CN112234657B - Power system optimal scheduling method based on new energy joint output and demand response - Google Patents
Power system optimal scheduling method based on new energy joint output and demand response Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention discloses an electric power system optimal scheduling method based on new energy combined output and demand response, which comprises the following steps: constructing a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy sources; and solving the optimization objective function to obtain a load reduction balancing scheme responded by the demand side in the new energy scene by taking the minimized new energy output cost, the response cost of the demand side in different new energy scenes and the rescheduling cost as the optimization objective function of the power scheduling optimization model so as to control the response access power of the demand side. The method comprises the steps of considering nonlinear correlation and demand response among new energy sources in the power system, obtaining an optimized scheduling model based on new energy output fluctuation and demand side response, and establishing an interface relation between the nonlinear correlation and the demand side response of the new energy sources so as to balance load reduction of the demand side response in each new energy source scene and improve the new energy consumption level.
Description
Technical Field
The invention relates to the technical field of optimal scheduling of an electric power system, in particular to an optimal scheduling method of the electric power system based on new energy joint output and demand response.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The new energy resource occupation ratio in the power system is higher and higher, which brings great challenges to the safe and stable operation of the system. When the traditional random optimal scheduling method for the power system processes the new energy correlation, the complex nonlinear correlation between new energy outputs cannot be accurately described, and a certain error exists, so that the precise correlation processing method needs to be introduced into the optimal scheduling of the power system.
The Copula function is used for describing joint probability distribution among a plurality of variables, and the Copula function can realize correlation under strict monotone transformation, so that the Copula function has high accuracy on correlation processing, and is applied to power system optimization scheduling containing new energy.
However, the inventor thinks that a single Copula function still has a certain deviation in processing the complex new energy nonlinear dependence; in addition, while the output accuracy of the new energy nonlinear correlation relationship is considered, the consumption of the new energy is also required to be promoted, namely, the response of a demand side, and how to combine the nonlinear correlation between the new energy and the response of the demand side is required to ensure the output accuracy and the consumption level of the new energy to be the technical problem to be solved at present.
Disclosure of Invention
In order to solve the problems, the invention provides an electric power system optimization scheduling method based on new energy combined output and demand response, which considers nonlinear correlation and demand response among new energy in an electric power system, obtains an optimization scheduling model based on new energy output fluctuation and demand side response, and establishes an interface relation between the new energy nonlinear correlation and the demand side response so as to realize balance of load reduction of the demand side response in each new energy scene and improve the new energy consumption level.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a power system optimal scheduling method based on new energy joint output and demand response, including:
constructing a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy sources;
and solving the optimization objective function to obtain a load reduction balancing scheme responded by the demand side in the new energy scene by taking the minimized new energy output cost, the response cost of the demand side in different new energy scenes and the rescheduling cost as the optimization objective function of the power scheduling optimization model so as to control the response access power of the demand side.
In a second aspect, the present invention provides an electric power system optimization scheduling system based on new energy joint output and demand response, including:
the model construction module is used for constructing a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy sources;
and the optimization scheduling module is used for solving the optimization objective function to obtain a load reduction balancing scheme of the demand side response in the new energy scene by taking the minimized new energy output cost, the demand side response cost in different new energy scenes and the rescheduling cost as the optimization objective function of the power scheduling optimization model so as to control the demand side response access power.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention considers the stochastic output fluctuation and the demand response characteristic of each energy form of the power system, constructs an optimization model of the energy forms such as new energy and the demand side response, introduces the nonlinear correlation and the demand side response of the new energy into the optimization scheduling of the power system, and establishes the stochastic optimization target and the constraint considering the nonlinear correlation and the demand side response of the new energy so as to realize the optimization of the result obtained by the safety and stability constraint under a certain confidence level.
According to the method, the complex nonlinear correlation among the outputs of the new energy is accurately processed based on the M-Copula function, so that a combined output model of a plurality of new energy is obtained, the limitation problem of a single Copula function is solved, and the accurate calculation of the comprehensive correlation of the new energy is realized.
According to the invention, the new energy nonlinear correlation and the demand side response are introduced into the electric power system optimization scheduling, and the interface relation between the new energy correlation processing method and the demand side response model is established, so that the accuracy of the electric power system optimization scheduling result is improved, and the consumption of new energy is promoted.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of an electric power system optimal scheduling method based on new energy joint output and demand response according to embodiment 1 of the present invention;
fig. 2 is a new energy output calculation scenario set provided in embodiment 1 of the present invention;
fig. 3 shows a demand-side response load reduction amount in different new energy scenarios according to embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the present embodiment provides a power system optimal scheduling method based on new energy joint output and demand response, including:
s1: constructing a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy sources;
s2: and taking the minimized new energy output cost, the demand side response cost and the rescheduling cost in different new energy scenes as an optimization objective function of the power scheduling optimization model, and solving the optimization objective function to obtain a load reduction balancing scheme of the demand side response in the new energy scenes so as to control the demand side response access power.
In step S1, the new energy includes photovoltaic power, wind power, a thermal power generating unit, and the like; a plurality of new energy power generation systems are accessed to an electric power system, and due to the fact that nonlinear correlation relations exist among new energy, a combined output model of a plurality of new energy is constructed by adopting an M-Copula function, and the nonlinear correlation relations among new energy output models of different new energy are accurately processed;
in this embodiment, taking photovoltaic power generation as an example, the photovoltaic power generation has a certain randomness, and its output fluctuation model is:
wherein Gamma is a Gamma function; beta is a beta 1 、β 2 Is a parameter of Beta distribution.
The M-Copula function is:
C M-Copula (x 1 ,x 2 ,…,x n )＝h 1 C 1 (x 1 ,x 2 ,…,x n ,θ 1 )+h 2 C 2 (x 1 ,x 2 ,…,x n ,θ 2 )+…+h nc C nc (x 1 ,x 2 ,…,x n ,θ nc )
wherein, C M-Copula (. h) is the M-Copula function; h is 1 、h 2 Waiting for weight coefficients representing different Copula functions;
preferably, h 1 、h 2 The equal weight coefficient can be obtained by a least square method;
in this embodiment, a new energy joint output model is obtained by weighting each new energy output model by using an M-Copula function; namely, different Copula functions are adopted to produce respective joint output models, and the multiple joint output models are weighted to obtain a final joint output model.
In this embodiment, the demand-side response is an excitation-based demand response, and the demand-side response model is:
wherein, C IBDR A cost function that is an incentive-type demand-side response; p IBDR The load after the excitation is reduced; k 1 、K 2 Respectively, coefficients of a demand side response cost function; theta.theta. IBDR An intention factor being an excitatory demand side response;
a demand side response cost function after weighting is carried out on each new energy scene; n is a radical of SCE The number of new energy scenes; w is a k Is the probability of scene k.
In step S2, the objective function is:
min f＝min(f TPU +f DR +f RE )
wherein, f TPU Is a cost function of the thermal power generating unit; p TPU,i,t 、P PV,l,t 、P DR,t Respectively outputting power of the thermal power generating unit, outputting new energy and reducing load; a is i 、b i 、c i Respectively representing coefficients of a cost function of the thermal power generating unit; n is a radical of TPU The number of the thermal power generating units is; f. of DR Response cost functions of the demand side under different scene sets are obtained; f. of RE Rescheduling costs for different sets of scenarios; d t A cost factor for rescheduling; p is L,t Is the load power;
preferably, the rescheduling cost means that real-time power balance is satisfied through real-time unit output variation.
In this embodiment, under the constraint condition that the new energy output and the demand-side response power balance are satisfied, that is:
wherein the content of the first and second substances,
indicating a confidence level.
Preferably, except for power balance constraint, under any new energy scene, the output upper and lower limit constraint, the climbing rate constraint, the demand side response scale constraint, the load reduction times and the rate constraint of the thermal power generating unit need to be met; the scene set considering the nonlinear correlation of the new energy and the response of the demand side is subjected to optimization calculation, so that the accuracy of the system random optimization scheduling result can be realized, and the consumption level of the new energy is improved.
In this embodiment, accurate sampling is performed by a monte carlo method based on an M-Copula function, so as to obtain a computation scene set considering the new energy nonlinear correlation, as shown in fig. 2, the limitation of a single Copula function can be avoided, and accurate description of the new energy comprehensive correlation is realized.
After the calculation scene set is reduced to a reasonable scale, an objective function considering the nonlinear correlation of new energy and the response of the demand side and a constraint condition are adopted to solve, and the demand side response load reduction amount of various scenes under a certain confidence coefficient condition is obtained, as shown in fig. 3, it can be seen that the peak time of the load reduction amount is basically consistent with the peak time of the new energy output, and the effect of the demand side response on the aspect of improving the new energy consumption level is proved.
Calculating the cost of the thermal power generating unit, the response cost of the demand side and the rescheduling cost under different new energy correlation conditions, wherein the cost, the response cost and the rescheduling cost are shown in a table 1;
TABLE 1 tables of various cost cases (Unit: USD) under different correlation conditions
It can be seen from table 1 that under different correlation conditions, the cost of the thermal power generating unit is basically unchanged, but the demand side response cost and the rescheduling cost are obviously changed, and the fluctuation degree of the new energy is further increased along with the increase of the correlation of the new energy, so that the demand side response cost and the rescheduling cost are obviously increased, and the accuracy of the correlation processing method and the validity of the demand side response in the embodiment are verified.
Calculating the cost of the thermal power generating unit, the response cost of the demand side and the rescheduling cost under different response scales of the demand side, wherein the cost is shown in a table 2;
TABLE 2 various cost situation tables (Unit: USD) for different demand side response scales
It can be seen from table 2 that the cost change of the thermal power generating unit is small under different demand side response scales, and meanwhile, the cost of the demand side response is significantly increased with the increase of the maximum response power, but the reduction range of the rescheduling cost is larger after the load side demand response scale is increased, so that the total cost is reduced, and it is verified that the demand side response has relatively high economic benefit after reaching a certain scale.
According to the method, the nonlinear correlation of the new energy and the response of the demand side are introduced into the optimized scheduling of the power system, the actual condition of the power system is accurately modeled, the nonlinear correlation of the new energy is accurately modeled, the interface relation between the new energy correlation processing method and the response model of the demand side is established, the accuracy of the optimized scheduling result of the power system is improved, and the consumption of the new energy is promoted.
Example 2
The embodiment provides an electric power system optimal scheduling system based on new energy combined output and demand response, which includes:
the model construction module is used for constructing a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy sources;
and the optimization scheduling module is used for solving the optimization objective function to obtain a load reduction balancing scheme of the demand side response in the new energy scene by taking the minimized new energy output cost, the demand side response cost in different new energy scenes and the rescheduling cost as the optimization objective function of the power scheduling optimization model so as to control the demand side response access power.
It should be noted that the above modules correspond to steps S1 to S2 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (8)
1. A power system optimal scheduling method based on new energy combined output and demand response is characterized by comprising the following steps:
constructing a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy sources;
taking the minimized new energy output cost, the demand side response cost and the rescheduling cost in different new energy scenes as an optimization objective function of the power scheduling optimization model, and solving the optimization objective function to obtain a load reduction balancing scheme of the demand side response in the new energy scenes so as to control the demand side response access power;
the new energy comprises photovoltaic, wind power and thermal power generating units; weighting each new energy output model by adopting an M-Copula function to obtain a new energy combined output model; adopting different Copula functions to produce respective combined output models, and weighting the combined output models to obtain a final combined output model;
the M-Copula function is:
C M-Copula (x 1 ，x 2 ，…，x n )＝h 1 C 1 (x 1 ，x 2 ，…，x n ，θ 1 )+h 2 C 2 (x 1 ，x 2 ，…，x n ，θ 2 )+…+h m C m (x 1 ，x 2 ，…，x n ，θ m )
wherein, C M-Copula () is an M-Copula function; h is 1 、h 2 ...h m Representing the weight coefficients of different Copula functions;
the demand side response is an excitation-based demand response, and the demand side response model is as follows:
wherein, C IBDR A cost function that is an incentive-type demand-side response; p IBDR Reducing the load after the excitation; k 1 、K 2 Respectively, coefficients of a demand side response cost function; theta.theta. IBDR An intent factor that is an excitatory demand-side response;
a demand side response cost function after weighting is carried out on each new energy scene; n is a radical of SCE The number of new energy scenes; w is a k Is the probability of scene k;
under different new energy scenes, as the correlation among a plurality of new energy resources is increased, the response cost and the rescheduling cost of a demand side are increased; as the response power of the demand side response increases, the rescheduling cost decreases.
2. The optimal scheduling method for electric power system based on new energy combined contribution and demand response of claim 1, wherein the weighted weighting coefficients are obtained by a least square method.
3. The optimal scheduling method for electric power system based on new energy combined contribution and demand response of claim 1, wherein the demand-side response is an excitation-based demand-side response, and the demand-side response model is constructed based on excited load reduction.
4. The optimal scheduling method for electric power system based on new energy combined contribution and demand response of claim 1, wherein the optimal objective function is solved under the constraint condition that the new energy combined contribution and demand side response power balance is satisfied.
5. The method of claim 1, wherein the rescheduling cost refers to meeting a real-time new-energy contribution and demand-side response power balance through real-time new-energy contribution changes.
6. A power system optimal scheduling system based on new energy joint output and demand response is characterized by comprising:
the model building module is used for building a power dispatching optimization model according to the demand side response model and the combined output model of the plurality of new energy resources;
the optimization scheduling module is used for solving an optimization objective function by taking the minimized new energy output cost, the demand side response cost and the rescheduling cost in different new energy scenes as the optimization objective function of the power scheduling optimization model to obtain a load reduction balancing scheme of the demand side response in the new energy scene so as to control the demand side response access power; the new energy comprises photovoltaic, wind power and thermal power generating units; weighting each new energy output model by adopting an M-Copula function to obtain a new energy combined output model; adopting different Copula functions to produce respective combined output models, and weighting the combined output models to obtain a final combined output model;
the M-Copula function is:
C M-Copula (x 1 ，x 2 ，…，x n )＝h 1 C 1 (x 1 ，x 2 ，…，x n ，θ 1 )+h 2 C 2 (x 1 ，x 2 ，…，x n ，θ 2 )+…+h m C m (x 1 ，x 2 ，…，x n ，θ m )
wherein, C M-Copula (. h) is the M-Copula function; h is 1 、h 2 ...h m Representing the weight coefficients of different Copula functions;
the demand side response is an excitation-based demand response, and the demand side response model is as follows:
wherein, C IBDR A cost function that is an incentive-type demand-side response; p is IBDR The load after the excitation is reduced; k 1 、K 2 Respectively, coefficients of a demand side response cost function; theta IBDR An intention factor being an excitatory demand side response;
a demand side response cost function after weighting is carried out on each new energy scene; n is a radical of hydrogen SCE The number of new energy scenes; w is a k Is the probability of scene k;
under different new energy scenes, as the correlation among a plurality of new energy resources increases, the response cost and the rescheduling cost of a demand side increase; as the response power of the demand side response increases, the rescheduling cost decreases.
7. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-5.
8. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 5.
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