scenario pattern for photovoltaic power generation is shown in fig. 3. A typical scenario pattern of user loading is shown in fig. 4. A typical scenario pattern of a commercial load is shown in fig. 5.
(3) Referring to the electricity utilization rule of residential loads and commercial loads, the 32 bus node loads of the IEEE-33 power distribution system are used as 24-period average loads, the load time sequence simulation is generated, the nodes 23, 24 and 31 are commercial loads, and the rest are residential loads. The 24 moment load of each node is shown in fig. 6.
(4) The maximum load of the IEEE33 node standard distribution network is 5.5MW, the maximum permeability of photovoltaic power generation is set to be 30%, the minimum unit capacity of photovoltaic is 0.1MW,32 nodes can be used as photovoltaic installation positions, and the maximum photovoltaic capacity of a single node is 0.3MW. The investment cost of the 1MW photovoltaic is 500 ten thousand yuan, and the annual operation maintenance cost is 5 ten thousand yuan. The photovoltaic power generation online electricity price is 0.045 ten thousand yuan/MW.h, the resident electricity price is 0.055 ten thousand yuan/MW.h, and the commercial electricity price is 0.075 ten thousand yuan/MW.h. The service life of the photovoltaic power generation equipment is 20 years, and the discount rate in the service life is 0.05. 4 typical scenes of photovoltaic power generation are obtained through cluster analysis, 4 joint time sequence scenes are constructed, distributed photovoltaic site selection and volume determination optimization of the power distribution network is carried out through a simple genetic algorithm and an improved genetic algorithm according to probability distribution of the joint time sequence scenes, and specific results are shown in figure 7.
It can be seen that the improved genetic algorithm basically achieves the optimal result after 20 iterations, and the total economic benefit reaches 1090 ten thousand yuan in the service life cycle of the photovoltaic equipment. The photovoltaic installation capacity is 16MW, and the investment is 800 ten thousand yuan at a time.
Table 1 photovoltaic mounting locations and capacities
(5) Meanwhile, in order to achieve the purpose, the super capacitor is adopted to evaluate the photovoltaic power generation power. For photovoltaic power generation typical scenario 1, the stored energy charge-discharge power is shown in fig. 8.
The photovoltaic equipment is distributed in unit capacity, and the most energy storage capacity is 0.0488805. Similarly, the energy storage device capacity optimization analysis is carried out on scenes 2, 3 and 4, the optimal capacities are 0.0381265, 0.0011615 and 0.0134965 respectively, and four scene capacity maximum values 0.0488805 are selected. And the total installation capacity of the photovoltaic is 16MW, and each installation position is required to be configured with a super capacitor of 0.782 MW.
While the foregoing is directed to embodiments, aspects and advantages of the present invention, other and further details of the invention may be had by the foregoing description, it will be understood that the foregoing embodiments are merely exemplary of the invention, and that any changes, substitutions, alterations, etc. which may be made herein without departing from the spirit and principles of the invention.
Claims (6)
1. The investment decision-making method for the distributed photovoltaic and energy storage power station of the power distribution network is characterized by comprising the following steps of:
step 1: the method comprises the steps of inputting a historical power generation power data set and a historical power consumption load data set of a photovoltaic power station, extracting a photovoltaic output typical scene and a power consumption load typical scene by adopting an improved K center point clustering algorithm, calculating distances among different daily power generation power curves of the distributed photovoltaic power station by adopting a dynamic time bending distance, constructing a distance matrix among the different daily power generation power curves of the distributed photovoltaic power station, carrying out clustering analysis on the different daily power generation power curves of the distributed photovoltaic power station by adopting a K center point clustering algorithm with optimal profile coefficient as a target, selecting each cluster center point as a photovoltaic output typical scene, calculating the distances among the different daily load curves of the power consumption load by adopting a dynamic time bending distance, constructing a distance matrix among the different daily load curves of the load, carrying out clustering analysis on the different daily load curves of various types of the load by adopting a K center point clustering algorithm with optimal profile coefficient as a target, and selecting each cluster center point as the power consumption load typical scene;
step 2: then constructing a photovoltaic and load combined time sequence scene according to the photovoltaic output time sequence characteristic of the photovoltaic output typical scene and the power consumption load time sequence characteristic of the power consumption load typical scene;
step 3: from the perspective of occurrence probability of a photovoltaic and load combined time sequence scene, determining the installation position and capacity of a distributed photovoltaic by adopting an improved genetic simulated annealing algorithm with the aim of maximizing the economic benefit of a photovoltaic power station, constructing a fitness function, adopting decimal integer codes for chromosomes, randomly selecting two individuals to compare the fitness of the two individuals, and storing the individuals with higher fitness; the method comprises the steps of directly copying an individual with highest fitness in a current population to the next generation, increasing crossover probability and mutation probability when the population falls into a local optimal solution trend, correspondingly reducing crossover probability and mutation probability when the population diverges in a solution space, independently and randomly selecting two genes of each individual as disturbance points through a group of new individuals generated by the genetic algorithm, receiving the new individuals if the fitness of the individuals is increased, otherwise, receiving the new individuals according to the probability:
T k+1 ＝α·T k
wherein f k+1 And f k The adaptation values of the new individual and the old individual respectively; p (T) k+1 ) At T k+1 Probability of acceptance at temperature; alpha is a cooling coefficient;
step 4: and adopting the super capacitor to stabilize the fluctuation characteristic of the output of the distributed photovoltaic power generation, and determining the installation position and the capacity of the energy storage power station by taking the minimum capacity as a target.
2. The method for investment decision-making for distributed photovoltaic and energy storage power stations of power distribution network according to claim 1, characterized in that said dynamic time bending distance calculation formula is as follows:
assume that there are two time sequences a= { a 1 ,…,a l ,…,a m Sum b= { B 1 ,…,b j ,…,b n First, a matrix M of M x n is constructed, and the element M (i, j) is a i And b j The distance between the two sequences is found, and then a curved path which minimizes the accumulated distance between the two sequences is found in the matrix; the curved path w= { W 1 ,…,w l ,…,w K A matrix M is a set of consecutive elements, K is the number of curved path elements, and satisfies the following constraint:
1) Bounded restraint: max (m, n) is more than or equal to K and less than or equal to m+n-1;
2) Boundary constraint: element w 1 =m (1, 1) and w K =m (M, n) is the start and end of the curved path, respectively;
3) Continuity constraint: given element w l =m (i, j), its adjacent elementsElement w l-1 M (i ', j') is required to satisfy i-i '1, j-j' 1, i.e., the curved path elements are adjacent;
4) Monotonicity constraint: given element w l =m (i, j), its adjacent element w l-1 =m (i ', j') needs to satisfy i-i '. Gtoreq.0, j-j'. Gtoreq.0;
there are a plurality of curved paths in the matrix M that satisfy the above constraint, and the dynamic time-curved distance of the time series A and B is the smallest curved path;
the curved path is solved by adopting a dynamic programming algorithm, and the optimal solution substructure is as follows:
d(i,j)＝M(i,j)+min{d(i-1,j-1),d(i-1,j),d(i,j-1)}
where i=1, 2, …, m, j=1, 2, …, n, d (0, 0) =0, d (i, 0) =d (0, j-j+++;
the above time sequence a= { a 1 ,…,a i ,…,a m Sum b= { B 1 ,…,b j ,…,b n Dynamic time warping distance of D dtw (A,B)＝d(m,n)。
3. The method for investment decision-making for a distributed photovoltaic and energy storage power station of a power distribution network according to claim 2, wherein said profile factor is defined as follows:
for a data set D of n objects, suppose D is divided into k clusters C 1 ，...，C k The method comprises the steps of carrying out a first treatment on the surface of the For each object o e D, calculating an average distance a (o) between o and other objects o 'of the cluster to which o belongs and a minimum average distance b (o) from o to all cluster objects o' not belonging to o;
suppose o ε C i And i is more than or equal to 1 and less than or equal to k, then
The contour coefficient of object o is defined as
Wherein dist (o, o ') is the distance between objects o and o';
dist (o, o ") is the distance between objects o and o';
the value of the profile factor is between-1 and 1.
4. The investment decision-making method for the distributed photovoltaic and energy storage power stations of the power distribution network according to claim 1, wherein the specific steps of constructing a photovoltaic and load combined time sequence scene are as follows:
(1) The occurrence probability of each photovoltaic output typical scene and each electricity load typical scene is calculated according to the following specific formula:
wherein P is gi Probability of occurrence for the ith photovoltaic output typical scene, |C gi I is the number of samples of the ith cluster of the power generation power curve of the distributed photovoltaic power station, N g Generating power curve quantity for the distributed photovoltaic power station; p (P) lj For the j-th power load typical scene occurrence probability, |C lj I is the number of samples of the jth cluster of the load curve, N l The number of the load curves;
(2) Building a photovoltaic and load joint time sequence scene, wherein the occurrence probability of each scene is as follows:
P gi×li ＝P gi ×P lj
wherein P is gi×lj And combining the time sequence scene occurrence probability for the ith photovoltaic output typical scene and the jth electricity load typical scene.
5. The investment decision-making method for distributed photovoltaic and energy storage power stations of power distribution network according to claim 1, wherein the specific steps of said step 3 are as follows:
(1) The distributed photovoltaic installation location and capacity optimization targets are as follows:
wherein: p (P) tol Is the total income of photovoltaic power generation, S eg Selling electricity benefits for photovoltaic power generation; c (C) pv Investment and installation cost for the distributed photovoltaic equipment; c (C) yun Delta C, the operation and maintenance cost of distributed photovoltaic loss Energy conservation and loss reduction benefits for photovoltaic installation power generation; p (P) i The occurrence probability of the ith photovoltaic load joint time sequence scene is given, and m is the number of the photovoltaic load joint time sequence scenes;
the investment and installation cost of the distributed photovoltaic equipment is as follows:
wherein: n is the number of nodes of the photovoltaic access power grid;photovoltaic installation capacity for node i; c (C) PVe The equipment cost per unit capacity of photovoltaic; c (C) PVk Installation cost per unit capacity of photovoltaic;
the operation and maintenance cost of the distributed photovoltaic equipment is as follows:
wherein: c (C) PVy The operation and maintenance cost of photovoltaic with unit capacity per year; m is m PV The investment recovery period of the photovoltaic; r is the discount rate;
the electricity generation and electricity selling benefits in the photovoltaic equipment investment recovery period are as follows:
wherein: j (J) i The electricity selling price of the node i; p (P) ij Node i, jth annual energy production;
the energy-saving and loss-reducing benefits of the photovoltaic power generation grid are as follows:
wherein: j (J) buy The electricity purchasing price is the online electricity price; p (P) loss The power distribution network loses each year before photovoltaic installation,the power grid loss after photovoltaic installation;
(2) The improved genetic simulated annealing algorithm is adopted to develop and determine the distributed photovoltaic capacity, and the specific contents are as follows:
1) Since the goal of distributed photovoltaic planning is to maximize the photovoltaic power plant investment economy, the fitness function is constructed as:
wherein F (x) is an objective function of an individual, namely the investment economic benefit of the photovoltaic power station; f (x) is an fitness function of the individual; t (T) 0 Initial temperature to simulate annealing problems; n (N) max K is the current iteration number, and alpha is the expansion coefficient;
2) The distributed photovoltaic installation position and capacity belong to discrete variables, and the chromosome adopts decimal integer coding as follows:
X＝[x 1 ,x 2 ,…,x n ]
in which x is i =0, indicating that position i does not mount a photovoltaic; x is x i =m, representing bitI is arranged, m photovoltaic units with unit capacity are arranged, i is more than or equal to 1 and less than or equal to n;
3) Randomly selecting two individuals to compare the fitness of the two individuals, and storing the individuals with higher fitness; directly copying the individuals with the highest fitness in the current population to the next generation;
4) Adaptive crossover and mutation operations: increasing crossover probability p when a population has a tendency to fall into a locally optimal solution c Probability of variation p m Corresponding decrease in p when populations diverge in solution space c And p m ，p c And p m The following formula is shown:
in which k is 0.ltoreq.k i ≤1(i＝1,2,3,4)，k i Self-adaptive constant, f max The method is the maximum adaptability in the current generation of the group; f (f) avg Is the average fitness in the current generation of the population; f is the greater fitness used to cross two individuals; f' is the fitness of the individual to be mutated;
5) Acceptance of the annealing process for new individuals: a group of new individuals generated by the genetic algorithm, wherein two genes of each individual are independently and randomly selected as disturbance points, if the individual fitness is increased, the new individuals are accepted, otherwise, the new individuals are accepted according to the probability of the expression;
T k+1 ＝α·T k
wherein f k+1 And f k The adaptation values of the new individual and the old individual respectively; p (T) k+1 ) At T k+1 Probability of acceptance at temperature; alpha is the cooling coefficient.
6. The investment decision-making method for distributed photovoltaic and energy storage power stations of the power distribution network according to claim 1, wherein in the step 4, super-capacitors are adopted to stabilize fluctuation of output of distributed photovoltaic power generation, and the installation position and capacity of the energy storage power stations are determined with the aim of minimum capacity:
according to the photovoltaic power data with the time span of 360 days and the energy storage system control strategy, calculating the 360-day charge and discharge power of the energy storage system, and calculating the energy storage capacity required by the j th day;
wherein Cap (j) is the energy storage capacity configured on the j th day; p (P) ji The charging and discharging power value at the j th day i moment; Δt is the sampling interval; 1 to m 1 、m 2 ～m 3 、m j ～m s And the time of data sampling is the time of uninterrupted charge and discharge of the stored energy.
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