CN115864459A - Network-building type energy storage capacity configuration method and device combined with robust optimization - Google Patents
Network-building type energy storage capacity configuration method and device combined with robust optimization Download PDFInfo
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Abstract
The invention relates to the technical field of renewable energy sources, in particular to a network-building type energy storage capacity configuration method and a network-building type energy storage capacity configuration device combined with robust optimization, wherein the method comprises the steps of building a robust uncertain set of actual power deviation; constructing a space constraint parameter calculation expression in the uncertain convergence set of the robustness; quantifying the robustness of the system by utilizing the probability value of the renewable energy output outside the extreme condition; constructing a network-building type energy storage optimization configuration model under the extreme condition of power deviation of power generation and power utilization; and solving the network-forming type energy storage optimization configuration model by utilizing a whale algorithm. On the basis of predicting power generation and power consumption, the invention constructs a robust collection capable of flexibly adjusting boundaries by considering the probability characteristics of actual power deviation, and introduces uncertain space constraint parameters to flexibly adjust the boundaries of the constructed uncertain collection. The capacity of the energy storage power station is configured on the basis of scientific representation of power uncertainty so as to accurately match the fluctuation of the use requirement, and further the utilization rate of the energy storage is improved.
Description
Technical Field
The invention relates to the technical field of renewable energy sources, in particular to a network-building type energy storage capacity configuration method and device combined with robust optimization.
Background
The renewable energy has the characteristics of large power generation randomness and difficulty in accurate prediction, and the flexible response capability of the system is insufficient after grid connection. The network type energy storage has the capability of fast peak regulation and frequency regulation, and is one of the excellent technical means for realizing dynamic fast matching of supply and demand and improving the flexible regulation capability of a power grid. The method is widely applied to aspects of coordinating a power generation plan, meeting the power grid frequency requirement and the like. However, the energy storage has a problem of underutilization due to fluctuation of use demand. The method is mainly characterized in that the investment cost is too high or the return income period is longer during the early-stage planning of energy storage. Therefore, scientific description of the uncertainty of power generation, and thus accurate characterization of the demand for energy storage usage, is a major challenge in energy storage configuration.
Methods for describing uncertainty in the power system are mainly classified into a standby method and a stochastic programming method, but the two methods may cause results to be uneconomical or unreliable in the context of a novel power system characterized by complex morphological evolution and multiple uncertainties. Then, methods such as fuzzification, scene analysis, spectrum analysis, point estimation method, random chance constraint planning and the like are continuously proposed, although the feasibility of the result can be improved, various problems still exist, such as subjective membership function selection in the fuzzy chance constraint method; methods such as scene analysis, spectrum analysis, random chance constraint and the like all need a large amount of sample data, results are limited by the number of scenes, multi-scene description uncertainty calculation is complex, and solving efficiency and precision are difficult to guarantee; the variability of the point estimation method due to the diversity of the statistical samples places the inference results on the sample quality. The robust optimization finds a decision scheme under the worst situation by setting the fluctuation range of uncertain variables, and develops a new idea for describing the uncertainty of power. However, at present, uncertain collections of fixed boundaries are mostly established in robust optimization, and the economical efficiency is ignored when the robustness of the system is improved, so that the result is too conservative; the robustness is neglected when the system economy is improved, so that the result is unreliable. Even if the preference optimization mode is used for determining the collection boundary, the defects that the system robustness cannot be quantified, the economy is effectively balanced and the like exist. Therefore, how to reasonably adjust the boundary of the uncertain set and quantify the robustness of the system so as to scientifically balance the economy and the robustness, and the complex relation between the coupling of a plurality of uncertain variables and the economy and the robustness of the system needs further research.
Disclosure of Invention
The invention provides a network-building type energy storage capacity configuration method and device combined with robust optimization, overcomes the defects of the prior art, and can effectively solve the problem that the boundary of a built uncertain set cannot be adjusted in an uncertain description method used in the capacity configuration of the existing energy storage power station.
One of the technical schemes of the invention is realized by causing the following measures: a network-building type energy storage capacity configuration method combined with robust optimization comprises the following steps:
constructing a robust uncertain collection of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of power generation and power consumption prediction;
constructing a space constraint parameter calculation expression in the robust uncertain convergence set by using a Lindeberg-levy center limit theorem;
constructing the output of a robust uncertain aggregation renewable energy source station under extreme conditions by utilizing a linear dual theory and a Lagrange function;
quantifying the robustness of the system by utilizing the probability value of the output of the renewable energy outside the extreme condition;
constructing a network-building type energy storage optimization configuration model under the extreme condition of power deviation of power generation and power utilization by taking the minimum overall cost in a large-scale renewable energy access system as a target;
solving the network-forming type energy storage optimization configuration model by utilizing a whale algorithm, and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
The following is further optimization or/and improvement of the technical scheme of the invention:
the robust uncertain collection of the actual power deviation of the wind power output constructed by using infinite norm constraint and 1-norm constraint on the basis of the predicted power is as follows:
in the formula (I), the compound is shown in the specification,
for the actual output of the wind farm i at time t->
To predict force, is>
Respectively an upper limit and a lower limit of the output deviation;N w number for wind farm>
Is an infinite norm; />
Is perturbation quantity 1-norm constraint and corresponds to the space clustering effect of actual wind power output; />
The deviation coefficient of the wind power plant i at the time t is obtained; the same way an uncertain set can be constructed for other uncertain variables in the system.
The construction of the spatial constraint parameter calculation expression in the robust uncertain convergence set by using the Lindeberg-levy central limit theorem comprises the following steps:
determining the uncertain space constraint parameters of wind power output, comprising the following steps:
(1) In the first step
Have>
If the power deviation is a random variable which is independently identically distributed, then->
Are also independently identically distributed, which is desired to be->
Variance is ^ er>
(2)
Is greater than or equal to>
The following formula:
wherein E (-) represents expectation, D (-) represents variance;
(3) Standard variable
Obeying a standard Gaussian distribution, whose cumulative distribution function>
For an arbitrary probability a w The following equation relationship is satisfied: />
In the formula, alpha w Is the confidence probability;
(4) Deducing wind power output uncertainty space constraint parameters
The following were used:
and determining the spatial constraint parameters of other uncertain variables through the step of determining the wind power output uncertainty spatial constraint parameters.
The method for constructing the output of the robust uncertain centralized renewable energy source station by utilizing the linear dual theory and the Lagrange function under the extreme condition comprises the following steps:
constructing the output power of the wind power under the extreme condition by using a linear dual theory and a Lagrange function, wherein the robust uncertain combination is concentrated;
(1) By linear dual theory, structure
Lagrange function of (a):
(2) Due to the fact that
The power of the wind power output power under the extreme condition of output deviation can be obtained as follows:
since the linear programming optimal solution is at the vertex, the above equation is simplified:
(3) According to the combination of the most extreme condition in the time period t, the deviation coefficient of the output of only one wind power plant is less than 1, the wind power plant is set as j, and the total output is as follows:
in the formula (I), the compound is shown in the specification,
is a rounded-down symbol;
and determining the output power of other uncertain quantities in the extreme case by using the steps.
The construction of the network type energy storage optimization configuration model under the extreme condition of power deviation of power generation and power utilization by taking the minimum overall cost in the large-scale renewable energy access system as the target comprises the following steps:
aiming at the minimum overall cost of the access system, a target function of an energy storage optimization configuration model is constructed as shown in the specificationA number, wherein the overall cost includes: power generation cost of thermal power generating unit under extreme condition of renewable energy power generation prediction error
The start-stop cost of the unit->
Energy storage annual initial investment cost->
Maintenance cost->
Cost of frequency modulation C PFR ；
Considering the operation of a unit in an access system and network security factors, determining constraint conditions of a network-building type energy storage optimization configuration model;
(1) And power balance constraint:
in the formula (I), the compound is shown in the specification,
is the absence of power at time t>
Is a load->
The charging and discharging power for energy storage to meet the balance of power supply and demand; />
Are all; extreme power conditions;
(2) The stored energy operating constraints include:
1. and (3) charge and discharge power constraint:
2. and (3) charge state constraint:
3. the charge is equal to the discharge in the total scheduling period:
(3) Thermal power unit dynamic frequency output restraint includes:
1. and (3) restraining the output of frequency modulation:
in the formula, K i Is the power frequency static characteristic coefficient, delta f of the thermal power generating unit i max It is indicated that the maximum frequency deviation is,
the frequency modulation dead zone is a frequency modulation dead zone of the generator set i;
2. and (3) state constraint participating in frequency modulation:
3. and (3) frequency modulation capacity constraint:
(4) And (3) constraint of frequency modulation capacity requirement:
in the formula (I), the compound is shown in the specification,
for dynamic FM capacity requirements, Δ PRN Is the disturbance amount of the renewable energy source predicted output.
The solving of the network-building type energy storage optimization configuration model by using the whale algorithm comprises the following steps:
for boundary constraint in the network-building type energy storage optimization configuration model, a climbing constraint is dynamically updated into boundary constraint by using a boundary-crossing processing mode in a heuristic algorithm;
for power balance constraint in the network-building type energy storage optimization configuration model, a dynamic relaxation constraint processing mode is adopted;
processing the charge state constraint of the energy storage system in the network-building type energy storage optimization configuration model through a filter technology;
and (4) solving the optimal solution of the network type energy storage optimization configuration model by utilizing an optimization mechanism of a whale algorithm.
The second technical scheme of the invention is realized by the following measures: a network-building energy storage capacity configuration device incorporating robust optimization, comprising:
the collection construction unit is used for constructing a robust uncertain collection of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of predicting power generation and power consumption;
the constraint parameter determining unit is used for constructing a spatial constraint parameter calculation expression in the robust uncertain convergence set by using a Lindeberg-levy central limit theorem;
the processing and determining unit is used for constructing the output of the renewable energy station in the robust uncertain aggregation by utilizing a linear dual theory and a Lagrange function;
the quantization unit quantizes the system robustness by using the probability value of the renewable energy output outside the extreme case;
the model construction unit is used for constructing a network-building type energy storage optimization configuration model under the extreme condition of power generation and power utilization deviation by taking the minimum overall cost in a large-scale renewable energy access system as a target;
and the solving unit is used for solving the network-forming type energy storage optimization configuration model by utilizing a whale algorithm and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
The invention relates to a network-building type energy storage capacity configuration based on precise probability distribution information robust optimization, which is characterized in that on the basis of predicting power generation and power consumption, a robust collection capable of flexibly adjusting boundaries is built by taking into account the probability characteristics of actual power deviation, and the boundaries of the built uncertain collection are flexibly adjusted by introducing uncertain space constraint parameters. The capacity of the energy storage power station is configured on the basis of scientific representation of power uncertainty so as to accurately match the fluctuation of the use requirement, and further the utilization rate of the energy storage is improved.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 shows the predicted values of daily load, wind speed, irradiation intensity and temperature on a typical day of the present invention.
FIG. 3 shows the relationship between the confidence probability and the space constraint parameter in the wind power uncertain set and the POE.
FIG. 4 is a flow chart of the solution of the energy storage optimization configuration model in the present invention.
FIG. 5 is a graph illustrating the effect of confidence probability of uncertainty on the economics of energy storage configuration results in the present invention.
FIG. 6 is the impact of the indeterminate amount of spatial clustering effect on the economics of energy storage configuration results in the present invention.
FIG. 7 shows the influence of the uncertain quantity on the frequency modulation output of the energy storage and thermal power generating unit.
FIG. 8 is a schematic diagram of the apparatus of the present invention.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described with reference to the following examples and figures:
example 1: as shown in fig. 1, an embodiment of the present invention discloses a method for configuring a network-type energy storage capacity in combination with robust optimization, including:
s11, constructing a robust uncertain set of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of power generation and power consumption prediction;
s12, constructing a calculation expression of space constraint parameters in the robust uncertain convergence set by using a Lindeberg-levy central limit theorem;
s13, constructing the output of the renewable energy station in the robust uncertain aggregation by utilizing a linear dual theory and a Lagrange function;
s14, quantifying the robustness of the system by utilizing the probability value of the output of the renewable energy outside the extreme case;
s15, constructing a network-building type energy storage optimization configuration model under the extreme condition of power generation and power utilization deviation by taking the minimum overall cost in a large-scale renewable energy access system as a target;
and S16, solving the network-forming type energy storage optimization configuration model by using a whale algorithm, and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
The invention provides a network-building type energy storage capacity configuration method combined with robust optimization. On the basis of predicting power generation and power utilization power, a robust uncertain set capable of flexibly adjusting boundaries is constructed by considering probability characteristics of actual power deviation, and power generation and power utilization uncertainty in a large-scale renewable energy access system is scientifically described and accurately analyzed. And the uncertain set boundary is flexibly adjusted by utilizing the space constraint parameters, and the defect that the economy and the robustness cannot be considered in the interval robust optimization result is overcome. On the basis of flexibly adjusting the interval robustness of the uncertain collection boundary, the robustness of the system is quantized by using the external operation probability value under the extreme condition, and the defect that the current robustness cannot be quantized is overcome, so that the economy and the robustness of the result are visually balanced. On the basis of scientific description and characterization of power generation uncertainty, a large-scale renewable energy access system is used as a research object, a thermal power generating unit is used as main rotating equipment of the access system, energy storage is used as necessary supplement, a network-forming type energy storage optimization configuration model considering dynamic frequency requirements of the access system is constructed, and influence of uncertain influence factors on a network-forming type energy storage optimization configuration result is analyzed.
Example 2: the embodiment of the invention discloses a network-building type energy storage capacity configuration method combined with robust optimization, which comprises the following steps:
s21, constructing a robust uncertain set of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of power generation and power consumption prediction;
in the above steps, taking wind power output as an example, on the basis of predicting power generation and power consumption (as shown in fig. 2), constructing a robust uncertain collection of actual power deviation by using infinite norm constraint and 1-norm constraint as follows:
in the formula (I), the compound is shown in the specification,
for the actual output of the wind farm i at time t->
To predict force, is>
Respectively, the upper and lower limits of the output deviation. N is a radical of w Number for wind farm>
Is an infinite norm; />
Is a perturbation 1-norm constraint and corresponds to the space of wind power output in practiceInter-cluster effect. The space clustering effect can be explained as that the deviation of the power of each wind power plant in the same scheduling period cannot reach the maximum value at the same time, so that the uncertain space constraint parameters of the wind power plants are introduced
And the deviation coefficient of the wind power plant i at the moment t. The robust uncertain collection can be constructed for other uncertain variables in the system in the same way.
In the step, 1-norm constraint corresponds to the space clustering effect of renewable energy output in practical problems, that is, the actual power deviation of all power stations in a renewable energy power station group in the same time section cannot reach the maximum at the same time. The spatial constraint parameters introducing uncertainty are thus flexible to adjust the boundaries of the constructed uncertain set.
S22, constructing a calculation expression of space constraint parameters in the robust uncertain convergence set by using a Lindeberg-levy central limit theorem;
the steps comprise:
step S201, taking wind power output as an example, constructing a calculation expression of space constraint parameters in the uncertain convergence of robust, comprising:
(1) In step S21
Has->
If the power deviation is a random variable which is independently and identically distributed, then->
Are also independently identically distributed, which is desired to be->
Variance is +>
(2)
Is greater than or equal to>
The following formula:
wherein E (-) represents expectation, D (-) represents variance;
(3) Standard variable
Obeying a standard Gaussian distribution, whose cumulative distribution function>
For an arbitrary probability a w The following equation relationship is satisfied: />
In the formula, alpha w Is the confidence probability;
if not enough historical data is available as a sample, it can be assumed that>
Obeying a Gaussian distribution; hypothesis->
Obeying a Gaussian distribution with an expectation of 0 and a variance of >>
(4) According to the uncertainty space of the wind power output quittingBeam parameter
As follows:
step S202, the process of determining the space constraint parameter segments of other uncertain variables is the same as step S201.
S23, constructing the output of the renewable energy station in the robust uncertain aggregation by utilizing a linear dual theory and a Lagrange function;
step S301, taking wind power output as an example, constructing a Lagrange function by utilizing a linear dual theory, and constructing a robust uncertain set of output power of a wind power field under an extreme condition;
(1) By linear dual theory, structure
Lagrange function of (a):
(2) Due to the fact that
Obtaining the power of the wind power output power under the extreme condition of output deviation as follows:
since the linear programming optimal solution is at the vertex, the above equation is simplified:
(3) According to the combination of the most extreme condition in the time period t, the deviation coefficient of the output of only one wind power plant is less than 1, the wind power plant is set as j, and the total output is as follows:
in the formula (I), the compound is shown in the specification,
is a rounded-down symbol;
step S302, the output power of other uncertain quantity in extreme cases is determined by using the step S302.
Step S24, quantifying the robustness of the system by utilizing the probability value of the output of the renewable energy outside the extreme case;
taking wind power output as an example, on the basis of S22 and S23, setting an event a as that the wind power output is outside the established uncertain set, and then the probability of occurrence of a can be expressed as follows:
the POE of the system when only the uncertainty of the wind power output deviation is considered is expressed as follows:
total number N of different wind farms w At different confidence probabilities alpha w The theoretical results of POE for the following system are shown in figure 3.
S25, constructing a network-building type energy storage optimization configuration model under the extreme condition of power generation and power utilization deviation by taking the minimum overall cost in a large-scale renewable energy access system as a target;
the steps comprise:
step S501, aiming at the minimum overall cost of the access system, constructing an objective function of an energy storage optimization configuration model shown as the following, wherein the overall cost is the minimumThis includes: power generation cost of thermal power generating unit under extreme condition of renewable energy power generation prediction error
The start-stop cost of the unit->
Energy storage annual initial investment cost->
Maintenance cost->
Cost of frequency modulation C PFR ；
In the formula, N G The number of the thermoelectric generator sets in the system, a i 、b i 、c i As a fuel cost factor, d i 、e i Is the threshold point effect coefficient;
indicating the operating state (< >) of the generator i>
Is running); />
The Boolean type variable is the starting and stopping state of the thermal power unit, and the unit is changed from being stopped to being started and is based on the condition>
Is 1 or is 0, and the unit is changed from start to stop>
1, otherwise 0./>
Indicating whether a unit i is involved in frequency modulation (< >) at time t>
Indicating participation). />
For the total output power of the thermal power unit i at time t, wherein>
For meeting the output power of the power supply and demand balance, the device>
Is the frequency modulation power; />
And the cost coefficient of the start and the stop of the unit is obtained. />
In the formula (I), the compound is shown in the specification,
the unit power cost and the unit capacity cost of the energy storage system are respectively;
rated power and capacity of the energy storage system respectively; gamma is capital discount rate; t is rt The life cycle of the energy storage system;
in the formula, C maint And maintaining the cost coefficient for the year-average of the energy storage system.
In the formula (I), the compound is shown in the specification,
respectively carrying out frequency modulation quotations on the generator set and the energy storage system; />
The stored frequency modulation power is the t moment.
Step S502, considering the operation of a unit in an access system and network safety factors, and determining constraint conditions of a network-building type energy storage optimization configuration model; the method comprises the following steps: the method comprises the following steps of access system power balance constraint, energy storage operation constraint, thermal power generating unit dynamic frequency output constraint and frequency modulation capacity requirement constraint. The method comprises the following specific steps:
(1) And (3) power balance constraint:
in the formula (I), the compound is shown in the specification,
for the absence of power at time t>
Is based on load and/or is based on>
The charging and discharging power for energy storage to meet the balance of power supply and demand; />
Are all; extreme power conditions;
(2) An energy storage operation constraint comprising:
1. charge and discharge power constraint:
2. and (3) charge state constraint, wherein the charge state constraint is set to ensure the normal operation of the energy storage system:
the charge state of the energy storage system at the current moment is not only related to the charge state at the previous moment, but also closely related to the charge and discharge electric quantity at the moment, and a specific calculation formula of the charge state at the current moment is as follows:
in the formula eta s 、η c 、η d Respectively, the self-discharge rate and the charge/discharge rate of the stored energy.
3. The charging capacity is equal to the discharging capacity in the total dispatching cycle, and the configuration ensures that the ESS can be continuously and circularly used:
(3) Thermal power unit dynamic frequency output restraint includes:
1. and (3) restraining the output of frequency modulation:
in the formula, K i Is the power frequency static characteristic coefficient, delta f of the thermal power generating unit i max The maximum deviation of the frequency is indicated,
is a frequency modulation dead zone of the generator set i;
2. and (3) state constraint participating in frequency modulation:
3. and (3) frequency modulation capacity constraint:
(4) And the requirement constraint of frequency modulation capacity is that the thermal power generating unit and the energy storage participate in frequency modulation and the requirement of primary frequency modulation capacity is met. If the frequency modulation capacity requirement exceeds the maximum frequency modulation power of the thermal power generating unit, the stored energy participates in frequency modulation, and the frequency modulation shortage of the thermal power generating unit is made up, and the method is specifically as follows:
in the formula (I), the compound is shown in the specification,
for dynamic FM capacity requirements, Δ P RN The disturbance quantity of the output is predicted by the renewable energy sources, and the disturbance quantity of the uncertain power generation and utilization set constructed corresponding to the S1 is under the extreme condition.
And S26, solving the network-forming type energy storage optimization configuration model by using a whale algorithm, and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
In the steps, a network-building type energy storage optimization configuration model is solved by utilizing a whale algorithm, and the method comprises the following steps:
step S601, for boundary constraint in a network-building type energy storage optimization configuration model, a climbing constraint is dynamically updated into boundary constraint by using a boundary-crossing processing mode in a heuristic algorithm;
step S602, for power balance constraint in the network-building type energy storage optimization configuration model, a dynamic relaxation constraint processing mode is adopted;
step S603, processing the charge state constraint of the energy storage system in the network-building type energy storage optimization configuration model through a filter technology;
step S604, solving an optimal solution of a network type energy storage optimization configuration model by utilizing an optimization mechanism of a whale algorithm; the specific implementation steps are shown in fig. 4.
In the above steps, the influence of the uncertain influencing factors on the grid-connected energy storage optimization configuration result is analyzed, that is, the influence of the confidence probability of the uncertain quantity and the spatial clustering effect on the economy of the grid-connected energy storage optimization configuration result and the frequency modulation output of the thermal power generating unit is analyzed, as shown in fig. 5 to 7.
Example 3: as shown in fig. 8, an embodiment of the present invention discloses a network-forming type energy storage capacity configuration device combined with robust optimization, including:
the collection construction unit is used for constructing a robust uncertain collection of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of power generation and power consumption prediction;
the constraint parameter determining unit is used for constructing a spatial constraint parameter calculation expression in the robust uncertain convergence set by using a Lindeberg-levy central limit theorem;
the processing and determining unit is used for constructing the output of the renewable energy station in the robust uncertain aggregation by utilizing a linear dual theory and a Lagrange function;
the quantization unit quantizes the system robustness by using the probability value of the renewable energy output outside the extreme case;
the model construction unit is used for constructing a network-building type energy storage optimization configuration model under the extreme condition of power generation and power utilization deviation by taking the minimum overall cost in a large-scale renewable energy access system as a target;
and the solving unit is used for solving the network-forming type energy storage optimization configuration model by utilizing a whale algorithm and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
Example 4: the embodiment of the invention discloses a storage medium, wherein a computer program which can be read by a computer is stored on the storage medium, and the computer program is set to execute a power grid weak link identification method based on extreme ice disasters when running.
The storage medium may include, but is not limited to: u disk, read-only memory, removable hard disk, magnetic or optical disk, etc. various media capable of storing computer programs.
Example 5: the embodiment of the invention discloses electronic equipment which comprises a processor and a memory, wherein a computer program is stored in the memory and loaded and executed by the processor to realize a power grid weak link identification method based on extreme ice disasters.
The processor may be a central processing unit CPU, general purpose processor, digital signal processor DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. Or a combination that performs a computing function, e.g., comprising one or more microprocessors, DSPs, and microprocessors, etc. The memory may include, but is not limited to: u disk, read-only memory, removable hard disk, magnetic or optical disk, etc. various media capable of storing computer programs.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect, and unnecessary technical features can be increased or decreased according to actual needs to meet the requirements of different situations.
Claims (9)
1. A method for configuring the network type energy storage capacity by combining robust optimization is characterized by comprising the following steps:
constructing a robust uncertain collection of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of power generation and power consumption prediction;
constructing a space constraint parameter calculation expression in the robust uncertain convergence set by using a Lindeberg-levy central limit theorem;
constructing the output of a robust uncertain aggregation renewable energy source station under extreme conditions by utilizing a linear dual theory and a Lagrange function;
quantifying the robustness of the system by utilizing the probability value of the output of the renewable energy outside the extreme condition;
constructing a network-building type energy storage optimization configuration model under the extreme condition of power deviation of power generation and power utilization by taking the minimum overall cost in a large-scale renewable energy access system as a target;
solving the network-forming type energy storage optimization configuration model by utilizing a whale algorithm, and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
2. The method for configuring the energy storage capacity of the network structure type combined with the robust optimization according to claim 1, wherein a robust uncertain set of actual power deviation of wind power output constructed by infinite norm constraint and 1-norm constraint on the basis of predicted power is as follows:
in the formula (I), the compound is shown in the specification,
for the actual output of the wind farm i at time t->
To predict force, is>
Respectively an upper limit and a lower limit of the output deviation; n is a radical of w Number for wind farm>
Is an infinite norm; />
Is perturbation quantity 1-norm constraint and corresponds to the space clustering effect of actual wind power output; />
For wind powerThe deviation coefficient of field i at time t; and similarly, constructing an uncertain set of other uncertain variables in the system.
3. The method for configuring network type energy storage capacity combined with robust optimization according to claim 2, wherein the constructing a spatial constraint parameter calculation expression in a robust uncertain set by using a Lindeberg-levy central limit theorem comprises:
determining the uncertain space constraint parameters of wind power output, comprising the following steps:
(1) In the first step
Have>
If the power deviation is a random variable which is independently and identically distributed, then->
Are also independently identically distributed, which is desired to be->
Variance is ^ er>
(2)
Is greater than or equal to>
The following formula: />
Wherein E (-) represents expectation, D (-) represents variance;
(3) Standard variable
Obeying a standard Gaussian distribution, whose cumulative distribution function>
For an arbitrary probability a w The following equation relationship is satisfied:
in the formula, alpha w Is the confidence probability;
(4) Deducing wind power output uncertainty space constraint parameters
The following were used:
and determining the spatial constraint parameters of other uncertain variables through the step of determining the wind power output uncertainty spatial constraint parameters.
4. The method for configuring energy storage capacity in a form of a network in combination with robust optimization according to claim 1, 2 or 3, wherein the constructing the output of the renewable energy station in the robust uncertain set of renewable energy stations in the extreme case by using the linear dual theory and the Lagrangian function comprises:
constructing output power of the wind power under the extreme condition in the uncertain combination of robustness by utilizing a linear dual theory and constructing a Lagrange function;
(1) By linear dual theory, structure
Lagrange function of (a):
(2) Due to the fact that
The power of the wind power output power under the extreme condition of output deviation is obtained as follows:
since the linear programming optimal solution is at the vertex, the above equation is simplified:
(3) According to the combination of the most extreme condition in the time period t, the deviation coefficient of the output of only one wind power plant is less than 1, the wind power plant is set as j, and the total output is as follows:
in the formula (I), the compound is shown in the specification,
is a rounded-down symbol;
and determining the output power of other uncertain quantities in the extreme case by using the steps.
5. The method for configuring network type energy storage capacity in combination with robust optimization according to any one of claims 1 to 4, wherein the constructing a network type energy storage optimization configuration model under extreme power deviation conditions of power generation and power utilization with the goal of minimizing overall cost in a large-scale renewable energy access system comprises:
with the minimum overall cost of an access system as a target, constructing an objective function of an energy storage optimization configuration model as shown in the following, wherein the overall cost comprises the following steps: power generation cost of thermal power generating unit under extreme condition of renewable energy power generation prediction error
The start-stop cost of the unit->
Annual initial investment cost in energy storage>
Maintenance cost->
Cost of frequency modulation C PFR ；
Considering the operation of a unit in an access system and network security factors, determining constraint conditions of a network-building type energy storage optimization configuration model;
(1) And power balance constraint:
in the formula (I), the compound is shown in the specification,
is the absence of power at time t>
Is based on load and/or is based on>
The charging and discharging power for energy storage to meet the balance of power supply and demand; />
Are all; extreme power conditions;
(2) The stored energy operating constraints include:
1. and (3) charge and discharge power constraint:
2. and (3) charge state constraint:
3. the charge is equal to the discharge in the total scheduling period:
(3) Thermal power unit dynamic frequency output restraint includes:
1. and (3) restraining the output of frequency modulation:
in the formula, K i Is the power frequency static characteristic coefficient, delta f of the thermal power generating unit i max The maximum deviation of the frequency is indicated,
the frequency modulation dead zone is a frequency modulation dead zone of the generator set i;
2. and (3) state constraint participating in frequency modulation:
3. and (3) frequency modulation capacity constraint:
(4) And (3) constraint of frequency modulation capacity requirement:
in the formula (I), the compound is shown in the specification,
for dynamic FM capacity requirements, Δ P RN Is the disturbance quantity of the predicted output of the renewable energy source.
6. The method for configuring the networked energy storage capacity in combination with the robust optimization according to any one of claims 1 to 4, wherein the solving of the networked energy storage optimization configuration model by using a whale algorithm comprises:
for boundary constraint in the network-building type energy storage optimization configuration model, a climbing constraint is dynamically updated into boundary constraint by using a boundary-crossing processing mode in a heuristic algorithm;
for power balance constraint in the network-building type energy storage optimization configuration model, a dynamic relaxation constraint processing mode is adopted;
for the charge state constraint of an energy storage system in the network-building type energy storage optimization configuration model, processing the charge state constraint through a filter technology;
and (4) solving the optimal solution of the network type energy storage optimization configuration model by utilizing an optimization mechanism of a whale algorithm.
7. A robustly optimized meshed energy storage capacity configuration device applying the method of any one of claims 1 to 6, comprising:
the collection construction unit is used for constructing a robust uncertain collection of actual power deviation by using infinite norm constraint and 1-norm constraint on the basis of power generation and power consumption prediction;
the constraint parameter determining unit is used for constructing a spatial constraint parameter calculation expression in the robust uncertain convergence set by using a Lindeberg-levy central limit theorem;
the processing and determining unit is used for constructing the output of the renewable energy station in the robust uncertain aggregation by utilizing a linear dual theory and a Lagrange function;
the quantization unit quantizes the system robustness by using the probability value of the renewable energy output outside the extreme case;
the model construction unit is used for constructing a network-building type energy storage optimization configuration model under the extreme condition of power generation and power utilization deviation by taking the minimum overall cost in a large-scale renewable energy access system as a target;
and the solving unit is used for solving the network-forming type energy storage optimization configuration model by utilizing a whale algorithm and analyzing the influence of uncertain influence factors on the network-forming type energy storage optimization configuration result.
8. A storage medium having stored thereon a computer program readable by a computer, the computer program being arranged to execute the networked energy storage capacity configuration apparatus in combination with robust optimization according to any of claims 1 to 6 when running.
9. An electronic device, characterized in that it comprises a processor and a memory, in which a computer program is stored, which computer program is loaded and executed by the processor to implement the network-type energy storage capacity configuration apparatus in combination with robust optimization according to any of claims 1 to 6.
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| CN117332997B (en) * | 2023-12-01 | 2024-02-23 | 国网江苏省电力有限公司经济技术研究院 | Low-carbon optimal scheduling method, device and equipment for comprehensive energy system |
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