CN117196107A - Power grid optimization method and system for participation of multiple types of energy storage in electric carbon market - Google Patents
Power grid optimization method and system for participation of multiple types of energy storage in electric carbon market Download PDFInfo
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Abstract
The invention provides a power grid optimization method and a system for participating in an electric carbon market by multi-type energy storage, belonging to the field of power grid optimization, wherein the method comprises the following steps: constructing a value evaluation system; the value evaluation system comprises a target layer, a criterion layer and an index layer; the target layer is the multidimensional value of the multi-type energy storage participating in the electric carbon market, the criterion layer comprises the carbon reduction benefit and the economic benefit of the multi-type energy storage participating in the electric carbon market, and the index layer comprises a plurality of secondary indexes; determining the weight of each secondary index by adopting an entropy weight method; according to the weights of each candidate decision scheme and each secondary index, determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method; and determining an optimal decision scheme of the power grid according to the comprehensive evaluation indexes of the candidate decision schemes so as to optimize the power grid. The invention evaluates the carbon reduction benefits and economic benefits of the participation of the multi-type energy storage in the electric carbon market, promotes the application of the multi-type energy storage in the electric power system, and improves the reliability, the economy and the environmental sustainability of the power grid.
Description
Technical Field
The invention relates to the field of power grid optimization, in particular to a power grid optimization method and system for participating in electric carbon markets by multi-type energy storage based on a TOPSIS method.
Background
The energy storage technology can effectively regulate the change of the voltage, the frequency and the phase of the power grid caused by new energy power generation, so that large-scale wind power and photovoltaic power generation can be conveniently and reliably integrated into a conventional power grid. At present, the value evaluation methods of various energy storage systems are few, and the benefits of the energy storage systems in the carbon market are not considered.
Therefore, it is highly desirable to provide an evaluation method for the value of various energy storage systems, so as to promote the rapid development and comprehensive popularization of the multi-type energy storage technology.
Disclosure of Invention
The invention aims to provide a power grid optimization method and system for participating in an electric carbon market by multi-type energy storage, which can improve the reliability, the economy and the environmental sustainability of a power grid.
In order to achieve the above object, the present invention provides the following solutions:
a method for grid optimization of multiple types of energy storage participating in an electric carbon market, comprising:
constructing a value evaluation system; the value evaluation system comprises a target layer, a criterion layer and an index layer; the target layer is multi-dimensional value of multi-type energy storage participating in an electric carbon market, the criterion layer comprises a plurality of first-level indexes, and the index layer comprises a plurality of second-level indexes under each first-level index; the first-level indexes are respectively the carbon reduction benefits and the economic benefits of the participation of the multi-type energy storage in the electric carbon market; the secondary indexes under the carbon reduction benefit are respectively system carbon quota, thermal power generating unit carbon emission, system outsourcing electric power carbon emission, new energy power generation carbon reduction, multi-energy storage carbon emission, multi-energy storage carbon reduction and system carbon quota residual total; the secondary indexes under the economic benefit are ladder carbon trade benefits, leveling power generation cost, system purchase energy cost, system electricity selling benefits and system net benefits respectively;
acquiring a historical dataset; the historical data set comprises values of secondary indexes of each year in a historical setting period;
according to the historical data set, determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method;
determining a plurality of candidate decision schemes; each candidate decision scheme comprises candidate values of secondary indexes;
according to the weights of each candidate decision scheme and each secondary index, determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method;
and determining an optimal decision scheme of the power grid according to the comprehensive evaluation indexes of the candidate decision schemes so as to optimize the power grid.
Optionally, determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method according to the historical data set, which specifically comprises the following steps:
constructing an original evaluation matrix according to the historical data set; the elements in the original evaluation matrix are respectively the values of each secondary index in each year in the historical setting period;
normalizing the original evaluation matrix to obtain a normalized matrix; the elements in the standardized matrix are the standardized values of each secondary index each year;
aiming at any secondary index in the value evaluation system, determining the information entropy of the secondary index according to the standard value of the secondary index every year in the standardized matrix;
and determining the weight of the secondary index according to the information entropy of the secondary index.
Optionally, the following formula is used to determine the canonical value of the jth secondary index in the ith year:
wherein r is ij A is the standard value of the j-th secondary index in the i-th year ij The value of the second-level index is the j-th index of the I-th year, and I is the year number.
Optionally, the information entropy of the jth secondary index is determined using the following formula:
the weight of the jth secondary index is determined using the following formula:
wherein E is j Information entropy of jth secondary index, r ij Is the standard value of the j second level index of the ith year, I is the year number, m j The J-th secondary index is weighted, and J is the number of secondary indexes.
Optionally, determining the system carbon quota, the thermal power generating unit carbon emission, the system outsourcing power carbon emission, the new energy power generation carbon emission reduction, the multi-energy storage carbon emission reduction and the residual total amount of the system carbon quota in each candidate decision scheme by adopting the following formula:
E CEQ ＝Q e ×B e ×F 1 ×F r ×F f +Q h ×B h +η e P buy ；
E CFP ＝β e P CFP ；
E PCE ＝β s P buy ；
E＝E CEQ +E ENG,CR +E NEG,CR -E ENG,CE -E NEG,CE -E CFP -E PCE ；
wherein E is CEQ For system carbon quota, E CFP Is the carbon emission of the thermal power unit, E PCE Carbon emission of outsourcing power of system E NEG,CE Generating carbon emission for new energy, E NEG,CR The carbon reduction amount for new energy power generation E ENG,CE For a plurality of energy storage carbon emission, E ENG,CR For multi-energy storage and carbon reduction, E is the total residual quantity of the carbon quota of the system, Q e For the power supply quantity of the unit B e Reference value F for power supply of class to which the machine set belongs 1 Correction coefficient for unit cooling mode, F r Correction coefficient for heat supply of machine set, F f For the load factor correction coefficient of the unit, Q h Heat supply quantity of machine set B h For the heat supply reference value eta of the category of the unit e Carbon quota coefficient of unit electric quantity, P buy Beta for outsourcing electric quantity of system e Carbon emission coefficient beta for unit electric quantity of coal-fired unit s Carbon emission coefficient, P, of unit electric quantity of outsourced electric power CFP Is a unitGenerating capacity, N is the number of new energy types, ρ s,n Carbon emission factor ρ as n-th new energy source a,n Carbon emission reduction factor P as n-th new energy NEG,n Generating energy of the nth new energy, B is the quantity of energy storage types, ρ cha,b Carbon emission factor ρ for class b energy storage dis,b Carbon emission reduction factor, P, for class b energy storage ENG,cha,b Charge amount for class b energy storage, P ENG,dis,b The discharge amount of the energy storage of the b type.
Optionally, the step carbon trade benefits, normalized power generation costs, system purchase energy costs, system electricity sales benefits, and system net benefits in each candidate decision scheme are determined using the following formulas:
C S ＝P sell ·C sell ；
C＝C S +C car -C B ；
wherein C is car For the trading benefit of the ladder carbon, LCOE is the leveling power generation cost, C B To the cost of system purchasing energy, C S For system electricity selling benefits, C is system net benefits, C is carbon trade reference price of market, d is carbon emission interval length, alpha is carbon trade price increase amplitude, E is system carbon quota residual total, K is equipment number in an electric power system, I is year number, and IC k For initial investment of equipment k, OC i,k For the i-th operating cost of the device k, MC i,k For maintenance cost of equipment k, i-th year, G i,k R is the power generation of the equipment k k P, the discount rate of the device k buy To purchase electricity outside the system, C buy In order to realize the price of the electricity purchase of the system,n is the number of new energy types, P NEG,n Generating energy of nth new energy, C NEG,n The power generation and internet electricity price of the nth new energy source is that of the energy storage type, B is the quantity of the energy storage type, P ENG,cha,b Charge amount for class b energy storage, C ENG,cha,b Charging electricity price for b-type energy storage, P CFP For generating power of the unit, C CFP The power price of the thermal power generating unit is the power price, P sell For selling electricity, C sell Is the price of electricity.
Optionally, according to the weights of each candidate decision scheme and each secondary index, a TOPSIS method is adopted to determine the comprehensive evaluation index of each candidate decision scheme, which specifically comprises:
establishing an initial index matrix according to each candidate decision scheme; the elements in the initial index matrix are candidate values of secondary indexes in each candidate decision scheme respectively;
normalizing the initial index matrix to determine a normalized decision matrix; the elements in the normalized decision matrix are the normalized values of the secondary indexes in each candidate decision scheme respectively;
determining a weighted canonical matrix according to the canonical decision matrix and the weight of each secondary index; the elements in the weighting specification matrix are respectively the weighting values of the secondary indexes in each candidate decision scheme;
determining an ideal solution and a negative ideal solution according to the initial index matrix;
for any candidate decision scheme, calculating the Euclidean distance from the candidate decision scheme to the ideal solution and the Euclidean distance from the candidate decision scheme to the negative ideal solution;
and determining the comprehensive evaluation index of the candidate decision scheme according to the Euclidean distance from the candidate decision scheme to the ideal solution and the Euclidean distance from the candidate decision scheme to the negative ideal solution.
Optionally, the ideal solution includes an ideal solution for each secondary indicator, and the negative ideal solution includes a negative ideal solution for each secondary indicator;
determining an ideal solution and a negative ideal solution according to the initial index matrix, wherein the method specifically comprises the following steps:
for any secondary index, if the secondary index is the secondary index under the benefit of carbon reduction, adopting a formulaDetermining an ideal solution of said secondary index using the formula +.>Determining a negative ideal solution of the secondary index;
if the secondary index is the secondary index under the economic benefit, adopting a formulaDetermining an ideal solution of said secondary index using the formula +.>Determining a negative ideal solution of the secondary index;
wherein,for the ideal solution of the j-th secondary index, < >>Is the negative ideal solution of the j-th secondary index, x mj Is the candidate value of the j-th secondary index in the candidate decision scheme m.
Optionally, the following formula is used to determine the overall evaluation index of the candidate decision scheme m:
wherein,comprehensive evaluation index for candidate decision scheme m, +.>Euclidean distance from candidate decision scheme m to negative ideal solution, +.>The euclidean distance from the candidate decision scheme m to the ideal solution.
In order to achieve the above purpose, the present invention also provides the following solutions:
a grid optimization system for participating in an electric carbon market with multiple types of energy storage, comprising:
the system construction module is used for constructing a value evaluation system; the value evaluation system comprises a target layer, a criterion layer and an index layer; the target layer is multi-dimensional value of multi-type energy storage participating in an electric carbon market, the criterion layer comprises a plurality of first-level indexes, and the index layer comprises a plurality of second-level indexes under each first-level index; the first-level indexes are respectively the carbon reduction benefits and the economic benefits of the participation of the multi-type energy storage in the electric carbon market; the secondary indexes under the carbon reduction benefit are respectively system carbon quota, thermal power generating unit carbon emission, system outsourcing electric power carbon emission, new energy power generation carbon reduction, multi-energy storage carbon emission, multi-energy storage carbon reduction and system carbon quota residual total; the secondary indexes under the economic benefit are ladder carbon trade benefits, leveling power generation cost, system purchase energy cost, system electricity selling benefits and system net benefits respectively;
the historical data acquisition module is used for acquiring a historical data set; the historical data set comprises values of secondary indexes of each year in a historical setting period;
the weight determining module is respectively connected with the system constructing module and the historical data obtaining module and is used for determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method according to the historical data set;
a candidate scheme determination module for determining a plurality of candidate decision schemes; each candidate decision scheme comprises candidate values of secondary indexes;
the evaluation index determining module is respectively connected with the weight determining module and the candidate scheme determining module and is used for determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method according to the weights of each candidate decision scheme and each secondary index;
the optimal scheme determining module is connected with the evaluation index determining module and is used for determining an optimal decision scheme of the power grid according to the comprehensive evaluation index of each candidate decision scheme so as to optimize the power grid.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the value evaluation system constructed by the invention, the carbon reduction benefits and the economic benefits of the multi-type energy storage participating in the electric carbon market are considered, the comprehensive evaluation indexes of the candidate decision schemes are determined by adopting the TOPSIS method, and finally the optimal decision scheme of the power grid is determined according to the comprehensive evaluation indexes of the candidate decision schemes so as to optimize the power grid, so that the carbon reduction benefits and the economic benefits of the multi-type energy storage participating in the electric carbon market and the carbon market can be evaluated, the application of the multi-type energy storage in the electric power system is facilitated, and the reliability, the economical efficiency and the environmental sustainability of the power grid are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall flow chart of a power grid optimization method for participating in an electric carbon market by multi-type energy storage provided by the invention;
FIG. 2 is a detailed flow chart of a method for optimizing a power grid by participating in an electric carbon market with multi-type energy storage provided by the invention;
FIG. 3 is a schematic diagram of a value assessment system;
fig. 4 is a schematic diagram of a grid optimization system for participating in an electric carbon market with multi-type energy storage provided by the invention.
Symbol description: the system comprises a 1-system construction module, a 2-historical data acquisition module, a 3-weight determination module, a 4-candidate scheme determination module, a 5-evaluation index determination module and a 6-optimal scheme determination module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a power grid optimization method and system for participating in an electric carbon market by multi-type energy storage, which promote the application of the multi-type energy storage in a power system and improve the reliability, economy and environmental sustainability of the power grid.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1 and fig. 2, the present embodiment provides a power grid optimization method for participating in an electric carbon market by using multiple types of energy storage, including:
step 100: and (5) constructing a value evaluation system. As shown in FIG. 3, the value evaluation system comprises a target layer, a criterion layer and an index layer.
The target layer is multi-type energy storage and participates in multi-dimensional value of the electric carbon market. The criterion layer comprises a plurality of first-level indexes, and the index layer comprises a plurality of second-level indexes under each first-level index.
The first-level indexes are respectively the carbon reduction benefits and the economic benefits of the participation of the multi-type energy storage in the electric carbon market.
The secondary indexes under the carbon reduction benefit are respectively system carbon quota, thermal power generating unit carbon emission, system outsourcing electric power carbon emission, new energy power generation carbon reduction, multi-energy storage carbon emission, multi-energy storage carbon reduction and system carbon quota residual total.
The secondary indexes under the economic benefit are ladder carbon trade benefits, leveling power generation cost, system purchase energy cost, system electricity selling benefits and system net benefits respectively.
Step 200: a historical dataset is obtained. The historical data set comprises values of secondary indexes of each year in a historical setting period.
Step 300: and determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method according to the historical data set.
Further, step 300 includes:
(31) And constructing an original evaluation matrix A according to the historical data set. The elements in the original evaluation matrix are respectively the values of each secondary index in each year in the history setting period:
wherein a is ij The value of the j-th secondary index in the i-th year is obtained.
(32) And carrying out normalization processing on the original evaluation matrix to obtain a normalization matrix R. The elements in the normalized matrix are normalized values of each secondary index each year. Specifically, the following formula is used to determine the canonical value of the jth secondary index in the ith year:
wherein r is ij A is the standard value of the j-th secondary index in the i-th year ij The value of the second-level index is the j-th index of the I-th year, and I is the year number.
Then normalize the matrix to
(33) And determining the information entropy of any secondary index in the value evaluation system according to the standard value of the secondary index in the standardized matrix every year. Specifically, the normalized matrix is summed up by columns, then the information entropy of each secondary index is calculated according to the formula of the information entropy, and the information entropy of the jth secondary index is determined by adopting the following formula:
(34) And determining the weight of the secondary index according to the information entropy of the secondary index. Specifically, the weight of the jth secondary index is determined using the following formula:
wherein E is j Information entropy of jth secondary index, r ij Is the standard value of the j second level index of the ith year, I is the year number, m j The J-th secondary index is weighted, and J is the number of secondary indexes.
In addition, the invention can average the normalized matrix of each secondary index according to the columns to obtain the weight of each secondary index, and the weight of the j secondary index is calculated by adopting the following formula:
step 400: a plurality of candidate decision schemes is determined. Each candidate decision scheme includes candidate values for the secondary indicators. Specifically, the invention adopts the following formula to determine the candidate value of each secondary index in each candidate decision scheme.
①E CEQ ＝Q e ×B e ×F 1 ×F r ×F f +Q h ×B h +η e P buy ；
Wherein E is CEQ For the system carbon quota, the unit is tCO 2 ，Q e The unit is MWh and B e The unit of the power supply reference value is tCO for the class to which the unit belongs 2 /MWh，F 1 Correction coefficient for unit cooling mode, F r The heat supply correction coefficient of the coal-fired unit is 1-0.22 multiplied by heat supply ratio F f For the load factor correction coefficient of the unit, Q h Heat supply unit of GJ, B h For the heat supply reference value of the category to which the unit belongs, the unit tCO 2 /GJ，η e Carbon quota coefficient of unit electric quantity, P buy The unit of the power is MWh.
②E CFP ＝β e P CFP ；
Wherein E is CFP Is the carbon emission of the thermal power unit, and the unit is tCO 2 ，β e The carbon emission coefficient of the unit electric quantity of the coal-fired unit is tCO 2 /MWh，P CFP The unit is MWh for generating capacity of the unit.
③E PCE ＝β s P buy ；
Wherein E is PCE For the outsourcing electric power carbon emission of the system, the unit is tCO 2 ，β s The carbon emission coefficient of the unit electric quantity of outsourcing power is MWh.
④
Wherein E is NEG,CE Generating carbon emission for new energy, wherein the unit is tCO 2 N is the number of new energy types, ρ s,n The unit of carbon emission factor is tCO which is the n-th new energy 2 /MWh，P NEG,n The unit of the generated energy is MWh.
⑤
Wherein E is NEG,CR The unit of the carbon reduction amount for new energy power generation is tCO 2 ，ρ a,n The unit of carbon emission reduction factor is tCO which is the n-th new energy 2 /MWh。
⑥
Wherein E is ENG,CE Is the unit of tCO for multiple energy storage carbon emission 2 B is the quantity of energy storage types, ρ cha,b Carbon emission factor for class b energy storage in tCO 2 /MWh，P ENG,cha,b The charge amount for the b-th class of stored energy is given in MWh.
⑦
Wherein E is ENG,CR For multi-energy storage and carbon reduction, the unit is tCO 2 ，ρ dis,b Carbon emission reduction factor for class b energy storage in tCO 2 /MWh，P ENG,dis,b The discharge amount of the type b energy storage is MWh.
⑧E＝E CEQ +E ENG,CR +E NEG,CR -E ENG,CE -E NEG,CE -E CFP -E PCE ；
Wherein E is the residual total amount of the carbon quota of the system, and the unit is tCO 2 。
⑨
Wherein C is car For step carbon trade benefits, c is the carbon trade reference price of the market, d is the carbon emission interval length, alpha is the carbon trade price increase amplitude, and E is the total carbon quota remaining amount of the system.
⑩
Wherein LCOE is the leveling power generation cost, K is the number of devices in the power system, I is the year number, and IC k For initial investment of equipment k, OC i,k For the i-th operating cost of the device k, MC i,k For maintenance cost of equipment k, i-th year, G i,k R is the power generation of the equipment k k Is the discount rate of device k.
Wherein C is B Is the system purchase energy cost, the unit is kiloyuan, P buy To purchase electricity outside the system, C buy For the price of system electricity purchase, the unit cell/kWh, N is the number of new energy types, P NEG,n Generating energy of nth new energy, C NEG,n Generating power and surfing electricity price for nth new energy and listbit/kWh, B is the number of energy storage types, P ENG,cha,b Charge amount for class b energy storage, C ENG,cha,b Charging electricity price for class b energy storage, unit cell/kWh, P CFP For generating power of the unit, C CFP The method is characterized by comprising the step of connecting the power unit to the power grid, wherein the power unit is unit cell/kWh.
C S ＝P sell ·C sell ；
Wherein C is S The unit of the electricity selling income of the system is kiloyuan, P sell Is the electricity selling quantity, the unit is MWh, C sell The unit is Yuan/kWh for selling electricity.
C＝C S +C car -C B ；
Wherein, C is the net benefit of the system in kiloyuan.
Step 500: and determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method according to the weights of each candidate decision scheme and each secondary index.
Further, step 500 includes:
(51) And establishing an initial index matrix X according to each candidate decision scheme. The elements in the initial index matrix are candidate values of secondary indexes in each candidate decision scheme respectively:
wherein x is mj And the candidate value of the j-th secondary index in the candidate decision schemes M is represented, and M is the number of the candidate decision schemes.
(52) And carrying out normalization processing on the initial index matrix to determine a normalization decision matrix Y. The elements in the normalized decision matrix are the normalized values of the secondary indexes in each candidate decision scheme respectively: y= { Y mj }，Wherein y is mj Is the canonical value of the j-th secondary index in the candidate decision scheme m.
(53) And determining a weighted canonical matrix Z according to the canonical decision matrix and the weight of each secondary index. The elements in the weighted canonical matrix are weighted values of each secondary index in each candidate decision scheme respectively: z= { Z mj }，z mj ＝m j ·y mj The method comprises the steps of carrying out a first treatment on the surface of the Wherein z is mj Is the weighted value of the j-th secondary index in the candidate decision scheme m.
(54) Determining an ideal solution x according to the initial index matrix * Negative ideal solution x n * . Specifically, the ideal solution includes an ideal solution for each secondary index, and the negative ideal solution includes a negative ideal solution for each secondary index.
For any secondary index, if the secondary index is the secondary index under the benefit of carbon reduction, adopting a formulaDetermining an ideal solution of said secondary index using the formula +.>And determining a negative ideal solution of the secondary index.
If the secondary index is the secondary index under the economic benefit, adopting a formulaDetermining an ideal solution of said secondary index using the formula +.>And determining a negative ideal solution of the secondary index.
Wherein,for the ideal solution of the j-th secondary index, < >>Is the negative ideal solution of the j-th secondary index, x mj Is the candidate value of the j-th secondary index in the candidate decision scheme m.
(55) For any candidate decision scheme, the Euclidean distance of the candidate decision scheme to the ideal solution and the Euclidean distance of the candidate decision scheme to the negative ideal solution are calculated.
Specifically, the formula is adoptedCalculating Euclidean distance from the candidate decision scheme m to the ideal solution; using the formula->Calculating the Euclidean distance from the candidate decision scheme m to the negative ideal solution; wherein,euclidean distance from candidate decision scheme m to ideal solution,>the Euclidean distance from the candidate decision scheme m to the negative ideal solution.
(56) And determining the comprehensive evaluation index of the candidate decision scheme according to the Euclidean distance from the candidate decision scheme to the ideal solution and the Euclidean distance from the candidate decision scheme to the negative ideal solution.
Specifically, the following formula is used to determine the overall evaluation index of the candidate decision scheme m:
wherein,comprehensive evaluation index for candidate decision scheme m, +.>Euclidean distance from candidate decision scheme m to negative ideal solution, +.>The euclidean distance from the candidate decision scheme m to the ideal solution.
Step 600: and determining an optimal decision scheme of the power grid according to the comprehensive evaluation indexes of the candidate decision schemes so as to optimize the power grid.
Specifically, the candidate decision schemes are ranked according to the magnitude of the comprehensive evaluation index so as to judge the influence degree of different candidate decision schemes on the power grid benefit, and the higher the influence degree is, the higher the attention degree of the corresponding candidate decision schemes is.
The invention can evaluate the carbon reduction benefits and economic benefits of the participation of the multi-type energy storage in the electric power market and the carbon market, is beneficial to promoting the application of the multi-type energy storage in the electric power system and improves the reliability, the economy and the environmental sustainability of the power grid.
Example two
In order to execute the corresponding method of the above embodiment to achieve the corresponding functions and technical effects, a grid optimization system for participating in the electric carbon market by multi-type energy storage is provided below.
As shown in fig. 4, the power grid optimization system for participating in an electric carbon market by using multi-type energy storage provided by the embodiment includes: the system comprises a system construction module 1, a historical data acquisition module 2, a weight determination module 3, a candidate scheme determination module 4, an evaluation index determination module 5 and an optimal scheme determination module 6.
Wherein, the system construction module 1 is used for constructing a value evaluation system. The value evaluation system comprises a target layer, a criterion layer and an index layer. The target layer is multi-dimensional value of multi-type energy storage participating in an electric carbon market, the criterion layer comprises a plurality of first-level indexes, and the index layer comprises a plurality of second-level indexes under each first-level index. The first-level indexes are respectively the carbon reduction benefits and the economic benefits of the participation of the multi-type energy storage in the electric carbon market. The secondary indexes under the carbon reduction benefit are respectively system carbon quota, thermal power generating unit carbon emission, system outsourcing electric power carbon emission, new energy power generation carbon reduction, multi-energy storage carbon emission, multi-energy storage carbon reduction and system carbon quota residual total. The secondary indexes under the economic benefit are ladder carbon trade benefits, leveling power generation cost, system purchase energy cost, system electricity selling benefits and system net benefits respectively.
The historical data acquisition module 2 is used for acquiring a historical data set. The historical data set comprises values of secondary indexes of each year in a historical setting period.
The weight determining module 3 is respectively connected with the system constructing module 1 and the historical data obtaining module 2, and the weight determining module 3 is used for determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method according to the historical data set.
The candidate determination module 4 is configured to determine a plurality of candidate decision schemes. Each candidate decision scheme includes candidate values for the secondary indicators.
The evaluation index determining module 5 is respectively connected with the weight determining module 3 and the candidate scheme determining module 4, and is used for determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method according to the weights of each candidate decision scheme and each secondary index.
The optimal solution determining module 6 is connected with the evaluation index determining module 5, and the optimal solution determining module 6 is used for determining an optimal decision scheme of the power grid according to the comprehensive evaluation index of each candidate decision scheme so as to optimize the power grid.
Compared with the prior art, the power grid optimization system for participating in the electric carbon market by the multi-type energy storage provided by the embodiment has the same beneficial effects as the power grid optimization method for participating in the electric carbon market by the multi-type energy storage provided by the embodiment, and is not repeated here.
Example III
The embodiment provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to run the computer program to cause the electronic device to execute the multi-type energy storage method of the first embodiment to participate in the grid optimization method of the electric carbon market.
Alternatively, the electronic device may be a server.
In addition, the embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the multi-type energy storage participation electric carbon market grid optimization method of the first embodiment when being executed by a processor.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The power grid optimization method for the multi-type energy storage participation electric carbon market is characterized by comprising the following steps of:
constructing a value evaluation system; the value evaluation system comprises a target layer, a criterion layer and an index layer; the target layer is multi-dimensional value of multi-type energy storage participating in an electric carbon market, the criterion layer comprises a plurality of first-level indexes, and the index layer comprises a plurality of second-level indexes under each first-level index; the first-level indexes are respectively the carbon reduction benefits and the economic benefits of the participation of the multi-type energy storage in the electric carbon market; the secondary indexes under the carbon reduction benefit are respectively system carbon quota, thermal power generating unit carbon emission, system outsourcing electric power carbon emission, new energy power generation carbon reduction, multi-energy storage carbon emission, multi-energy storage carbon reduction and system carbon quota residual total; the secondary indexes under the economic benefit are ladder carbon trade benefits, leveling power generation cost, system purchase energy cost, system electricity selling benefits and system net benefits respectively;
acquiring a historical dataset; the historical data set comprises values of secondary indexes of each year in a historical setting period;
according to the historical data set, determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method;
determining a plurality of candidate decision schemes; each candidate decision scheme comprises candidate values of secondary indexes;
according to the weights of each candidate decision scheme and each secondary index, determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method;
and determining an optimal decision scheme of the power grid according to the comprehensive evaluation indexes of the candidate decision schemes so as to optimize the power grid.
2. The method for optimizing a power grid for participation in an electric carbon market by using multi-type energy storage according to claim 1, wherein the method for determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method according to the historical data set is characterized by comprising the following specific steps:
constructing an original evaluation matrix according to the historical data set; the elements in the original evaluation matrix are respectively the values of each secondary index in each year in the historical setting period;
normalizing the original evaluation matrix to obtain a normalized matrix; the elements in the standardized matrix are the standardized values of each secondary index each year;
aiming at any secondary index in the value evaluation system, determining the information entropy of the secondary index according to the standard value of the secondary index every year in the standardized matrix;
and determining the weight of the secondary index according to the information entropy of the secondary index.
3. The method for optimizing a power grid for participation in an electric carbon market by multi-type energy storage according to claim 2, wherein the specification value of the jth secondary index of the ith year is determined by adopting the following formula:
wherein r is ij A is the standard value of the j-th secondary index in the i-th year ij The value of the j second-level index in the ith year is IIs a number of years.
4. The method for optimizing a power grid for participation in an electric carbon market by multi-type energy storage according to claim 2, wherein the information entropy of the j-th secondary index is determined by adopting the following formula:
the weight of the jth secondary index is determined using the following formula:
wherein E is j Information entropy of jth secondary index, r ij Is the standard value of the j second level index of the ith year, I is the year number, m j The J-th secondary index is weighted, and J is the number of secondary indexes.
5. The method for optimizing a power grid in which multiple types of energy storage participate in an electricity-carbon market according to claim 1, wherein the following formulas are adopted to determine the system carbon quota, the thermal power unit carbon emission, the system outsourcing electricity carbon emission, the new energy power generation carbon reduction, the multiple energy storage carbon emission, the multiple energy storage carbon reduction and the total system carbon quota remaining in each candidate decision scheme:
E CEQ ＝Q e ×B e ×F 1 ×F r ×F f +Q h ×B h +η e P buy ；
E CFP ＝β e P CFP ；
E PCE ＝β s P buy ；
E＝E CEQ +E ENG,CR +E NEG,CR -E ENG,CE -E NEG,CE -E CFP -E PCE ；
wherein E is CEQ For system carbon quota, E CFP Is the carbon emission of the thermal power unit, E PCE Carbon emission of outsourcing power of system E NEG,CE Generating carbon emission for new energy, E NEG,CR The carbon reduction amount for new energy power generation E ENG,CE For a plurality of energy storage carbon emission, E ENG,CR For multi-energy storage and carbon reduction, E is the total residual quantity of the carbon quota of the system, Q e For the power supply quantity of the unit B e Reference value F for power supply of class to which the machine set belongs 1 Correction coefficient for unit cooling mode, F r Correction coefficient for heat supply of machine set, F f For the load factor correction coefficient of the unit, Q h Heat supply quantity of machine set B h For the heat supply reference value eta of the category of the unit e Carbon quota coefficient of unit electric quantity, P buy Beta for outsourcing electric quantity of system e Carbon emission coefficient beta for unit electric quantity of coal-fired unit s Carbon emission coefficient, P, of unit electric quantity of outsourced electric power CFP N is the number of new energy types and ρ is the generating capacity of the unit s,n Carbon emission factor ρ as n-th new energy source a,n Carbon emission reduction factor P as n-th new energy NEG,n Generating energy of the nth new energy, B is the quantity of energy storage types, ρ cha,b Carbon emission factor ρ for class b energy storage dis,b Carbon emission reduction factors for class b energy storage,P ENG,cha,b charge amount for class b energy storage, P ENG,dis,b The discharge amount of the energy storage of the b type.
6. The method of claim 1, wherein the step carbon trading revenue, the normalized power generation cost, the system energy purchase cost, the system electricity sales revenue and the system net revenue in each candidate decision scheme are determined by using the following formula:
C S ＝P sell ·C sell ；
C＝C S +C car -C B ；
wherein C is car For the trading benefit of the ladder carbon, LCOE is the leveling power generation cost, C B To the cost of system purchasing energy, C S For system electricity selling benefits, C is system net benefits, C is carbon trade reference price of market, d is carbon emission interval length, alpha is carbon trade price increase amplitude, E is system carbon quota residual total, K is equipment number in an electric power system, I is year number, and IC k For initial investment of equipment k, OC i,k For the i-th operating cost of the device k, MC i,k For maintenance cost of equipment k, i-th year, G i,k R is the power generation of the equipment k k P, the discount rate of the device k buy To purchase electricity outside the system, C buy For the price of system electricity purchase, N is the number of new energy types, P NEG,n Generating energy of nth new energy, C NEG,n Is the firstGenerating electricity of n new energy sources, wherein B is the quantity of energy storage types and P is the electricity price of the electricity generation on the internet ENG,cha,b Charge amount for class b energy storage, C ENG,cha,b Charging electricity price for b-type energy storage, P CFP For generating power of the unit, C CFP The power price of the thermal power generating unit is the power price, P sell For selling electricity, C sell Is the price of electricity.
7. The method for optimizing the power grid of the multi-type energy storage participation electric carbon market according to claim 1, wherein the comprehensive evaluation index of each candidate decision scheme is determined by adopting a TOPSIS method according to the weights of each candidate decision scheme and each secondary index, and the method specifically comprises the following steps:
establishing an initial index matrix according to each candidate decision scheme; the elements in the initial index matrix are candidate values of secondary indexes in each candidate decision scheme respectively;
normalizing the initial index matrix to determine a normalized decision matrix; the elements in the normalized decision matrix are the normalized values of the secondary indexes in each candidate decision scheme respectively;
determining a weighted canonical matrix according to the canonical decision matrix and the weight of each secondary index; the elements in the weighting specification matrix are respectively the weighting values of the secondary indexes in each candidate decision scheme;
determining an ideal solution and a negative ideal solution according to the initial index matrix;
for any candidate decision scheme, calculating the Euclidean distance from the candidate decision scheme to the ideal solution and the Euclidean distance from the candidate decision scheme to the negative ideal solution;
and determining the comprehensive evaluation index of the candidate decision scheme according to the Euclidean distance from the candidate decision scheme to the ideal solution and the Euclidean distance from the candidate decision scheme to the negative ideal solution.
8. The method of grid optimization for multi-type energy storage participation in an electric carbon market according to claim 7, wherein the ideal solution comprises an ideal solution for each secondary index, and the negative ideal solution comprises a negative ideal solution for each secondary index;
determining an ideal solution and a negative ideal solution according to the initial index matrix, wherein the method specifically comprises the following steps:
for any secondary index, if the secondary index is the secondary index under the benefit of carbon reduction, adopting a formulaDetermining an ideal solution of said secondary index using the formula +.>Determining a negative ideal solution of the secondary index;
if the secondary index is the secondary index under the economic benefit, adopting a formulaDetermining an ideal solution of said secondary index using the formula +.>Determining a negative ideal solution of the secondary index;
wherein,for the ideal solution of the j-th secondary index, < >>Is the negative ideal solution of the j-th secondary index, x mj Is the candidate value of the j-th secondary index in the candidate decision scheme m.
9. The method for optimizing a power grid for participation in an electric carbon market according to claim 7, wherein the comprehensive evaluation index of the candidate decision scheme m is determined using the following formula:
wherein,comprehensive evaluation index for candidate decision scheme m, +.>Euclidean distance from candidate decision scheme m to negative ideal solution, +.>The euclidean distance from the candidate decision scheme m to the ideal solution.
10. A grid optimization system for participating in an electric carbon market with multiple types of energy storage, the grid optimization system for participating in an electric carbon market with multiple types of energy storage comprising:
the system construction module is used for constructing a value evaluation system; the value evaluation system comprises a target layer, a criterion layer and an index layer; the target layer is multi-dimensional value of multi-type energy storage participating in an electric carbon market, the criterion layer comprises a plurality of first-level indexes, and the index layer comprises a plurality of second-level indexes under each first-level index; the first-level indexes are respectively the carbon reduction benefits and the economic benefits of the participation of the multi-type energy storage in the electric carbon market; the secondary indexes under the carbon reduction benefit are respectively system carbon quota, thermal power generating unit carbon emission, system outsourcing electric power carbon emission, new energy power generation carbon reduction, multi-energy storage carbon emission, multi-energy storage carbon reduction and system carbon quota residual total; the secondary indexes under the economic benefit are ladder carbon trade benefits, leveling power generation cost, system purchase energy cost, system electricity selling benefits and system net benefits respectively;
the historical data acquisition module is used for acquiring a historical data set; the historical data set comprises values of secondary indexes of each year in a historical setting period;
the weight determining module is respectively connected with the system constructing module and the historical data obtaining module and is used for determining the weight of each secondary index in the value evaluation system by adopting an entropy weight method according to the historical data set;
a candidate scheme determination module for determining a plurality of candidate decision schemes; each candidate decision scheme comprises candidate values of secondary indexes;
the evaluation index determining module is respectively connected with the weight determining module and the candidate scheme determining module and is used for determining the comprehensive evaluation index of each candidate decision scheme by adopting a TOPSIS method according to the weights of each candidate decision scheme and each secondary index;
the optimal scheme determining module is connected with the evaluation index determining module and is used for determining an optimal decision scheme of the power grid according to the comprehensive evaluation index of each candidate decision scheme so as to optimize the power grid.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117670136A (en) * | 2023-12-13 | 2024-03-08 | 杨童睿 | TOPSIS analysis-based optimal power generation mode determination method and system |
| CN118134347A (en) * | 2024-05-06 | 2024-06-04 | 国网浙江省电力有限公司丽水市莲都区供电公司 | Method, system, equipment and medium for simulating inter-provincial spot purchase electricity of receiving-end power grid |
| CN118485320A (en) * | 2024-07-16 | 2024-08-13 | 福建福大建筑规划设计研究院有限公司 | Urban water relation-oriented urban storage and evacuation space optimization method and simulation system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117670136A (en) * | 2023-12-13 | 2024-03-08 | 杨童睿 | TOPSIS analysis-based optimal power generation mode determination method and system |
| CN118134347A (en) * | 2024-05-06 | 2024-06-04 | 国网浙江省电力有限公司丽水市莲都区供电公司 | Method, system, equipment and medium for simulating inter-provincial spot purchase electricity of receiving-end power grid |
| CN118485320A (en) * | 2024-07-16 | 2024-08-13 | 福建福大建筑规划设计研究院有限公司 | Urban water relation-oriented urban storage and evacuation space optimization method and simulation system |
| CN118485320B (en) * | 2024-07-16 | 2024-09-27 | 福建福大建筑规划设计研究院有限公司 | Urban water relation-oriented urban storage and evacuation space optimization method and simulation system |
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