CN115940216B - Energy storage capacity optimization configuration method considering flexible load influence - Google Patents
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Abstract
The invention relates to the technical field of power systems, in particular to an energy storage capacity optimization configuration method considering the influence of flexible load, which comprises the steps of collecting new energy output related parameters, power consumption load related parameters and operation parameters of a region to be configured; according to typical daily load electricity consumption data, researching the characteristics of the electricity consumption load, dividing the electricity consumption load into a flexible load and a fixed load, and accumulating historical data of each load; subtracting the total flexible load from the traditional load technology to obtain a fixed load P L in the system; fitting an independent probability density distribution function of the load shedding, load transferring and load shifting based on historical data of the load shedding, load shifting and load shifting; combining three flexible load independent probability density distribution functions to establish a ternary joint probability distribution model; obtaining a typical daily flexible load output scene to obtain a typical scene setThe invention achieves the effects of reducing load peaks, filling load valleys and smoothing load curves, and realizes safe, efficient and continuous operation of the system.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to an energy storage capacity optimization configuration method considering the influence of flexible load.
Background
Under the dual-carbon background, china greatly advances energy transformation, high-permeability new energy is connected with the grid, intermittent performance and randomness impact on the stable and safe operation of the novel power system, and in order to maintain the power supply and demand balance of the power grid, an energy storage system is configured on the new energy side to solve the challenges brought by the new energy grid connection. The flexible load can effectively improve the power grid load curve, reduce the peak load and the running cost of the system, and the energy storage system has the characteristics of high response speed, flexible adjustment, convenient control and the like, can accurately regulate and control the energy of the novel power system, can provide peak regulation and frequency modulation for the power grid through the charge and discharge capacity, and greatly improves the flexibility, the safety and the stability of the power system accessed by high-proportion new energy.
Because of uncertainty of new energy output and load fluctuation, the peak-valley difference of the power grid is large, and the peak regulation problem of the power grid is increasingly serious. The energy storage can play a key role in peak clipping and valley filling, stabilizing new energy output fluctuation and improving the reliability of the novel power system due to the characteristics of the energy storage, but if the load power curve of the system is adjusted only by the energy storage device, the running benefit of the system can be reduced, and the sustainable development of the system is not facilitated, so that the energy storage capacity optimization configuration method considering the influence of the flexible load is provided, and has a vital significance for the safety, stability and sustainable development of the novel power system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the energy storage capacity optimizing configuration method considering the influence of the flexible load, considers the influence of the flexible load in the system and the economical efficiency of the energy storage system, reasonably adjusts the load curve according to the operation characteristics of the flexible load participated in the system, establishes the energy storage optimizing configuration model containing the flexible load, effectively stabilizes the fluctuation of the load curve, achieves the effects of reducing the load peak, filling the load valley and smoothing the load curve, and realizes the safe, efficient and continuous operation of the system.
The invention provides an energy storage capacity optimization configuration method considering the influence of flexible load, which adopts the technical scheme that the method comprises the following steps:
s1, collecting new energy output related parameters, electricity load related parameters and operation parameters of a region to be configured;
S2, researching the characteristics of the electric load according to the electric data of the typical daily load, dividing the electric load into a flexible load and a fixed load, and accumulating historical data of each load, wherein the flexible load comprises a load-reducible load, a translatable load and a transferable load;
S3, subtracting the total flexible load from the traditional load technology to obtain a fixed load P L in the system;
S4, fitting independent probability density distribution functions of the load-shedding load, the load-transferability load and the load-transferability load by using a kernel function non-parameter estimation method based on the historical data of the load-shedding load, the load-transferability load and the load-transferability load;
S5, establishing a ternary joint probability distribution model by utilizing a vine-copula function and combining three flexible load independent probability density distribution functions;
S6, obtaining a typical daily flexible load output scene by combining the ternary joint probability distribution model through Monte Carlo sampling technology, and obtaining a typical scene set by using a K-means clustering method Wherein the method comprises the steps ofIn order to make it possible to cut down the load vector,In order to be able to translate the load vector,The load vector can be transferred, and T is the time of each flexible load participating in regulation and control;
S7, establishing three flexible load linear models;
S8, providing an energy storage capacity optimization configuration objective function;
s9, setting constraint conditions;
and S10, carrying out optimization solution on the objective function by using a particle swarm optimization algorithm.
Preferably, in the step S7, the establishing three flexible load linear models includes:
Assuming that each flexible load participates in demand side response at time t 0, the ratio of the three flexible loads participating in demand response is r 1、r2、r3, and the three flexible loads are
The load amount reduced by the jth node at the moment t 0 can be reduced, and the load amount is P 1j＝r1·PLS1jt0,PLS1jt0, and the total load amount can be reduced by the jth node;
Translational load amount of jth node at time t 0 of translatable load A translatable aggregate load for the jth node;
Load transfer amount of jth node at moment t 0 capable of transferring load The total load can be transferred for the j-th node;
The fixed load quantity of the jth node at the fixed load t 0 moment is Three flexible loads for the j-th node,Is the sum of all load amounts of the jth node.
Preferably, in the step S8, the energy storage capacity optimization configuration objective function includes formulas (1) - (5):
Wherein f ess is an energy storage construction and operation cost function, f LSi is a complement function of the ith flexible load, P ess (t) is a force output value at the moment of energy storage t, and P i (t) is the load quantity which can be reduced or can be flattened/transferred at the moment of the ith flexible load t;
Wherein alpha is a unit energy storage operation coefficient, P all is the total load quantity in the area to be solved, P pv is the power generated by photovoltaic power generation, r is the discount rate, y is the operation planning year of the energy storage device, r i is the proportion of flexible load to participate in demand response, Is the total amount of flexible load;
PLSi＝g(i,Ppv,PL) (4)
Wherein, C i is the ith flexible load complement coefficient, P LSi is the flexible load quantity containing the ith flexible load capable of participating in demand response, T t is the flexible load capable of reducing, transferring or translating time, f LSi is the government complement of annual flexible load action, P pv is the photovoltaic power generation force in the system, and g (i, P pv,PL) is the flexible load quantity of the system under the action of the ith flexible load.
Preferably, in S9, the constraint condition includes a system power balance constraint condition:
wherein, P LSi→j is the load quantity transferred from the ith flexible load to the node j, P PV is the power generated by photovoltaic power generation, r i is the proportion of the flexible load participating in the demand response, P G is the generator output, and P 1 is the system rigid load.
Preferably, in S9, the constraint condition includes a flexible load capacity constraint condition:
PLSimin≤PLSi≤PLSimax(i＝1,2,3)；
Wherein, P LSimin、PLSimax is the minimum and maximum load quantity of the ith flexible load which can be regulated and controlled, and P LSi is the flexible load quantity containing the ith flexible load which can participate in demand response.
Preferably, in S9, the constraint condition includes a reliability constraint condition:
Llost≤0.1％；
Wherein L lost is the system load loss rate, m is the time for reducing the load, P LS1 (t) is the total flexible load in the ith, and P all (t) is the total system load.
The beneficial effects of the invention are as follows: the method considers three flexible electricity utilization characteristics of load reduction, load translation and load transfer, and the three flexible loads are matched with the new energy output in a coordinated operation mode, so that a load curve is reasonably regulated and controlled; and finally, establishing an energy storage optimal configuration model containing flexible load. In the process of capacity configuration of the energy storage system, the peak clipping and valley filling effects of three flexible loads are prioritized, and compared with the traditional method, the configuration method has the advantages of smaller load fluctuation, higher running reliability and better system benefit, and provides important technical support for sustainable development of the system.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise. "plurality" means "two or more".
Example 1
Fig. 1 shows a schematic flow chart of an energy storage capacity optimizing configuration method according to a preferred embodiment of the present application (fig. 1 shows a first embodiment of the present application), which takes into account the influence of flexible load, and for convenience of explanation, only the portions relevant to the present embodiment are shown, and the detailed description is as follows:
The invention provides an energy storage capacity optimization configuration method considering the influence of flexible load, which adopts the technical scheme that the method comprises the following steps:
s1, collecting new energy output related parameters, electricity load related parameters and operation parameters of a region to be configured;
S2, researching the characteristics of the electric load according to the electric data of the typical daily load, dividing the electric load into a flexible load and a fixed load, and accumulating historical data of each load, wherein the flexible load comprises a load-reducible load, a translatable load and a transferable load;
S3, subtracting the total flexible load from the traditional load technology to obtain a fixed load P L in the system;
S4, fitting independent probability density distribution functions of the load-shedding load, the load-transferability load and the load-transferability load by using a kernel function non-parameter estimation method based on the historical data of the load-shedding load, the load-transferability load and the load-transferability load;
S5, establishing a ternary joint probability distribution model by utilizing a vine-copula function and combining three flexible load independent probability density distribution functions;
S6, obtaining a typical daily flexible load output scene by combining the ternary joint probability distribution model through Monte Carlo sampling technology, and obtaining a typical scene set by using a K-means clustering method Wherein the method comprises the steps ofIn order to make it possible to cut down the load vector,In order to be able to translate the load vector,T is the time for each flexible load to participate in regulation for the transferable load vector.
S7, establishing three flexible load linear models;
S8, providing an energy storage capacity optimization configuration objective function;
s9, setting constraint conditions;
and S10, carrying out optimization solution on the objective function by using a particle swarm optimization algorithm.
In one embodiment, in S7, building three flexible load linear models includes:
Assuming that each flexible load participates in demand side response at time t 0, the ratio of the three flexible loads participating in demand response is r 1、r2、r3, and the three flexible loads are
The load amount reduced by the jth node at the moment t 0 can be reduced, and the load amount is P 1j＝r1·PLS1jt0,PLS1jt0, and the total load amount can be reduced by the jth node;
Translational load amount of jth node at time t 0 of translatable load Translating the load electric energy for the j-th node to replace the load quantity;
Load transfer amount of jth node at moment t 0 capable of transferring load The total load can be transferred for the j-th node;
The fixed load quantity of the jth node at the fixed load t 0 moment is Three flexible loads for the j-th node,Is the sum of all load amounts of the jth node.
In one embodiment, in S8, the energy storage capacity optimizing configuration objective function includes formulas (1) - (5):
Wherein f ess is an energy storage construction and operation cost function, f LSi is a complement function of the ith flexible load, P ess (t) is a force output value at the moment of energy storage t, and P i (t) is the load quantity which can be reduced or can be flattened/transferred at the moment of the ith flexible load t;
Wherein alpha is a unit energy storage operation coefficient, P all is the total load quantity in the area to be solved, P pv is the power generated by photovoltaic power generation, r is the discount rate, 6% is generally taken, y is the operation planning year of the energy storage device, r i is the proportion of flexible load to participate in demand response, Is the total amount of flexible load;
PLSi＝g(i,Ppv,PL) (4)
Wherein C i is the ith flexible load complement coefficient, P LSi is the flexible load quantity containing the ith flexible load capable of participating in demand response, T t is the flexible load capable of reducing, transferring or translating time, f LSi is the government complement of annual flexible load action, P pv is the photovoltaic power generation force in the system, and g (i, P pv,PL) is the flexible load quantity of the system under the action of the ith flexible load.
In one embodiment, in S9, the constraint includes a system power balance constraint:
wherein, P LSi→j is the load quantity transferred from the ith flexible load to the node j, P PV is the power generated by photovoltaic power generation, r i is the proportion of the flexible load participating in the demand response, P G is the generator output, and P 1 is the system rigid load.
In one embodiment, in S9, the constraint includes a flexible load capacity constraint:
PLSimin≤PLSi≤PLSimax(i＝1,2,3) (7)
Wherein, P LSimin、PLSimax is the minimum and maximum load quantity of the ith flexible load which can be regulated and controlled, and P LSi is the flexible load quantity containing the ith flexible load which can participate in demand response.
In one embodiment, in S9, the constraint includes a reliability constraint:
Llost≤0.1％ (9)
Wherein L lost is the system load loss rate, m is the time for reducing the load, P LS1 (t) is the total flexible load in the ith, and P all (t) is the total system load.
It should be understood that the specific order or hierarchy of steps in the processes disclosed are examples of exemplary approaches. Based on design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment of this invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. As will be apparent to those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, as used in the specification or claims, the term "comprising" is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean "non-exclusive or".
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application 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 application, and are intended to be included in the scope of the present application.
Claims (4)
1. The energy storage capacity optimizing configuration method considering the influence of flexible load is characterized by comprising the following steps:
s1, collecting new energy output related parameters, electricity load related parameters and operation parameters of a region to be configured;
S2, researching the characteristics of the electric load according to the electric data of the typical daily load, dividing the electric load into a flexible load and a fixed load, and accumulating historical data of each load, wherein the flexible load comprises a load-reducible load, a translatable load and a transferable load;
S3, subtracting the total flexible load from the traditional load technology to obtain a fixed load PL in the system;
S4, fitting independent probability density distribution functions of the load-shedding load, the load-transferability load and the load-transferability load by using a kernel function non-parameter estimation method based on the historical data of the load-shedding load, the load-transferability load and the load-transferability load;
S5, establishing a ternary joint probability distribution model by utilizing a vine-copula function and combining three flexible load independent probability density distribution functions;
S6, obtaining a typical daily flexible load output scene by combining the ternary joint probability distribution model through Monte Carlo sampling technology, and obtaining a typical scene set by using a K-means clustering method Wherein the method comprises the steps ofIn order to make it possible to cut down the load vector,In order to be able to translate the load vector,The load vector can be transferred, and T is the time of each flexible load participating in regulation and control;
S7, establishing three flexible load linear models;
S8, providing an energy storage capacity optimization configuration objective function;
s9, setting constraint conditions;
s10, optimizing and solving the objective function by using a particle swarm optimization algorithm;
In the step S7, the establishing three flexible load linear models includes:
Assuming that each flexible load participates in demand side response at time t 0, the ratio of the three flexible loads participating in demand response is r 1、r2、r3, and the three flexible loads are
The load amount reduced by the jth node at the moment t 0 can be reduced, and the load amount is P 1j＝r1·PLS1jt0,PLS1jt0, and the total load amount can be reduced by the jth node;
Translational load amount of jth node at time t 0 of translatable load A translatable aggregate load for the jth node;
Load transfer amount of jth node at moment t 0 capable of transferring load The total load can be transferred for the j-th node;
The fixed load quantity of the jth node at the fixed load t 0 moment is Three flexible loads for the j-th node,The sum of all load amounts of the jth node;
In the step S8, the energy storage capacity optimizing configuration objective function includes formulas (1) - (5):
Wherein f ess is an energy storage construction and operation cost function, f LSi is a complement function of the ith flexible load, P ess (t) is a force output value at the moment of energy storage t, and P i (t) is the load quantity which can be reduced or can be flattened/transferred at the moment of the ith flexible load t;
Wherein alpha is a unit energy storage operation coefficient, P all is the total load quantity in the area to be solved, P pv is the power generated by photovoltaic power generation, r is the discount rate, y is the operation planning period of the energy storage device, Is the total amount of flexible load;
PLSi＝g(i,Ppv,PL) (4)
Wherein, C i is the ith flexible load complement coefficient, P LSi is the flexible load quantity containing the ith flexible load capable of participating in demand response, T t is the flexible load capable of reducing, transferring or translating time, f LSi is the government complement of annual flexible load action, P pv is the photovoltaic power generation force in the system, and g (i, P pv,PL) is the flexible load quantity of the system under the action of the ith flexible load.
2. The energy storage capacity optimizing configuration method considering the influence of flexible load according to claim 1, wherein in S9, the constraint condition includes a system power balance constraint condition:
Where P LSi→j is the load amount transferred to node j by the ith flexible load, P G is the generator output, and P 1 is the system stiffness load.
3. The energy storage capacity optimizing configuration method considering the influence of flexible load according to claim 1, wherein in S9, the constraint condition includes a flexible load capacity constraint condition:
PLSimin≤PLSi≤PLSimax(i＝1,2,3)；
Wherein, P LSimin、PLSimax is the minimum and maximum load quantity of the ith flexible load which can be regulated and controlled, and P LSi is the flexible load quantity containing the ith flexible load which can participate in demand response.
4. The energy storage capacity optimizing configuration method considering the influence of flexible load according to claim 1, wherein in S9, the constraint condition includes a reliability constraint condition:
Llost≤0.1％；
Wherein L lost is the system load loss rate, m is the time for reducing the load, P LS1 (t) is the total flexible load in the ith, and P all (t) is the total system load.
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