CN117196107A - A grid optimization method and system for multiple types of energy storage to participate in the electricity carbon market - Google Patents

Abstract

The invention provides a power grid optimization method and system for multiple types of energy storage to participate in the electric carbon market, which belongs to the field of power grid optimization. The method includes: constructing a value evaluation system; the value evaluation system includes a target layer, a criterion layer and an indicator layer; the target layer is The multi-dimensional value of multiple types of energy storage participating in the electric carbon market. The criterion layer includes the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electric carbon market. The indicator layer includes multiple secondary indicators; the entropy weight method is used to determine the value of each secondary indicator. Weight; based on the weight of each candidate decision-making scheme and each secondary indicator, use the TOPSIS method to determine the comprehensive evaluation index of each candidate decision-making scheme; determine the optimal decision-making scheme of the power grid based on the comprehensive evaluation index of each candidate decision-making scheme to optimize the power grid . This invention evaluates the carbon reduction and economic benefits of multiple types of energy storage participating in the electricity carbon market, promotes the application of multiple types of energy storage in the power system, and improves the reliability, economy and environmental sustainability of the power grid.

Description

A grid optimization method and system for multiple types of energy storage to participate in the electricity carbon market

Technical field

The present invention relates to the field of power grid optimization, and in particular to a power grid optimization method and system based on the TOPSIS method for multiple types of energy storage to participate in the electric carbon market.

Background technique

Energy storage technology can effectively adjust the changes in grid voltage, frequency and phase caused by new energy power generation, allowing large-scale wind power and photovoltaic power generation to be easily and reliably integrated into the conventional power grid. At present, there are few methods for evaluating the value of various energy storage systems, and the benefits of energy storage systems in the carbon market are not taken into account.

Therefore, it is urgent to propose an evaluation method for the value of multiple energy storage systems to promote the rapid development and comprehensive promotion of multiple types of energy storage technologies.

Contents of the invention

The purpose of the present invention is to provide a power grid optimization method and system for multiple types of energy storage to participate in the electric carbon market, which can improve the reliability, economy and environmental sustainability of the power grid.

In order to achieve the above objects, the present invention provides the following solutions:

A grid optimization method for multiple types of energy storage to participate in the electricity carbon market, including:

Construct a value evaluation system; the value evaluation system includes a target layer, a criterion layer and an indicator layer; the target layer is the multi-dimensional value of multiple types of energy storage participating in the electric carbon market, the criterion layer includes multiple first-level indicators, and the The indicator layer includes multiple secondary indicators under each primary indicator; the multiple primary indicators are the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electric carbon market; the secondary indicators under the carbon reduction benefits are System carbon quota, thermal power unit carbon emissions, system purchased power carbon emissions, new energy power generation carbon emissions, new energy power generation carbon reduction, multiple energy storage carbon emissions, multiple energy storage carbon reduction and system carbon quota remaining The total amount; the secondary indicators under the economic benefits are ladder carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income;

Obtain a historical data set; the historical data set includes the values of each secondary indicator every year within the historical set period;

According to the historical data set, the entropy weight method is used to determine the weight of each secondary indicator in the value evaluation system;

Determine multiple candidate decision-making solutions; each candidate decision-making solution includes candidate values for each secondary indicator;

Based on the weight of each candidate decision-making scheme and each secondary indicator, the TOPSIS method is used to determine the comprehensive evaluation index of each candidate decision-making scheme;

The optimal decision-making plan for the power grid is determined based on the comprehensive evaluation index of each candidate decision-making plan to optimize the power grid.

Optionally, based on the historical data set, the entropy weight method is used to determine the weight of each secondary indicator in the value evaluation system, specifically including:

Construct an original evaluation matrix based on the historical data set; the elements in the original evaluation matrix are the values of each secondary indicator each year within the historical set period;

The original evaluation matrix is standardized to obtain a standardized matrix; the elements in the standardized matrix are the standardized values of each secondary indicator each year;

For any secondary indicator in the value evaluation system, determine the information entropy of the secondary indicator based on the normative value of the secondary indicator for each year in the normalized matrix;

The weight of the secondary indicator is determined based on the information entropy of the secondary indicator.

Optionally, use the following formula to determine the normative value of the j-th secondary indicator in year i:

Among them, r _ij is the normative value of the j-th secondary indicator in the i-th year, a _ij is the value of the j-th secondary indicator in the i-th year, and I is the number of years.

Optionally, use the following formula to determine the information entropy of the j-th secondary indicator:

The following formula is used to determine the weight of the j-th secondary indicator:

Among them, E _j is the information entropy of the j-th secondary indicator, r _ij is the normative value of the j-th secondary indicator in the i-th year, I is the number of years, m _j is the weight of the j-th secondary indicator, and J is the second level indicator. The number of level indicators.

Optionally, use the following formula to determine the system carbon quota, carbon emissions of thermal power units, system purchased power carbon emissions, new energy power generation carbon emissions, new energy power generation carbon reduction, and multi-energy storage carbon emissions in each candidate decision-making scheme. Emissions, multi-energy storage carbon reduction, and total system carbon quota remaining:

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 ;

Among them, E _CEQ is the system carbon quota, E _CFP is the carbon emissions of thermal power units, E _PCE is the system carbon emissions of purchased power, E _{NEG, CE} is the carbon emissions of new energy power generation, E _{NEG, CR} is the reduction of new energy power generation. Carbon amount, E _{ENG, CE} is the carbon emission of multiple energy storage, E _{ENG, CR} is the carbon reduction amount of multiple energy storage, E is the total remaining carbon quota of the system, Q _e is the power supply of the unit, B _e is the category of the unit to which it belongs. Power supply reference value, F ₁ is the unit cooling method correction coefficient, F _r is the unit heat supply correction coefficient, F _f is the unit load coefficient correction coefficient, Q _h unit heat supply, B _h is the heating reference value of the category to which the unit belongs , η _e is the carbon quota coefficient per unit of electricity, P _buy is the purchased electricity of the system, β _e is the carbon emission coefficient of coal-fired units per unit of electricity, β _s is the carbon emission coefficient of unit of purchased electricity, P _CFP is the power generation of the unit, N is the number of new energy types, ρ _s,n is the carbon emission factor of the nth type of new energy, ρ _a,n is the carbon emission reduction factor of the nth type of new energy, P _NEG,n is the power generation of the nth type of new energy quantity, B is the number of energy storage types, ρ _cha,b is the carbon emission factor of type b energy storage, ρ _dis,b is the carbon emission reduction factor of type b energy storage, P _ENG,cha,b is the carbon emission factor of type b energy storage The charging amount of type b energy storage, P _ENG,dis,b is the discharge amount of type b energy storage.

Optionally, use the following formula to determine the tiered carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income in each candidate decision-making scheme:

C _S =P _sell ·C _sell ;

C＝C _S +C _car -C _B ;

Among them, C _car is the ladder carbon trading income, LCOE is the levelized cost of power generation, C _B is the system energy purchase cost, C _S is the system electricity sales income, C is the system net income, c is the market carbon trading benchmark price, d is the length of the carbon emission interval, α is the growth rate of carbon trading price, E is the total remaining carbon quota of the system, K is the number of equipment in the power system, I is the number of years, IC _k is the initial investment of equipment k, OC _i,k is The operating cost of equipment k in year i, MC _i,k is the maintenance cost of equipment k in year i, G _i,k is the power generation of equipment k, r _k is the discount rate of equipment k, and P _buy is the system outsourcing Electricity, C _buy is the system power purchase price, N is the quantity of new energy types, P _NEG,n is the power generation of the nth type of new energy, C _NEG,n is the power generation price of the nth type of new energy, B is the storage The number of energy types, P _ENG,cha,b is the charging amount of type b energy storage, C _ENG,cha,b is the charging electricity price of type b energy storage, P _CFP is the power generation of the unit, C _CFP is the grid-connected thermal power unit Price of electricity, P _sell is the electricity sold, C _sell is the price of electricity sold.

Optionally, based on the weight of each candidate decision-making scheme and each secondary indicator, use the TOPSIS method to determine the comprehensive evaluation index of each candidate decision-making scheme, specifically including:

Establish an initial indicator matrix according to each candidate decision-making scheme; the elements in the initial indicator matrix are the candidate values of each secondary indicator in each candidate decision-making scheme;

The initial indicator matrix is normalized to determine a standardized decision matrix; the elements in the normalized decision matrix are the standardized values of each secondary indicator in each candidate decision scheme;

Determine a weighted specification matrix based on the normalized decision matrix and the weight of each secondary indicator; the elements in the weighted specification matrix are respectively the weighted values of each secondary indicator in each candidate decision plan;

Determine the ideal solution and the negative ideal solution according to the initial indicator matrix;

For any candidate decision-making solution, calculate the Euclidean distance between the candidate decision-making solution and the ideal solution and the Euclidean distance between the candidate decision-making solution and the negative ideal solution;

The comprehensive evaluation index of the candidate decision-making solution is determined based on the Euclidean distance between the candidate decision-making solution and the ideal solution and the Euclidean distance between the candidate decision-making solution and the negative ideal solution.

Optionally, the ideal solution includes the ideal solution of each secondary indicator, and the negative ideal solution includes the negative ideal solution of each secondary indicator;

Determine the ideal solution and the negative ideal solution based on the initial indicator matrix, specifically including:

For any secondary indicator, if the secondary indicator is a secondary indicator under carbon reduction benefits, the formula is used To determine the ideal solution for the secondary indicator, use the formula/> Determine the negative ideal solution of the secondary indicator;

If the secondary indicator is a secondary indicator under economic benefits, the formula To determine the ideal solution for the secondary indicator, use the formula/> Determine the negative ideal solution of the secondary indicator;

in, is the ideal solution of the j-th secondary index,/> is the negative ideal solution of the jth secondary indicator, and x _mj is the candidate value of the jth secondary indicator in the candidate decision plan m.

Optionally, use the following formula to determine the comprehensive evaluation index of candidate decision solution m:

in, is the comprehensive evaluation index of candidate decision plan m,/> is the Euclidean distance from the candidate decision solution m to the negative ideal solution,/> is the Euclidean distance between candidate decision solution m and the ideal solution.

In order to achieve the above objects, the present invention also provides the following solutions:

A grid optimization system for multiple types of energy storage to participate in the electricity carbon market, including:

System building module, used to construct a value evaluation system; the value evaluation system includes a target layer, a criterion layer and an indicator layer; the target layer is the multi-dimensional value of multiple types of energy storage participating in the electric carbon market, and the criterion layer includes multiple First-level indicators, the indicator layer includes multiple second-level indicators under each first-level indicator; the multiple first-level indicators are the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electricity carbon market; the carbon reduction benefits are The secondary indicators are system carbon quota, thermal power unit carbon emissions, system purchased power carbon emissions, new energy power generation carbon emissions, new energy power generation carbon reduction, multiple energy storage carbon emissions, multiple energy storage carbon reduction amount and the total remaining amount of system carbon quota; the secondary indicators under the above-mentioned economic benefits are ladder carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income;

A historical data acquisition module is used to acquire a historical data set; the historical data set includes the values of each secondary indicator every year within the historical set period;

A weight determination module, respectively connected to the system construction module and the historical data acquisition module, is used to determine the weight of each secondary index in the value evaluation system using the entropy weight method based on the historical data set;

The candidate solution determination module is used to determine multiple candidate decision solutions; each candidate decision solution includes candidate values of each secondary indicator;

An evaluation index determination module is respectively connected to the weight determination module and the candidate solution determination module, and is used to determine the comprehensive evaluation index of each candidate decision solution according to the weight of each candidate decision solution and each secondary indicator using the TOPSIS method;

The optimal solution determination module is connected to the evaluation index determination module and is used to determine the optimal decision-making solution of the power grid based on the comprehensive evaluation index of each candidate decision-making solution to optimize the power grid.

According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the value evaluation system constructed by the present invention considers the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electric carbon market, and uses the TOPSIS method to determine each candidate decision The comprehensive evaluation index of the plan is finally determined based on the comprehensive evaluation index of each candidate decision-making plan to determine the optimal decision-making plan for the power grid to optimize the power grid and evaluate the carbon reduction and economic benefits of multiple types of energy storage participating in the power market and carbon market. It is conducive to promoting the application of multiple types of energy storage in power systems and improving the reliability, economy and environmental sustainability of the power grid.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the drawings needed to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the drawings of the present invention. Embodiments, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without exerting creative efforts.

Figure 1 is an overall flow chart of a power grid optimization method for multiple types of energy storage to participate in the electric carbon market provided by the present invention;

Figure 2 is a detailed flow chart of the power grid optimization method for multiple types of energy storage to participate in the electric carbon market provided by the present invention;

Figure 3 is a schematic diagram of the value evaluation system;

Figure 4 is a schematic diagram of a power grid optimization system in which multiple types of energy storage participate in the electric carbon market provided by the present invention.

Symbol explanation: 1-system building module, 2-historical data acquisition module, 3-weight determination module, 4-candidate solution determination module, 5-evaluation index determination module, 6-optimal solution determination module.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

The purpose of the present invention is to provide a power grid optimization method and system for multiple types of energy storage to participate in the electric carbon market, promote the application of multiple types of energy storage in the power system, and improve the reliability, economy and environmental sustainability of the power grid.

In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

Embodiment 1

As shown in Figures 1 and 2, this embodiment provides a grid optimization method for multiple types of energy storage to participate in the electric carbon market, including:

Step 100: Build a value evaluation system. As shown in Figure 3, the value evaluation system includes the target layer, the criterion layer and the indicator layer.

The target layer is the multi-dimensional value of multiple types of energy storage participating in the electric carbon market. The criterion layer includes multiple first-level indicators, and the indicator layer includes multiple second-level indicators under each first-level indicator.

Multiple first-level indicators are the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electricity carbon market.

The secondary indicators under carbon reduction benefits are system carbon quota, carbon emissions of thermal power units, carbon emissions of system purchased power, carbon emissions of new energy power generation, carbon reduction of new energy power generation, carbon emissions of multiple energy storage, and multiple Energy storage carbon reduction amount and the total remaining system carbon quota.

The secondary indicators under economic benefits are ladder carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income.

Step 200: Obtain historical data set. The historical data set includes the values of each secondary indicator every year within the historical set period.

Step 300: Based on the historical data set, use the entropy weight method to determine the weight of each secondary indicator in the value evaluation system.

Further, step 300 includes:

(31) Construct the original evaluation matrix A based on the historical data set. The elements in the original evaluation matrix are the values of each secondary indicator each year within the historical set period:

Among them, a _ij is the value of the j-th secondary indicator in the i-th year.

(32) Perform normalization processing on the original evaluation matrix to obtain the normalized matrix R. The elements in the normalized matrix are the standardized values of each secondary indicator for each year. Specifically, the following formula is used to determine the normative value of the j-th secondary indicator in the i-th year:

Among them, r _ij is the normative value of the j-th secondary indicator in the i-th year, a _ij is the value of the j-th secondary indicator in the i-th year, and I is the number of years.

Then the normalized matrix is

(33) For any secondary indicator in the value evaluation system, determine the information entropy of the secondary indicator based on the normative value of the secondary indicator for each year in the normalized matrix. Specifically, the normalized matrix is summed by column, and then the information entropy of each secondary indicator is calculated according to the formula of information entropy. The following formula is used to determine the information entropy of the jth secondary indicator:

(34) Determine the weight of the secondary indicator based on the information entropy of the secondary indicator. Specifically, the following formula is used to determine the weight of the j-th secondary indicator:

Among them, E _j is the information entropy of the j-th secondary indicator, r _ij is the normative value of the j-th secondary indicator in the i-th year, I is the number of years, m _j is the weight of the j-th secondary indicator, and J is the second level indicator. The number of level indicators.

In addition, the present invention can also average the normalized matrix of each secondary indicator by column to obtain the weight of each secondary indicator. Specifically, the following formula is used to calculate the weight of the jth secondary indicator:

Step 400: Determine multiple candidate decision solutions. Each candidate decision-making solution includes candidate values for each secondary indicator. Specifically, the present invention uses the following formula to determine the candidate values of each secondary indicator in each candidate decision-making scheme.

①E _CEQ =Q _e ×B _e ×F ₁ ×F _r ×F _f +Q _h ×B _h +η _e P _buy ;

Among them, E _CEQ is the system carbon quota, the unit is tCO ₂ , Q _e is the power supply of the unit, the unit is MWh, B _e is the power supply baseline value of the unit category, the unit is tCO ₂ /MWh, F ₁ is the unit cooling method correction Coefficient, F _r is the unit heat supply correction coefficient, the coal-fired unit heat supply correction coefficient is 1-0.22×heat supply ratio, F _f is the unit load coefficient correction coefficient, Q _h unit heat supply, the unit is GJ, B _h is the heating reference value of the unit category, in tCO ₂ /GJ, η _e is the carbon quota coefficient per unit of electricity, and P _buy is the power purchased from the system, in MWh.

②E _CFP = β _e P _CFP ;

Among them, E _CFP is the carbon emissions of thermal power units in tCO ₂ , β _e is the carbon emission coefficient per unit of electricity of coal-fired units in tCO ₂ /MWh, and P _CFP is the power generation of the unit in MWh.

③E _PCE = β _s P _buy ;

Among them, E _PCE is the carbon emission of the purchased power of the system, the unit is tCO ₂ , and β _s is the carbon emission coefficient of the purchased power unit, the unit is MWh.

④

Among them, E _{NEG, CE} is the carbon emission of new energy power generation, the unit is tCO ₂ , N is the number of new energy types, ρ _s,n is the carbon emission factor of the nth type of new energy, the unit is tCO ₂ /MWh, P _NEG,n is the power generation of the nth type of new energy, in MWh.

⑤

Among them, E _NEG,CR is the carbon reduction amount of new energy power generation, in tCO ₂ , and ρ _a,n is the carbon emission reduction factor of the nth type of new energy, in tCO ₂ /MWh.

⑥

Among them, E _ENG,CE is the carbon emission of multiple energy storage, the unit is tCO ₂ , B is the number of energy storage types, ρ _cha,b is the carbon emission factor of type b energy storage, the unit is tCO ₂ /MWh, P _ENG,cha,b is the charging capacity of type b energy storage, in MWh.

⑦

Among them, E _ENG,CR is the carbon reduction amount of multiple energy storage, the unit is tCO ₂ , ρ _dis,b is the carbon emission reduction factor of type b energy storage, the unit is tCO ₂ /MWh, P ENG,dis,b is the carbon emission reduction factor of type b energy storage, and P _ENG,dis,b is the carbon emission reduction factor of type b energy storage. The discharge capacity of type b energy storage, in MWh.

⑧E＝E _CEQ +E _ENG,CR +E _NEG,CR -E _ENG,CE -E _NEG,CE -E _CFP -E _PCE ;

Among them, E is the total remaining carbon quota of the system, and the unit is tCO ₂ .

⑨

Among them, C _car is the income from ladder carbon trading, c is the carbon trading benchmark price in the market, d is the length of the carbon emission interval, α is the growth rate of carbon trading price, and E is the total remaining amount of system carbon quota.

⑩

Among them, LCOE is the levelized cost of power generation, K is the number of equipment in the power system, I is the number of years, IC _k is the initial investment of equipment k, OC _i,k is the operating cost of equipment k in the i-th year, MC _i,k is the maintenance cost of equipment k in the i-th year, G _i,k is the power generation of equipment k, and r _k is the discount rate of equipment k.

Among them, C _B is the system energy purchase cost, the unit is thousands of yuan, P _buy is the system's external power purchase, C _buy is the system power purchase price, the unit is yuan/kWh, N is the number of new energy types, P _{NEG, n} is the The power generation of n type new energy, C _NEG,n is the on-grid electricity price of type n new energy, unit yuan/kWh, B is the number of energy storage types, P _ENG,cha,b is the charging of type b energy storage Amount, C _ENG,cha,b is the charging electricity price of type b energy storage, unit yuan/kWh, P _CFP is the power generation of the unit, C _CFP is the on-grid electricity price of the thermal power unit, unit yuan/kWh.

C _S =P _sell ·C _sell ;

Among them, C _S is the system's electricity sales revenue, the unit is thousands of yuan, P _sell is the electricity sales, the unit is MWh, C _sell is the electricity sales price, the unit is yuan/kWh.

C＝C _S +C _car -C _B ;

Among them, C is the net income of the system, in thousands of yuan.

Step 500: Based on the weight of each candidate decision-making scheme and each secondary indicator, use the TOPSIS method to determine the comprehensive evaluation index of each candidate decision-making scheme.

Further, step 500 includes:

(51) Establish an initial index matrix X based on each candidate decision-making solution. The elements in the initial indicator matrix are the candidate values of each secondary indicator in each candidate decision-making scheme:

Among them, x _mj represents the candidate value of the j-th secondary indicator in the candidate decision solution m, and M is the number of candidate decision solutions.

(52) Perform normalization processing on the initial indicator matrix and determine the normalized decision matrix Y. The elements in the normalized decision matrix are the standardized values of each secondary indicator in each candidate decision scheme: Y={y _mj }, Among them, y _mj is the normative value of the j-th secondary indicator in the candidate decision plan m.

(53) According to the normalized decision matrix and the weight of each secondary indicator, determine the weighted normative matrix Z. The elements in the weighted specification matrix are the weighted values of each secondary indicator in each candidate decision-making scheme: Z = {z _mj }, z _mj = m _j ·y _mj ; where z _mj is the mth in the candidate decision-making scheme m The weighted values of j secondary indicators.

(54) Determine the ideal solution x ^* and the negative ideal solution x _n ^* according to the initial index matrix. Specifically, the ideal solution includes the ideal solution of each secondary indicator, and the negative ideal solution includes the negative ideal solution of each secondary indicator.

For any secondary indicator, if the secondary indicator is a secondary indicator under carbon reduction benefits, the formula is used To determine the ideal solution for the secondary indicator, use the formula/> Determine the negative ideal solution of the secondary indicator.

If the secondary indicator is a secondary indicator under economic benefits, the formula To determine the ideal solution for the secondary indicator, use the formula/> Determine the negative ideal solution of the secondary indicator.

in, is the ideal solution of the j-th secondary index,/> is the negative ideal solution of the jth secondary indicator, and x _mj is the candidate value of the jth secondary indicator in the candidate decision plan m.

(55) For any candidate decision-making solution, calculate the Euclidean distance between the candidate decision-making solution and the ideal solution and the Euclidean distance between the candidate decision-making solution and the negative ideal solution.

Specifically, using the formula Calculate the Euclidean distance from the candidate decision solution m to the ideal solution; use the formula/> Calculate the Euclidean distance from the candidate decision solution m to the negative ideal solution; where, is the Euclidean distance between the candidate decision solution m and the ideal solution,/> is the Euclidean distance between candidate decision solution m and the negative ideal solution.

(56) Determine the comprehensive evaluation index of the candidate decision solution based on the Euclidean distance between the candidate decision solution and the ideal solution and the Euclidean distance between the candidate decision solution and the negative ideal solution.

Specifically, the following formula is used to determine the comprehensive evaluation index of candidate decision solution m:

in, is the comprehensive evaluation index of candidate decision plan m,/> is the Euclidean distance from the candidate decision solution m to the negative ideal solution,/> is the Euclidean distance between candidate decision solution m and the ideal solution.

Step 600: Determine the optimal decision-making solution for the power grid based on the comprehensive evaluation index of each candidate decision-making solution to optimize the power grid.

Specifically, each candidate decision-making scheme is sorted according to the size of the comprehensive evaluation index to determine the degree of impact of different candidate decision-making schemes on power grid benefits. The higher the degree of impact, the higher the degree of attention to the corresponding candidate decision-making scheme.

The invention can evaluate the carbon reduction and economic benefits of multiple types of energy storage participating in the power market and the carbon market, and is conducive to promoting the application of multiple types of energy storage in the power system and improving the reliability, economy and environmental sustainability of the power grid.

Embodiment 2

In order to implement the method corresponding to the above-mentioned Embodiment 1 and achieve corresponding functions and technical effects, a power grid optimization system for multiple types of energy storage to participate in the electric carbon market is provided below.

As shown in Figure 4, the power grid optimization system for multiple types of energy storage participating in the electric carbon market provided by this embodiment includes: system building module 1, historical data acquisition module 2, weight determination module 3, candidate solution determination module 4, and evaluation index determination Module 5 and optimal solution determination module 6.

Among them, system building module 1 is used to build a value evaluation system. The value evaluation system includes a target layer, a criterion layer and an indicator layer. The target layer is the multi-dimensional value of multiple types of energy storage participating in the electric carbon market. The criterion layer includes multiple first-level indicators. The indicator layer includes multiple second-level indicators under each first-level indicator. Multiple first-level indicators are the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electricity carbon market. The secondary indicators under the carbon reduction benefits are system carbon quota, thermal power unit carbon emissions, system purchased power carbon emissions, new energy power generation carbon emissions, new energy power generation carbon reduction, and multi-energy storage carbon emissions. , the carbon reduction amount of multiple energy storage and the total remaining system carbon quota. The secondary indicators under the above-mentioned economic benefits are ladder carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income.

Historical data acquisition module 2 is used to acquire historical data sets. The historical data set includes the values of each secondary indicator every year within the historical set period.

The weight determination module 3 is connected to the system construction module 1 and the historical data acquisition module 2 respectively. The weight determination module 3 is used to determine each secondary index in the value evaluation system based on the historical data set using the entropy weight method. the weight of.

The candidate solution determination module 4 is used to determine multiple candidate decision solutions. Each candidate decision-making solution includes candidate values for each secondary indicator.

The evaluation index determination module 5 is connected to the weight determination module 3 and the candidate plan determination module 4 respectively. The evaluation index determination module is used to determine each candidate decision based on the weight of each candidate decision plan and each secondary indicator, using the TOPSIS method. Comprehensive evaluation index of the program.

The optimal solution determination module 6 is connected to the evaluation index determination module 5. The optimal solution determination module 6 is used to determine the optimal decision solution of the power grid based on the comprehensive evaluation index of each candidate decision solution to optimize the power grid.

Compared with the existing technology, the grid optimization system for multiple types of energy storage participating in the electric carbon market provided in this embodiment has the same beneficial effects as the grid optimization method for multiple types of energy storage participating in the electric carbon market provided in Embodiment 1, and will not be discussed here. Repeat.

Embodiment 3

This embodiment provides an electronic device, including a memory and a processor. The memory is used to store a computer program. The processor runs the computer program to enable the electronic device to execute the grid optimization method of multiple types of energy storage participating in the electric carbon market in Embodiment 1.

Optionally, the above-mentioned electronic device may be a server.

In addition, embodiments of the present invention also provide a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, the grid optimization method for multiple types of energy storage participating in the electric carbon market of Embodiment 1 is implemented.

Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on its differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other.

This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and the core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the present invention There will be changes in the specific implementation methods and application scope of the ideas. In summary, the contents of this description should not be construed as limitations of the present invention.

Claims (10)

1. A grid optimization method for multiple types of energy storage to participate in the electricity carbon market, characterized in that the grid optimization method for multiple types of energy storage to participate in the electricity carbon market includes: Construct a value evaluation system; the value evaluation system includes a target layer, a criterion layer and an indicator layer; the target layer is the multi-dimensional value of multiple types of energy storage participating in the electric carbon market, the criterion layer includes multiple first-level indicators, and the The indicator layer includes multiple secondary indicators under each primary indicator; the multiple primary indicators are the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electric carbon market; the secondary indicators under the carbon reduction benefits are System carbon quota, thermal power unit carbon emissions, system purchased power carbon emissions, new energy power generation carbon emissions, new energy power generation carbon reduction, multiple energy storage carbon emissions, multiple energy storage carbon reduction and system carbon quota remaining The total amount; the secondary indicators under the economic benefits are ladder carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income; Obtain a historical data set; the historical data set includes the values of each secondary indicator every year within the historical set period; According to the historical data set, the entropy weight method is used to determine the weight of each secondary indicator in the value evaluation system; Determine multiple candidate decision-making solutions; each candidate decision-making solution includes candidate values for each secondary indicator; Based on the weight of each candidate decision-making scheme and each secondary indicator, the TOPSIS method is used to determine the comprehensive evaluation index of each candidate decision-making scheme; The optimal decision-making plan for the power grid is determined based on the comprehensive evaluation index of each candidate decision-making plan to optimize the power grid. 2. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 1, characterized in that, based on the historical data set, the entropy weight method is used to determine the weight of each secondary index in the value evaluation system. , specifically including: Construct an original evaluation matrix based on the historical data set; the elements in the original evaluation matrix are the values of each secondary indicator each year within the historical set period; The original evaluation matrix is standardized to obtain a standardized matrix; the elements in the standardized matrix are the standardized values of each secondary indicator each year; For any secondary indicator in the value evaluation system, determine the information entropy of the secondary indicator based on the normative value of the secondary indicator for each year in the normalized matrix; The weight of the secondary indicator is determined based on the information entropy of the secondary indicator. 3. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 2, characterized in that the following formula is used to determine the normative value of the j-th secondary indicator in the i-th year: Among them, r _ij is the normative value of the j-th secondary indicator in the i-th year, a _ij is the value of the j-th secondary indicator in the i-th year, and I is the number of years. 4. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 2, characterized in that the following formula is used to determine the information entropy of the j-th secondary indicator: The following formula is used to determine the weight of the j-th secondary indicator: Among them, E _j is the information entropy of the j-th secondary indicator, r _ij is the normative value of the j-th secondary indicator in the i-th year, I is the number of years, m _j is the weight of the j-th secondary indicator, and J is the second level indicator. The number of level indicators. 5. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 1, characterized in that the following formula is used to determine the system carbon quota, thermal power unit carbon emissions, and system purchased power in each candidate decision-making plan. Carbon emissions, carbon emissions from new energy power generation, carbon reduction from new energy power generation, carbon emissions from multiple energy storage, carbon reduction from multiple energy storage and total system carbon quota remaining: 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 ; Among them, E _CEQ is the system carbon quota, E _CFP is the carbon emissions of thermal power units, E _PCE is the system carbon emissions of purchased power, E _{NEG, CE} is the carbon emissions of new energy power generation, E _{NEG, CR} is the reduction of new energy power generation. Carbon amount, E _{ENG, CE} is the carbon emission of multiple energy storage, E _{ENG, CR} is the carbon reduction amount of multiple energy storage, E is the total remaining carbon quota of the system, Q _e is the power supply of the unit, B _e is the category of the unit to which it belongs. Power supply reference value, F ₁ is the unit cooling method correction coefficient, F _r is the unit heat supply correction coefficient, F _f is the unit load coefficient correction coefficient, Q _h unit heat supply, B _h is the heating reference value of the category to which the unit belongs , η _e is the carbon quota coefficient per unit of electricity, P _buy is the purchased electricity of the system, β _e is the carbon emission coefficient of coal-fired units per unit of electricity, β _s is the carbon emission coefficient of unit of purchased electricity, P _CFP is the power generation of the unit, N is the number of new energy types, ρ _s,n is the carbon emission factor of the nth type of new energy, ρ _a,n is the carbon emission reduction factor of the nth type of new energy, P _NEG,n is the power generation of the nth type of new energy quantity, B is the number of energy storage types, ρ _cha,b is the carbon emission factor of type b energy storage, ρ _dis,b is the carbon emission reduction factor of type b energy storage, P _ENG,cha,b is the carbon emission factor of type b energy storage The charging amount of type b energy storage, P _ENG,dis,b is the discharge amount of type b energy storage. 6. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 1, characterized in that the following formula is used to determine the ladder carbon trading income, levelized power generation cost, and system energy purchase in each candidate decision-making plan. Cost, system electricity sales income and system net income: C _S =P _sell ·C _sell ; C＝C _S +C _car -C _B ; Among them, C _car is the ladder carbon trading income, LCOE is the levelized cost of power generation, C _B is the system energy purchase cost, C _S is the system electricity sales income, C is the system net income, c is the market carbon trading benchmark price, d is the length of the carbon emission interval, α is the growth rate of carbon trading price, E is the total remaining carbon quota of the system, K is the number of equipment in the power system, I is the number of years, IC _k is the initial investment of equipment k, OC _i,k is The operating cost of equipment k in year i, MC _i,k is the maintenance cost of equipment k in year i, G _i,k is the power generation of equipment k, r _k is the discount rate of equipment k, and P _buy is the system outsourcing Electricity, C _buy is the system power purchase price, N is the quantity of new energy types, P _NEG,n is the power generation of the nth type of new energy, C _NEG,n is the power generation price of the nth type of new energy, B is the storage The number of energy types, P _ENG,cha,b is the charging amount of type b energy storage, C _ENG,cha,b is the charging electricity price of type b energy storage, P _CFP is the power generation of the unit, C _CFP is the grid-connected thermal power unit Price of electricity, P _sell is the electricity sold, C _sell is the price of electricity sold. 7. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 1, characterized in that according to the weight of each candidate decision-making scheme and each secondary indicator, the TOPSIS method is used to determine the comprehensive value of each candidate decision-making scheme. Evaluation index, specifically including: Establish an initial indicator matrix according to each candidate decision-making scheme; the elements in the initial indicator matrix are the candidate values of each secondary indicator in each candidate decision-making scheme; The initial indicator matrix is normalized to determine a standardized decision matrix; the elements in the normalized decision matrix are the standardized values of each secondary indicator in each candidate decision scheme; Determine a weighted specification matrix based on the normalized decision matrix and the weight of each secondary indicator; the elements in the weighted specification matrix are respectively the weighted values of each secondary indicator in each candidate decision plan; Determine the ideal solution and the negative ideal solution according to the initial indicator matrix; For any candidate decision-making solution, calculate the Euclidean distance between the candidate decision-making solution and the ideal solution and the Euclidean distance between the candidate decision-making solution and the negative ideal solution; The comprehensive evaluation index of the candidate decision-making solution is determined based on the Euclidean distance between the candidate decision-making solution and the ideal solution and the Euclidean distance between the candidate decision-making solution and the negative ideal solution. 8. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 7, characterized in that the ideal solution includes the ideal solution of each secondary indicator, and the negative ideal solution includes the ideal solution of each secondary indicator. Negative ideal solution; Determine the ideal solution and the negative ideal solution based on the initial indicator matrix, specifically including: For any secondary indicator, if the secondary indicator is a secondary indicator under carbon reduction benefits, the formula is used To determine the ideal solution for the secondary indicator, use the formula/> Determine the negative ideal solution of the secondary indicator; If the secondary indicator is a secondary indicator under economic benefits, the formula To determine the ideal solution for the secondary indicator, use the formula/> Determine the negative ideal solution of the secondary indicator; in, is the ideal solution of the j-th secondary index,/> is the negative ideal solution of the jth secondary indicator, and x _mj is the candidate value of the jth secondary indicator in the candidate decision plan m. 9. The power grid optimization method for multiple types of energy storage to participate in the electric carbon market according to claim 7, characterized in that the following formula is used to determine the comprehensive evaluation index of the candidate decision plan m: in, is the comprehensive evaluation index of candidate decision plan m,/> is the Euclidean distance from the candidate decision solution m to the negative ideal solution,/> is the Euclidean distance between candidate decision solution m and the ideal solution. 10. A grid optimization system for multiple types of energy storage to participate in the electricity carbon market, characterized in that the grid optimization system for multiple types of energy storage to participate in the electricity carbon market includes: System building module, used to construct a value evaluation system; the value evaluation system includes a target layer, a criterion layer and an indicator layer; the target layer is the multi-dimensional value of multiple types of energy storage participating in the electric carbon market, and the criterion layer includes multiple First-level indicators, the indicator layer includes multiple second-level indicators under each first-level indicator; the multiple first-level indicators are the carbon reduction benefits and economic benefits of multiple types of energy storage participating in the electricity carbon market; the carbon reduction benefits are The secondary indicators are system carbon quota, thermal power unit carbon emissions, system purchased power carbon emissions, new energy power generation carbon emissions, new energy power generation carbon reduction, multiple energy storage carbon emissions, multiple energy storage carbon reduction amount and the total remaining amount of system carbon quota; the secondary indicators under the above-mentioned economic benefits are ladder carbon trading income, levelized power generation cost, system energy purchase cost, system electricity sales income and system net income; A historical data acquisition module is used to acquire a historical data set; the historical data set includes the values of each secondary indicator every year within the historical set period; A weight determination module, respectively connected to the system construction module and the historical data acquisition module, is used to determine the weight of each secondary index in the value evaluation system using the entropy weight method based on the historical data set; The candidate solution determination module is used to determine multiple candidate decision solutions; each candidate decision solution includes candidate values of each secondary indicator; An evaluation index determination module is respectively connected to the weight determination module and the candidate solution determination module, and is used to determine the comprehensive evaluation index of each candidate decision solution according to the weight of each candidate decision solution and each secondary indicator using the TOPSIS method; The optimal solution determination module is connected to the evaluation index determination module and is used to determine the optimal decision-making solution of the power grid based on the comprehensive evaluation index of each candidate decision-making solution to optimize the power grid.