Source-storage-load system hybrid energy storage capacity configuration method, device, equipment and medium

Abstract

The present invention relates to the technical field of hybrid energy storage capacity configuration, and in particular to a hybrid energy storage capacity configuration method, device, equipment and medium for a source-storage-load system, the method comprising: setting multi-party demand attribute indicators; constructing a double-layer optimization configuration model, the upper layer of the double-layer optimization configuration model is an electrochemical energy storage optimization configuration model, and the lower layer is a pumped storage optimization configuration model, and considering the optimization operation under two scenarios when the wind power output is too much and the system regulation capacity is insufficient and when the load is too large but the wind power output is insufficient; constructing a user demand response model, considering the potential of flexible load that can be shifted and flexible load that can be reduced; solving the double-layer optimization configuration model to obtain the Pareto frontier optimal set; combining grey correlation analysis with subjective preference, comprehensively considering the objective mutual influence between various indicators and the subjective preference under actual conditions, and optimizing the weight factor setting; and deriving the optimal capacity configuration plan according to the approximate ideal solution sorting method.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid energy storage capacity configuration, and in particular to a hybrid energy storage capacity configuration method, device, equipment and medium for a source-storage-load system. Background Art

[0002] Renewable energy power generation technology has been a hot topic in recent years. In order to promote the consumption of new energy, alleviate the randomness and volatility brought by new energy, and reduce the overall energy consumption of the power grid, energy storage technology has also flourished. At this stage, the main forms of energy storage include electrochemical energy, magnetic field energy, electric field energy, and mechanical energy. Electrochemical energy storage is the main direction of research and development in various countries due to its excellent energy conversion rate, energy density, equipment response speed, and mobility. Pumped storage technology is the most widely used energy storage technology. In order to build a new power system and serve the realization of carbon peak and carbon neutrality, electrochemical energy storage technology and pumped storage technology have played an irreplaceable role.

[0003] Single electrochemical energy storage has a small capacity and is expensive, so it is not economical to use it to absorb high-power wind power. The pumped storage system has a large capacity, but cannot be switched frequently and cannot smooth out minute-level fluctuations. Existing technologies mostly consider configuring electrochemical energy storage to smooth out minute-level fluctuations or configuring pumped storage to perform hour-level peak shaving and valley filling, but few consider configuring batteries and pumped storage units at the same time on two time scales to solve both wave suppression and peak regulation needs.

[0004] Moreover, energy storage is an important solution to reduce fluctuations and promote consumption. It is also a feasible option to improve the reliability of users' electricity use and ensure low-cost electricity use. However, in the existing technology, the research on the optimal configuration of hybrid energy storage capacity mostly only considers unilateral needs. The relationship between indicators such as information correlation has a certain impact on the final decision-making results. Only considering unilateral needs cannot meet planning needs.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a method, device, equipment and medium for configuring the hybrid energy storage capacity of a source-storage-load system, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for configuring the hybrid energy storage capacity of a source-storage-load system, comprising the following steps:

[0008] Set multiple demand attribute indicators, including economic indicators for energy storage companies, technical indicators for wind power companies, and social and environmental indicators on the user demand side;

[0009] Construct a two-layer optimization configuration model, the upper layer of which is an electrochemical energy storage optimization configuration model, and the lower layer is a pumped storage optimization configuration model, and considers the optimization operation in two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient;

[0010] Construct a user demand response model, considering the potential of flexible load shifting and flexible load reduction demand response;

[0011] Solving the two-layer optimization configuration model to obtain the optimal set of Pareto frontiers;

[0012] Combine grey correlation analysis with subjective preference, comprehensively consider the objective mutual influence between indicators and subjective preference under actual conditions, and optimize the weight factor setting;

[0013] The optimal capacity allocation scheme is derived based on the approach to ideal solution sorting method.

[0014] Furthermore, the energy storage business economic indicators include the cost f1 of configuring hybrid energy storage;

[0015] The technical indicators of wind power generators include the amount of wind power generator abandoned electricity f2;

[0016] The user demand side social indicators include user electricity satisfaction evaluation index f3;

[0017] The environmental indicators include the amount of coal f4 saved after the hybrid energy storage system is built.

[0018] Furthermore, the cost f1 of configuring hybrid energy storage includes:

[0019]

[0020] In the formula, C ei is the investment cost of electrochemical energy storage, C em is the operation and maintenance cost of electrochemical energy storage, C hi is the investment cost of pumped storage, C hm is the pumped storage operation and maintenance cost, Y e , Y h is the service life of electrochemical energy storage and pumped storage, B is the discount rate; E er is the rated capacity of the electrochemical energy storage battery; P er E is the rated power of the electrochemical energy storage battery; hr is the total installed capacity of pumped storage units; P hr is the total rated power of the pumped storage unit; k e1 is the investment cost of battery per unit capacity; k e2 is the investment cost of the battery per unit power; ce1 is the operation and maintenance cost of the battery per unit capacity; k h1 k is the investment cost of the unit capacity pumped storage unit; h2 is the investment cost of pumped storage per unit power; c h1 is the operation and maintenance cost of the unit capacity pumped storage unit; c h2 is the operation and maintenance cost of pumped storage per unit power.

[0021] Furthermore, the wind power company's abandoned electricity amount f2 includes:

[0022]

[0023] Where P qf1 (t), P qf2 (t) is the power abandoned in the wave suppression stage and the power abandoned in the peak load shifting stage; P w (t) is the power generated by the wind turbine.

[0024] Furthermore, the user electricity satisfaction evaluation index f3 includes:

[0025]

[0026] Where: P0(t) is the power consumption before the user responds; P L (t) is the power consumption after the user responds.

[0027] Furthermore, the amount of coal f4 saved after the hybrid energy storage system is built includes:

[0028]

[0029] In the formula, c out The coal consumption of the pumped storage unit replacing the peak load unit during pumping; c cost P is the unit coal consumption of the pumped storage unit when pumping water; hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively.

[0030] Furthermore, the electrochemical energy storage optimization configuration model includes four objective functions, including:

[0031]

[0032] The four objective functions correspond to multi-party demand attribute indicators respectively, and the first and second indicators are negative indicators, the smaller the indicator, the better, and the third and fourth indicators are positive indicators, the larger the indicator, the better.

[0033] Furthermore, the electrochemical energy storage optimization configuration model also includes the following constraints:

[0034] Electrochemical energy storage power constraints:

[0035] The real-time operating power of electrochemical energy storage cannot exceed the rated power:

[0036] 0≤P ch (t)≤y ch (t)P er ;

[0037] 0≤P dis (t)≤y dis (t)P er ;

[0038] y dis (t)+y ch (t)≤1;

[0039] Where: y ch (t) and y dis (t) are Boolean variables representing the charging and power generation states of electrochemical energy storage; P ch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t;

[0040] Electrochemical energy storage state of charge constraints:

[0041] The state of charge of the energy storage must be between certain upper and lower limits:

[0042]

[0043] S OCmin ≤S OC (t)≤S OCmax ;

[0044] Where: S OC (t), S OC (t-1) is the charge state of the electrochemical energy storage battery at time t and t-1; η ch and η dis are the energy conversion efficiency of electrochemical energy storage during charging and discharging, S OCmax and S OCmin are the upper and lower limits of the electrochemical energy storage state of charge, respectively.

[0045] Furthermore, the pumped storage optimization configuration model considers two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient, taking into account the wind abandonment cost on the power generation side and the load loss cost on the user side, and its objective function includes:

[0046]

[0047] Where: c qfis the unit wind curtailment cost; c loss P is the load loss cost per unit of electricity; qf (t) is the total wind power abandoned in the system; P loss (t) is the load loss power.

[0048] Furthermore, the pumped storage optimization configuration model also includes the following constraints:

[0049] Power constraints of pumped storage units:

[0050] Under pumping or power generation conditions, the operating power of each pumped storage unit is between certain upper and lower limits:

[0051] y p (t)P hp,min ≤P hp (t)≤y p (t)P hp,max ;

[0052] y g (t)P hg,min ≤P hg (t)≤y g (t)P hg,max ;

[0053] y p (t)+y g (t)≤1;

[0054] Where: P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively; P hp,max and P hp,min are the upper and lower limits of the unit’s pumping power, P hg,max and P hg,min are the upper and lower limits of the unit's power generation respectively; p (t) and y g (t) Boolean variables indicating whether the system is in pumping mode or power generation mode at time t, with 1 for yes and 0 for no;

[0055] Reservoir water level and change constraints:

[0056] The real-time water level of the power station reservoir is between certain upper and lower limits, and the initial and final water levels in each dispatching cycle must be consistent:

[0057] E h,min ≤E h (t)≤E h,max ;

[0058] E h (t) = E h (t-1)+(η pP hp (t)-η g P hg (t))Δt;

[0059] E h (t0) = E(t end );

[0060] Where: E h (t) and E h (t-1) is the water level of the upper reservoir of the power station at time t and t-1; E h,max 、E h,min are the upper and lower limits of the water level of the reservoir in the power station respectively; η p and η g E is the water-to-electricity conversion coefficient under pumping and power generation conditions; h (t0), E(t end ) are the water levels of the upper reservoir of the power station at the beginning and end of the dispatching period. The storage capacity constraint of the upper reservoir also represents the constraint of the lower reservoir.

[0061] Wind curtailment and load shedding constraints:

[0062] 0≤P qf (t)≤P w (t);

[0063] 0≤P loss (t)≤P L (t);

[0064] Power balance constraints:

[0065] P w (t)-P qf (t)+P hg (t)+P dis (t) = P L (t)-P loss (t)+P hp (t)+P ch (t)

[0066] Where: P qf (t) is the wind power curtailment power, P w (t) is the power generated by the wind turbine, P L (t) is the power consumption after the user responds, P loss (t) is the load loss power, P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit, P ch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t.

[0067] Furthermore, the consideration of the potential of flexible load shifting and flexible load reduction demand response includes:

[0068]

[0069] Where: t d is the duration of the load that can be translated; a(t) is a Boolean variable, 1 indicates that the load is translated, and 0 indicates that it is not translated; τ is the starting time of the translation interval;

[0070] P cuta (t) = P cutb (t)-γ t b(t)P cutb (t);

[0071] Where: P cuta (t) is the load power after reduction; P cutb (t) is the load power before reduction; γ t is the load reduction coefficient; b(t) is a Boolean variable, 1 indicates that the load is reduced, and 0 indicates that there is no reduction.

[0072] Furthermore, solving the two-layer optimization configuration model to obtain the optimal set of Pareto frontiers includes the following steps:

[0073] The third generation non-dominated sorting genetic algorithm is used to nest the GUROBI solver to iteratively solve the optimization model, including:

[0074] Read the data and initialize the group, using the electrochemical energy storage battery rated power, the electrochemical energy storage battery rated capacity, the pumped storage rated power, and the pumped storage rated capacity as search variables;

[0075] Call the fitness function and GUROBI to solve the fitness value of each individual upper and lower layers;

[0076] Update population: After selection, recombination, and mutation, a new population is generated and merged with the initial population to obtain G2;

[0077] Perform non-dominated sorting on the merged population and construct a set with non-dominated levels of 1, 2, …, l;

[0078] From the non-dominated level 1 to level 1, the individuals of the population are sequentially incorporated into the empty population G3. If |G3| is equal to the population size, no selection is required and G3 is directly used as the next-generation population. If |G3| is larger than the population size, some individuals need to be selected to make |G3| equal to the population size.

[0079] Repeat the above process until the maximum number of iterations is reached to obtain the optimal set of Pareto frontiers.

[0080] Furthermore, the grey correlation analysis is combined with subjective preference, the objective mutual influence between the indicators and the subjective preference under actual conditions are comprehensively considered, and the weight factor setting is optimized, including the following steps:

[0081] Construct a normalized multi-criteria decision matrix:

[0082] Suppose there are m objects and n evaluation indicators.

[0083]

[0084] In the formula, x mn represents the normalized decision value of the nth indicator of the mth solution;

[0085] Calculate the correlation coefficient:

[0086] Each column of the decision matrix is ​​regarded as a subsequence. The parent sequence Y is formed by taking the maximum value from each row. The correlation coefficient gamma between each subsequence criterion and the parent sequence is calculated according to the grey correlation analysis GCA:

[0087] Y=[y1,y2,...,y i ] T ;

[0088]

[0089] Where Y is the parent sequence; i is an element in the parent sequence; x i k is an element in the decision matrix; g i (k) is the correlation coefficient of each sample; H is the maximum difference between the two poles; h is the minimum difference between the two poles; ξ is the resolution coefficient, which is taken as 0.5 in this paper; gamma(k) is the grey correlation degree of the subsequence;

[0090] Derive the weight matrix:

[0091] The correlation coefficient and subjective preference are combined to form a comprehensive weight based on the above correlation coefficient using the following calculation equation:

[0092]

[0093] W k =(w GCA (k)+w SUB (k)) / 2;

[0094] In the formula, w GCA (k) is the objective weight of relevance; w SUB (k) is the subjective preference weight considering practical factors; W k is the comprehensive weight.

[0095] Furthermore, the method of deriving the optimal capacity configuration scheme according to the approach-to-ideal solution sorting method includes:

[0096] Calculate the weighted decision matrix Z = (z ij ) m×n :

[0097] z ik =W k x ik ;

[0098] Calculate the maximum value corresponding to each parameter:

[0099]

[0100] Calculate the optimal and worst distances of each solution:

[0101]

[0102] Calculate the comprehensive evaluation value:

[0103]

[0104] An optimal capacity configuration scheme is obtained according to the comprehensive evaluation value.

[0105] The present invention also includes a source-storage-load system hybrid energy storage capacity configuration device, using the above method, including:

[0106] The multi-party demand unit is used to set multi-party demand attribute indicators, including economic indicators of energy storage companies, technical indicators of wind power companies, and social indicators and environmental indicators on the user demand side;

[0107] A two-layer optimization configuration model modeling unit is used to construct a two-layer optimization configuration model, wherein the upper layer of the two-layer optimization configuration model is an electrochemical energy storage optimization configuration model, and the lower layer is a pumped storage optimization configuration model, and considers the optimization operation under two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient;

[0108] A user demand response model modeling unit is used to construct a user demand response model, taking into account the potential of flexible load shifting and flexible load reduction demand response;

[0109] A model solving unit, used for solving the two-layer optimization configuration model to obtain the optimal set of Pareto frontiers;

[0110] The weight unit is used to combine the grey relational analysis with subjective preference, comprehensively consider the objective mutual influence between the indicators and the subjective preference under actual conditions, and optimize the weight factor setting;

[0111] The capacity configuration unit is used to derive the optimal capacity configuration plan according to the approximate ideal solution sorting method.

[0112] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0113] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0114] The beneficial effects of the present invention are as follows: the present invention considers the multi-faceted needs of multiple roles more comprehensively by setting multi-attribute indicators, including economic indicators of energy storage companies, technical indicators of wind power companies, social indicators on the user demand side, and environmental indicators; and by constructing a two-layer optimization configuration model, the upper layer is an electrochemical energy storage optimization configuration model, the main function of which is to smooth the minute-level wind power output fluctuations, and the lower layer is a pumped storage optimization configuration model, the main function of which is to complete the hour-level wind power consumption and peak-shaving and valley-filling, considering multi-functional optimization operation; and constructs a user demand response model, considering the potential of flexible loads that can be translated and flexible loads that can be reduced; and proposes a subjective and objective comprehensive decision-making method that considers the attributes of multiple demand, combines GCA (grey correlation analysis) with subjective preferences, comprehensively considers the objective mutual influence between the indicators and the subjective preferences under actual conditions, and optimizes the weight factor setting. Finally, the optimal capacity planning scheme is derived according to the approximate ideal solution sorting method. This standard combination method not only solves the problem that the weights of multiple indicators are difficult to determine, but also improves the accuracy of the evaluation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0116] Figure 1 is a flow chart of the method in Example 1;

[0117] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0118] Figure 3 This is the source-storage-load system architecture diagram in Example 2;

[0119] Figure 4 This is a comprehensive decision-making flow chart in Example 2;

[0120] Figure 5 Schematic diagram of weight factors of different decision-making methods under extreme weather conditions in Example 2;

[0121] Figure 6 Schematic diagram of weight factors of different decision-making methods under suitable weather conditions in Example 2;

[0122] Figure 7 Schematic diagram of hybrid energy storage charging and discharging in Example 2;

[0123] Figure 8 This is a schematic diagram of the stroke suppression effect in Example 2;

[0124] Fig. 9 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0125] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0126] Embodiment 1:

[0127] like Figure 1 As shown: A method for configuring the hybrid energy storage capacity of a source-storage-load system comprises the following steps:

[0128] Set multiple demand attribute indicators, including economic indicators for energy storage companies, technical indicators for wind power companies, and social and environmental indicators on the user demand side;

[0129] Construct a two-layer optimization configuration model. The upper layer of the two-layer optimization configuration model is the electrochemical energy storage optimization configuration model, and the lower layer is the pumped storage optimization configuration model. It also considers the optimization operation in two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient.

[0130] Construct a user demand response model, considering the potential of flexible load shifting and flexible load reduction demand response;

[0131] Solve the two-layer optimization configuration model and obtain the optimal set of Pareto frontiers;

[0132] Combine grey correlation analysis with subjective preference, comprehensively consider the objective mutual influence between indicators and subjective preference under actual conditions, and optimize the weight factor setting;

[0133] The optimal capacity allocation scheme is derived based on the approach to ideal solution sorting method.

[0134] By setting multi-attribute indicators, including economic indicators of energy storage companies, technical indicators of wind power companies, social indicators on the user demand side, and environmental indicators, the multi-faceted needs of multiple roles are taken into account more comprehensively. In addition, a two-layer optimization configuration model is constructed. The upper layer is an electrochemical energy storage optimization configuration model, whose main function is to smooth the minute-level wind power output fluctuations, and the lower layer is a pumped storage optimization configuration model, whose main function is to complete the hour-level wind power consumption and peak-shaving and valley-filling, considering multi-functional optimization operation. A user demand response model is constructed, considering the potential of flexible loads that can be shifted and flexible loads that can be reduced. A subjective and objective comprehensive decision-making method considering the attributes of multiple demand attributes is proposed, combining GCA (grey correlation analysis) with subjective preferences, comprehensively considering the objective mutual influence between the indicators and the subjective preferences under actual conditions, and optimizing the weight factor setting. Finally, the optimal capacity planning scheme is derived based on the approximate ideal solution sorting method. This standard combination method not only solves the problem of the difficulty in determining the weights of multiple indicators, but also improves the accuracy of the evaluation process.

[0135] In this embodiment, the energy storage business economic indicators include the cost f1 of configuring hybrid energy storage;

[0136] The technical indicators of wind power generators include the amount of wind power generators abandoned, f2;

[0137] The user demand side social indicators include the user electricity satisfaction evaluation index f3;

[0138] Environmental indicators include the amount of coal saved after the hybrid energy storage system is built f4.

[0139] The cost f1 of configuring hybrid energy storage includes:

[0140]

[0141] In the formula, C ei is the investment cost of electrochemical energy storage, C em is the operation and maintenance cost of electrochemical energy storage, C hi is the investment cost of pumped storage, C hm is the pumped storage operation and maintenance cost, Y e , Y h is the service life of electrochemical energy storage and pumped storage, B is the discount rate; E er is the rated capacity of the electrochemical energy storage battery; P er E is the rated power of the electrochemical energy storage battery; hr is the total installed capacity of pumped storage units; P hr is the total rated power of the pumped storage unit; k e1 is the investment cost of battery per unit capacity; k e2 is the investment cost of the battery per unit power; ce1 is the operation and maintenance cost of the battery per unit capacity; k h1 k is the investment cost of the unit capacity pumped storage unit; h2 is the investment cost of pumped storage per unit power; c h1 is the operation and maintenance cost of the unit capacity pumped storage unit; c h2 is the operation and maintenance cost of pumped storage per unit power.

[0142] The amount of wind power generators' abandoned electricity f2 includes:

[0143]

[0144] Where P qf1 (t), P qf2 (t) is the power abandoned in the wave suppression stage and the power abandoned in the peak load shifting stage; P w (t) is the power generated by the wind turbine.

[0145] The evaluation index f3 of user electricity satisfaction includes:

[0146]

[0147] Where: P0(t) is the power consumption before the user responds; P L (t) is the power consumption after the user responds.

[0148] The amount of coal saved after the hybrid energy storage system is built includes:

[0149]

[0150] In the formula, c out The coal consumption of the pumped storage unit replacing the peak load unit during pumping; c cost P is the unit coal consumption of the pumped storage unit when pumping water; hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively.

[0151] In this embodiment, the electrochemical energy storage optimization configuration model includes four objective functions, including:

[0152]

[0153] The four objective functions correspond to multi-party demand attribute indicators respectively, and the first and second indicators are negative indicators, the smaller the indicator, the better; the third and fourth indicators are positive indicators, the larger the indicator, the better.

[0154] Among them, the electrochemical energy storage optimization configuration model also includes the following constraints:

[0155] Electrochemical energy storage power constraints:

[0156] The real-time operating power of electrochemical energy storage cannot exceed the rated power:

[0157] 0≤P ch (t)≤y ch (t)P er ;

[0158] 0≤P dis (t)≤y dis (t)P er ;

[0159] y dis (t)+y ch (t)≤1;

[0160] Where: y ch (t) and y dis (t) are Boolean variables representing the charging and power generation states of electrochemical energy storage; P ch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t;

[0161] Electrochemical energy storage state of charge constraints:

[0162] The state of charge of the energy storage must be between certain upper and lower limits:

[0163]

[0164] S OCmin ≤S OC (t)≤S OCmax ;

[0165] Where: S OC (t), S OC (t-1) is the charge state of the electrochemical energy storage battery at time t and t-1; η ch and η dis are the energy conversion efficiency of electrochemical energy storage during charging and discharging, S OCmax and S OCmin are the upper and lower limits of the electrochemical energy storage state of charge, respectively.

[0166] The pumped storage optimization configuration model considers two scenarios: when the wind power output is too high and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient. It takes into account the wind abandonment cost on the power generation side and the load loss cost on the user side. Its objective function includes:

[0167]

[0168] Where: c qf is the unit wind curtailment cost; c lossP is the load loss cost per unit of electricity; qf (t) is the total wind power abandoned in the system; P loss (t) is the load loss power.

[0169] The pumped storage optimal configuration model also includes the following constraints:

[0170] Power constraints of pumped storage units:

[0171] Under pumping or power generation conditions, the operating power of each pumped storage unit is between certain upper and lower limits:

[0172] y p (t)P hp,min ≤P hp (t)≤y p (t)P hp,max ;

[0173] y g (t)P hg,min ≤P hg (t)≤y g (t)P hg,max ;

[0174] y p (t)+y g (t)≤1;

[0175] Where: P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively; P hp,max and P hp,min are the upper and lower limits of the unit’s pumping power, P hg,max and P hg,min are the upper and lower limits of the unit's power generation respectively; p (t) and y g (t) Boolean variables indicating whether the system is in pumping mode or power generation mode at time t, with 1 for yes and 0 for no;

[0176] Reservoir water level and change constraints:

[0177] The real-time water level of the power station reservoir is between certain upper and lower limits, and the initial and final water levels in each dispatching cycle must be consistent:

[0178] E h,min ≤E h (t)≤E h,max ;

[0179] E h (t) = E h (t-1)+(η p P hp (t)-ηg P hg (t))Δt;

[0180] E h (t0) = E(t end );

[0181] Where: E h (t) and E h (t-1) is the water level of the upper reservoir of the power station at time t and t-1; E h,max 、E h,min are the upper and lower limits of the water level of the reservoir in the power station respectively; η p and η g E is the water-to-electricity conversion coefficient under pumping and power generation conditions; h (t0), E(t end ) are the water levels of the upper reservoir of the power station at the beginning and end of the dispatching period. The storage capacity constraint of the upper reservoir also represents the constraint of the lower reservoir.

[0182] Wind curtailment and load shedding constraints:

[0183] 0≤P qf (t)≤P w (t);

[0184] 0≤P loss (t)≤P L (t);

[0185] Power balance constraints:

[0186] P w (t)-P qf (t)+P hg (t)+P dis (t) = P L (t)-P loss (t)+P hp (t)+P ch (t)

[0187] Where: P qf (t) is the wind power curtailment power, P w (t) is the power generated by the wind turbine, P L (t) is the power consumption after the user responds, P loss (t) is the load loss power, P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit, P ch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t.

[0188] As a preference for the above embodiment, the potential of flexible load shifting and flexible load reduction demand response is considered, including:

[0189]

[0190] Where: t d is the duration of the load that can be translated; a(t) is a Boolean variable, 1 indicates that the load is translated, and 0 indicates that it is not translated; τ is the starting time of the translation interval;

[0191] P cuta (t) = P cutb (t)-γ t b(t)P cutb (t);

[0192] Where: P cuta (t) is the load power after reduction; P cutb (t) is the load power before reduction; γ t is the load reduction coefficient; b(t) is a Boolean variable, 1 indicates that the load is reduced, and 0 indicates that there is no reduction.

[0193] In this embodiment, solving the two-layer optimization configuration model to obtain the Pareto frontier optimal set includes the following steps:

[0194] The third generation non-dominated sorting genetic algorithm is used to nest the GUROBI solver to iteratively solve the optimization model, including:

[0195] Read the data and initialize the group, using the electrochemical energy storage battery rated power, the electrochemical energy storage battery rated capacity, the pumped storage rated power, and the pumped storage rated capacity as search variables;

[0196] Call the fitness function and GUROBI to solve the fitness value of each individual upper and lower layers;

[0197] Update population: After selection, recombination, and mutation, a new population is generated and merged with the initial population to obtain G2;

[0198] Perform non-dominated sorting on the merged population and construct a set with non-dominated levels of 1, 2, …, l;

[0199] From the non-dominated level 1 to level 1, the individuals of the population are sequentially incorporated into the empty population G3. If |G3| is equal to the population size, no selection is required and G3 is directly used as the next-generation population. If |G3| is larger than the population size, some individuals need to be selected to make |G3| equal to the population size.

[0200] Repeat the above process until the maximum number of iterations is reached to obtain the optimal set of Pareto frontiers.

[0201] Among them, the grey correlation analysis is combined with subjective preference, the objective mutual influence between the indicators and the subjective preference under actual conditions are comprehensively considered, and the weight factor setting is optimized, including the following steps:

[0202] Construct a normalized multi-criteria decision matrix:

[0203] Suppose there are m objects and n evaluation indicators.

[0204]

[0205] In the formula, x mn represents the normalized decision value of the nth indicator of the mth solution;

[0206] Calculate the correlation coefficient:

[0207] Each column of the decision matrix is ​​regarded as a subsequence. The parent sequence Y is formed by taking the maximum value from each row. The correlation coefficient gamma between each subsequence criterion and the parent sequence is calculated according to the grey correlation analysis GCA:

[0208] Y=[y1,y2,...,y i ] T ;

[0209]

[0210] Where Y is the parent sequence; i is an element in the parent sequence; x ik is an element in the decision matrix; g i (k) is the correlation coefficient of each sample; H is the maximum difference between the two poles; h is the minimum difference between the two poles; ξ is the resolution coefficient, which is taken as 0.5 in this paper; gamma(k) is the grey correlation degree of the subsequence;

[0211] Derive the weight matrix:

[0212] The correlation coefficient and subjective preference are combined to form a comprehensive weight based on the above correlation coefficient using the following calculation equation:

[0213]

[0214] W k =(w GCA (k)+w SUB (k)) / 2;

[0215] In the formula, w GCA (k) is the objective weight of relevance; w SUB (k) is the subjective preference weight considering practical factors; W k is the comprehensive weight.

[0216] The optimal capacity configuration scheme is derived based on the approach to ideal solution sorting method, including:

[0217] Calculate the weighted decision matrix Z = (z ij ) m×n :

[0218] z ik =W k x ik ;

[0219] Calculate the maximum value corresponding to each parameter:

[0220]

[0221] Calculate the optimal and worst distances of each solution:

[0222]

[0223] Calculate the comprehensive evaluation value:

[0224]

[0225] The optimal capacity configuration plan is obtained based on the comprehensive evaluation value.

[0226] like Figure 2 As shown, this embodiment also includes a source-storage-load system hybrid energy storage capacity configuration device, using the above method, including:

[0227] The multi-party demand unit is used to set multi-party demand attribute indicators, including economic indicators of energy storage companies, technical indicators of wind power companies, and social indicators and environmental indicators on the user demand side;

[0228] A two-layer optimization configuration model modeling unit is used to construct a two-layer optimization configuration model. The upper layer of the two-layer optimization configuration model is an electrochemical energy storage optimization configuration model, and the lower layer is a pumped storage optimization configuration model. It also considers the optimization operation in two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient.

[0229] A user demand response model modeling unit is used to construct a user demand response model, taking into account the potential of flexible load shifting and flexible load reduction demand response;

[0230] A model solving unit, used to solve the double-layer optimization configuration model and obtain the optimal set of Pareto frontiers;

[0231] The weight unit is used to combine the grey relational analysis with subjective preference, comprehensively consider the objective mutual influence between the indicators and the subjective preference under actual conditions, and optimize the weight factor setting;

[0232] The capacity configuration unit is used to derive the optimal capacity configuration plan according to the approximate ideal solution sorting method.

[0233] Embodiment 2:

[0234] like Figures 3 to 4 As shown: A method for optimizing the configuration of hybrid energy storage capacity of a source-storage-load system comprises the following steps:

[0235] 1: Set multiple demand attribute indicators, including economic indicators of energy storage companies, technical indicators of wind power companies, social indicators on the user demand side, and environmental indicators;

[0236] 2: Construct a two-layer optimization configuration model. The upper layer is the electrochemical energy storage optimization configuration model, whose main function is to smooth the minute-level wind power output fluctuations to meet the national grid connection requirements. The lower layer is the pumped storage optimization configuration model, whose main function is to complete the hourly consumption and peak-shaving of wind power, and consider the two situations of optimizing operation when the wind power output is too much and the system regulation capacity is insufficient and when the load is too large but the wind power output is insufficient;

[0237] 3: Build a user demand response model, considering the potential of flexible load shifting and flexible load reduction demand response;

[0238] 4: The third generation non-dominated sorting genetic algorithm (NSGA-Ⅲ) is used to iteratively solve the optimization model with a nested GUROBI solver to obtain the optimal set of Pareto frontiers;

[0239] 5: A subjective and objective comprehensive decision-making method considering multiple demand attributes is proposed. GCA is combined with subjective preferences, and the objective mutual influence between indicators and subjective preferences under actual conditions are comprehensively considered to optimize the weight factor setting. Finally, the optimal capacity planning scheme is derived based on the approximate ideal solution sorting method. This standard combination method not only solves the problem of difficulty in determining the weights of multiple indicators, but also improves the accuracy of the evaluation process.

[0240] 2. The method for optimizing the configuration of the hybrid energy storage capacity of the source-storage-load system according to claim 1, characterized in that it also includes:

[0241] Economic indicators for energy storage companies include:

[0242] Considering that the cost of hybrid energy storage configuration by energy storage companies is expected to be as low as possible, this paper takes into account the hybrid energy storage cost including the electrochemical energy storage investment cost C ei 、Operation and maintenance cost of electrochemical energy storage C em , Pumped Storage Investment Cost C hi and pumped storage operation and maintenance cost C hm . The expression is as follows:

[0243]

[0244] Where: Y e , Y h is the service life of electrochemical energy storage and pumped storage. B is the discount rate

[0245] The economic cost expressions of each part are as follows:

[0246]

[0247] Where: E er is the rated capacity of the electrochemical energy storage battery; P er E is the rated power of the electrochemical energy storage battery; hr is the total installed capacity of pumped storage units; P hr is the total rated power of the pumped storage unit; k e1 is the investment cost of battery per unit capacity; k e2 is the investment cost of the battery per unit power; c e1 is the operation and maintenance cost of the battery per unit capacity; k h1 k is the investment cost of the unit capacity pumped storage unit; h2 is the investment cost of pumped storage per unit power; c h1 is the operation and maintenance cost of the unit capacity pumped storage unit; c h2 is the operation and maintenance cost of pumped storage per unit power.

[0248] Technical indicators for wind power generators include:

[0249] Considering that wind power generators need to meet the grid-connected fluctuation requirements and peak load regulation, the amount of power wasted in this paper includes the amount of power wasted in the wave suppression stage and the amount of power wasted in the peak load reduction and valley filling stage.

[0250]

[0251] Where: P qf1 (t), P qf2 (t) is the amount of power abandoned during the wave suppression stage and the amount of power abandoned during the peak load shifting and valley filling stage; P w (t) is the power generated by the wind turbine.

[0252] Demand-side social indicators include:

[0253] Considering the policy of encouraging users to participate in demand response to reduce peak load pressure, this paper constructs a model of user adjustable load. However, users still hope to ensure the comfort of electricity use. Therefore, this paper sets the user satisfaction evaluation index:

[0254]

[0255] Where: P0(t) is the power consumption before the user responds; P L(t) is the power consumption after the user responds.

[0256] Environmental indicators include:

[0257] The amount of coal saved by the system after the pumped storage is completed is calculated by the following formula.

[0258]

[0259] Among them, c out The coal consumption of the pumped storage unit replacing the peak load unit during pumping; c cost P is the unit coal consumption of the pumped storage unit when pumping water; hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively.

[0260] The objective function of the upper electrochemical energy storage optimization configuration model includes:

[0261] The electrochemical energy storage optimization configuration model is the upper model of this paper. Its main function is to smooth the minute-level wind power output fluctuations to meet the national grid connection requirements. The setting of the objective function takes into account the above four scheme indicators. This paper uses a multi-objective optimization algorithm to calculate the multi-index optimization problem, in which the first and second indicators are negative indicators, the smaller the better; the third and fourth indicators are positive indicators, the larger the better.

[0262]

[0263] The constraints of the upper electrochemical energy storage optimization configuration model include:

[0264] (1) Electrochemical energy storage power constraints

[0265] Considering that excessive charge and discharge current will damage the performance of electrochemical energy storage, the real-time operating power of electrochemical energy storage cannot exceed the rated power:

[0266] 0≤P ch (t)≤y ch (t)P er

[0267] 0≤P dis (t)≤y dis (t)P er

[0268] y dis (t)+y ch (t)≤1

[0269] Where: y ch (t) and y dis (t) are Boolean variables representing the charging and power generation states of electrochemical energy storage; Pch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t.

[0270] (2) Electrochemical energy storage state of charge constraints

[0271] During operation, the remaining capacity of electrochemical energy storage must meet certain constraints, and the charge state of the energy storage must be between certain upper and lower limits.

[0272]

[0273] S OCmin ≤S OC (t)≤S OCmax

[0274] Where: S OC (t), S OC (t-1) is the charge state of the electrochemical energy storage battery at time t and t-1; η ch and η dis S are the energy conversion efficiencies during charging and discharging of electrochemical energy storage. OCmax and S OCmin are the upper and lower limits of the electrochemical energy storage state of charge, respectively.

[0275] The objective function of the optimal configuration model of the lower pumped storage unit includes:

[0276] The lower model is the pumped storage optimization configuration model, whose main function is to complete the hourly consumption of wind power and peak shaving and valley filling. Considering two situations: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient, taking into account the wind abandonment cost on the power generation side and the load loss cost on the user side, the objective function expression is as follows:

[0277]

[0278] Where: c qf is the unit wind curtailment cost; c loss P is the load loss cost per unit of electricity; qf (t) is the total wind power abandoned in the system; P loss (t) is the load loss power.

[0279] The constraints of the optimal configuration model of the lower pumped storage unit include:

[0280] (1) Power constraints of pumped storage units

[0281] Under pumping or power generation conditions, the operating power of each pumped storage unit is between certain upper and lower limits:

[0282] y p (t)Php,min ≤P hp (t)≤y p (t)P hp,max

[0283] y g (t)P hg,min ≤P hg (t)≤y g (t)P hg,max

[0284] y p (t)+y g (t)≤1

[0285] Where: P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively; P hp,max and P hp,min are the upper and lower limits of the unit’s pumping power, P hg,max and P hg,min are the upper and lower limits of the unit's power generation respectively; p (t) and y g (t) are Boolean variables indicating whether the system is in pumping condition or power generation condition at time t, with 1 for yes and 0 for no.

[0286] (2) Reservoir water level and change constraints

[0287] The real-time water level of the power station reservoir is between certain upper and lower limits, and the initial and final water levels in each dispatching cycle must be consistent.

[0288] E h,min ≤E h (t)≤E h,max

[0289] E h (t) = E h (t-1)+(η p P hp (t)-η g P hg (t))Δt

[0290] E h (t0) = E(t end )

[0291] Where: E h (t) and E h (t-1) is the water level of the upper reservoir of the power station at time t and t-1; E h,max 、E h,min are the upper and lower limits of the water level of the reservoir in the power station respectively; η p and η gE is the water-to-electricity conversion coefficient under pumping and power generation conditions; h (t0), E(t end ) are the water levels of the upper reservoir of the power station at the beginning and end of the dispatching period. The storage capacity constraint on the upper reservoir also represents the constraint on the lower reservoir.

[0292] (3) Wind curtailment and load loss constraints

[0293] 0≤P qf (t)≤P w (t)

[0294] 0≤P loss (t)≤P L (t)

[0295] (4) Power balance constraints

[0296] P w (t)-P qf (t)+P hg (t)+P dis (t) = P L (t)-P loss (t)+P hp (t)+P ch (t)

[0297] Translatable load models, including:

[0298] A shiftable load is a flexible load with variable working time and constant total power consumption. Its shifting time must be within an acceptable range to ensure the continuity and comfort of power consumption for users. The relevant formula is as follows:

[0299]

[0300] Where: t d is the duration of the translatable load; a(t) is a Boolean variable, 1 indicates that the load is translated, and 0 indicates that it is not translated; τ is the starting time of the translation interval.

[0301] Load-cutting models include:

[0302] Curtailable load is a flexible load whose working time can be interrupted and power consumption can be adjusted. The curtailment formula is as follows:

[0303] P cuta (t) = P cutb (t)-γ t b(t)P cutb (t)

[0304] Where: P cuta (t) is the load power after reduction; P cutb(t) is the load power before reduction; γ t is the load reduction coefficient; b(t) is a Boolean variable, 1 indicates that the load is reduced, and 0 indicates that there is no reduction.

[0305] Combining grey relational analysis with subjective preference, comprehensively considering the objective mutual influence between indicators and subjective preference under actual conditions, and optimizing the weight factor setting, including the following steps:

[0306] Read the data and initialize the group, using the electrochemical energy storage battery rated power, the electrochemical energy storage battery rated capacity, the pumped storage rated power, and the pumped storage rated capacity as search variables;

[0307] Call the fitness function and GUROBI to solve the fitness value of each individual upper and lower layers;

[0308] Update the population. After selection, recombination, and mutation, a new population is generated and merged with the initial population to obtain G2.

[0309] Perform non-dominated sorting on the merged population and construct a set with non-dominated levels of 1, 2, …, l.

[0310] Population individuals are incorporated into the empty population G3 from non-dominated levels 1 to l in turn. If |G3| is equal to the population size, no selection is required and G3 is directly used as the next-generation population; if |G3| is larger than the population size, some individuals need to be selected to make |G3| equal to the population size.

[0311] Repeat the above process until the maximum number of iterations is reached to obtain the optimal set of Pareto frontiers.

[0312] The optimal capacity allocation scheme is derived according to the approach to ideal solution sorting method, which includes the following steps:

[0313] Step 1: Construct a normalized multi-criteria decision matrix

[0314] Suppose there are m objects and n evaluation indicators.

[0315]

[0316] Among them, x mn Represents the normalized decision value of the nth indicator of the mth solution.

[0317] Step 2 Calculate the correlation coefficient

[0318] Each column of the decision matrix is ​​considered as a subsequence. The parent sequence Y is formed by taking the maximum value from each row. The correlation coefficient gamma between each subsequence criterion and the parent sequence is calculated according to GCA.

[0319] Y=[y1,y2,...,y i ]T

[0320]

[0321]

[0322] Among them, Y is the parent sequence; y i is an element in the parent sequence; x ik is an element in the decision matrix; g i (k) is the correlation coefficient of each sample; H is the maximum difference between the two poles; h is the minimum difference between the two poles; ξ is the resolution coefficient, which is taken as 0.5 in this paper; gamma(k) is the grey correlation degree of the subsequence;

[0323] Step 3: Derive the weight matrix

[0324] The correlation coefficient and subjective preference are combined to form a comprehensive weight using the following calculation equation based on the above correlation coefficient.

[0325]

[0326] W k =(w GCA (k)+w SUB (k)) / 2

[0327] Among them, w GCA (k) is the objective weight of relevance; w SUB (k) is the subjective preference weight considering practical factors; W k is the comprehensive weight.

[0328] Step 4: Calculate the weighted decision matrix Z = (z ij ) m×n

[0329] z ik =W k x ik

[0330] Step 5: Calculate the maximum value corresponding to each parameter

[0331]

[0332] Step 6: Calculate the optimal and worst distances for each solution:

[0333]

[0334] Step 7: Calculate the comprehensive evaluation value

[0335]

[0336] In this example, the NSGA-III nested GUROBI algorithm is used to solve the multi-objective problem and obtain the Pareto frontier data containing 30 solutions. Then, the comprehensive decision-making method considering multiple demand attributes is used, combined with the superior and inferior solution distance method to solve the optimal configuration solution.

[0337] Considering that the user-side demand will have different preferences due to actual factors such as weather, for example, in extreme weather such as hot or cold weather, the user's electricity consumption takes into account the comfort demand, the response desire decreases, the load reduction and shifting decrease, and the subjective preference weight of the social indicator taking into account user satisfaction is relatively large. Table 1 shows the configuration results of different decision-making methods under extreme weather.

[0338] Table 1 Configuration results of different decision-making methods under extreme weather

[0339] Configuration Result <![CDATA[E er (MWh)]]> <![CDATA[P er (MW)]]> <![CDATA[E hr (MWh)]]> <![CDATA[P hr (MW)]]> Comprehensive weight 25.43 123.87 872.89 396.55 Preference Weight 78.63 179.75 1578.90 355.62 Relevance Weight 59.25 129.54 847.11 352.59 Indicator results Economic indicators (ten thousand yuan) Technical indicators (%) Social indicators (%) Environmental indicators (t) Comprehensive weight 26494.00 39.19 84.23 326.48 Preference Weight 41954.09 38.18 85.77 375.48 Relevance Weight 25859.06 40.48 82.35 350.76

[0340] The weight factors obtained by different decision-making methods under extreme weather conditions are as follows: Figure 5 As shown, the specific values ​​are: 0.15, 0.18, 0.48, 0.19 under comprehensive weight; 0.1, 0.1, 0.7, 0.1 under preference weight; 0.20, 0.27, 0.26, 0.27 under correlation weight. It can be seen that the preference weight setting is too biased and inaccurate, and does not take into account the relationship and nature of the indicators themselves, resulting in excessive cost of hybrid energy storage configuration, which is not conducive to the economic indicators of the energy storage business side. The correlation weight only takes into account the relationship between indicators, and does not take into account the actual weather factors, resulting in low user satisfaction and a large wind abandonment rate, which is the technical indicator of the power generator. Therefore, the decision-making method adopted in this study neutralizes the advantages of the above two methods, making the economic indicators 36.8% lower than the preference weight, and making the social indicators 2.3% higher than the correlation weight. This standard combination method not only solves the problem of difficulty in determining the weights of multiple indicators, but also improves the accuracy of the evaluation process. The final evaluation results of its decision-making scheme can take into account the objective correlation between indicators and reflect the subjective preferences under different actual conditions.

[0341] If the weather is sunny and comfortable, the user's response desire increases, and the load reduction and shifting amount increase. At this time, the subjective preference weight of the social indicator taking into account user satisfaction is small. Table 2 shows the configuration results of different decision-making methods under suitable weather conditions when the subjective preference is biased towards the interests of energy storage companies.

[0342] Table 2 Configuration results of different decision-making methods under suitable weather conditions

[0343]

[0344]

[0345] The weight factors obtained by different decision-making methods under suitable weather conditions are as follows: Figure 6 As shown, the specific values ​​are: 0.45, 0.18, 0.18, 0.19 under comprehensive weight; 0.7, 0.1, 0.1, 0.1 under preference weight; 0.20, 0.27, 0.26, 0.27 under correlation weight. It can be seen that the technical indicators, social indicators, and environmental indicators under preference weight are not ideal, which is not conducive to the balance of multiple demands. The economic indicators under correlation weight are too large. Therefore, the decision-making method adopted in this study can balance the demands of multiple parties, make the positive indicators as large as possible, and make the negative indicators as small as possible. It not only solves the problem of difficulty in determining the weights of multiple indicators, but also improves the accuracy of the evaluation process.

[0346] After adopting the method in this embodiment, the hybrid energy storage charging and discharging and wind speed suppression effects are as follows: Figures 7 to 8 As shown in the figure, it not only considers the configuration of batteries and pumped storage units at the same time under two time scales to solve the two needs of wave suppression and peak regulation, but also reduces fluctuations and promotes wind power consumption.

[0347] See also Fig. 9 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0348] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0349] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0350] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0351] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0352] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0353] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0354] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0355] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0356] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0357] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for configuring the capacity of a hybrid energy storage system of a source-storage-load system, characterized in that: The steps include: Set multiple demand attribute indicators, including economic indicators for energy storage companies, technical indicators for wind power companies, and social and environmental indicators on the user demand side; Construct a two-layer optimization configuration model, the upper layer of which is an electrochemical energy storage optimization configuration model, and the lower layer is a pumped storage optimization configuration model, and considers the optimization operation in two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient; Construct a user demand response model, considering the potential of flexible load shifting and flexible load reduction demand response; Solving the two-layer optimization configuration model to obtain the optimal set of Pareto frontiers; Combine grey correlation analysis with subjective preference, comprehensively consider the objective mutual influence between indicators and subjective preference under actual conditions, and optimize the weight factor setting; The optimal capacity allocation scheme is derived based on the approach to ideal solution sorting method; The energy storage business economic indicators include the cost of configuring hybrid energy storage f1; The technical indicators of wind power generators include the amount of wind power generator abandoned electricity f2; The user demand side social indicators include user electricity satisfaction evaluation index f3; The environmental indicators include the amount of coal saved after the hybrid energy storage system is built, f4; The cost f1 of configuring hybrid energy storage includes: In the formula, C ei is the investment cost of electrochemical energy storage, C em is the operation and maintenance cost of electrochemical energy storage, C hi is the investment cost of pumped storage, C hm is the pumped storage operation and maintenance cost, Y e , Y h is the service life of electrochemical energy storage and pumped storage, b is the discount rate; E er is the rated capacity of the electrochemical energy storage battery; P er E is the rated power of the electrochemical energy storage battery; hr is the total installed capacity of pumped storage units; P hr is the total rated power of the pumped storage unit; k e1 is the investment cost of battery per unit capacity; k e2 is the investment cost of the battery per unit power; c e1 is the operation and maintenance cost of the battery per unit capacity; k h1 k is the investment cost of the unit capacity pumped storage unit; h2 is the investment cost of pumped storage per unit power; c h1 is the operation and maintenance cost of the unit capacity pumped storage unit; c h2 is the operation and maintenance cost of pumped storage per unit power; The wind power company's abandoned amount f2 includes: Where P qf1 (t), P qf2 (t) is the power abandoned in the wave suppression stage and the power abandoned in the peak load shifting stage; P w (t) is the power generated by the wind turbine; The user electricity satisfaction evaluation index f3 includes: Where: P0(t) is the power consumption before the user responds; P L (t) is the power consumption after the user responds; The amount of coal f4 saved after the hybrid energy storage system is completed includes: In the formula, c out The coal consumption of the pumped storage unit replacing the peak load unit during pumping; c cost P is the unit coal consumption of the pumped storage unit when pumping water; hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively; The electrochemical energy storage optimization configuration model includes four objective functions, including: The four objective functions correspond to the multi-party demand attribute indicators respectively, and the first and second indicators are negative indicators, the smaller the indicators, the better, and the third and fourth indicators are positive indicators, the larger the indicators, the better; The electrochemical energy storage optimization configuration model also includes the following constraints: Electrochemical energy storage power constraints: The real-time operating power of electrochemical energy storage cannot exceed the rated power: 0≤P ch (t)≤y ch (t)P er ; 0≤P dis (t)≤y dis (t)P er ; y dis (t)+y ch (t)≤1; Where: y ch (t) and y dis (t) are Boolean variables representing the charging and power generation states of electrochemical energy storage; P ch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t; Electrochemical energy storage state of charge constraints: The state of charge of the energy storage must be between certain upper and lower limits: S OCmin ≤S OC (t)≤S OCmax ; Where: S OC (t), S OC (t-1) is the charge state of the electrochemical energy storage battery at time t and t-1; h ch and h dis are the energy conversion efficiency of electrochemical energy storage during charging and discharging, S OCmax and S OCmin are the upper and lower limits of the electrochemical energy storage state of charge, respectively; The pumped storage optimization configuration model considers two scenarios: when the wind power output is too high and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient. It takes into account the wind abandonment cost on the power generation side and the load loss cost on the user side. Its objective function includes: Where: c qf is the unit wind curtailment cost; c loss P is the load loss cost per unit of electricity; qf (t) is the total wind power abandoned in the system; P loss (t) is the load loss power; The pumped storage optimization configuration model also includes the following constraints: Power constraints of pumped storage units: Under pumping or power generation conditions, the operating power of each pumped storage unit is between certain upper and lower limits: y p (t)P hp,min ≤P hp (t)≤y p (t)P hp,max ; y g (t)P hg,min ≤P hg (t)≤y g (t)P hg,max ; y p (t)+y g (t)≤1; Where: P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit respectively; P hp,max and P hp,min are the upper and lower limits of the unit’s pumping power, P hg,max and P hg,min are the upper and lower limits of the unit's power generation respectively; p (t) and y g (t) Boolean variables indicating whether the system is in pumping mode or power generation mode at time t, with 1 for yes and 0 for no; Reservoir water level and change constraints: The real-time water level of the power station reservoir is between certain upper and lower limits, and the initial and final water levels in each dispatching cycle must be consistent: E h,min ≤E h (t)≤E h,max ; E h (t)=E h (t-1)+(h p P hp (t)-h g P hg (t))△t; E h (t0)=E(t end ); Where: E h (t) and E h (t-1) is the water level of the upper reservoir of the power station at time t and t-1; E h,max 、E h,min are the upper and lower limits of the water level of the reservoir in the power station respectively; h p and h g E is the water-to-electricity conversion coefficient under pumping and power generation conditions; h (t0), E(t end ) are the water levels of the upper reservoir of the power station at the beginning and end of the dispatching period. The storage capacity constraint of the upper reservoir also represents the constraint of the lower reservoir. Wind curtailment and load shedding constraints: 0≤P qf (t)≤P w (t); 0≤P loss (t)≤P L (t); Power balance constraints: P w (t)-P qf (t)+P hg (t)+P dis (t)=P L (t)-P loss (t)+P hp (t)+P ch (t); Where: P qf (t) is the wind power curtailment power, P w (t) is the power generated by the wind turbine, P L (t) is the power consumption after the user responds, P loss (t) is the load loss power, P hp (t) and P hg (t) are the pumping power and generating power of the pumped storage unit, P ch (t), P dis (t) is the charging power and discharging power of the electrochemical energy storage battery at time t; The method combines the grey correlation analysis with subjective preference, comprehensively considers the objective mutual influence between the indicators and the subjective preference under actual conditions, and optimizes the weight factor setting, including the following steps: Construct a normalized multi-criteria decision matrix: Suppose there are m objects and n evaluation indicators. In the formula, x mn represents the normalized decision value of the nth indicator of the mth solution; Calculate the correlation coefficient: Each column of the decision matrix is ​​regarded as a subsequence. The parent sequence Y is formed by taking the maximum value from each row. The correlation coefficient gamma between each subsequence criterion and the parent sequence is calculated according to the grey correlation analysis GCA: Y=[y1,y2,…,y i ] T ; Where Y is the parent sequence; i is an element in the parent sequence; x ik is an element in the decision matrix; g i (k) is the correlation coefficient of each sample; H is the maximum difference between the two poles; h is the minimum difference between the two poles; x is the resolution coefficient, which is 0.5; gamma(k) is the grey correlation degree of the subsequence; Derive the weight matrix: The correlation coefficient and subjective preference are combined to form a comprehensive weight based on the above correlation coefficient using the following calculation equation: W k =(w GCA (k)+w SUB (k)) / 2; In the formula, w GCA (k) is the objective weight of relevance; w SUB (k) is the subjective preference weight considering practical factors; W k is the comprehensive weight.

2. The method for configuring the hybrid energy storage capacity of the source-storage-load system according to claim 1, characterized in that: The potential for demand response to consider flexible loads that can be shifted and flexible loads that can be curtailed includes: Where: t d is the duration of the load that can be translated; a(t) is a Boolean variable, 1 indicates that the load is translated, and 0 indicates that it is not translated; τ is the starting time of the translation interval; P cuta (t)=P cutb (t)-γ t b(t)P cutb (t); Where: P cuta (t) is the load power after reduction; P cutb (t) is the load power before reduction; γ t is the load reduction coefficient; b(t) is a Boolean variable, 1 indicates that the load is reduced, and 0 indicates that there is no reduction.

3. The source-storage-load system hybrid energy storage capacity configuration method according to claim 1, characterized in that: Solving the two-layer optimization configuration model to obtain the Pareto frontier optimal set includes the following steps: The third generation non-dominated sorting genetic algorithm is used to nest the GUROBI solver to iteratively solve the optimization model, including: Read the data and initialize the group, using the electrochemical energy storage battery rated power, the electrochemical energy storage battery rated capacity, the pumped storage rated power, and the pumped storage rated capacity as search variables; Call the fitness function and GUROBI to solve the fitness value of each individual upper and lower layers; Update population: After selection, recombination, and mutation, a new population is generated and merged with the initial population to obtain G2; Perform non-dominated sorting on the merged population and construct a set with non-dominated levels of 1, 2, …, l; From the non-dominated level 1 to level 1, the individuals of the population are sequentially incorporated into the empty population G3. If |G3| is equal to the population size, no selection is required and G3 is directly used as the next-generation population. If |G3| is larger than the population size, some individuals need to be selected to make |G3| equal to the population size. Repeat the above process until the maximum number of iterations is reached to obtain the optimal set of Pareto frontiers.

4. The source-storage-load system hybrid energy storage capacity configuration method according to claim 1, characterized in that: The method of deriving the optimal capacity configuration scheme according to the approach to ideal solution sorting method includes: Calculate the weighted decision matrix Z = (z ik ) m×n : z ik =W k x ik ; Calculate the maximum value corresponding to each parameter: Calculate the optimal and worst distances of each solution: Calculate the comprehensive evaluation value: An optimal capacity configuration scheme is obtained according to the comprehensive evaluation value.

5. A hybrid energy storage capacity configuration device for a source-storage-load system, characterized in that: Use of the method according to any one of claims 1 to 4, comprising: The multi-party demand unit is used to set multi-party demand attribute indicators, including economic indicators of energy storage companies, technical indicators of wind power companies, and social indicators and environmental indicators on the user demand side; A two-layer optimization configuration model modeling unit is used to construct a two-layer optimization configuration model, wherein the upper layer of the two-layer optimization configuration model is an electrochemical energy storage optimization configuration model, and the lower layer is a pumped storage optimization configuration model, and considers the optimization operation under two scenarios: when the wind power output is too much and the system regulation capacity is insufficient, and when the load is too large but the wind power output is insufficient; A user demand response model modeling unit is used to construct a user demand response model, taking into account the potential of flexible load shifting and flexible load reduction demand response; A model solving unit, used for solving the two-layer optimization configuration model to obtain the optimal set of Pareto frontiers; The weight unit is used to combine the grey relational analysis with subjective preference, comprehensively consider the objective mutual influence between the indicators and the subjective preference under actual conditions, and optimize the weight factor setting; The capacity configuration unit is used to derive the optimal capacity configuration plan according to the approximate ideal solution sorting method.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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