Energy storage type selection and site selection method and system for new energy station

Abstract

The invention discloses an energy storage type selection and site selection method and system for a new energy station. The method comprises the following steps: constructing an evaluation system for energy storage type selection and site selection of the new energy station and an energy storage type selection and site selection scheme set in the new energy station; an entropy weight method is adopted to obtain an energy storage comprehensive evaluation value of each type selection and site selection scheme in an energy storage access scheme set in a new energy station, and a relative proximity index for reflecting economic feasibility of the energy storage type selection and site selection scheme and node voltage stability of the new energy station of each type selection and site selection scheme is obtained based on a TOPSIS method; and taking the relatively approximate index as the final model selection and site selection scheme of the energy storage of the new energy station. According to the method, the energy storage technical characteristics and the power grid requirements are comprehensively considered, the new energy station energy storage evaluation system is constructed, the comprehensive evaluation effect on the energy storage technical performance, the economic characteristics, the carbon emission reduction capacity and the income capacity is achieved, the decision making process is simplified, and optimal configuration and efficient utilization of energy storage resources are facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage site selection, and in particular, relates to an energy storage site selection method and system for a new energy station. Background Art

[0002] Energy storage technology plays a vital role in the power system, and its selection and site selection are crucial to ensuring the stability and economy of the power grid. However, the existing methods for selecting and site-selecting energy storage for new energy stations have some obvious defects. In principle, these methods often do not fully consider the dynamic characteristics of different types of energy storage systems, and selection and site selection are usually regarded as two independent and complex processes, which increases the difficulty of decision-making for energy storage operators and system operators.

[0003] Due to the different dynamic characteristics of different types of energy storage, they are suitable for different occasions. For example, energy-type energy storage is more suitable for large-scale energy transfer and long-term energy balance, while power-type energy storage is more suitable for providing instantaneous power support and frequency regulation. Therefore, it is particularly important to choose the right energy storage technology to meet the needs of a specific power grid.

[0004] In the power system, there are multiple potential nodes where energy storage facilities can be configured. However, configuring energy storage at different nodes has different effects on the dynamic and economic performance of the system. The selection of energy storage configuration points (energy storage site selection) has a significant impact on the overall performance and economic benefits of the power grid. For example, the configuration location of energy storage facilities will affect the voltage stability, power flow and frequency regulation capabilities of the power grid. Therefore, the configuration points of energy storage need to be carefully selected to ensure that energy storage resources can achieve the greatest benefits in the power grid. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention proposes a method and system for selecting energy storage type and site for a new energy station. The technical solution of the present invention is as follows:

[0006] According to a first aspect of the present invention, a method for selecting energy storage type and site for a new energy station is provided, the method comprising:

[0007] Construct an evaluation system for energy storage selection and site selection of new energy stations and a set of energy storage selection and site selection schemes in new energy stations; the evaluation system uses the comprehensive evaluation value of energy storage as the criterion layer, and uses the first-level indicator for evaluating the energy storage technical performance of new energy stations, the second-level indicator for evaluating the energy storage economic characteristics of new energy stations, the third-level indicator for evaluating the energy storage carbon emission reduction capacity of new energy stations, and the fourth-level indicator for evaluating the energy storage revenue capacity of new energy stations as the criterion layer, and uses the second-level indicators under each first-level indicator of the target layer as the indicator layer;

[0008] The entropy weight method is used to obtain the comprehensive evaluation value of energy storage for each selection and site selection scheme in the set of energy storage access schemes within the new energy station. Based on the TOPSIS method, the relative proximity index of each selection and site selection scheme, which is used to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the new energy station node, is obtained. The scheme with the largest relative proximity index is taken as the final selection and site selection scheme for the new energy station energy storage.

[0009] Furthermore, the secondary indicators under the first primary indicator include volume energy density, mass energy density, power density, charge-discharge energy conversion efficiency, charge retention capacity and cycle life;

[0010] The secondary indicators under the second primary indicator include initial investment cost, maintenance and operation cost, replacement cost and charging cost;

[0011] The secondary indicators under the third primary indicator include carbon emission reduction coefficient;

[0012] The secondary indicators under the fourth primary indicator include the benefits of new energy consumption, the benefits of reducing the assessment of new energy grid connection, the benefits of auxiliary services and the benefits of delaying the construction of conventional units;

[0013] Furthermore, the volume energy density ED v and mass energy density ED m The calculation formula is as follows:

[0014]

[0015] Where, E is the reversible electromotive force; L is the amount of charge stored in the energy storage; V and M are the maximum volumes of the energy storage, respectively; the power density ED w The calculation formula is as follows:

[0016]

[0017] Where W max is the maximum power of energy storage input and output; V is the maximum volume of energy storage space;

[0018] The charge-discharge energy conversion efficiency η ESU It is the ratio of the total discharge amount to the total charge amount during the service life of the energy storage. The calculation formula is as follows:

[0019]

[0020] In the formula, E D is the total discharge capacity of energy storage; E C is the total energy storage charge;

[0021] The charge retention capacity η s The calculation formula is as follows:

[0022]

[0023] In the formula, c b is the discharge capacity before energy storage; c a The discharge capacity tested after storing energy for a period of time;

[0024] The calculation formula of the cycle life L is as follows:

[0025]

[0026] Where L is the energy storage cycle life; n1, n2, ..., n b are the maximum charge and discharge times of each energy storage; b is the number of energy storage types

[0027] The initial investment cost C v Including capacity cost C E and power cost C P , the calculation formulas are as follows:

[0028]

[0029] Where U E is the unit energy storage capacity cost; Q E is the energy storage capacity; U p is the unit power cost; I P is the installed capacity;

[0030] The maintenance and operation cost C O Including capacity maintenance cost C E,O , Power maintenance cost C P,O and labor operation cost C labor The calculation formulas are as follows:

[0031]

[0032] Where U E,O is the maintenance cost per unit energy storage capacity; Q E is the energy storage capacity; U P,O is the maintenance cost per unit power; I P is the installed capacity; n labor U is the number of project operation and maintenance personnel; labor The cost per person per year;

[0033] The replacement cost C R The calculation formula is as follows:

[0034]

[0035] Where U R is the replacement cost per unit energy storage capacity; Q Eis the energy storage capacity; N is a natural number;

[0036] The charging cost C C The calculation formula is as follows:

[0037]

[0038] Where U C is the charging unit price; Q C N is the power of each charge; y is the number of annual charging times; θ DoD is the discharge depth; η ESU is the charge and discharge efficiency.

[0039] The carbon reduction factor is:

[0040]

[0041] In the formula, C is the installed capacity of the energy storage equipment / power station; D is the carbon emissions reduced from conventional thermal power under the energy storage discharge scenario; Gi is the i-th power generation of the energy storage power station; Fgrid is the emission factor of the new energy station released by the Ministry of Ecology and Environment; α is the number of cycles in the whole life cycle; ε is the discharge efficiency; n is a natural number;

[0042] The new energy consumption income R R for:

[0043]

[0044] Where P N E is the feed-in tariff for renewable energy; ab The total amount of new energy wasted electricity reduced by energy storage; E ab,i The amount of new energy curtailment reduced by energy storage on day i;

[0045] Reducing the assessment income of new energy grid connection R e for:

[0046] R e =α(p1'-p 1) +β(p2'-p2)+γ(p3'-p3)

[0047] In the formula, α, β, and γ are the penalty points for changes in active power, power forecasts, and other assessments of renewable energy in a certain region; p1', p2', and p3' are the corresponding penalty points for changes in active power, power forecasts, and other assessments when energy storage is not configured; p1, p2, and p3 are the corresponding penalty points for changes in active power, power forecasts, and other assessments when energy storage is configured;

[0048] Ancillary service revenue R as for:

[0049]

[0050] Where ω1 and ω2 are the compensation coefficients for energy storage participating in peak load regulation and frequency regulation auxiliary services; E p is the amount of electricity that energy storage participates in frequency regulation auxiliary services; D is the frequency regulation mileage; K is the frequency regulation coefficient; i and j are the days that energy storage participates in peak regulation and frequency regulation; m and n are the total number of days that energy storage participates in peak regulation and frequency regulation in a year;

[0051] Delaying the benefits of new units cp for:

[0052]

[0053] In the formula, C f E is the construction cost of conventional coal-fired power units; i is the total amount of energy storage discharged on the i-th day; T is the annual maximum power operating hours of the conventional unit; P pr Peak load regulation for conventional units; P max is the maximum output of conventional units; Q is the peak load coefficient of conventional units.

[0054] Furthermore, the method for acquiring the energy storage access solution set in the new energy station includes:

[0055] The types of energy storage at new energy stations and their corresponding site selection schemes are obtained, thereby forming a new energy station energy storage type selection and site selection scheme; the set of new energy station energy storage type selection and site selection schemes is represented by a matrix N:

[0056]

[0057] where k N represents the number of energy storage selection options, n represents the number of energy storage access points in the new energy station, and the element N in the matrix N ij It represents the energy storage selection and site selection scheme in which type i energy storage is connected to access point j.

[0058] Furthermore, the specific method of using the entropy weight method to obtain the comprehensive evaluation value of energy storage for each selection and site selection scheme in the set of energy storage access schemes in the new energy station includes:

[0059] The original data matrix of the single-type energy storage TOPSIS method is constructed, wherein the elements of each column of the matrix represent different secondary indicators, and there are r secondary indicators in total. The elements of each row of the matrix represent different energy storage candidate nodes, and there are n energy storage candidate nodes in total. The original matrix is ​​expressed as:

[0060]

[0061] In the formula, O k ijis the value of the jth secondary index of the kth type of energy storage at the ith candidate access point; each row of the matrix represents a different candidate energy storage node, and each column represents the secondary index of a different type of energy storage. i is the sequence number of the selection and site selection scheme, i = 1, 2, ..., n, n is the number of candidate access points for energy storage; j is 1, 2, ..., r, r is the total number of secondary indicators;

[0062] The expression of the original data matrix of the TOPSIS method for different types of energy storage is:

[0063] O=[O 1 O 2 … O k ] n×kr

[0064] In the formula, the matrix O is composed of k single-type energy storage TOPSIS method original data matrix O m Composition (m=1,2,...,k).

[0065] The dimensionless standardization preprocessing of the larger the better type and the smaller the better type is used to unify the positive and negative indicators, and the weight proportion of each indicator is calculated according to the following formula G k ij :

[0066]

[0067]

[0068] In the formula, K k ij is the dimensionless processing result of the jth secondary index of the kth type of energy storage at the ith candidate access point, O k ij is the value of the jth secondary index of the k-type energy storage at the i-th candidate access point; max(O k j ) is the maximum value of the j-th secondary index of the k-type energy storage under all the candidate access points, min(O k j ) is the minimum value of the j-th secondary index of the k-type energy storage under all the candidate access points, K ij and G ij The ranges are: 0≤K k ij ≤1,0≤G k ij ≤1.

[0069] The information entropy value A of the i-th candidate access point of type k energy storage is calculated as follows: k i , the information entropy redundancy B of the i-th candidate access point of type k energy storagek i , the index weight D of the i-th candidate access point of type k energy storage k i :

[0070]

[0071] In the formula, A k i is the information entropy value of the i-th candidate access point of type k energy storage, is the information entropy redundancy of the i-th candidate access point of type k energy storage, D k i is the index weight of the i-th candidate access point of the k-type energy storage; G ij k is the weight ratio of the jth secondary indicator of the kth type of energy storage at the i-th candidate access point.

[0072] Furthermore, the specific method for obtaining the relative proximity index of each selection and site selection scheme based on the TOPSIS method to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the node of the new energy station includes:

[0073] First, construct the normalized decision matrix H of k-type energy storage k =(H k ij ) n×r , where element H k ij Calculated as follows:

[0074]

[0075] In the formula, H k ij The k-type energy storage is in the normalized decision matrix H k represents the normalized decision quantity of the jth secondary indicator under the i-th candidate access point;

[0076] The next step is to construct a weighted normalized decision matrix U k =(U k ij ) n×r , where the element U k ij It can be expressed as:

[0077] In the matrix U, the decision elements U of all secondary indicators under the i-th candidate access point of type k energy storage are k ij The maximum value of is the positive ideal solution under the i-th candidate access point, U k ijThe minimum value of is the negative ideal solution under the jth candidate access point, that is:

[0078]

[0079] On this basis, the distance of the positive ideal solution under the i-th candidate access point of the k-type energy storage is calculated and the distance to the negative ideal solution

[0080]

[0081] Finally, the relative proximity index F of the k-type energy storage at the j-th candidate access point is calculated respectively: k dj :

[0082]

[0083] Comparison of the relative proximity index F of k types of energy storage at different candidate access points k dj The maximum value is taken as the distance index F of this type of energy storage k d :

[0084]

[0085] Comparison of distance index F for different types of energy storage k d The maximum value is taken as the distance index F of the system d , select F d The corresponding energy storage type k and access point j are used as the selection and site selection results of the system energy storage.

[0086] Furthermore, the distance index F d The expression is:

[0087] According to a second aspect of the present invention, there is provided a system for selecting energy storage types and locations for new energy sites, including an evaluation system and set building module, an energy storage comprehensive evaluation value acquisition module, and a relative proximity index acquisition module;

[0088] The evaluation system and set construction module are used to construct an evaluation system for energy storage selection and site selection of new energy stations and a set of energy storage selection and site selection schemes in new energy stations; the evaluation system uses the comprehensive evaluation value of energy storage as the criterion layer, and uses the first-level indicator for evaluating the energy storage technical performance of new energy stations, the second-level indicator for evaluating the energy storage economic characteristics of new energy stations, the third-level indicator for evaluating the energy storage carbon emission reduction capacity of new energy stations, and the fourth-level indicator for evaluating the energy storage revenue capacity of new energy stations as the criterion layer, and uses the second-level indicators under each first-level indicator of the target layer as the indicator layer;

[0089] The energy storage comprehensive evaluation value acquisition module is used to obtain the energy storage comprehensive evaluation value of each selection and site selection scheme in the energy storage access scheme set in the new energy station by using the entropy weight method;

[0090] The relative proximity index acquisition module is used to obtain the relative proximity index of each selection and site selection scheme based on the TOPSIS method, which is used to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the new energy station node, and the one with the largest relative proximity index is used as the final selection and site selection scheme for the new energy station energy storage.

[0091] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] The present invention proposes a method and system for selecting and site selection for energy storage at new energy stations. The method comprehensively considers the characteristics of energy storage technology and power grid demand, adopts the technical feature of building a new energy station energy storage evaluation system, solves the problem that existing methods do not fully consider the dynamic characteristics of different types of energy storage systems, and achieves a comprehensive evaluation effect on energy storage technology performance, economic characteristics, carbon emission reduction capabilities and profitability.

[0094] The method of the present invention adopts the entropy weight method to obtain the technical characteristics of the comprehensive evaluation value of energy storage, solves the problem of strong subjectivity in the weight allocation of evaluation indicators in the traditional method in the process of energy storage selection and site selection, and realizes a comprehensive and objective evaluation of energy storage technology performance, economic characteristics, carbon emission reduction capacity and profitability. The method uses the TOPSIS index to evaluate the dynamic characteristics of different types of energy storage and their adaptability under different energy storage configuration points, providing a standardized energy storage site selection method for energy storage operators and system operators.

[0095] The method of the present invention solves the problem of increased decision-making difficulty caused by treating energy storage selection and site selection as independent processes by constructing a set of technical features for energy storage selection and site selection schemes for new energy stations, realizes integrated consideration of energy storage selection and site selection, simplifies the decision-making process, and helps to achieve optimal configuration and efficient utilization of energy storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 This is a flow chart of a method for selecting energy storage type and site for a new energy station of the present invention;

[0097] Figure 2 The block diagram is a evaluation system in a method for selecting a type and site for energy storage of a new energy station according to the present invention. DETAILED DESCRIPTION

[0098] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0099] Embodiment 1:

[0100] A method for selecting a new energy station for energy storage is provided in the present invention. Figure 1 As shown, the method includes:

[0101] S1. Construct an evaluation system for energy storage selection and site selection of new energy stations and a set of energy storage selection and site selection schemes in new energy stations; the evaluation system uses the comprehensive evaluation value of energy storage as the criterion layer, and uses the first-level indicator for evaluating the energy storage technical performance of new energy stations, the second-level indicator for evaluating the energy storage economic characteristics of new energy stations, the third-level indicator for evaluating the energy storage carbon emission reduction capacity of new energy stations, and the fourth-level indicator for evaluating the energy storage revenue capacity of new energy stations as the criterion layer, and uses the second-level indicators under each first-level indicator of the target layer as the indicator layer;

[0102] S2. Based on simulation data, the entropy weight method is used to obtain the comprehensive evaluation value of energy storage for each selection and site selection scheme in the set of energy storage access schemes within the new energy station;

[0103] S3. Based on the TOPSIS method, obtain the relative proximity index of each selection and site selection scheme to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the new energy site node, and take the one with the largest relative proximity index as the final selection and site selection scheme for the new energy site energy storage.

[0104] Embodiment 2:

[0105] This embodiment is further designed on the basis of the first embodiment in that: Figure 2 As shown, the secondary indicators under the first-level indicator in this example include volume energy density, mass energy density, power density, charge-discharge energy conversion efficiency, charge retention capacity and cycle life;

[0106] Secondary indicators under the second primary indicator include initial investment cost, maintenance and operation cost, replacement cost and charging cost;

[0107] Secondary indicators under the third primary indicator include carbon reduction coefficient;

[0108] The secondary indicators under the 4th first-level indicator include the benefits of new energy consumption, the benefits of reducing new energy grid connection assessment, the benefits of auxiliary services and the benefits of delaying the construction of conventional units.

[0109] Embodiment three:

[0110] This embodiment is further designed on the basis of the second embodiment in that the volume energy density ED v and mass energy density ED m The calculation formula is as follows:

[0111]

[0112] In the formula, E is the reversible electromotive force, that is, the theoretical working voltage; L is the amount of charge stored in the energy storage; V and M are the maximum volumes of the energy storage (the energy storage system set up in the new energy station);

[0113] Power density ED w The calculation formula is as follows:

[0114]

[0115] Where W max is the maximum power of energy storage input and output; V is the maximum volume of energy storage space;

[0116] Charge and discharge energy conversion efficiency η ESU It is the ratio of the total discharge amount to the total charge amount during the service life of the energy storage. The calculation formula is as follows:

[0117]

[0118] In the formula, E D is the total discharge capacity of energy storage; E C is the total energy storage charge;

[0119] Charge retention capacity η s The calculation formula is as follows:

[0120]

[0121] In the formula, c b is the discharge capacity before energy storage; c a The discharge capacity tested after storing energy for a period of time;

[0122] The calculation formula of cycle life L is as follows:

[0123]

[0124] Where L is the energy storage cycle life; n1, n2, ..., n b are the maximum charge and discharge times of each energy storage; b is the number of energy storage types;

[0125] Initial investment cost C v , generally refers to the sum of the costs of design, software and hardware, procurement, engineering construction, etc. when building an energy storage system. The initial investment cost Cv Including capacity cost C E and power cost C P The capacity cost is the cost of equipment and construction related to the energy storage capacity in the energy storage system, such as the cost of the reservoir in the pumped storage power station and the purchase cost of the container in the battery energy storage; the power cost refers to the cost of equipment and construction related to the power in the energy storage system, such as the cost of purchasing, installing and constructing the compressed air energy storage expander and compressor, the initial investment cost C v , Capacity cost C E and power cost C P The calculation formulas are as follows:

[0126]

[0127] Where U E,O is the unit energy storage capacity cost (yuan / (MW·h)); Q E is the energy storage capacity (MW·h); U P,O is the unit power cost (yuan / MW); I P is the installed capacity (MW);

[0128] Maintenance and operation cost C O , generally refers to the cost incurred during the operation and maintenance of the energy storage system, mainly including capacity maintenance cost C E,O , Power maintenance cost C P,O and labor operation cost C labor , respectively represent the capacity-related equipment maintenance cost, power-related equipment maintenance cost and operation and maintenance labor cost, and the calculation formulas are as follows:

[0129]

[0130] Where U E,OM is the maintenance cost per unit energy storage capacity (yuan / (MW·h)); Q E is the energy storage capacity (MW-h); U P,OM is the maintenance cost per unit power; I P is the installed capacity (MW); n labor U is the number of project operation and maintenance personnel; labor The cost per person per year;

[0131] Replacement cost C R , generally refers to the cost incurred in the process of replacing energy storage equipment components due to equipment life factors. The calculation formula is as follows:

[0132]

[0133] Where U R is the replacement cost per unit energy storage capacity (yuan / (MW·h)); QE is the energy storage capacity (MW·h); N is a natural number;

[0134] Charging cost C C , generally refers to all the costs required for charging the energy storage system from the grid or renewable energy power source during its entire life cycle. The calculation formula is as follows:

[0135]

[0136] Where U C is the charging unit price (yuan / (MW·h)); Q C is the power of each charge (MW·h); N y is the number of annual charging times; θ DoD is the discharge depth; η ESU is the charge and discharge efficiency.

[0137] The carbon reduction factor for energy storage during its entire life cycle is assumed to be used to replace fossil fuel thermal power plants:

[0138]

[0139] In the formula, C is the installed capacity of the energy storage equipment / power station; D is the amount of carbon emissions reduced from conventional thermal power under the energy storage discharge scenario; Gi is the i-th power generation of the energy storage power station; Fgrid is the emission factor of new energy stations released by the Ministry of Ecology and Environment (t / (MW·h)); α is the number of cycles in the entire life cycle; ε is the discharge efficiency; and n is a natural number.

[0140] New energy consumption income R R for:

[0141]

[0142] Where P N E is the feed-in tariff for renewable energy; ab The total amount of new energy wasted electricity reduced by energy storage; E ab,i The amount of new energy curtailment reduced by energy storage on day i;

[0143] Reducing the assessment income of new energy grid connection R e for:

[0144] R e =α(p1'-p1)+β(p2'-p2)+γ(p3'-p3)

[0145] In the formula, α, β, and γ are the penalty points for changes in active power, power forecasts, and other assessments of renewable energy in a certain region; p1', p2', and p3' are the corresponding penalty points for changes in active power, power forecasts, and other assessments when energy storage is not configured; p1, p2, and p3 are the corresponding penalty points for changes in active power, power forecasts, and other assessments when energy storage is configured;

[0146] Ancillary service revenue R as for:

[0147]

[0148] Where ω1 and ω2 are the compensation coefficients for energy storage participating in peak load regulation and frequency regulation auxiliary services; E p is the amount of electricity that energy storage participates in frequency regulation auxiliary services; D is the frequency regulation mileage; K is the frequency regulation coefficient; i and j are the days that energy storage participates in peak regulation and frequency regulation; m and n are the total number of days that energy storage participates in peak regulation and frequency regulation in a year;

[0149] Delaying the benefits of new units cp for:

[0150]

[0151] In the formula, C f E is the construction cost of conventional coal-fired power units; i is the total amount of energy storage discharged on the i-th day; T is the annual maximum power operating hours of the conventional unit; P pr Peak load regulation for conventional units; P max is the maximum output of conventional units; Q is the peak load coefficient of conventional units.

[0152] Embodiment 4:

[0153] This embodiment is further designed on the basis of the first embodiment in that the method for obtaining the energy storage access solution set in the new energy station in this embodiment includes:

[0154] Obtain the type of energy storage at the new energy station and its corresponding site selection plan, thereby forming a new energy station energy storage type selection and site selection plan; the type of energy storage (i.e., energy storage type selection plan) includes: pumped storage type energy storage, electrochemical energy storage type energy storage, lead-acid battery type energy storage, flywheel type energy storage, gravity type energy storage and other common types of energy storage; each type selection plan corresponds to one or more site selection plans, and one type selection plan and its corresponding one site selection plan are used as one type selection and site selection plan in the set of new energy station energy storage type selection and site selection plans. The set of new energy station energy storage type selection and site selection plans is represented by a matrix N:

[0155]

[0156] where k Nrepresents the number of energy storage selection options, n represents the number of energy storage access points in the new energy station, and the element N in the matrix N ij It represents the energy storage selection and site selection scheme in which type i energy storage is connected to access point j.

[0157] Embodiment five:

[0158] This embodiment is further designed on the basis of the fourth embodiment in that the specific method of using the entropy weight method in this embodiment to obtain the comprehensive evaluation value of energy storage for each selection and site selection scheme in the set of energy storage access schemes in the new energy station includes:

[0159] Based on the simulation data, the original data matrix of the single-type energy storage TOPSIS method is constructed, where the elements of each column of the matrix represent different secondary indicators, and there are r secondary indicators in total. In this example, r=14. The elements of each row of the matrix represent different energy storage candidate nodes, and there are n energy storage candidate nodes in total. The original matrix is ​​expressed as:

[0160]

[0161] In the formula, O k ij is the value of the jth secondary index of the kth type of energy storage at the ith candidate access point; each row of the matrix represents a different energy storage candidate node, and each column represents a secondary index of a different type of energy storage, for example, O 1 23 and O 2 23 They represent the third secondary index (power density index) of the first type of energy storage (pumped storage) and the second type of energy storage (electrochemical energy storage) at the candidate node 2 respectively; i is the sequence number of the selection and site selection scheme, i = 1, 2, ..., n, n is the number of the candidate access points for energy storage; j is 1, 2, ..., r, r is the total number of secondary indexes;

[0162] The expression of the original data matrix of the TOPSIS method for different types of energy storage is:

[0163] O=[O 1 O 2 … O k ] n×kr

[0164] In the formula, the matrix O is composed of k single-type energy storage TOPSIS method original data matrix O m Composition (m=1,2,...,k).

[0165] Since the indicators in actual measurement differ in physical meaning, measurement unit and order of magnitude, standardization preprocessing should be performed before calculating the comprehensive weight. That is, the absolute data of each indicator should be converted into relative values, the dimensional influence of different indicators should be eliminated, and the homogenization of heterogeneous indicators should be achieved.

[0166] The dimensionless standardization preprocessing of the larger the better type and the smaller the better type is used to unify the positive and negative indicators, and the weight proportion of each indicator is calculated according to the following formula G k ij :

[0167]

[0168]

[0169] In the formula, K k ij is the dimensionless processing result of the jth secondary index of the kth type of energy storage at the ith candidate access point, O k ij is the value of the jth secondary index of the k-type energy storage at the i-th candidate access point; max(O k j ) is the maximum value of the j-th secondary index of the k-type energy storage under all the candidate access points, min(O k j ) is the minimum value of the j-th secondary index of the k-type energy storage under all the candidate access points, K ij and G ij The ranges are: 0≤K k ij ≤1,0≤G k ij ≤1.

[0170] Information entropy can measure the degree of dispersion of each indicator. The smaller the information entropy of an indicator, the greater the degree of dispersion of its data, which can provide more differentiated information, and therefore has a higher weight. The information entropy value A of the i-th candidate access point of the k-type energy storage is calculated as follows: k i , the information entropy redundancy (difference coefficient) of the i-th candidate access point of type k energy storage is B k i , the index weight (entropy weight) D of the i-th candidate access point of type k energy storage k i :

[0171]

[0172] In the formula, A k i is the information entropy value of the i-th candidate access point of type k energy storage, Bk i is the information entropy redundancy of the i-th candidate access point of type k energy storage, D k i is the index weight of the i-th candidate access point of the k-type energy storage; G ij k is the weight ratio of the jth secondary indicator of the kth type of energy storage at the i-th candidate access point.

[0173] Embodiment six:

[0174] This embodiment is further designed on the basis of the fifth embodiment in that the specific method for obtaining the relative proximity index of each selection and site selection scheme based on the TOPSIS method to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the node of the new energy station includes:

[0175] First, construct the normalized decision matrix H of k-type energy storage k =(H k ij ) n×r , where element H k ij Calculated as follows:

[0176]

[0177] In the formula, H k ij The k-type energy storage is in the normalized decision matrix H k represents the normalized decision quantity of the jth secondary indicator under the i-th candidate access point;

[0178] The next step is to construct a weighted normalized decision matrix U k =(U k ij ) n×r , where the element U k ij It can be expressed as:

[0179] In the matrix U, under the i-th candidate access point of energy storage of type k, the decision elements U of all (r in total) secondary indicators are k ij The maximum value of is the positive ideal solution under the i-th candidate access point, U k ij The minimum value of is the negative ideal solution under the jth candidate access point, that is:

[0180]

[0181] On this basis, the distance of the positive ideal solution under the i-th candidate access point of the k-type energy storage is calculated and the distance to the negative ideal solution

[0182]

[0183] Finally, the relative proximity index F of the k-type energy storage at the j-th candidate access point is calculated respectively: k dj :

[0184]

[0185] Comparison of the relative proximity index F of k types of energy storage at different candidate access points k dj The maximum value is taken as the distance index F of this type of energy storage k d :

[0186]

[0187] Comparison of distance index F for different types of energy storage k d The maximum value is taken as the distance index F of the system d , select F d The corresponding energy storage type k and access point j are used as the selection and site selection results of the system energy storage.

[0188] Embodiment seven:

[0189] This embodiment is further designed on the basis of the fifth embodiment in that the distance index F in this embodiment is d The calculation formula is as follows:

[0190]

[0191] Embodiment eight:

[0192] A system for selecting energy storage type and site for a new energy station of the present invention comprises an evaluation system and set construction module, an energy storage comprehensive evaluation value acquisition module and a relative proximity index acquisition module;

[0193] An evaluation system and set construction module, used to construct an evaluation system for energy storage selection and site selection for new energy stations and a set of energy storage selection and site selection plans for new energy stations; the evaluation system uses the comprehensive evaluation value of energy storage as the criterion layer, and uses the first-level indicator for evaluating the energy storage technical performance of new energy stations, the second-level indicator for evaluating the energy storage economic characteristics of new energy stations, the third-level indicator for evaluating the energy storage carbon emission reduction capacity of new energy stations, and the fourth-level indicator for evaluating the energy storage revenue capacity of new energy stations as the criterion layer, and uses the second-level indicators under each first-level indicator of the target layer as the indicator layer;

[0194] The energy storage comprehensive evaluation value acquisition module is used to obtain the energy storage comprehensive evaluation value of each selection and site selection scheme in the energy storage access scheme set in the new energy station by using the entropy weight method;

[0195] The relative proximity index acquisition module is used to obtain the relative proximity index of each selection and site selection scheme based on the TOPSIS method, which is used to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the node of the new energy station, and the one with the largest relative proximity index is used as the final selection and site selection scheme for the new energy station energy storage.

[0196] Embodiment nine:

[0197] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0198] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0199] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for selecting energy storage type and site for a new energy station, characterized in that: The method comprises: Construct an evaluation system for energy storage selection and site selection of new energy stations and a set of energy storage selection and site selection schemes in new energy stations; the evaluation system uses the comprehensive evaluation value of energy storage as the criterion layer, and uses the first-level indicator for evaluating the energy storage technical performance of new energy stations, the second-level indicator for evaluating the energy storage economic characteristics of new energy stations, the third-level indicator for evaluating the energy storage carbon emission reduction capacity of new energy stations, and the fourth-level indicator for evaluating the energy storage revenue capacity of new energy stations as the criterion layer, and uses the second-level indicators under each first-level indicator of the target layer as the indicator layer; The entropy weight method is used to obtain the comprehensive evaluation value of energy storage for each selection and site selection scheme in the set of energy storage access schemes within the new energy station. Based on the TOPSIS method, the relative proximity index of each selection and site selection scheme, which is used to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the new energy station node, is obtained. The scheme with the largest relative proximity index is taken as the final selection and site selection scheme for the new energy station energy storage.

2. The method for selecting energy storage type and site for a new energy station according to claim 1, characterized in that: The secondary indicators under the first primary indicator include volume energy density, mass energy density, power density, charge-discharge energy conversion efficiency, charge retention capacity and cycle life; The secondary indicators under the second primary indicator include initial investment cost, maintenance and operation cost, replacement cost and charging cost; The secondary indicators under the third primary indicator include carbon emission reduction coefficient; The secondary indicators under the fourth first-level indicator include the benefits of new energy consumption, the benefits of reducing new energy grid connection assessment, the benefits of auxiliary services and the benefits of delaying the construction of conventional units.

3. The method for selecting energy storage type and site for a new energy station according to claim 2, characterized in that: The volume energy density ED v and mass energy density ED m The calculation formula is as follows: Where, E is the reversible electromotive force; L is the amount of charge stored in the energy storage; V and M are the maximum volumes of the energy storage, respectively; The power density ED w The calculation formula is as follows: Where W max is the maximum power of energy storage input and output; V is the maximum volume of energy storage space; The charge-discharge energy conversion efficiency η ESU It is the ratio of the total discharge amount to the total charge amount during the service life of the energy storage. The calculation formula is as follows: In the formula, E D is the total discharge capacity of energy storage; E C is the total energy storage charge; The charge retention capacity η s The calculation formula is as follows: In the formula, c b is the discharge capacity before energy storage; c a The discharge capacity tested after storing energy for a period of time; The calculation formula of the cycle life L is as follows: Where L is the energy storage cycle life; n1, n2, ..., n b are the maximum charge and discharge times of each energy storage; b is the number of energy storage types; The initial investment cost C v Including capacity cost C E and power cost C P , the calculation formulas are as follows: Where U E is the unit energy storage capacity cost; Q E is the energy storage capacity; U p is the unit power cost; I P is the installed capacity; The maintenance and operation cost C O Including capacity maintenance cost C E,O , Power maintenance cost C P,O and labor operation cost C labor The calculation formulas are as follows: Where U E,O is the maintenance cost per unit energy storage capacity; Q E is the energy storage capacity; U P,O is the maintenance cost per unit power; I P is the installed capacity; n labor U is the number of project operation and maintenance personnel; labor The cost per person per year; The replacement cost C R The calculation formula is as follows: Where U R is the replacement cost per unit energy storage capacity; Q E is the energy storage capacity; N is a natural number; The charging cost C C The calculation formula is as follows: Where U C is the charging unit price; Q C N is the power of each charge; y is the number of annual charging times; θ DoD is the discharge depth; η ESU is the charge and discharge efficiency; The carbon reduction factor is: In the formula, C is the installed capacity of the energy storage equipment / power station; D is the carbon emissions reduced from conventional thermal power under the energy storage discharge scenario; Gi is the i-th power generation of the energy storage power station; Fgrid is the emission factor of the new energy station released by the Ministry of Ecology and Environment; α is the number of cycles in the whole life cycle; ε is the discharge efficiency; n is a natural number; The new energy consumption income R R for: Where P N E is the feed-in tariff for renewable energy; ab The total amount of new energy wasted electricity reduced by energy storage; E ab,i The amount of new energy curtailment reduced by energy storage on day i; Reducing the assessment income of new energy grid connection R e for: R e =α(p1′-p1)+β(p2'-p2)+γ(p3'-p3) In the formula, α, β, and γ are the penalty points for the change in active power, power forecast, and other assessments of renewable energy in a certain area; p1′, p2′, and p3′ are the corresponding penalty points for the change in active power, power forecast, and other assessments when energy storage is not configured; p1, p2, and p3 are the corresponding penalty points for the change in active power, power forecast, and other assessments when energy storage is configured; Ancillary service revenue R as for: Where ω1 and ω2 are the compensation coefficients for energy storage participating in peak load regulation and frequency regulation auxiliary services; E p is the amount of electricity that energy storage participates in frequency regulation auxiliary services; D is the frequency regulation mileage; K is the frequency regulation coefficient; i and j are the days that energy storage participates in peak regulation and frequency regulation; m and n are the total number of days that energy storage participates in peak regulation and frequency regulation in a year; Delaying the benefits of new units cp for: In the formula, C f E is the construction cost of conventional coal-fired power units; i is the total amount of energy storage discharged on day i; T is the annual maximum power operating hours of conventional units; P pr Peak load regulation for conventional units; P max is the maximum output of conventional units; Q is the peak load coefficient of conventional units.

4. The method for selecting energy storage type and site for a new energy station according to claim 1, characterized in that: The method for acquiring the energy storage access solution set in the new energy station includes: The types of energy storage at new energy stations and their corresponding site selection schemes are obtained, thereby forming a new energy station energy storage type selection and site selection scheme; the set of new energy station energy storage type selection and site selection schemes is represented by a matrix N: where k N represents the number of energy storage selection options, n represents the number of energy storage access points in the new energy station, and the element N in the matrix N ij It represents the energy storage selection and site selection scheme in which type i energy storage is connected to access point j.

5. The method for selecting energy storage type and site for a new energy station according to claim 4, characterized in that: The specific method of using the entropy weight method to obtain the comprehensive evaluation value of energy storage for each selection and site selection scheme in the set of energy storage access schemes in the new energy station includes: The original data matrix of the single-type energy storage TOPSIS method is constructed, wherein the elements of each column of the matrix represent different secondary indicators, and there are r secondary indicators in total. The elements of each row of the matrix represent different energy storage candidate nodes, and there are n energy storage candidate nodes in total. The original matrix is ​​expressed as: In the formula, O k ij is the value of the jth secondary index of the kth type of energy storage at the ith candidate access point; each row of the matrix represents a different candidate energy storage node, and each column represents the secondary index of a different type of energy storage. i is the sequence number of the selection and site selection scheme, i = 1, 2, ..., n, n is the number of candidate access points for energy storage; j is 1, 2, ..., r, r is the total number of secondary indicators; The expression of the original data matrix of the TOPSIS method for different types of energy storage is: O=[O 1 The 2 …The k ] n×kr In the formula, the matrix O is composed of k single-type energy storage TOPSIS method original data matrix O m Composition (m = 1, 2, ..., k); The dimensionless standardization preprocessing of the larger the better type and the smaller the better type is used to unify the positive and negative indicators, and the weight proportion of each indicator is calculated according to the following formula G k ij : In the formula, K k ij is the dimensionless processing result of the jth secondary index of the kth type of energy storage at the ith candidate access point, O k ij is the value of the jth secondary index of the k-type energy storage at the i-th candidate access point; max(O k j ) is the maximum value of the j-th secondary index of the k-type energy storage under all the candidate access points, min(O k j ) is the minimum value of the j-th secondary index of the k-type energy storage under all the candidate access points, K ij and G ij The ranges are: 0≤K k ij ≤1,0≤G k ij ≤1; The information entropy value A of the i-th candidate access point of type k energy storage is calculated as follows: k i , the information entropy redundancy B of the i-th candidate access point of type k energy storage k i , the index weight D of the i-th candidate access point of type k energy storage k i : In the formula, A k i is the information entropy value of the i-th candidate access point of type k energy storage, B k i is the information entropy redundancy of the i-th candidate access point of type k energy storage, D k i is the index weight of the i-th candidate access point of the k-type energy storage; G ij k is the weight ratio of the jth secondary indicator of the kth type of energy storage at the i-th candidate access point.

6. The method for selecting energy storage type and site for a new energy station according to claim 5, characterized in that: The specific method for obtaining the relative proximity index of each selection and site selection scheme based on the TOPSIS method to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the node of the new energy station includes: First, construct the normalized decision matrix H of k-type energy storage k =(H k ij ) n×r , where element H k ij Calculated as follows: In the formula, H k ij The k-type energy storage is in the normalized decision matrix H k represents the normalized decision quantity of the jth secondary indicator under the i-th candidate access point; The next step is to construct a weighted normalized decision matrix U k =(U k ij ) n×r , where the element U k ij It can be expressed as: In the matrix U, the decision elements U of all secondary indicators under the i-th candidate access point of type k energy storage are k ij The maximum value of is the positive ideal solution under the i-th candidate access point, U k ij The minimum value of is the negative ideal solution under the jth candidate access point, that is: On this basis, the distance of the positive ideal solution under the i-th candidate access point of the k-type energy storage is calculated and the distance to the negative ideal solution Finally, the relative proximity index F of the k-type energy storage at the j-th candidate access point is calculated respectively: k dj : Comparison of the relative proximity index F of k types of energy storage at different candidate access points k dj The maximum value is taken as the distance index F of this type of energy storage k d : Comparison of distance index F for different types of energy storage k d The maximum value is taken as the distance index F of the system d , select F d The corresponding energy storage type k and access point j are used as the selection and site selection results of the system energy storage.

7. The method for selecting energy storage type and site for a new energy station according to claim 5, characterized in that: The distance index F d The expression is:

8. A system for selecting energy storage type and site for a new energy station, characterized in that: It includes the evaluation system and set construction module, the energy storage comprehensive evaluation value acquisition module and the relative proximity index acquisition module; The evaluation system and set construction module are used to construct an evaluation system for energy storage selection and site selection of new energy stations and a set of energy storage selection and site selection schemes in new energy stations; the evaluation system uses the comprehensive evaluation value of energy storage as the criterion layer, and uses the first-level indicator for evaluating the energy storage technical performance of new energy stations, the second-level indicator for evaluating the energy storage economic characteristics of new energy stations, the third-level indicator for evaluating the energy storage carbon emission reduction capacity of new energy stations, and the fourth-level indicator for evaluating the energy storage revenue capacity of new energy stations as the criterion layer, and uses the second-level indicators under each first-level indicator of the target layer as the indicator layer; The energy storage comprehensive evaluation value acquisition module is used to obtain the energy storage comprehensive evaluation value of each selection and site selection scheme in the energy storage access scheme set in the new energy station by using the entropy weight method; The relative proximity index acquisition module is used to obtain the relative proximity index of each selection and site selection scheme based on the TOPSIS method, which is used to reflect the economic feasibility of the energy storage selection and site selection scheme and the voltage stability of the new energy station node, and the one with the largest relative proximity index is used as the final selection and site selection scheme for the new energy station energy storage.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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