Network-side independent energy storage random optimization configuration method based on multi-market transaction operation

Abstract

The invention discloses a network-side independent energy storage stochastic optimization configuration method based on multi-market transaction operation, and the method comprises the following steps: building an independent energy storage optimization configuration model participating in multi-market transaction operation, obtaining a minimum system total cost target function, and calculating a stage constraint condition of the minimum system total cost target function; an original problem is divided into a main problem and a plurality of sub-problems, a double-loop iteration solving algorithm is established to solve loop iteration between the main problem and the plurality of sub-problems in combination with an independent energy storage optimal configuration model, and an optimal independent energy storage configuration scheme and an optimal system operation strategy are obtained. The method comprehensively considers the participation of energy storage in multi-market transaction operation such as peak-valley arbitrage, frequency modulation and leasing, achieves the effect of improving the comprehensive income and utilization rate of energy storage, and solves the problems that in the prior art, single market transaction is performed from the adjustment demand of a single subject, and the transaction efficiency is poor. The energy storage utilization rate is low; and the investment cost recovery period is long.

Description

Technical Field

[0001] The present invention relates to the technical field of random optimization of grid-side independent energy storage, and in particular to a random optimization configuration method of grid-side independent energy storage based on multi-market transaction operation. Background Art

[0002] Energy storage, as a flexible resource with bidirectional rapid regulation capabilities, can alleviate transmission congestion and reduce the power abandonment rate of renewable energy generation through short-term charging and discharging. Therefore, how to reasonably optimize the configuration of energy storage devices in the power grid to improve the system's ability to absorb renewable energy is of great significance to the safe and economical operation of the power grid.

[0003] However, traditional energy storage planning is mostly based on the regulation needs of a single entity such as the grid or power supply side. Energy storage can only make profits by participating in a single market transaction, which is prone to problems such as low energy storage utilization and long investment cost recovery period. There are also few studies that evaluate the value of energy storage system planning from the perspective of the operating benefits of the entire power system.

[0004] In the existing technology, there is a single market transaction based on the regulation needs of a single entity, which leads to low energy storage utilization and a long investment cost recovery period. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a random optimization configuration method of grid-side independent energy storage based on multi-market trading operations, so as to solve the problem in the prior art of conducting a single market transaction based on the regulation needs of a single entity.

[0006] To achieve the above object, the present invention provides the following technical solutions: The random optimization configuration method of grid-side independent energy storage based on multi-market transaction operation includes the following steps: establishing an independent energy storage optimization configuration model participating in multi-market transaction operation, obtaining the minimum system total cost objective function, and calculating the stage constraints of the minimum system total cost objective function; dividing the original problem into a main problem and several sub-problems, establishing a double-loop iterative solution algorithm and combining the independent energy storage optimization configuration model to solve the loop iteration between the main problem and several sub-problems, and obtaining the optimal independent energy storage configuration plan and system operation strategy.

[0007] In a preferred embodiment, the independent energy storage optimization configuration model participating in multi-market transactions is established to obtain the minimum system total cost objective function, specifically: obtaining first data of the generator set, and calculating the planned operating cost of the generator set on the day before; obtaining second data of the independent energy storage system, and calculating the cost of the independent energy storage system; obtaining third data of the wind farm, and combining the calculated wind farm cost; combining the planned operating cost of the generator set on the day before, the independent energy storage system cost and the wind farm cost, to establish the independent energy storage optimization configuration model participating in multi-market transactions, and obtain the minimum system total cost objective function.

[0008] In a preferred embodiment, the stage constraints for calculating the minimum system total cost objective function are specifically: calculating the day-ahead scheduling stage constraints, configuring independent energy storage and formulating a system scheduling plan; calculating the intraday re-dispatching stage constraints to respond to the uncertainty of wind power output.

[0009] In a preferred embodiment, the original problem is divided into a main problem and several sub-problems, specifically: optimizing the configuration of independent energy storage according to the predicted wind power output and load forecast, optimizing the active output of the generator sets within the scheduling period, and reasonably allocating the charging and discharging power of the energy storage, to obtain the main problem of day-ahead scheduling; making effective re-scheduling decisions based on the real-time operation data of the system, reducing the grid deviation and wind abandonment of wind power, to obtain several intra-day re-scheduling sub-problems.

[0010] In a preferred embodiment, the dual-loop iterative solution algorithm is combined with an independent energy storage optimization configuration model to solve the loop iteration between the main problem and several sub-problems, specifically: obtaining an approximate value of the expected cost of intra-day redispatch, optimizing the configuration of independent energy storage according to the predicted wind power output and load forecast, optimizing the active output of the generator set within the dispatch cycle, and reasonably allocating the charging and discharging power of the energy storage; making effective redispatch decisions based on the real-time operation data of the system to reduce the grid deviation and wind abandonment of wind power; introducing virtual slack variables to form an unbalanced quantity objective function, and punishing the unbalanced quantity objective function by real numbers.

[0011] In a preferred embodiment, the introduction of virtual slack variables forms an imbalance objective function, and the imbalance objective function is penalized by a real number, specifically including: when the imbalance objective function value is not zero, returning to the outer loop of the double loop algorithm to adjust the day-ahead operation mode of the system; when the imbalance objective function value is zero, returning to the inner loop of the double loop algorithm to calculate the current intraday expected rescheduling cost; when the absolute value of the difference between the approximate value of the intraday expected rescheduling cost and the current intraday expected rescheduling cost is less than the convergence criterion, the current intraday expected rescheduling cost is set to the actual value of the intraday expected rescheduling cost, otherwise re-entering the loop.

[0012] In a preferred embodiment, the stage constraints of the minimum system total cost objective function include day-ahead scheduling stage constraints and intraday re-scheduling stage constraints, specifically including: day-ahead scheduling stage constraints include calculation of generator set constraints, power flow constraints, independent energy storage investment and construction constraints, independent energy storage participation in frequency regulation power constraints, independent energy storage capacity constraints, wind farm leased energy storage charging and discharging power constraints, and node power balance constraints; intraday re-scheduling stage constraints include generator set output adjustment constraints, power flow constraints, wind power output constraints, and node power balance constraints.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By establishing a two-stage random optimization configuration model for grid-side independent energy storage that takes into account the participation of energy storage in multiple market transactions, the participation of energy storage in multiple market transactions such as peak-valley arbitrage, frequency regulation and leasing is comprehensively considered, thereby achieving the improvement of the comprehensive benefits and utilization rate of energy storage, and effectively solving the problem of low energy storage utilization in existing technologies.

[0014] 2. By forming a double-loop iterative solution algorithm based on Benders decomposition, the two-stage stochastic optimization configuration model of grid-side independent energy storage taking into account the participation of energy storage in multi-market transactions is effectively solved, so that the original problem is decomposed into a main problem and several sub-problems, and the cyclic iterative solution between the main problem and the sub-problems is solved, thereby achieving the optimal independent energy storage configuration plan and system operation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A method flow chart of a random optimization configuration method for grid-side independent energy storage based on multi-market trading operation provided in an embodiment of the present application; Figure 2 A framework diagram of a two-stage random optimization configuration model of grid-side independent energy storage for a random optimization configuration method of grid-side independent energy storage based on multi-market trading operations provided in an embodiment of the present application; Figure 3 An algorithm flow chart based on Benders decomposition provided in an embodiment of the present application; Figure 4 An improved IEEE RTS-79 system grid diagram provided for an embodiment of the present application; Figure 5 The day-ahead wind power output and load demand forecast curve provided in the embodiment of the present application; Figure 6 A time-of-use electricity price setting curve diagram provided in an embodiment of the present application; Figure 7 A bar chart of system frequency modulation power requirements provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0018] Embodiment 1, Figure 1 The present invention provides a random optimization configuration method for grid-side independent energy storage based on multi-market trading operation, comprising the following steps: Step S1, establishing an independent energy storage optimization configuration model participating in multi-market trading operations, obtaining a minimum system total cost objective function, and calculating the stage constraints of the minimum system total cost objective function; Step S11, combining the planned operation cost of the generator set, the cost of the independent energy storage system and the cost of the wind farm, establishes the objective function of the independent energy storage optimization configuration model. The calculation formula of the objective function of the independent energy storage optimization configuration model is shown in formula (1): (1) Where: is the objective function of the independent energy storage optimization configuration model, is the planned operating cost of the generator set on the day before, is the cost of the independent energy storage system, is the wind farm cost, For the scene The probability of For scene collection, The daily cost of independent energy storage investment, is the energy storage life loss cost, The market revenue of energy storage day-ahead frequency regulation is For energy storage peak-valley arbitrage benefits, The leasing costs paid to independent energy storage operators for wind farms, For intraday scenarios The penalty cost of wind curtailment in downstream wind farms, For intraday scenarios The penalty cost of wind power grid access deviation in downstream wind farms.

[0019] Step S11-1, calculate the planned operating cost of the generator set on the day before, and the calculation formula is shown in formula (2): (2) Where: Plan the operating costs for the generator sets on the day before; is a set of scheduling time periods; Assemble for the generator set; , and are the power generation cost coefficients respectively; is the active output of generator set g at time t; is a 0-1 variable indicating the start / stop status of the generator set g at time t; is the startup cost of generator set g at time t; is the downtime cost of generator set g at time t; and The upward reserve and downward reserve capacity of generator set g at time t, It is the standby cost coefficient fee.

[0020] Step S11-2, calculating the cost of the independent energy storage system, wherein the cost of the independent energy storage system includes the daily cost of energy storage investment, the life loss cost of energy storage, the day-ahead frequency regulation market income of energy storage, and the peak-valley arbitrage income of energy storage; The daily cost of energy storage investment is calculated as shown in formula (3): (3) Where: B is the AC busbar set, is the independent energy storage set connected to bus node i; r is the discount rate; TB is the economic service life of the energy storage device; is the unit capacity investment cost of energy storage u; is the configured capacity of energy storage u.

[0021] The energy storage life loss cost is calculated as shown in formula (4): (4) Where: The life loss cost per unit charge and discharge of energy storage; and They represent the charging and discharging power of energy storage u used for leasing at time t respectively; and They represent the power purchased and sold by energy storage u to the grid at time t respectively; and They respectively represent the charging and discharging power of the energy storage u participating in frequency modulation at time t; is the time interval.

[0022] The calculation formula for the day-ahead frequency regulation market revenue of energy storage is shown in formula (5): (5) Where: is the unit capacity price of energy storage u at time t; is the price per mileage of energy storage u at time t; is the frequency modulation mileage coefficient, which is the hourly historical average of the frequency modulation mileage calculated from the historical AGC signal.

[0023] The calculation formula for energy storage peak-valley arbitrage profit is shown in formula (6): (6) Where: To sell electricity to the power grid in time of use; The power purchase price of the power grid.

[0024] Step S11-3, calculating the wind farm cost, wherein the wind farm cost includes the leasing cost paid by the wind farm to the independent energy storage operator, the wind farm daily wind abandonment penalty cost, and the wind farm daily wind power access deviation penalty cost; The leasing cost paid by the wind farm to the independent energy storage operator is calculated as shown in formula (7): (7) Where: W is the wind farm set; , They are the charging and discharging unit prices set for independent energy storage for wind farm w at time t; , They are respectively the charging and discharging power required by wind farm w for independent energy storage at time t.

[0025] The calculation formula of the wind farm daily wind abandonment penalty cost is shown in formula (8): (8) Where: is the penalty cost coefficient for wind curtailment; is the wind curtailment amount of wind farm w at time t in scenario s.

[0026] The calculation formula of the penalty cost of wind power grid access deviation within a wind farm day is shown in formula (9): (9) Where: is the penalty cost coefficient for wind power grid access deviation, is the wind power grid-connected deviation of wind farm w at time t in scenario s.

[0027] The stage constraints of the minimum system total cost objective function are calculated to obtain the minimum system total cost objective function, and the stage constraints include the day-ahead scheduling stage constraints and the intra-day rescheduling stage constraints.

[0028] Step S12, calculate the constraints of the day-ahead dispatch stage, configure the independent energy storage and formulate a dispatch plan for the system; the constraints of the day-ahead dispatch stage include calculating the constraints of the generator set, the power flow constraints, the construction constraints of the independent energy storage, the power constraints of the independent energy storage participating in the frequency regulation, the independent energy storage capacity constraints, the charging and discharging power constraints of the energy storage rented by the wind farm, and the node power balance constraints.

[0029] Step S12-1, calculating the constraints of the generator set, including the active output constraint of the generator set, the ramp constraint of the generator set, the spare capacity constraint of the generator set, the minimum start and stop time constraint of the generator set, the startup cost constraint of the generator set, and the shutdown cost constraint of the generator set; The active output constraint of the generator set is calculated as follows: (10) Where: Formula (10) is the active output constraint of the generator set, and They are the lower and upper bounds of the unit g output respectively.

[0030] The climbing constraint of the generator set is calculated as follows: (11) Where: Formula (11) is the climbing constraint of the generator set, where and They are the upward and downward climbing rate limits of unit g respectively.

[0031] The generator set reserve capacity constraint is calculated as follows: (12) Where: Formula (12) is the generator set reserve capacity constraint, where and They are respectively the maximum upward and downward reserves that unit g can provide.

[0032] The minimum start and stop time constraint of the generator set is calculated as follows: (13) Where: Formula (13) is the minimum start and stop time constraint of the generator set, where and are the time that unit g has been in the on state and in the off state at time t-1, respectively. and are the minimum start-up time and minimum shutdown time of unit g respectively.

[0033] The startup cost constraint of the generator set is calculated as follows: (14) Where: Formula (14) is the startup cost of the generator set, Represents the single startup cost of unit g.

[0034] The shutdown cost constraint of the generator set is calculated as follows: (15) Where: Formula (15) is the shutdown cost of the generator set, Represents the single shutdown cost of unit g.

[0035] Step S12-2, calculating power flow constraints, which include branch power flow constraints and node voltage phase angle constraints; Branch power flow constraint, the calculation formula is as follows (16): (16) Where: Formula (16) is the branch flow constraint, is the power flow of line ij at time t, is the admittance of line ij, is the voltage phase angle of node i at time t, is the transmission capacity of line ij.

[0036] The node voltage phase angle constraint is calculated as follows: (17) Where: Equation (17) is the node voltage phase angle constraint, where and are the upper and lower limits of the voltage phase angle respectively.

[0037] Step S12-3, calculate the independent energy storage construction constraints, the calculation formula is as follows (18): (18) Where: Set a cap on the amount of independent energy storage; A binary variable indicating whether independent energy storage u is configured; and are the upper and lower limits of the capacity configured for energy storage u respectively.

[0038] Step S12-4, calculate the power constraint of independent energy storage participating in frequency regulation, the calculation formula is as follows: (19) Where: and Binary variables representing the charging and discharging states of the energy storage system; and They are the frequency regulation, charging and discharging power demands of the ancillary service market respectively.

[0039] Step S12-5, calculate the independent energy storage capacity constraints, which include the total charging power constraints of the independent energy storage, the total discharging power constraints of the independent energy storage, and the capacity constraints of the independent energy storage. To ensure the safe operation and continuous service of the independent energy storage, the charge of the energy storage after the reserved rental charging and discharging power on the day before should be constrained.

[0040] The total charging power constraint of independent energy storage is calculated as follows: (20) Where: and are the total charging and discharging powers after the energy storage u reserves the rental charging and discharging power at time t a day ago.

[0041] The total discharge power constraint of independent energy storage is calculated as follows: (twenty one) Where: and are the total charging and discharging powers after the energy storage u reserves the rental charging and discharging power at time t a day ago, and are the minimum and maximum charging power of energy storage u, and are the minimum and maximum discharge powers of energy storage u respectively.

[0042] The capacity constraint of independent energy storage is calculated by equations (22) to (23): (twenty two) (twenty three) Where: Equations (22)-(23) are capacity constraints of independent energy storage, where The capacity after leasing charging and discharging power is reserved for energy storage u at time t the day before. and are the charging and discharging efficiencies of energy storage u, and They are the lower limit coefficient and upper limit coefficient of the remaining capacity of the energy storage system respectively.

[0043] In addition, it should also be ensured that the energy storage charge remains within a safe range and maintains continuous service after the leased charging and discharging power is called, as shown in equations (24) to (27): (twenty four) (25) (26) (27) In formula (24) (25): and They are the total charging and discharging power of energy storage u after calling the rented charging and discharging power at time t within the day; It is the capacity of energy storage u after calling the leased charging and discharging power at time t within the day.

[0044] Step S12-6, calculate the charging and discharging power constraints of the energy storage leased by the wind farm, as shown in equations (28) to (29): (28) (29) Where: Formula (28) is the charging and discharging power limit constraint of the wind farm renting energy storage, where , They are the lower and upper limits of independent energy storage charging for wind farms. , are the lower and upper limits of wind farms using independent energy storage discharge, and are binary variables representing the charging and discharging status of the rented energy storage in wind farms. Formula (29) constrains the total charging and discharging power demand of all wind farms to be equal to the total charging and discharging power of all independent energy storage devices for leasing.

[0045] Step S12-7, calculate the node power balance constraint, the calculation formula is as follows (30): (30) Where: , , , and They are the generator set set, wind farm set, independent energy storage set, branch set and load set connected to bus node i respectively; is the predicted power of wind farm w at time t; is the power of load d at time t.

[0046] Step S13, calculating the constraints of the intraday redispatch phase to respond to the uncertainty of wind power output; Step S13-1, calculate the output adjustment constraint of the generator set, the calculation formula is as follows: (31) (32) Where: Formula (31) is the generator output adjustment constraint under the intraday wind power uncertainty scenario, where and are the output and output adjustment of unit g at time t in scenario s respectively.

[0047] Where: Formula (32) constrains that the output of the generator set in each scenario should be adjusted within the reserve range.

[0048] Step S13-2, calculate the power flow constraint, the calculation formula is as follows: (33) (34) Where: Formula (33) is the branch power flow constraint under each scenario, Formula (34) is the node voltage phase angle constraint under each scenario, and They are respectively the power flow of line ij at time t and the voltage phase angle of bus node i in scenario s.

[0049] Step S13-3, calculate the wind power output constraint, the calculation formula is as follows: (35) (36) (37) (38) (39) (40) Where: Equations (35)-(36) are the maximum wind curtailment constraints under each scenario, where is the actual output of wind farm w at time t in scenario s, is the theoretical output of wind farm w at time t in the generated scenario s. Equation (37) is the grid-connected deviation constraint of wind farms in each scenario. To facilitate model solution, equation (38)-(40) is used to linearize equation (37), where and is an auxiliary continuous variable.

[0050] Step S13-4, calculate the node power balance constraint, the calculation formula is shown in formula (41): (41) Step S2, dividing the original problem into a main problem and several sub-problems, establishing a double-loop iterative solution algorithm and combining it with an independent energy storage optimization configuration model to solve the loop iteration between the main problem and several sub-problems, and obtaining the optimal independent energy storage configuration solution and system operation strategy; Step S21, optimizing the configuration of independent energy storage according to the predicted wind power output and load forecast, optimizing the active output of the generator sets within the scheduling period, and reasonably allocating the charging and discharging power of the energy storage, obtaining the main problem of day-ahead scheduling, and calculating the objective function and constraints of the main problem of day-ahead scheduling; The objective function and constraints of the main problem of day-ahead scheduling are calculated as shown in formula (42): (42) Where: It is an approximation of the expected cost of intraday redispatching and is continuously updated during the loop iteration process.

[0051] Step S22, make effective re-dispatching decisions based on the real-time operation data of the system, reduce the grid deviation and wind abandonment of wind power, obtain several re-dispatching sub-problems within a day, and calculate the objective function and constraint conditions of the sub-problem model.

[0052] The objective function and constraint conditions of the sub-problem model are calculated as shown in formula (43): (43) Where: the superscript * indicates that it is the optimal solution of the current main problem (constant); is the expected cost of redispatching at time t in scenario s; is the dual multiplier corresponding to the constraint condition.

[0053] Considering the serious congestion of transmission lines and the ramp rate limit of generators in some extreme scenarios, even wind curtailment cannot meet the system power balance, resulting in no solution to the intraday redispatch subproblem in this scenario. Therefore, this paper introduces a virtual slack variable v and forms an imbalance objective function, and then uses a large positive real number M for penalty to ensure that this subproblem always has a solution.

[0054] When the imbalance objective function is not 0, it is necessary to return to the Benders cuts in equation (44) and adjust the day-ahead operation mode of the system to ensure the feasibility of the intra-day sub-problem.

[0055] (44) When the imbalance objective function at all times in any scenario s is equal to 0, the current intraday redispatching expected cost is calculated by equation (45): (45) When the absolute value of the difference between the approximate value of the expected cost of intraday redispatching and the current expected cost of intraday redispatching is less than the convergence criterion, the solution of the model has converged, where the convergence criterion is a very small positive number. At this time, the current expected cost of intraday redispatching is the actual value of the expected cost of intraday redispatching; otherwise, return to Benders cuts as in formula (46): (46) Embodiment 2, Figure 2 A framework diagram of a two-stage random optimization configuration model of grid-side independent energy storage of a random optimization configuration method of grid-side independent energy storage based on multi-market trading operation provided in an embodiment of the present application is given.

[0056] Embodiment 3, Figure 3 This is a flowchart of an algorithm based on Benders decomposition provided in an embodiment of the present application.

[0057] Embodiment 4, Figure 4 Improved IEEE RTS-79 system network diagram provided for the embodiment of the present application; Figure 4 Explain as follows: This embodiment adopts the Monte Carlo simulation method to randomly generate 800 uncertain scenarios based on the predicted data of wind power output, and then uses the probability distance fast previous generation elimination technology to reduce the scenarios, and finally obtains 8 typical uncertain scenarios, which are marked as Figure 4 middle.

[0058] This paper proposes to configure independent energy storage at node 14 (U1), node 15 (U2), node 17 (U3) and node 22 (U4). The important parameters of energy storage configuration are shown in Table 1: Table 1 Important parameters related to independent energy storage

[0059] Embodiment 5, Figure 5 This is the time-of-use electricity price setting.

[0060] Embodiment 6, Figure 6 The frequency modulation power requirement of the system.

[0061] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0062] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0063] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0064] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0065] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0066] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A random optimization configuration method for grid-side independent energy storage based on multi-market trading operation, characterized in that: The following steps are involved: Establish an independent energy storage optimization configuration model that participates in multi-market transactions, obtain the minimum system total cost objective function, and calculate the stage constraints of the minimum system total cost objective function; The original problem is divided into a main problem and several sub-problems. A double-loop iterative solution algorithm is established and combined with an independent energy storage optimization configuration model to solve the loop iteration between the main problem and several sub-problems, and the optimal independent energy storage configuration plan and system operation strategy are obtained.

2. The random optimization configuration method for grid-side independent energy storage based on multi-market trading operation as claimed in claim 1 is characterized in that: The independent energy storage optimization configuration model participating in multi-market transactions is established to obtain the minimum system total cost objective function, which is specifically: Obtain the first data of the generator set and calculate the planned operating cost of the generator set on the day before; Obtaining second data of the independent energy storage system and calculating the cost of the independent energy storage system; Obtain the third data of the wind farm and combine it to calculate the cost of the wind farm; Combining the planned operating costs of generator sets, the costs of independent energy storage systems and wind farms, an independent energy storage optimization configuration model participating in multi-market transactions is established to obtain the objective function of minimum total system cost.

3. The random optimization configuration method for grid-side independent energy storage based on multi-market transaction operation as claimed in claim 2 is characterized in that: The stage constraints for calculating the minimum system total cost objective function are specifically: Calculate the constraints of the day-ahead dispatch phase, configure independent energy storage, and develop a dispatch plan for the system; The constraints of the intraday redispatch phase are calculated to respond to the uncertainty of wind power output.

4. The random optimization configuration method for grid-side independent energy storage based on multi-market trading operation as claimed in claim 3 is characterized in that: The original problem is divided into a main problem and several sub-problems, specifically: According to the wind power forecast and load forecast, the independent energy storage is optimally configured, the active power output of the generator sets within the dispatch period is optimized, and the charging and discharging power of the energy storage is reasonably allocated to obtain the main problem of day-ahead dispatch; According to the real-time operation data of the system, effective redispatching decisions are made to reduce the grid deviation and wind abandonment of wind power, and several intra-day redispatching sub-problems are obtained.

5. The random optimization configuration method for grid-side independent energy storage based on multi-market trading operation as claimed in claim 4 is characterized in that: The dual-loop iterative solution algorithm is established to solve the loop iteration between the main problem and several sub-problems in combination with the independent energy storage optimization configuration model, specifically: Get the approximate value of the expected cost of intraday redispatch, optimize the configuration of independent energy storage according to the predicted wind power output and load forecast, optimize the active output of the generator sets within the dispatch cycle, and reasonably allocate the charging and discharging power of the energy storage: Make effective re-dispatch decisions based on real-time system operation data to reduce the grid deviation and wind curtailment of wind power; A virtual slack variable is introduced to form an imbalance objective function, and the imbalance objective function is penalized by a real number.

6. The random optimization configuration method for grid-side independent energy storage based on multi-market trading operation as claimed in claim 5 is characterized in that: The introduction of virtual slack variables to form an imbalance objective function and the use of real numbers to penalize the imbalance objective function specifically include: When the value of the imbalance objective function is not zero, return to the outer loop of the double loop algorithm to adjust the day-ahead operation mode of the system; When the value of the imbalance objective function is zero, return to the inner loop of the double loop algorithm to calculate the current intraday expected cost of redispatching; When the absolute value of the difference between the approximate value of the intraday redispatching expected cost and the current intraday redispatching expected cost is less than the convergence criterion, the current intraday redispatching expected cost is set as the actual value of the intraday redispatching expected cost, otherwise the loop is re-entered.

7. The random optimization configuration method for grid-side independent energy storage based on multi-market trading operation as claimed in claim 6 is characterized in that: The stage constraints of the minimum system total cost objective function include day-ahead scheduling stage constraints and intraday rescheduling stage constraints, specifically including: The constraints in the day-ahead dispatch phase include calculation of generator set constraints, power flow constraints, independent energy storage investment constraints, independent energy storage participation in frequency regulation power constraints, independent energy storage capacity constraints, wind farm rental energy storage charging and discharging power constraints, and node power balance constraints. The constraints in the intraday redispatching stage include generator output adjustment constraints, power flow constraints, wind power output constraints, and node power balance constraints.

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