CN116454944A - Energy storage device optimal configuration method and system based on random production simulation - Google Patents
Energy storage device optimal configuration method and system based on random production simulation Download PDFInfo
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Abstract
The invention provides an energy storage device optimal configuration method and system based on random production simulation, comprising the following steps: acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions; generating a wind power time sequence based on second-order Markov chain-Monte Carlo sampling according to wind power fluctuation quantity distribution, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling by a photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set; taking the wind-discarding light-discarding rate and the power supply reliability as opportunity constraints, and establishing a random planning model of the energy storage configuration of the power system; and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device. The problem that the existing energy storage device is difficult to meet the actual dynamic energy storage requirement of electric power is solved.
Description
Technical Field
The invention relates to the technical field of energy storage system optimization, in particular to an energy storage device optimal configuration method and system based on random production simulation.
Background
Grid-connected power generation of new energy sources such as wind power, photovoltaic and the like requires that a power grid can provide flexible resources to cope with the characteristics of randomness, intermittence, fluctuation and the like of new energy source output. With the large-scale installation of new energy into grid-connected power generation, the wind and light absorption pressure of a power grid can exist for a long time. The energy storage device is used as a flexible resource, can realize electric quantity transfer, effectively relieves the imbalance problem between electric energy supply and load demands, and provides a solution for improving the new energy power generation duty ratio. However, the existing energy storage construction cost is high, the energy storage capacity configuration is too large, so that the investment and operation cost of a power grid is greatly increased to be unfavorable for sustainable development of new energy power generation, and the effect of participating in power grid adjustment on improving the wind-solar energy absorption level is not obvious if the energy storage capacity configuration is too small, so that the research on the configuration problem of an energy storage device is very important.
The capacity and power allocation of the energy storage device at present mainly comprises a typical scene method and a production simulation method. The typical scene method utilizes the load and the new energy scene of the typical day to configure the energy storage capacity and the power, the scene covered by the typical day is single, the randomness of the variables such as wind power, photovoltaic output, load and the like can not be reflected, and the large deviation between the planning result and the actual operation is easy to occur. The production simulation method simulates annual wind power, photovoltaic output and load curves based on the randomness characteristics of wind power and photovoltaic, and solves equations of all time periods simultaneously to obtain the capacities and powers of various heat and power storage devices with minimum annual total cost. The production simulation method can well reflect the probability characteristics of random variables, but conservation of configuration results exists. With the perfection of demand response incentive policies and the popularization of intelligent terminals, more users will actively participate in power grid regulation in the future. If the execution effect of the demand response policy can be properly considered in the system planning and configuration stage, the planning cost can be reduced, the resource utilization rate can be improved, and the development process of the demand response of the power system can be promoted.
Therefore, wind power, photovoltaic and load scenes are obtained based on random production simulation, and the energy storage device optimizing configuration random planning model is established by combining opportunity constraint conditions of the wind discarding and light discarding rate and the power supply reliability, so that the method has important practical significance for improving the utilization rate of the existing resources and promoting the development of power marketization.
Disclosure of Invention
The invention provides an energy storage device optimal configuration method and system based on random production simulation, which are used for solving the problem that the existing energy storage device is difficult to meet the actual dynamic energy storage requirement of electric power.
The invention provides an energy storage device optimal configuration method based on random production simulation, which comprises the following steps:
acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions;
sampling based on a second-order Markov chain-Monte Carlo method according to the fluctuation amount distribution of the wind power to generate a wind power time sequence, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling of the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
taking the wind-discarding light-discarding rate and the power supply reliability as opportunity constraints, and establishing a random planning model of the energy storage configuration of the power system;
and relaxing the opportunity constraint condition, and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device.
According to the energy storage device optimal configuration method based on random production simulation, wind power output and photovoltaic output historical data are obtained, and wind power fluctuation amount distribution and photovoltaic output probability density functions are obtained by analyzing the wind power output and photovoltaic output historical data, and the method specifically comprises the following steps:
analyzing the wind power output historical data to obtain a state transition matrix of wind power output, and generating wind power fluctuation quantity distribution;
and analyzing the photovoltaic output historical data, and obtaining a photovoltaic output probability density function based on a non-parameter kernel density estimation method.
According to the energy storage device optimal configuration method based on random production simulation, provided by the invention, a wind power time sequence is generated based on sampling of a second-order Markov chain-Monte Carlo method according to wind power fluctuation amount distribution, and the method specifically comprises the following steps:
the fluctuation quantity of the wind power meets the position scale distribution, and a state transition matrix is adopted to quantify the fluctuation characteristic of the wind power;
and generating a wind power time sequence based on second-order Markov chain-Monte Carlo sampling, and generating a state transition matrix.
According to the energy storage device optimal configuration method based on random production simulation, the clustering algorithm is utilized to reduce an original scene set to obtain a representative scene set, and the method specifically comprises the following steps:
acquiring an original scene set, selecting a cluster number, randomly generating a plurality of initial cluster centers, and calculating the distance from each scene to the cluster center;
aiming at any scene, finding the nearest scene by comparing the distance between the scene and each cluster center, classifying the nearest scene into a cluster, and calculating the scene mean value of each cluster to serve as a new cluster center until the cluster center is not changed;
multiplying the obtained photovoltaic output scene by scene probabilities of a wind power scene and a load scene to obtain the probability of each combined scene, and generating a representative scene set.
According to the energy storage device optimal configuration method based on random production simulation, provided by the invention, the wind-discarding light-discarding rate and the power supply reliability are taken as opportunity constraints, and a random planning model of energy storage configuration of an electric power system is established, and the method specifically comprises the following steps:
determining a random programming model objective function of energy storage configuration;
constraint adjustment of the stochastic programming model of the energy storage configuration comprises: opportunity constraint conditions, network safety constraint, wind power output constraint, photovoltaic output constraint, conventional thermal power generating unit power constraint and climbing constraint, cogeneration unit electricity/heat output constraint, cogeneration unit climbing constraint, heat storage device heat storage/release power constraint, heat storage device charge/discharge power constraint, heat storage device capacity constraint, power storage device capacity constraint, periodic power storage capacity constraint, periodic heat constraint and electric boiler operation power constraint.
According to the energy storage device optimizing configuration method based on random production simulation, the opportunity constraint condition is relaxed, the random programming model is converted into a deterministic linear programming model, and the configuration capacity and the configuration power of the energy storage device are obtained, and the method specifically comprises the following steps:
and converting the stochastic programming model into a mixed integer linear programming model, and calling a day-ahead dispatching optimization algorithm to solve the energy storage configuration linear programming model by using an open source solver, so as to finally obtain the configuration capacity and power of each energy storage device.
The invention also provides an energy storage device optimal configuration system based on random production simulation, which comprises the following steps:
the data analysis module is used for acquiring the historical data of wind power output and photovoltaic output, analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation quantity distribution and photovoltaic output probability density functions;
the data processing module is used for generating a wind power time sequence based on second-order Markov chain-Monte Carlo sampling according to the wind power fluctuation quantity distribution, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling according to the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
the model building module is used for taking the wind-abandoning light-abandoning rate and the power supply reliability as opportunity constraints and building a random planning model of the energy storage configuration of the power system;
and the energy storage configuration module is used for relaxing the opportunity constraint condition, converting the stochastic programming model into a deterministic linear programming model and obtaining the configuration capacity and the configuration power of the energy storage device.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the energy storage device optimal configuration method based on random production simulation when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of optimizing configuration of an energy storage device based on random production simulation as described in any one of the above.
The invention also provides a computer program product, comprising a computer program which realizes the energy storage device optimal configuration method based on random production simulation when being executed by a processor.
According to the energy storage device optimal configuration method and system based on random production simulation, the energy storage optimal configuration random planning model is established through constraint conditions such as annual power generation plans and electric and thermal load scenes of each power plant, consideration of opportunity constraint conditions of electric and thermal load balance and wind-solar rejection rate, comprehensive consideration of tide constraint, unit climbing constraint, heat storage equipment operation control constraint and electricity storage equipment operation control constraint and the like. The method has the advantages that the opportunity constraint is relaxed in the solving process, the uncertainty model containing random variables is converted into a deterministic linear programming model, the energy storage device is optimally configured under the condition that the requirement of opportunity constraint confidence is met, the resource waste caused by the rigid requirement of load and wind-solar rejection rate is avoided, the further development of new energy and electric marketization is promoted, and the method has important practical significance.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an energy storage device optimizing configuration method based on random production simulation;
FIG. 2 is a second flow chart of an energy storage device optimizing configuration method based on random production simulation;
FIG. 3 is a third flow chart of an energy storage device optimizing configuration method based on random production simulation;
FIG. 4 is a schematic flow chart of an energy storage device optimizing configuration method based on random production simulation;
FIG. 5 is a schematic flow chart of an energy storage device optimizing configuration method based on random production simulation;
FIG. 6 is a schematic diagram of module connection of an energy storage device optimizing configuration system based on random production simulation;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Reference numerals:
110: a data analysis module; 120: a data processing module; 130: a model building module; 140: an energy storage configuration module;
710: a processor; 720: a communication interface; 730: a memory; 740: a communication bus.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following describes an energy storage device optimizing configuration method based on random production simulation with reference to fig. 1 to 5, which comprises the following steps:
s100, acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions;
s200, sampling based on a second-order Markov chain-Monte Carlo method according to the fluctuation amount distribution of the wind power to generate a wind power time sequence, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling according to the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
s300, taking the wind-abandoning light-abandoning rate and the power supply reliability as opportunity constraints, and establishing a random planning model of energy storage configuration of the power system;
s400, relaxing the opportunity constraint condition, and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device.
The wind power generation plan and load scene of the photovoltaic year are obtained through random production simulation, the opportunity constraint conditions of the wind discarding and light discarding rate and the power supply reliability are considered, and an energy storage device optimal configuration random planning model is established, so that the waste of energy storage device resources is avoided, and the planning and operation economy of the power system is improved.
The method comprises the steps of obtaining wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions, wherein the method specifically comprises the following steps:
s101, analyzing the wind power output historical data to obtain a state transition matrix of wind power output, and generating wind power fluctuation quantity distribution;
s102, analyzing the photovoltaic output historical data, and obtaining a photovoltaic output probability density function based on a non-parameter kernel density estimation method.
According to the invention, through analyzing the wind power output and photovoltaic output historical data, a large-scale original scene can be established in a more accurate auxiliary manner, and the subsequent reduction of the original scene is facilitated.
Sampling and generating a wind power time sequence based on a second-order Markov chain-Monte Carlo method according to the wind power fluctuation amount distribution, and specifically comprising the following steps:
s201, the fluctuation quantity of the wind power meets position scale distribution, and a state transition matrix is adopted to quantify wind power fluctuation characteristics;
s202, sampling based on a second-order Markov chain-Monte Carlo method to generate a wind power time sequence, and generating a state transition matrix.
In the invention, the fluctuation amount of wind power meets the t location-scale distribution, and the probability distribution is as follows:
wherein Γ (·) is a gamma function; μ is a position parameter; sigma is a scale parameter; v is a shape parameter.
The method comprises the steps of quantizing wind power fluctuation characteristics by adopting a state transition matrix, generating a wind power time sequence based on a second-order Markov chain-Monte Carlo method, and generating a state transition matrix as follows:
pij＝P(Xt＝j|Xt-1＝i,…,X1＝a)＝P(Xt＝j|Xt-1＝i)
the middle row corresponds to the current output state of wind power; the column corresponds to the output state at the next moment; element pij represents the probability of wind power transitioning from state i to state j; xt-1 is the state corresponding to the wind power value at the t-1 time; xt is the state corresponding to the wind power value at the t moment.
The probability density function of the photovoltaic output is estimated by a non-parameter nuclear density estimation method, and the photovoltaic output sample value is set as x 1 ,x 2 ,…,x N The kernel of its probability density function is estimated as the volt-age probability density expression as follows:
where N is the sample size, h is the bandwidth, and k (t) is the kernel function
The method comprises the steps of reducing an original scene set by using a clustering algorithm to obtain a representative scene set, and specifically comprises the following steps:
s301, acquiring an original scene set, selecting a cluster number, randomly generating a plurality of initial cluster centers, and calculating the distance from each scene to the cluster center;
s302, aiming at any scene, finding the nearest scene by comparing the distance between the scene and each cluster center, classifying the scene into a cluster, and calculating the scene mean value of each cluster to serve as a new cluster center until the cluster center is not changed;
s303, multiplying the obtained photovoltaic output scene by scene probabilities of the wind power scene and the load scene to obtain the probability of each combined scene, and generating a representative scene set.
In the invention, the photovoltaic original scene is reduced based on a clustering algorithm, and the main process is as follows:
1) First, the original scene set L= { L is obtained 1 ,l 2 ,...,l n Selecting a cluster number M, and randomly generating M initial cluster centers M= { M 1 ,m 2 ,...,m n Calculating the distance from each scene to the cluster center:
middle l i,t Is the t element, m of scene i j,t Is the t element of the cluster center j;
2) For any scene l i By comparison with each cluster center m j Finding the nearest scene m j Classifying the scenes into a cluster, and calculating the scene mean value of each cluster to serve as a new cluster center;
3) Repeating the step 2) until the clustering center is not changed;
4) Multiplying the obtained photovoltaic output scene by the scene probability of the wind power scene and the load scene to obtain the probability beta of each combined scene k 。
The energy storage configuration stochastic programming model objective function is as follows:
objective function min C total ＝C elec.inv +C heat.inv +C oper -S envi
First part C of objective function right type elec.inv Is the annual investment cost of the electricity storage device; second part C of the right formula of the objective function heat.inv Is the annual investment cost of the heat storage device; third part C in the right formula of the objective function oper The annual operation maintenance cost of the heat storage and electricity storage device is high; fourth part S in the right form of the objective function envi Is an environmental benefit brought by new energy consumption. C (C) elec.inv 、C heat.inv 、C oper And S is equal to envi The method is obtained by the following formula:
C oper ＝f·(C elec.inv +C heat.inv )+C OP,h +C OP,p
S envi ＝C hj ·W new
wherein r is the discount rate; y is the energy storage device lifetime; p (P) eh,N Is the power configuration of the power storage device; c (C) p Is the unit cost of the power storage device, S eh,N Is the power storage device configuration capacity; c (C) e The unit cost of the configuration capacity of the electricity storage device; p (P) h,N The configuration power of the heat storage device; c (C) hp Is the investment cost of the heat storage device configuration unit power, S h,N Is the configuration capacity of the heat storage device; c (C) hw The investment cost of the heat storage device configuration unit capacity is; f is the unit maintenance cost of the heat storage and electricity storage device; c (C) OP,h And C OP,p The operation cost of the heat storage and electricity storage devices is respectively; c (C) HS,j Is the unit operation cost of the heat storage device j; c (C) ES,l Is the unit operation cost of the electricity storage device;exothermic power for the heat storage device j; />Heat storage power for heat storage device j; />Discharging power for the electricity storage device; />Charging power for the power storage device l; c (C) hj Is the unit price for new energy compensation; w (W) new Is to consume new energy electric quantity; q c Is scene c probability; c (C) eh,k The unit power operation cost is configured for the electric boiler k; c (C) ehh,k The unit capacity operation cost is configured by the electric boiler k; />The electric power is used for the scene c electric boiler k; />K heat output of the electric boiler is the scene c; />And->Representing the operating state of the heat storage device j for scenario c for the 0-1 variable, +.>1 indicates that the heat storage device j releases heat, +.>A value of 1 indicates that the heat storage device j stores heat, and two variables are different from each other and 1.
The wind-discarding light-discarding rate and the power supply reliability are taken as opportunity constraints, and a random planning model of the energy storage configuration of the power system is established, which comprises the following steps:
s401, determining a random programming model objective function of energy storage configuration;
s402, constraint adjustment of the random programming model of the energy storage configuration comprises the following steps: opportunity constraint conditions, network safety constraint, wind power output constraint, photovoltaic output constraint, conventional thermal power generating unit power constraint and climbing constraint, cogeneration unit electricity/heat output constraint, cogeneration unit climbing constraint, heat storage device heat storage/release power constraint, heat storage device charge/discharge power constraint, heat storage device capacity constraint, power storage device capacity constraint, periodic power storage capacity constraint, periodic heat constraint and electric boiler operation power constraint.
In the invention, constraint conditions of the random programming model of the energy storage configuration are as follows:
1) Opportunistic constraints
In the method, in the process of the invention,the i heat output of the cogeneration unit is scene c; />Is scene c thermal load; />J heat loss of the heat storage device in the scene c; />G electric output of the conventional thermal power generating unit is the scene c; />The w output of the wind turbine generator is output; />The wind power is discarded; />Exerting force on the photovoltaic s; />Discarding the light power for the photovoltaic s; />I electric output of the cogeneration unit is scene c; />For scene c electrical load; η (eta) wind,curt Representing the wind abandoning rate; η (eta) solar,curt Representing the light rejection rate; η (eta) lim Representing the wind curtailment light rejection rate limit.
2) Network security constraints
In the method, in the process of the invention,transmitting power for scene c, branch b; />Branch b transmits the power limit.
3) Wind power output constraint
In the method, in the process of the invention,and planning output for the scene c wind turbine w.
4) Photovoltaic output restraint
In the method, in the process of the invention,and planning output for the photovoltaic unit s in the scene c.
5) Conventional thermal power generating unit power constraint and climbing constraint
In the method, in the process of the invention,and->The maximum and minimum technical output of the conventional unit g is obtained; />And->The upper limit and the lower limit of the climbing speed of the conventional unit g are set.
6) Electricity and heat output constraint of cogeneration unit
Wherein P is ei,max,i And P ei,min,i Respectively representing upper and lower limit constraints of the electric output under the pure coagulation working condition;the upper limit of the heat output of the cogeneration unit is shown.
7) Climbing constraint of cogeneration unit
In the method, in the process of the invention,and->Respectively representing the upper limit and the lower limit of the climbing of the electric output of the cogeneration unit; />And (3) withThe upper limit of the heat output of the cogeneration unit i is shown.
8) Heat storage device heat storage and release power constraint
In the method, in the process of the invention,and storing heat for the heat storage device j of the scene c.
9) Power constraint for charge and discharge of a power storage device
In the method, in the process of the invention,the electricity storage quantity of the electricity storage device l at the moment t is the electricity storage quantity of the scene c; p (P) l,max The maximum charge and discharge power of the power storage device l is shown.
10 Capacity constraint of a heat storage device
In the method, in the process of the invention,maximum heat storage capacity allowed for the heat storage device; delta h Is the self-heat release rate of the heat storage device.
11 Capacity constraint of a power storage device
Wherein eta is cha And eta dis The charge and discharge efficiency of the power storage device l are respectively;the method comprises the steps of storing the charge quantity of a device l at a moment t for a scene c; delta BS The self-discharge rate of the electricity storage device is obtained.
12 Cyclic power storage constraint
13 Cyclic heat storage capacity constraint)
14 Electric boiler operating power constraints
In the method, in the process of the invention,is the maximum operating power of the electric boiler.
The deterministic conversion of the opportunity constraint condition can utilize the out-of-limit hours of the line tide and the out-of-limit hours of the wind-solar rejection rate to be converted into corresponding determined equivalent forms, thereby simplifying the model
Wherein q is c 、d c,t 、S c,t And W is equal to c,t And are all 0-1 variables; q c The situation that the opportunity constraint condition is out of limit in the scene c is adopted; d, d c,t The situation that the line of the scene c is out of limit at the moment t; b is all line sets; p is p c Probability of scene c; s is S c,t The light rejection rate out-of-limit condition of the scene c at the moment t; w (W) c,t And (5) the out-of-limit condition of the wind curtailment rate of the scene c at the time t.
Relaxing the opportunity constraint condition, converting the stochastic programming model into a deterministic linear programming model, and obtaining the configuration capacity and the configuration power of the energy storage device, wherein the method specifically comprises the following steps of:
and converting the stochastic programming model into a mixed integer linear programming model, and calling a day-ahead dispatching optimization algorithm to solve the energy storage configuration linear programming model by using an open source solver, so as to finally obtain the configuration capacity and power of each energy storage device.
According to the energy storage device optimal configuration method based on random production simulation, through annual power generation plans and electricity and heat load scenes of each power plant obtained based on random production simulation, opportunity constraint conditions of electricity and heat load balance and wind-light rejection rate are considered, constraint conditions of tide constraint, unit climbing constraint, heat storage equipment operation control constraint, electricity storage equipment operation control constraint and the like are comprehensively considered, and an energy storage optimal configuration random planning model is established. The method has the advantages that the opportunity constraint is relaxed in the solving process, the uncertainty model containing random variables is converted into a deterministic linear programming model, the energy storage device is optimally configured under the condition that the requirement of opportunity constraint confidence is met, the resource waste caused by the rigid requirement of load and wind-solar rejection rate is avoided, the further development of new energy and electric marketization is promoted, and the method has important practical significance.
Referring to fig. 6, the invention also discloses an energy storage device optimizing configuration system based on random production simulation, which comprises:
the data analysis module 110 is configured to obtain wind power output and photovoltaic output historical data, and analyze the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and a photovoltaic output probability density function;
the data processing module 120 is configured to generate a wind power time sequence based on second-order markov chain-monte carlo sampling according to the wind power fluctuation amount distribution, obtain a large-scale original scene according to a scene generating method based on monte carlo sampling according to the photovoltaic output probability density function, and reduce the original scene set by using a clustering algorithm to obtain a representative scene set;
the model building module 130 is configured to build a random planning model of the energy storage configuration of the power system by taking the wind-abandoning light-abandoning rate and the power supply reliability as opportunity constraints;
and the energy storage configuration module 140 is used for relaxing the opportunity constraint condition, converting the stochastic programming model into a deterministic linear programming model, and obtaining the configuration capacity and the configuration power of the energy storage device.
The data analysis module 110 analyzes the wind power output history data to obtain a state transition matrix of wind power output, and generates wind power fluctuation quantity distribution;
and analyzing the photovoltaic output historical data, and obtaining a photovoltaic output probability density function based on a non-parameter kernel density estimation method.
The data processing module 120 adopts a state transition matrix to quantify wind power fluctuation characteristics through the wind power fluctuation quantity meeting position scale distribution;
and generating a wind power time sequence based on second-order Markov chain-Monte Carlo sampling, and generating a state transition matrix.
The method for reducing the original scene set by using the clustering algorithm to obtain a representative scene set specifically comprises the following steps:
acquiring an original scene set, selecting a cluster number, randomly generating a plurality of initial cluster centers, and calculating the distance from each scene to the cluster center;
aiming at any scene, finding the nearest scene by comparing the distance between the scene and each cluster center, classifying the nearest scene into a cluster, and calculating the scene mean value of each cluster to serve as a new cluster center until the cluster center is not changed;
multiplying the obtained photovoltaic output scene by scene probabilities of a wind power scene and a load scene to obtain the probability of each combined scene, and generating a representative scene set.
The model building module 130 determines a stochastic programming model objective function of the energy storage configuration;
constraint adjustment of the stochastic programming model of the energy storage configuration comprises: opportunity constraint conditions, network safety constraint, wind power output constraint, photovoltaic output constraint, conventional thermal power generating unit power constraint and climbing constraint, cogeneration unit electricity/heat output constraint, cogeneration unit climbing constraint, heat storage device heat storage/release power constraint, heat storage device charge/discharge power constraint, heat storage device capacity constraint, power storage device capacity constraint, periodic power storage capacity constraint, periodic heat constraint and electric boiler operation power constraint.
The energy storage configuration module 140 converts the random programming model into a mixed integer linear programming model, and invokes a day-ahead scheduling optimization algorithm to solve the energy storage configuration linear programming model by using an open source solver, so as to finally obtain the configuration capacity and power of each energy storage device.
According to the energy storage device optimal configuration system based on random production simulation, the energy storage optimal configuration random planning model is established through constraint conditions such as annual power generation plans and electric and thermal load scenes of each power plant obtained based on random production simulation, opportunity constraint conditions of electric and thermal load balance and wind-solar rejection rate are considered, tidal current constraint is comprehensively considered, and unit climbing constraint, heat storage equipment operation control constraint, electricity storage equipment operation control constraint and the like. The method has the advantages that the opportunity constraint is relaxed in the solving process, the uncertainty model containing random variables is converted into a deterministic linear programming model, the energy storage device is optimally configured under the condition that the requirement of opportunity constraint confidence is met, the resource waste caused by the rigid requirement of load and wind-solar rejection rate is avoided, the further development of new energy and electric marketization is promoted, and the method has important practical significance.
Fig. 7 illustrates a physical schematic diagram of an electronic device, as shown in fig. 7, which may include: processor 710, communication interface (Communications Interface) 720, memory 730, and communication bus 740, wherein processor 710, communication interface 720, memory 730 communicate with each other via communication bus 740. Processor 710 may invoke logic instructions in memory 730 to perform a method for optimizing configuration of an energy storage device based on random production modeling, the method comprising: acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions;
sampling based on a second-order Markov chain-Monte Carlo method according to the fluctuation amount distribution of the wind power to generate a wind power time sequence, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling of the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
taking the wind-discarding light-discarding rate and the power supply reliability as opportunity constraints, and establishing a random planning model of the energy storage configuration of the power system;
and relaxing the opportunity constraint condition, and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device.
Further, the logic instructions in the memory 730 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute a method for optimizing and configuring an energy storage device based on random production simulation, where the method includes: acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions;
sampling based on a second-order Markov chain-Monte Carlo method according to the fluctuation amount distribution of the wind power to generate a wind power time sequence, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling of the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
taking the wind-discarding light-discarding rate and the power supply reliability as opportunity constraints, and establishing a random planning model of the energy storage configuration of the power system;
and relaxing the opportunity constraint condition, and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device.
In yet another aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a method for optimizing configuration of an energy storage device based on random production simulation provided by the above methods, the method comprising: acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions;
sampling based on a second-order Markov chain-Monte Carlo method according to the fluctuation amount distribution of the wind power to generate a wind power time sequence, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling of the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
taking the wind-discarding light-discarding rate and the power supply reliability as opportunity constraints, and establishing a random planning model of the energy storage configuration of the power system;
and relaxing the opportunity constraint condition, and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An energy storage device optimal configuration method based on random production simulation is characterized by comprising the following steps:
acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions;
sampling based on a second-order Markov chain-Monte Carlo method according to the fluctuation amount distribution of the wind power to generate a wind power time sequence, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling of the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
taking the wind-discarding light-discarding rate and the power supply reliability as opportunity constraints, and establishing a random planning model of the energy storage configuration of the power system;
and relaxing the opportunity constraint condition, and converting the stochastic programming model into a deterministic linear programming model to obtain the configuration capacity and the configuration power of the energy storage device.
2. The method for optimizing configuration of an energy storage device based on random production simulation according to claim 1, wherein the obtaining wind power output and photovoltaic output historical data and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation amount distribution and photovoltaic output probability density functions specifically comprises:
analyzing the wind power output historical data to obtain a state transition matrix of wind power output, and generating wind power fluctuation quantity distribution;
and analyzing the photovoltaic output historical data, and obtaining a photovoltaic output probability density function based on a non-parameter kernel density estimation method.
3. The method for optimizing configuration of an energy storage device based on random production simulation according to claim 1, wherein the wind power time sequence is generated based on second-order markov chain-monte carlo sampling according to the wind power fluctuation amount distribution, and specifically comprises the following steps:
the fluctuation quantity of the wind power meets the position scale distribution, and a state transition matrix is adopted to quantify the fluctuation characteristic of the wind power;
and generating a wind power time sequence based on second-order Markov chain-Monte Carlo sampling, and generating a state transition matrix.
4. The method for optimizing configuration of an energy storage device based on random production simulation according to claim 1, wherein the method for reducing an original scene set by using a clustering algorithm to obtain a representative scene set specifically comprises:
acquiring an original scene set, selecting a cluster number, randomly generating a plurality of initial cluster centers, and calculating the distance from each scene to the cluster center;
aiming at any scene, finding the nearest scene by comparing the distance between the scene and each cluster center, classifying the nearest scene into a cluster, and calculating the scene mean value of each cluster to serve as a new cluster center until the cluster center is not changed;
multiplying the obtained photovoltaic output scene by scene probabilities of a wind power scene and a load scene to obtain the probability of each combined scene, and generating a representative scene set.
5. The method for optimizing configuration of an energy storage device based on random production simulation according to claim 1, wherein the method is characterized in that the method uses a wind-discarding light-discarding rate and power supply reliability as opportunity constraints to build a random planning model of energy storage configuration of an electric power system, and specifically comprises the following steps:
determining a random programming model objective function of energy storage configuration;
constraint adjustment of the stochastic programming model of the energy storage configuration comprises: opportunity constraint conditions, network safety constraint, wind power output constraint, photovoltaic output constraint, conventional thermal power generating unit power constraint and climbing constraint, cogeneration unit electricity/heat output constraint, cogeneration unit climbing constraint, heat storage device heat storage/release power constraint, heat storage device charge/discharge power constraint, heat storage device capacity constraint, power storage device capacity constraint, periodic power storage capacity constraint, periodic heat constraint and electric boiler operation power constraint.
6. The method for optimizing configuration of an energy storage device based on random production simulation of claim 1, wherein relaxing the opportunity constraint condition converts the random programming model into a deterministic linear programming model to obtain a configuration capacity and a configuration power of the energy storage device, and specifically comprises:
and converting the stochastic programming model into a mixed integer linear programming model, and calling a day-ahead dispatching optimization algorithm to solve the energy storage configuration linear programming model by using an open source solver, so as to finally obtain the configuration capacity and power of each energy storage device.
7. An energy storage device optimal configuration system based on random production simulation is characterized by comprising:
the data analysis module is used for acquiring wind power output and photovoltaic output historical data, and analyzing the wind power output and photovoltaic output historical data to obtain wind power fluctuation quantity distribution and photovoltaic output probability density functions;
the data processing module is used for generating a wind power time sequence based on second-order Markov chain-Monte Carlo sampling according to the wind power fluctuation quantity distribution, obtaining a large-scale original scene according to a scene generation method based on Monte Carlo sampling according to the photovoltaic output probability density function, and reducing the original scene set by using a clustering algorithm to obtain a representative scene set;
the model building module is used for taking the wind-abandoning light-abandoning rate and the power supply reliability as opportunity constraints and building a random planning model of the energy storage configuration of the power system;
and the energy storage configuration module is used for relaxing the opportunity constraint condition, converting the stochastic programming model into a deterministic linear programming model and obtaining the configuration capacity and the configuration power of the energy storage device.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for optimizing configuration of energy storage devices based on random production simulation as claimed in any one of claims 1 to 6 when executing the program.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the energy storage device optimizing configuration method based on random production simulation of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a method for optimizing the configuration of energy storage devices based on random production simulation according to any one of claims 1 to 6.
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