Cooperative scheduling method, device and equipment based on independent energy storage and storage medium

Abstract

The invention provides a cooperative scheduling method, device and equipment based on independent energy storage and a storage medium. The method comprises the steps of generating bid winning data of an independent energy storage power station based on a peak regulation market bidding model and a frequency regulation market bidding model; generating a first expected income difference and a second expected income difference based on the income calculation model and the bid winning data; determining a task type of each time period in the scheduling task according to the first expected income difference and the second expected income difference; executing a corresponding quotation strategy according to the task type of each time period, generating a real quotation of the independent energy storage power station, updating a peak regulation market bidding model and a frequency modulation market bidding model, and obtaining an updated bid-winning frequency modulation capacity and an updated bid-winning peak regulation charging power of the independent energy storage power station; and executing the task of each time period in the scheduling task based on the updated bid-winning frequency modulation capacity and the updated bid-winning peak regulation charging power. According to the invention, the cooperative scheduling of peak regulation and frequency modulation is realized for independent energy storage based on a market mechanism, and the energy storage service is optimized.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and power system management technology, and in particular to a collaborative scheduling method, device, equipment and storage medium based on independent energy storage. Background Art

[0002] Independent energy storage refers to a system that stores electrical energy through technical means and releases it when needed. This system does not rely on any specific energy source and can operate independently in a variety of power scenarios. Independent energy storage systems can not only provide instant electricity during peak electricity demand periods, but also absorb excess electricity during low electricity demand periods, thereby optimizing the balance between electricity supply and demand. Peak shaving is the process of adjusting the power supply to keep it consistent with the power demand in time. The purpose of peak shaving is to prevent shortages during peak power load periods and waste during low power periods, and to ensure the stable operation of the power system. Frequency regulation is to maintain the stability of the power system frequency, avoid the impact of frequency fluctuations on the safe operation of power equipment, and possible economic losses. However, the frequency regulation process requires a fast response, usually in seconds or milliseconds.

[0003] Most of the existing technologies use efficient energy storage devices such as lithium-ion batteries to directly release stored electricity for peak load regulation during peak demand periods. In terms of frequency regulation, these batteries can quickly respond to frequency changes and provide instantaneous power support; or use high-speed rotating flywheels to store energy and respond to short-term high-power peak load and frequency regulation needs. However, most of the existing research on peak load regulation and peak load regulation methods focuses on the discussion of a single technology or market mechanism, lacking a comprehensive analysis of the synergistic role of independent energy storage in multiple service markets. In particular, the combination of theory and practice is still insufficient in the coordinated optimization of peak load regulation and frequency regulation. Moreover, the application and research of existing energy storage technologies in the frequency regulation market mostly rely on idealized assumptions, ignoring the dynamic reflection of factors such as actual market fluctuations, policy changes and technological progress. The supply and demand matching, response time and pricing mechanism of frequency regulation services are in urgent need of improvement. How to achieve the flexible call of independent energy storage in the power system and optimize energy storage services is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention aims to provide a coordinated scheduling method, device, equipment and storage medium based on independent energy storage to solve the above technical problems, realize the coordinated scheduling of peak load and frequency regulation of independent energy storage based on market mechanism, and optimize energy storage services.

[0005] In order to solve the above technical problems, the present invention provides a coordinated scheduling method based on independent energy storage, comprising:

[0006] Generate winning bid data for independent energy storage power stations based on the peak load market bidding model and the frequency regulation market bidding model;

[0007] Generate a first expected profit difference and a second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data; wherein the first expected profit difference is the profit difference between the expected profit during the peak-shaving charging period and the frequency regulation profit; and the second expected profit difference is the profit difference between the expected profit during the peak-shaving discharging period and the frequency regulation profit;

[0008] Determine the task type of each time period in this scheduling task according to the first expected profit difference and the second expected profit difference; wherein the task type includes a charging time period, a discharging time period, and a non-peak-shaving charging and discharging time period;

[0009] Execute the corresponding quotation strategy according to the task type of each time period, generate the real quotation of the independent energy storage power station in the frequency regulation market, and update the peak regulation market bidding model and the frequency regulation market bidding model based on the real quotation to obtain the updated winning frequency regulation capacity and the updated winning peak regulation charging power of the independent energy storage power station, and execute the tasks of each time period in this scheduling task based on the updated winning frequency regulation capacity and the updated winning peak regulation charging power.

[0010] In the above scheme, the winning bid data of the independent energy storage power station is generated based on the peak-shaving market bidding model and the frequency regulation market bidding model, providing basic data for subsequent profit calculation and dispatch task execution, ensuring the winning bid capacity and power of the independent energy storage power station. The first expected profit difference and the second expected profit difference are generated based on the profit calculation model and the winning bid data. Through profit difference analysis, the economic benefits of the independent energy storage power station are determined, providing a basis for determining the dispatch task type and maximizing the economic benefits. According to the profit difference, the task type of each time period in this dispatch task is determined, including the charging period, the discharging period, and the non-peak-shaving charging and discharging period, optimizing the resource allocation of the independent energy storage power station in the peak-shaving and frequency regulation services, and improving the overall operation efficiency and economy. According to the task type, the corresponding quotation strategy is executed to generate the real quotation of the frequency regulation market. Through the real-time quotation strategy, the supply and demand matching and response time of the frequency regulation service are optimized, and the market competitiveness of the independent energy storage power station is improved. Based on the real quotation, the peak-shaving market bidding model and the frequency regulation market bidding model are updated to obtain the updated winning bid frequency regulation capacity and peak-shaving charging power, dynamically reflect market changes, improve the supply and demand matching and response time of the frequency regulation service, and improve the market adaptability and reliability of the frequency regulation service. Based on the updated winning frequency regulation capacity and peak-shaving charging power, the tasks in each period of this dispatching task are executed, the independent energy storage power station can be flexibly called in the power system, the energy storage service can be optimized, and the reliability and stability of the power system can be improved.

[0011] In one implementation, the generating of the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model specifically includes:

[0012] Obtain the initial peak-shaving quotation of the new energy station; wherein the expression of the initial peak-shaving quotation is:

[0013]

[0014] In the formula, C new,int,i,t is the initial peak load quotation, C i,max is the upper limit of the price offered by the i-th new energy station; C i,min is the lower limit of the quotation of the i-th new energy station; P i,q,t is the power abandonment power predicted by the i-th renewable energy station at time t; P i,q,max is the maximum power abandonment of the i-th renewable energy station at all times within a week; n is the number of renewable energy stations with peak load demand; S n,i is the installed capacity of the i-th new energy station; C max The maximum quotation limit stipulated by market trading rules; C i,min It is the lower limit of the maximum quotation stipulated by the market trading rules; k is the deviation coefficient, which is used to control the floating amount of the highest quotation;

[0015] Each independent energy storage power station participating in the secondary frequency regulation market transaction is input into the frequency regulation market quotation model, and a random number is taken in the quotation area provided by the frequency regulation market quotation model to obtain the frequency regulation declared price of each independent energy storage power station, and the frequency regulation declared price of each independent energy storage power station is sorted based on the frequency regulation market quotation order to obtain the sorted price of each independent energy storage power station in the frequency regulation market; wherein, the expression of the frequency regulation market quotation model is:

[0016]

[0017] In the formula, δ tp,i K is the average price of all frequency regulation bidding periods during the operation days of each energy storage power station in the past week, ctp is the frequency modulation quotation control coefficient, which is a constant coefficient greater than 1;

[0018] The expression of the FM market quotation sequence is:

[0019]

[0020] In the formula, C i is the ranking price of the i-th independent energy storage power station in the frequency regulation market; is the declared price of the ith independent energy storage power station in the frequency regulation market; K i is the frequency regulation performance index of the i-th independent energy storage power station; is the response time indicator; To adjust the speed index, To adjust the accuracy index;

[0021] The peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the frequency modulation market bidding model; wherein,

[0022] The peak-shaving market bidding model is expressed as:

[0023]

[0024] Where, F is the revenue of the energy storage power station; N T is the number of discharge periods in the peak regulation process; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the winning bid for peak-shaving power proposed by the i-th new energy station in the t-th period; ΔT is the number of hours in each operating period of the peak-shaving market;

[0025] The expression of the frequency modulation market bidding model is:

[0026]

[0027] Where P tp,i is the winning frequency regulation capacity of the i-th independent energy storage power station; n tp The number of independent energy storage power stations participating in the frequency regulation market bidding.

[0028] In one implementation, the peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency modulation market bidding model, specifically:

[0029] The solution of the peak-shaving market bidding model is constrained based on the first constraint condition; wherein the expression of the first constraint condition is:

[0030] P i,t ≤P new,ini,i,t ;

[0031] P c,t ≤P n,t ;

[0032]

[0033] Where P new,int,i,t is the peak load demand predicted by the i-th renewable energy station at time t, which is numerically equal to the power abandonment power P predicted by the i-th renewable energy station at time t i,q,t ;P c,t P is the peak-shaving charging power of the independent energy storage power station in period t;n,t is the upper limit of the available adjustable power of the independent energy storage power station in period t;

[0034] The solution of the frequency modulation market bidding model is constrained based on the second constraint condition; wherein the expression of the second constraint condition is:

[0035] P tp,i ≤P tp,i,max ;

[0036]

[0037] Where P tp,i,max P is the frequency regulation power reported by the ith independent energy storage power station in the frequency regulation market; grid is the secondary frequency regulation demand capacity value of the power grid in the current period;

[0038] The winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency regulation market bidding model; wherein the winning bid data includes: the winning peak-shaving quotation, the winning peak-shaving power and the winning frequency regulation capacity.

[0039] In one implementation, generating a first expected profit difference and a second expected profit difference of an independent energy storage power station based on a profit calculation model and the winning bid data specifically includes:

[0040] The first expected income of the independent energy storage power station in the peak-shaving charging period and the second expected income in the peak-shaving discharging period are generated according to the peak-shaving income calculation model; wherein the expression of the peak-shaving income calculation model is:

[0041] Qc,t=E3-E2;

[0042] Q f,t =E1;

[0043]

[0044] Where Qc,t is the first expected benefit of the independent energy storage power station during the charging period in the peak load regulation task; Q f,t is the second expected income of the independent energy storage power station during the discharge period in the peak-shaving task; E1 is the discharge income of the independent energy storage power station during the discharge period in the peak-shaving task; E2 is the charging cost of the independent energy storage power station during the charging period in the peak-shaving task; E3 is the charging income of the independent energy storage power station during the charging period in the peak-shaving task; T f is the total number of discharge periods; C f,t P is the unit revenue of the independent energy storage power station discharging to the grid at time t; f,t is the discharge power of the independent energy storage power station at time t; Δt is the number of hours in each operating period of the peak load market; T c is the total number of charging periods; Cc,t is the charging price at time t, P c,t is the peak-shaving charging power of the independent energy storage power station at time t; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the peak load-shaving power that the i-th new energy station bids for in the t-th period;

[0045] The frequency regulation benefit of the independent energy storage power station participating in the frequency regulation service is calculated according to the frequency regulation benefit calculation formula; wherein the frequency regulation benefit calculation formula is expressed as:

[0046]

[0047] In the formula, Q bj,t is the frequency modulation gain; C bj,t P is the winning bid price for each frequency regulation demand obtained after simulating the frequency regulation market for independent energy storage power stations; m,t is the expected winning bid frequency regulation capacity of the energy storage power station, which is numerically consistent with the winning bid frequency regulation capacity; N is the total number of time periods participating in the secondary frequency regulation;

[0048] The first expected benefit difference and the second expected benefit difference are generated according to the first expected benefit, the second expected benefit, and the frequency modulation benefit; wherein the expressions of the first expected benefit difference and the second expected benefit difference are:

[0049] Q cc,t =Q c,t -Q bj,t ;

[0050] Q fc,t =Q f,t -Q bj,t ;

[0051] In the formula, Q cc,t is the first expected return difference; Q fc,t is the second expected return difference.

[0052] In one implementation, determining the task type for each time period in the current scheduling task according to the first expected profit difference and the second expected profit difference specifically includes:

[0053] The maximum chargeable time and the maximum dischargeable time of the energy storage power station are calculated according to the power of the energy storage power station; wherein the expression of the maximum chargeable time and the maximum dischargeable time is:

[0054]

[0055] Where, T is the maximum charging time; P nis the rated capacity of the independent energy storage power station; η is the charge-discharge conversion efficiency of the independent energy storage power station, which is generally a constant; Q max and Q min The maximum and minimum values ​​of the remaining capacity range obtained by deducting the winning frequency regulation capacity from the available capacity range of the independent energy storage power station;

[0056] The first expected profit differences are arranged in descending order, and the time period types corresponding to the first expected profit differences of the first preset number are selected as the charging time period; wherein the first preset number of accumulated time periods is consistent with the maximum chargeable time, and the expression of the charging time period is:

[0057] [t1,t2......t T +=maxQ cc,t ;

[0058] The second expected return differences are arranged in descending order, and the time period types corresponding to the second expected return differences of the second preset number are selected as the discharge time period; wherein the second preset number of accumulated time periods is consistent with the maximum dischargeable time, and the expression of the discharge time period is:

[0059]

[0060] Eliminate the charging and discharging periods in this scheduling task, and divide the task types of all other periods into non-peak-shaving charging and discharging periods.

[0061] In one implementation, executing a corresponding quotation strategy according to the task type in each time period to generate a real quotation for the independent energy storage power station in the frequency regulation market specifically includes:

[0062] For a first period of time in which the task type is a charging period and a discharging period, a first frequency regulation quotation strategy is executed while planning peak regulation;

[0063] For the second period where the task type is non-peak-shaving charging and discharging period, only the second frequency regulation quotation strategy is executed;

[0064] The first frequency regulation quotation strategy is to randomly select values ​​according to the frequency regulation quotation intervals provided at different stages to generate a real quotation for the independent energy storage power station in the frequency regulation market;

[0065] During the charging phase, set the frequency modulation quotation interval to In the discharge phase, the frequency modulation quotation interval is set to Where N t The expected frequency modulation equivalent number in the tth period is obtained from historical data, K cp is a constant coefficient greater than or equal to 1;

[0066] The second frequency regulation quotation strategy is to compare the frequency regulation revenue of the second period with the average frequency regulation revenue of the historical operating time, select the corresponding frequency regulation quotation range for value taking according to the comparison result, and generate the real quotation of the independent energy storage power station in the frequency regulation market.

[0067] In one implementation, the updating of the peak-shaving market bidding model and the frequency regulation market bidding model based on the real bid to obtain the updated winning frequency regulation capacity and the updated winning peak-shaving charging power of the independent energy storage power station specifically includes:

[0068] Based on the real quotation, the updated ranking price of each independent energy storage power station in the frequency regulation market is updated, and the frequency regulation market bidding model is solved by the updated ranking price; according to the solution result of the frequency regulation bidding model, the updated winning frequency regulation capacity is obtained;

[0069] The updated available adjustable power upper limit is updated based on the updated winning frequency regulation capacity, a first updated constraint condition is obtained, and the peak-shaving market bidding model is solved according to the first updated constraint condition, and the updated winning peak-shaving charging power is obtained according to the solution result.

[0070] In a second aspect, the present application further provides a collaborative scheduling device based on independent energy storage, comprising: a data generation module, a revenue calculation module, a task determination module and a scheduling execution module;

[0071] The data generation module is used to generate the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model;

[0072] The profit calculation module is used to generate a first expected profit difference and a second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data; wherein the first expected profit difference is the profit difference between the expected profit during the peak-shaving charging period and the frequency regulation profit; the second expected profit difference is the profit difference between the expected profit during the peak-shaving discharging period and the frequency regulation profit;

[0073] The task determination module is used to determine the task type of each time period in this scheduling task according to the first expected profit difference and the second expected profit difference; wherein the task type includes a charging time period, a discharging time period and a non-peak-shaving charging and discharging time period;

[0074] The scheduling execution module is used to execute the corresponding bidding strategy according to the task type of each time period, generate the real quotation of the independent energy storage power station in the frequency regulation market, and update the peak regulation market bidding model and the frequency regulation market bidding model based on the real quotation, obtain the updated winning frequency regulation capacity and the updated winning peak regulation charging power of the independent energy storage power station, and execute the tasks of each time period in this scheduling task based on the updated winning frequency regulation capacity and the updated winning peak regulation charging power.

[0075] In the above scheme, the winning bid data of the independent energy storage power station is generated based on the peak-shaving market bidding model and the frequency regulation market bidding model, providing basic data for subsequent profit calculation and dispatch task execution, ensuring the winning bid capacity and power of the independent energy storage power station. The first expected profit difference and the second expected profit difference are generated based on the profit calculation model and the winning bid data. Through profit difference analysis, the economic benefits of the independent energy storage power station are determined, providing a basis for determining the dispatch task type and maximizing the economic benefits. According to the profit difference, the task type of each time period in this dispatch task is determined, including the charging period, the discharging period, and the non-peak-shaving charging and discharging period, optimizing the resource allocation of the independent energy storage power station in the peak-shaving and frequency regulation services, and improving the overall operation efficiency and economy. According to the task type, the corresponding quotation strategy is executed to generate the real quotation of the frequency regulation market. Through the real-time quotation strategy, the supply and demand matching and response time of the frequency regulation service are optimized, and the market competitiveness of the independent energy storage power station is improved. Based on the real quotation, the peak-shaving market bidding model and the frequency regulation market bidding model are updated to obtain the updated winning bid frequency regulation capacity and peak-shaving charging power, dynamically reflect market changes, improve the supply and demand matching and response time of the frequency regulation service, and improve the market adaptability and reliability of the frequency regulation service. Based on the updated winning frequency regulation capacity and peak-shaving charging power, the tasks in each period of this dispatching task are executed, the independent energy storage power station can be flexibly called in the power system, the energy storage service can be optimized, and the reliability and stability of the power system can be improved.

[0076] In one implementation, generating the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model specifically includes:

[0077] Obtain the initial peak-shaving quotation of the new energy station; wherein the expression of the initial peak-shaving quotation is:

[0078]

[0079] In the formula, C new,int,i,t is the initial peak load quotation, C i,max is the upper limit of the price offered by the i-th new energy station; C i,min is the lower limit of the quotation of the i-th new energy station; P i,q,t is the power abandonment power predicted by the i-th renewable energy station at time t; P i,q,max is the maximum power abandonment of the i-th renewable energy station at all times within a week; n is the number of renewable energy stations with peak load demand; S n,i is the installed capacity of the i-th new energy station; C max The maximum quotation limit stipulated by market trading rules; C i,min It is the lower limit of the maximum quotation stipulated by the market trading rules; k is the deviation coefficient, which is used to control the floating amount of the highest quotation;

[0080] Each independent energy storage power station participating in the secondary frequency regulation market transaction is input into the frequency regulation market quotation model, and a random number is taken in the quotation area provided by the frequency regulation market quotation model to obtain the frequency regulation declared price of each independent energy storage power station, and the frequency regulation declared price of each independent energy storage power station is sorted based on the frequency regulation market quotation order to obtain the sorted price of each independent energy storage power station in the frequency regulation market; wherein, the expression of the frequency regulation market quotation model is:

[0081]

[0082] In the formula, δ tp,i K is the average price of all frequency regulation bidding periods during the operation days of each energy storage power station in the past week, ctp is the frequency modulation quotation control coefficient, which is a constant coefficient greater than 1;

[0083] The expression of the FM market quotation sequence is:

[0084]

[0085] In the formula, C i is the ranking price of the i-th independent energy storage power station in the frequency regulation market; is the declared price of the ith independent energy storage power station in the frequency regulation market; K i is the frequency regulation performance index of the i-th independent energy storage power station; is the response time indicator; To adjust the speed index, To adjust the accuracy index;

[0086] The peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the frequency modulation market bidding model; wherein,

[0087] The peak-shaving market bidding model is expressed as:

[0088]

[0089] Where, F is the revenue of the energy storage power station; N T is the number of discharge periods in the peak regulation process; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the winning bid for peak-shaving power proposed by the i-th new energy station in the t-th period; ΔT is the number of hours in each operating period of the peak-shaving market;

[0090] The expression of the frequency modulation market bidding model is:

[0091]

[0092] Where P tp,i is the winning frequency regulation capacity of the i-th independent energy storage power station; n tp The number of independent energy storage power stations participating in the frequency regulation market bidding.

[0093] In one implementation, the peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency modulation market bidding model, specifically:

[0094] The solution of the peak-shaving market bidding model is constrained based on the first constraint condition; wherein the expression of the first constraint condition is:

[0095] P i,t ≤P new,ini,i,t ;

[0096] P c,t ≤P n,t ;

[0097]

[0098] Where P new,int,i,t is the peak load demand predicted by the i-th renewable energy station at time t, which is numerically equal to the power abandonment power P predicted by the i-th renewable energy station at time t i,q,t ;P c,t P is the peak-shaving charging power of the independent energy storage power station in period t; n,t is the upper limit of the available adjustable power of the independent energy storage power station in period t;

[0099] The solution of the frequency modulation market bidding model is constrained based on the second constraint condition; wherein the expression of the second constraint condition is:

[0100] P tp,i ≤P tp,i,max ;

[0101]

[0102] Where P tp,i,max P is the frequency regulation power reported by the ith independent energy storage power station in the frequency regulation market; grid is the secondary frequency regulation demand capacity value of the power grid in the current period;

[0103] The winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency regulation market bidding model; wherein the winning bid data includes: the winning peak-shaving quotation, the winning peak-shaving power and the winning frequency regulation capacity.

[0104] In one implementation, generating a first expected profit difference and a second expected profit difference of an independent energy storage power station based on a profit calculation model and the winning bid data specifically includes:

[0105] The first expected income of the independent energy storage power station in the peak-shaving charging period and the second expected income in the peak-shaving discharging period are generated according to the peak-shaving income calculation model; wherein the expression of the peak-shaving income calculation model is:

[0106] Qc,t=E3-E2;

[0107] Q f,t =E1;

[0108]

[0109] Where Qc,t is the first expected benefit of the independent energy storage power station during the charging period in the peak load regulation task; Q f,t is the second expected income of the independent energy storage power station during the discharge period in the peak-shaving task; E1 is the discharge income of the independent energy storage power station during the discharge period in the peak-shaving task; E2 is the charging cost of the independent energy storage power station during the charging period in the peak-shaving task; E3 is the charging income of the independent energy storage power station during the charging period in the peak-shaving task; T f is the total number of discharge periods; C f,t P is the unit revenue of the independent energy storage power station discharging to the grid at time t; f,t is the discharge power of the independent energy storage power station at time t; Δt is the number of hours in each operating period of the peak load market; T c is the total number of charging periods; C c,t is the charging price at time t, P c,t is the peak-shaving charging power of the independent energy storage power station at time t; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the peak load-shaving power that the i-th new energy station bids for in the t-th period;

[0110] The frequency regulation benefit of the independent energy storage power station participating in the frequency regulation service is calculated according to the frequency regulation benefit calculation formula; wherein the frequency regulation benefit calculation formula is expressed as:

[0111]

[0112] In the formula, Q bj,t is the frequency modulation gain; C bj,t P is the winning bid price for each frequency regulation demand obtained after simulating the frequency regulation market for independent energy storage power stations; m,t is the expected winning bid frequency regulation capacity of the energy storage power station, which is numerically consistent with the winning bid frequency regulation capacity; N is the total number of time periods participating in the secondary frequency regulation;

[0113] The first expected benefit difference and the second expected benefit difference are generated according to the first expected benefit, the second expected benefit, and the frequency modulation benefit; wherein the expressions of the first expected benefit difference and the second expected benefit difference are:

[0114] Q cc,t =Q c,t -Q bj,t ;

[0115] Q fc,t =Q f,t -Q bj,t ;

[0116] In the formula, Q cc,t is the first expected return difference; Q fc,t is the second expected return difference.

[0117] In one implementation, determining the task type for each time period in the current scheduling task according to the first expected profit difference and the second expected profit difference specifically includes:

[0118] The maximum chargeable time and the maximum dischargeable time of the energy storage power station are calculated according to the power of the energy storage power station; wherein the expression of the maximum chargeable time and the maximum dischargeable time is:

[0119]

[0120] Where, T is the maximum charging time; P n is the rated capacity of the independent energy storage power station; η is the charge-discharge conversion efficiency of the independent energy storage power station, which is generally a constant; Q max and Q min The maximum and minimum values ​​of the remaining capacity range obtained by deducting the winning frequency regulation capacity from the available capacity range of the independent energy storage power station;

[0121] The first expected profit differences are arranged in descending order, and the time period types corresponding to the first expected profit differences of the first preset number are selected as the charging time period; wherein the first preset number of accumulated time periods is consistent with the maximum chargeable time, and the expression of the charging time period is:

[0122] [t1,t2......t T +=maxQ cc,t ;

[0123] The second expected return differences are arranged in descending order, and the time period types corresponding to the second expected return differences of the second preset number are selected as the discharge time period; wherein the second preset number of accumulated time periods is consistent with the maximum dischargeable time, and the expression of the discharge time period is:

[0124]

[0125] Eliminate the charging and discharging periods in this scheduling task, and divide the task types of all other periods into non-peak-shaving charging and discharging periods.

[0126] In one implementation, executing a corresponding quotation strategy according to the task type in each time period to generate a real quotation for the independent energy storage power station in the frequency regulation market specifically includes:

[0127] For a first period of time in which the task type is a charging period and a discharging period, a first frequency regulation quotation strategy is executed while planning peak regulation;

[0128] For the second period where the task type is non-peak-shaving charging and discharging period, only the second frequency regulation quotation strategy is executed;

[0129] The first frequency regulation quotation strategy is to randomly select values ​​according to the frequency regulation quotation intervals provided at different stages to generate a real quotation for the independent energy storage power station in the frequency regulation market;

[0130] During the charging phase, set the frequency modulation quotation interval to In the discharge phase, the frequency modulation quotation interval is set to Where N t The expected frequency modulation equivalent number in the tth period is obtained from historical data, K cp is a constant coefficient greater than or equal to 1;

[0131] The second frequency regulation quotation strategy is to compare the frequency regulation revenue of the second period with the average frequency regulation revenue of the historical operating time, select the corresponding frequency regulation quotation range for value taking according to the comparison result, and generate the real quotation of the independent energy storage power station in the frequency regulation market.

[0132] In one implementation, the updating of the peak-shaving market bidding model and the frequency regulation market bidding model based on the real bid to obtain the updated winning frequency regulation capacity and the updated winning peak-shaving charging power of the independent energy storage power station specifically includes:

[0133] Based on the real quotation, the updated ranking price of each independent energy storage power station in the frequency regulation market is updated, and the frequency regulation market bidding model is solved by the updated ranking price; according to the solution result of the frequency regulation bidding model, the updated winning frequency regulation capacity is obtained;

[0134] The updated available adjustable power upper limit is updated based on the updated winning frequency regulation capacity, a first updated constraint condition is obtained, and the peak-shaving market bidding model is solved according to the first updated constraint condition, and the updated winning peak-shaving charging power is obtained according to the solution result.

[0135] In a third aspect, the present application also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the collaborative scheduling method based on independent energy storage as described above is implemented.

[0136] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the collaborative scheduling method based on independent energy storage as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] Figure 1 A schematic flow chart of a collaborative scheduling method based on independent energy storage provided in one embodiment of the present invention;

[0138] Figure 2 A schematic diagram of a module of a collaborative scheduling device based on independent energy storage provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0139] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0140] The terms "first" and "second" and the like in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0141] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0142] First, some terms in this application are explained to facilitate understanding by those skilled in the art.

[0143] (1) New energy stations: refers to areas where a variety of renewable energy power generation facilities are centrally constructed and managed, usually including wind farms, solar photovoltaic power stations, hydropower stations, biomass power stations and other new energy power generation forms. These stations are usually equipped with advanced monitoring and control systems to achieve centralized management and optimized scheduling of power generation facilities.

[0144] Example 1

[0145] See also Figure 1 , Figure 1 The flowchart of a collaborative scheduling method based on independent energy storage provided in one embodiment of the present invention is as follows. The embodiment of the present invention provides, including step 101 to step 104, each step is specifically as follows:

[0146] Step 101: Generate winning bid data for independent energy storage power stations based on the peak load market bidding model and the frequency regulation market bidding model.

[0147] In one embodiment, the generating of the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model specifically includes:

[0148] Obtain the initial peak-shaving quotation of the new energy station; wherein the expression of the initial peak-shaving quotation is:

[0149]

[0150] In the formula, C new,int,i,t is the initial peak load quotation, C i,max is the upper limit of the price offered by the i-th new energy station; C i,min is the lower limit of the quotation of the i-th new energy station; P i,q,t is the power abandonment power predicted by the i-th renewable energy station at time t; P i,q,max is the maximum power abandonment of the i-th renewable energy station at all times within a week; n is the number of renewable energy stations with peak load demand; S n,i is the installed capacity of the i-th new energy station; C max The maximum quotation limit stipulated by market trading rules; C i,min It is the lower limit of the maximum quotation stipulated by the market trading rules; k is the deviation coefficient, which is used to control the floating amount of the highest quotation;

[0151] Each independent energy storage power station participating in the secondary frequency regulation market transaction is input into the frequency regulation market quotation model, and a random number is taken in the quotation area provided by the frequency regulation market quotation model to obtain the frequency regulation declared price of each independent energy storage power station, and the frequency regulation declared price of each independent energy storage power station is sorted based on the frequency regulation market quotation order to obtain the sorted price of each independent energy storage power station in the frequency regulation market; wherein, the expression of the frequency regulation market quotation model is:

[0152] [δ tp, i ,δ tp, i K ctp ];

[0153] In the formula, δ tp,i K is the average price of all frequency regulation bidding periods during the operation days of each energy storage power station in the past week, ctp is the frequency modulation quotation control coefficient, which is a constant coefficient greater than 1;

[0154] The expression of the FM market quotation sequence is:

[0155]

[0156] In the formula, C i is the ranking price of the i-th independent energy storage power station in the frequency regulation market; is the declared price of the ith independent energy storage power station in the frequency regulation market; K i is the frequency regulation performance index of the i-th independent energy storage power station; is the response time indicator; To adjust the speed index, To adjust the accuracy index;

[0157] The peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the frequency modulation market bidding model; wherein,

[0158] The peak-shaving market bidding model is expressed as:

[0159]

[0160] Where, F is the revenue of the energy storage power station; N T is the number of discharge periods in the peak regulation process; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the winning bid for peak-shaving power proposed by the i-th new energy station in the t-th period; ΔT is the number of hours in each operating period of the peak-shaving market;

[0161] The expression of the frequency modulation market bidding model is:

[0162]

[0163] Where P tp,i is the winning frequency regulation capacity of the i-th independent energy storage power station; n tp The number of independent energy storage power stations participating in the frequency regulation market bidding.

[0164] When independent energy storage power stations participate in the auxiliary service peak-shaving market, new energy stations act as market participants, and independent energy storage power stations act as decision-makers to conduct bilateral transactions. Each new energy station in the auxiliary service peak-shaving market needs to complete transactions with the energy storage power station through bidding, so there is a bidding game among the new energy stations, and they need to compete and bargain through their bids based on factors such as their own predicted power generation, abandoned power, abandoned power rate, their own station capacity, and the capacity of other competitors in each period of the next peak-shaving process. In the embodiment of the present invention, the initial peak-shaving quotation C of the new energy station is obtained. new,int,i,t The initial peak-shaving quotation is the price curve reported at time t with a time length of 15 minutes, obtained by the i-th new energy station through the predicted power generation curve. i,max and C i,min It is set by the peak load auxiliary market and is a fixed value. The initial peak load quotation C reported by the independent energy storage power station for each new energy station new,int,i,t and peak demand P new,int,i,t Considering the constraints of the intended trading party's transaction power, the energy storage power station's charging power, and the energy storage capacity, the bidding is carried out to obtain the winning bid price C of each new energy power station after the simulated bidding is cleared. i,t and winning power P i,t .

[0165] In the secondary frequency regulation market, after the power grid releases the demand capacity for secondary frequency regulation, independent power stations, as providers of frequency regulation resources, bid to participate in market transactions. Each independent energy storage power station needs to declare the tradable frequency regulation capacity and frequency regulation price based on its own adjustable power and frequency regulation performance. The frequency regulation bidding model calculates the winning bid power and winning bid price of each energy storage power station.

[0166] In the embodiment of the present invention, during the frequency regulation market simulation bidding process, the average bid price of all frequency regulation bidding periods of each independent energy storage power station during the operation days of the past week is calculated as follows: tp,i Construct a quotation model for the frequency regulation market, and set the quotation range of each independent energy storage entity through the frequency regulation quotation control coefficient. For each independent energy storage power station participating in the secondary frequency regulation market transaction, randomly select numbers within the secondary frequency regulation price range provided by the frequency regulation market quotation model to obtain the secondary frequency regulation declared price of each independent energy storage power station. It should be noted that if the quotation range exceeds the upper limit set by the market rules, it shall be adjusted according to the quotation upper limit stipulated by the market; if the quotation range is lower than the lower limit set by the market rules, it shall be adjusted according to the quotation lower limit stipulated by the market.

[0167] In the bidding in the secondary frequency regulation market, firstly, the ranking price must be calculated comprehensively based on the secondary frequency regulation declared price of each independent energy storage power station and the frequency regulation performance index during the historical operation period; then, based on the secondary frequency regulation demand capacity released by the power grid, the energy storage power station with a lower ranking price is given priority to win the bid. Therefore, in the embodiment of the present invention, after obtaining the secondary frequency regulation declared price of each independent energy storage power station, the secondary frequency regulation declared price and reverse ranking of each independent energy storage power station are performed according to the quotation order of the frequency regulation market to obtain the ranking price of each independent energy storage power station in the frequency regulation market. Combined with the obtained initial peak-shaving curve and the ranking result, the peak-shaving market and frequency regulation market bidding process are simulated to solve the peak-shaving market bidding model and the frequency regulation market bidding model, and the corresponding winning bid data is obtained according to the solution results.

[0168] In one embodiment, the peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency modulation market bidding model, specifically:

[0169] The solution of the peak-shaving market bidding model is constrained based on the first constraint condition; wherein the expression of the first constraint condition is:

[0170] P i,t ≤P new,ini,i,t ;

[0171] P c,t ≤P n,t ;

[0172]

[0173] Where P new,int,i,t is the peak load demand predicted by the i-th renewable energy station at time t, which is numerically equal to the power abandonment power P predicted by the i-th renewable energy station at time t i,q,t ;P c,t P is the peak-shaving charging power of the independent energy storage power station in period t; n,t is the upper limit of the available adjustable power of the independent energy storage power station in period t;

[0174] The solution of the frequency modulation market bidding model is constrained based on the second constraint condition; wherein the expression of the second constraint condition is:

[0175] P tp,i ≤P tp,i,max ;

[0176]

[0177] Where P tp,i,max P is the frequency regulation power reported by the ith independent energy storage power station in the frequency regulation market;grid is the secondary frequency regulation demand capacity value of the power grid in the current period;

[0178] The winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency regulation market bidding model; wherein the winning bid data includes: the winning peak-shaving quotation, the winning peak-shaving power and the winning frequency regulation capacity.

[0179] In the embodiment of the present invention, the peak-shaving market bidding model is solved by taking the maximum peak-shaving revenue of the independent energy storage power station as the objective function and combining the first constraint condition. The first constraint condition includes: considering the peak-shaving power constraint of the new energy power station, the available peak-shaving power constraint of the energy storage power station and the peak-shaving power matching constraint. The peak-shaving bidding model is solved based on the above constraints to obtain the peak-shaving power provided by the independent energy storage power station, the peak-shaving bid price of the new energy station and the peak-shaving power of the new energy station.

[0180] In the frequency regulation auxiliary service market mechanism, it is necessary to take into account the minimum payment cost and the maximum frequency regulation performance of the power grid as the optimization goal. At the same time, the hourly transaction volume of each interested entity is used as the optimization variable, and the bidding and clearing process is carried out under multiple constraints. In an embodiment of the present invention, the minimum payment cost is used as the objective function, and the frequency regulation market bidding model is solved in combination with the second constraint. The second constraint includes: considering the frequency regulation capacity constraint and the frequency regulation power demand constraint declared by the energy storage power station. Based on the above constraints, the frequency regulation market bidding model is solved to obtain the winning frequency regulation capacity of the independent energy storage power station. The winning peak-shaving quotation, winning peak-shaving power and winning frequency regulation capacity obtained from the two model solution results are used as the winning data, and subsequent data processing is carried out.

[0181] Step 102: Generate a first expected profit difference and a second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data; wherein the first expected profit difference is the profit difference between the expected profit during the peak-shaving charging period and the frequency regulation profit; and the second expected profit difference is the profit difference between the expected profit during the peak-shaving discharging period and the frequency regulation profit.

[0182] In one embodiment, the generating of the first expected profit difference and the second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data specifically includes:

[0183] The first expected income of the independent energy storage power station in the peak-shaving charging period and the second expected income in the peak-shaving discharging period are generated according to the peak-shaving income calculation model; wherein the expression of the peak-shaving income calculation model is:

[0184] Qc,t=E3-E2;

[0185] Q f,t =E1;

[0186]

[0187]

[0188] Where Qc,t is the first expected benefit of the independent energy storage power station during the charging period in the peak load regulation task; Q f,t is the second expected income of the independent energy storage power station during the discharge period in the peak-shaving task; E1 is the discharge income of the independent energy storage power station during the discharge period in the peak-shaving task; E2 is the charging cost of the independent energy storage power station during the charging period in the peak-shaving task; E3 is the charging income of the independent energy storage power station during the charging period in the peak-shaving task; T f is the total number of discharge periods; C f,t P is the unit revenue of the independent energy storage power station discharging to the grid at time t; f,t is the discharge power of the independent energy storage power station at time t; Δt is the number of hours in each operating period of the peak load market; T c is the total number of charging periods; C c,t is the charging price at time t, P c,t is the peak-shaving charging power of the independent energy storage power station at time t; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the peak load-shaving power that the i-th new energy station bids for in the t-th period;

[0189] The frequency regulation benefit of the independent energy storage power station participating in the frequency regulation service is calculated according to the frequency regulation benefit calculation formula; wherein the frequency regulation benefit calculation formula is expressed as:

[0190]

[0191] In the formula, Q bj,t is the frequency modulation gain; C bj,t P is the winning bid price for each frequency regulation demand obtained after simulating the frequency regulation market for independent energy storage power stations; m,t is the expected winning bid frequency regulation capacity of the energy storage power station, which is numerically consistent with the winning bid frequency regulation capacity; N is the total number of time periods participating in the secondary frequency regulation;

[0192] The first expected benefit difference and the second expected benefit difference are generated according to the first expected benefit, the second expected benefit, and the frequency modulation benefit; wherein the expressions of the first expected benefit difference and the second expected benefit difference are:

[0193] Q cc,t =Q c,t -Q bj,t ;

[0194] Q fc,t =Q f,t -Q bj,t ;

[0195] In the formula, Q cc,t is the first expected return difference; Q fc,t is the second expected return difference.

[0196] The peak-shaving task can be divided into a charging period and a discharging period according to the charging and discharging conditions. The charging period refers to the time when the energy storage device reserves energy for the peak-shaving demand, while the discharging period is the time when the energy storage device releases energy to meet the peak-shaving demand. In the embodiment of the present invention, the revenue of the independent energy storage power station in the peak-shaving charging stage and the discharging stage is calculated respectively through the peak-shaving revenue model, and the two peak-shaving revenues are compared with the frequency regulation revenue respectively to obtain two revenue differences. In the peak-shaving task, the revenue difference of the charging period refers to the difference between the peak-shaving revenue per hour in the peak-shaving charging period and the frequency regulation revenue calculated according to the highest bid that can be won. This difference reflects the increase or decrease of the peak-shaving revenue relative to the frequency regulation revenue in the charging period; in the peak-shaving task, the revenue difference of the discharge period refers to the difference between the peak-shaving revenue per hour in the peak-shaving discharge period and the frequency regulation revenue calculated according to the highest bid that can be won. Taking the task arrangement of the independent energy storage power station in each time period as the optimization variable and maximizing the profit of the energy storage power station as the optimization goal, the optimal task allocation scheme is finally achieved. In the embodiment of the present invention, the task type of each time period in this task scheduling process is determined according to the difference between two expected profits, aiming to reasonably allocate the peak load and frequency regulation tasks to improve the overall profit of the energy storage power station in different time periods.

[0197] Step 103: Determine the task type for each time period in this scheduling task according to the first expected profit difference and the second expected profit difference; wherein the task type includes a charging time period, a discharging time period, and a non-peak-shaving charging and discharging time period.

[0198] In one embodiment, determining the task type of each time period in the current scheduling task according to the first expected profit difference and the second expected profit difference specifically includes:

[0199] The maximum chargeable time and the maximum dischargeable time of the energy storage power station are calculated according to the power of the energy storage power station; wherein the expression of the maximum chargeable time and the maximum dischargeable time is:

[0200]

[0201] Where, T is the maximum charging time; P n is the rated capacity of the independent energy storage power station; η is the charge-discharge conversion efficiency of the independent energy storage power station, which is generally a constant; Q max and Q min The maximum and minimum values ​​of the remaining capacity range obtained by deducting the winning frequency regulation capacity from the available capacity range of the independent energy storage power station;

[0202] The first expected profit differences are arranged in descending order, and the time period types corresponding to the first expected profit differences of the first preset number are selected as the charging time period; wherein the first preset number of accumulated time periods is consistent with the maximum chargeable time, and the expression of the charging time period is:

[0203] [t1,t2......t T +=maxQ cc,t ;

[0204] The second expected return differences are arranged in descending order, and the time period types corresponding to the second expected return differences of the second preset number are selected as the discharge time period; wherein the second preset number of accumulated time periods is consistent with the maximum dischargeable time, and the expression of the discharge time period is:

[0205]

[0206] Eliminate the charging and discharging periods in this scheduling task, and divide the task types of all other periods into non-peak-shaving charging and discharging periods.

[0207] In the embodiment of the present invention, the difference between the charging and discharging benefits and the frequency regulation benefits of each time period in the peak load phase of this scheduling task is sorted, and the corresponding specific time period is selected according to the maximum number of charging time periods and the maximum number of discharging time periods in this scheduling task, and its task type is divided into charging time period or discharging time period, so as to achieve the reasonable allocation of each time period in the scheduling process of this task. Furthermore, the time periods that are not selected are divided into non-peak load charging and discharging time periods.

[0208] In one embodiment, the corresponding quotation strategy is executed according to the task type in each time period to generate a real quotation of the independent energy storage power station in the frequency regulation market, specifically including:

[0209] For a first period of time in which the task type is a charging period and a discharging period, a first frequency regulation quotation strategy is executed while planning peak regulation;

[0210] For the second period where the task type is non-peak-shaving charging and discharging period, only the second frequency regulation quotation strategy is executed;

[0211] The first frequency regulation quotation strategy is to randomly select values ​​according to the frequency regulation quotation intervals provided at different stages to generate a real quotation for the independent energy storage power station in the frequency regulation market;

[0212] During the charging phase, set the frequency modulation quotation interval to In the discharge phase, the frequency modulation quotation interval is set to Where N t The expected frequency modulation equivalent number in the tth period is obtained from historical data, K cpis a constant coefficient greater than or equal to 1;

[0213] The second frequency regulation quotation strategy is to compare the frequency regulation revenue of the second period with the average frequency regulation revenue of the historical operating time, select the corresponding frequency regulation quotation range for value taking according to the comparison result, and generate the real quotation of the independent energy storage power station in the frequency regulation market.

[0214] In the embodiment of the present invention, different bidding strategies and bidding intervals are selected according to different task types in the time period. For the non-peak-shaving charging and discharging period in the current scheduling stage, only frequency regulation bidding is arranged. Specifically, the average frequency regulation revenue Q of the historical operation time is calculated. f,y,u The frequency modulation benefit value Q of this period bj,t For comparison, if Q bj,t >Q f,y,u , then the frequency modulation quotation Q of the current period rbj,t Set to any value within the range: Q rbj,t ∈[Q f,y,u ,K ctp Q f,y,u +; if Q bj,t <Q f,y,u , then the current period frequency modulation quotation Q rbj,t Set to Q bj,t . K ctp is a constant coefficient set to be greater than 1. Further, for T charging periods and T N During each discharge period, while planning the peak load regulation, the bidding for frequency regulation can be arranged. Preferably, the lower limit of the price in the frequency regulation quotation can also be set to the unit price after the peak load regulation income is converted.

[0215] Step 104: Execute the corresponding bidding strategy according to the task type of each time period, generate the real quotation of the independent energy storage power station in the frequency regulation market, and update the peak regulation market bidding model and the frequency regulation market bidding model based on the real quotation, obtain the updated winning frequency regulation capacity and the updated winning peak regulation charging power of the independent energy storage power station, and execute the tasks of each time period in this scheduling task based on the updated winning frequency regulation capacity and the updated winning peak regulation charging power.

[0216] In one embodiment, the peak-shaving market bidding model and the frequency regulation market bidding model are updated based on the real bid to obtain the updated winning frequency regulation capacity and the updated winning peak-shaving charging power of the independent energy storage power station, specifically including: updating the updated ranking price of each independent energy storage power station in the frequency regulation market based on the real bid, solving the frequency regulation market bidding model through the updated ranking price; obtaining the updated winning frequency regulation capacity according to the solution result of the frequency regulation bidding model; updating the updated available regulation power upper limit based on the updated winning frequency regulation capacity to obtain a first updated constraint condition and solving the peak-shaving market bidding model according to the first updated constraint condition, and obtaining the updated winning peak-shaving charging power according to the solution result.

[0217] In the embodiment of the present invention, after obtaining the real quotation of the independent energy storage power station in the frequency regulation market, it is substituted into the parameter in the expression of the frequency regulation market quotation sequence. The updated ranking price of each independent energy storage power station in the frequency regulation market is obtained. Then the updated ranking price is obtained. Substitute it into the expression in the frequency modulation market bidding model, update the frequency modulation market bidding model, and obtain the updated winning frequency modulation capacity P tp,i Based on the obtained updated winning frequency regulation capacity, the available regulation power upper limit P in the first constraint condition n,t Update, subtract the winning frequency regulation capacity from the available regulation power upper limit before the update, and you can get the updated available regulation power upper limit. By resolving the peak-shaving market bidding model with the first constraint condition that includes the updated available regulation power upper limit, you can get the peak-shaving charging power provided by the independent energy storage power station in each time period. Perform the peak-shaving and secondary frequency regulation tasks in each time period of this scheduling task according to the obtained updated winning frequency regulation capacity and updated winning frequency regulation charging power. In other time periods, the peak-shaving charging task can be performed according to the charge state of the energy storage to ensure that the charge level of the energy storage can meet the requirements of peak-shaving charging and secondary frequency regulation.

[0218] In an embodiment of the present invention, a collaborative scheduling device based on independent energy storage is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned collaborative scheduling method based on independent energy storage is implemented.

[0219] In an embodiment of the present invention, a computer-readable storage medium is also provided, and the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned collaborative scheduling method based on independent energy storage.

[0220] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to complete the present invention. One or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in a collaborative scheduling device based on independent energy storage.

[0221] The collaborative dispatching device based on independent energy storage can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The collaborative dispatching device based on independent energy storage may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art will understand that the above-mentioned components are merely examples of collaborative dispatching devices based on independent energy storage, and do not constitute a limitation on collaborative dispatching devices based on independent energy storage. It may include more or fewer components than the components, or a combination of certain components, or different components. For example, the collaborative dispatching device based on independent energy storage may also include input and output devices, network access devices, buses, etc.

[0222] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the collaborative dispatching device based on independent energy storage, and uses various interfaces and lines to connect the various parts of the entire collaborative dispatching device based on independent energy storage.

[0223] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the collaborative scheduling device based on independent energy storage by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0224] Among them, if the module based on the collaborative scheduling based on independent energy storage is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0225] The embodiment of the present invention provides a collaborative scheduling method based on independent energy storage, which generates the winning bid data of the independent energy storage power station based on the peak-shaving market bidding model and the frequency regulation market bidding model, provides basic data for subsequent profit calculation and scheduling task execution, and ensures the winning bid capacity and power of the independent energy storage power station. Based on the profit calculation model and the winning bid data, a first expected profit difference and a second expected profit difference are generated, and the economic benefits of the independent energy storage power station are determined through profit difference analysis, which provides a basis for determining the type of scheduling task and maximizes the economic benefits. According to the profit difference, the task type of each time period in this scheduling task is determined, including the charging period, the discharging period, and the non-peak-shaving charging and discharging period, and the resource allocation of the independent energy storage power station in the peak-shaving and frequency regulation services is optimized to improve the overall operation efficiency and economy. According to the task type, the corresponding quotation strategy is executed to generate a real quotation for the frequency regulation market. Through the real-time quotation strategy, the supply and demand matching and response time of the frequency regulation service are optimized, and the market competitiveness of the independent energy storage power station is improved. Based on the real bid, the peak-shaving market bidding model and the frequency regulation market bidding model are updated to obtain the updated winning frequency regulation capacity and peak-shaving charging power, dynamically reflect market changes, improve the supply and demand matching and response time of frequency regulation services, and improve the market adaptability and reliability of frequency regulation services. Based on the updated winning frequency regulation capacity and peak-shaving charging power, the tasks of each period in this dispatching task are executed, and the flexible call of independent energy storage power stations in the power system is realized, the energy storage service is optimized, and the reliability and stability of the power system are improved.

[0226] Example 2

[0227] See also Figure 2 , Figure 2 Schematic diagram of a module of a collaborative scheduling device based on independent energy storage provided in an embodiment of the present invention. The embodiment of the present invention provides a collaborative scheduling device based on independent energy storage, including: a data generation module 201, a benefit calculation module 202, a task determination module 203 and a scheduling execution module 204;

[0228] The data generation module 201 is used to generate the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model;

[0229] The profit calculation module 202 is used to generate a first expected profit difference and a second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data; wherein the first expected profit difference is the profit difference between the expected profit during the peak-shaving charging period and the frequency regulation profit; the second expected profit difference is the profit difference between the expected profit during the peak-shaving discharging period and the frequency regulation profit;

[0230] The task determination module 203 is used to determine the task type of each time period in this scheduling task according to the first expected profit difference and the second expected profit difference; wherein the task type includes a charging time period, a discharging time period and a non-peak-shaving charging and discharging time period;

[0231] The scheduling execution module 204 is used to execute the corresponding bidding strategy according to the task type of each time period, generate the real quotation of the independent energy storage power station in the frequency regulation market, and update the peak regulation market bidding model and the frequency regulation market bidding model based on the real quotation, obtain the updated winning frequency regulation capacity and the updated winning peak regulation charging power of the independent energy storage power station, and execute the tasks of each time period in this scheduling task based on the updated winning frequency regulation capacity and the updated winning peak regulation charging power.

[0232] In one embodiment, the generating of the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model specifically includes:

[0233] Obtain the initial peak-shaving quotation of the new energy station; wherein the expression of the initial peak-shaving quotation is:

[0234]

[0235] In the formula, C new,int,i,t is the initial peak load quotation, C i,max is the upper limit of the price offered by the i-th new energy station; C i,min is the lower limit of the quotation of the i-th new energy station; P i,q,t is the power abandonment power predicted by the i-th renewable energy station at time t; P i,q,max is the maximum power abandonment of the i-th renewable energy station at all times within a week; n is the number of renewable energy stations with peak load demand; S n,i is the installed capacity of the i-th new energy station; C max The maximum quotation limit stipulated by market trading rules; C i,min It is the lower limit of the maximum quotation stipulated by the market trading rules; k is the deviation coefficient, which is used to control the floating amount of the highest quotation;

[0236] Each independent energy storage power station participating in the secondary frequency regulation market transaction is input into the frequency regulation market quotation model, and a random number is taken in the quotation area provided by the frequency regulation market quotation model to obtain the frequency regulation declared price of each independent energy storage power station, and the frequency regulation declared price of each independent energy storage power station is sorted based on the frequency regulation market quotation order to obtain the sorted price of each independent energy storage power station in the frequency regulation market; wherein, the expression of the frequency regulation market quotation model is:

[0237] [δ tp, i ,δ tp, i K ctp ];

[0238] In the formula, δ tp,iK is the average price of all frequency regulation bidding periods during the operation days of each energy storage power station in the past week, ctp is the frequency modulation quotation control coefficient, which is a constant coefficient greater than 1;

[0239] The expression of the FM market quotation sequence is:

[0240]

[0241] In the formula, C i is the ranking price of the i-th independent energy storage power station in the frequency regulation market; is the declared price of the ith independent energy storage power station in the frequency regulation market; K i is the frequency regulation performance index of the i-th independent energy storage power station; is the response time indicator; To adjust the speed index, To adjust the accuracy index;

[0242] The peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the frequency modulation market bidding model; wherein,

[0243] The peak-shaving market bidding model is expressed as:

[0244]

[0245] Where, F is the revenue of the energy storage power station; N T is the number of discharge periods in the peak regulation process; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the winning bid for peak-shaving power proposed by the i-th new energy station in the t-th period; ΔT is the number of hours in each operating period of the peak-shaving market;

[0246] The expression of the frequency modulation market bidding model is:

[0247]

[0248] Where P tp,i is the winning frequency regulation capacity of the i-th independent energy storage power station; n tp The number of independent energy storage power stations participating in the frequency regulation market bidding.

[0249] In one embodiment, the peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency modulation market bidding model, specifically:

[0250] The solution of the peak-shaving market bidding model is constrained based on the first constraint condition; wherein the expression of the first constraint condition is:

[0251] P i,t ≤P new,ini,i,t ;

[0252] P c,t ≤P n,t ;

[0253]

[0254] Where P new,int,i,t is the peak load demand predicted by the i-th renewable energy station at time t, which is numerically equal to the power abandonment power P predicted by the i-th renewable energy station at time t i,q,t ;P c,t P is the peak-shaving charging power of the independent energy storage power station in period t; n,t is the upper limit of the available adjustable power of the independent energy storage power station in period t;

[0255] The solution of the frequency modulation market bidding model is constrained based on the second constraint condition; wherein the expression of the second constraint condition is:

[0256] P tp,i ≤P tp,i,max ;

[0257]

[0258] Where P tp,i,max P is the frequency regulation power reported by the ith independent energy storage power station in the frequency regulation market; grid is the secondary frequency regulation demand capacity value of the power grid in the current period;

[0259] The winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency regulation market bidding model; wherein the winning bid data includes: the winning peak-shaving quotation, the winning peak-shaving power and the winning frequency regulation capacity.

[0260] In one embodiment, the generating of the first expected profit difference and the second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data specifically includes:

[0261] The first expected income of the independent energy storage power station in the peak-shaving charging period and the second expected income in the peak-shaving discharging period are generated according to the peak-shaving income calculation model; wherein the expression of the peak-shaving income calculation model is:

[0262] Qc,t=E3-E2;

[0263] Qf,t =E1;

[0264]

[0265] Where Qc,t is the first expected benefit of the independent energy storage power station during the charging period in the peak load regulation task; Q f,t is the second expected income of the independent energy storage power station during the discharge period in the peak-shaving task; E1 is the discharge income of the independent energy storage power station during the discharge period in the peak-shaving task; E2 is the charging cost of the independent energy storage power station during the charging period in the peak-shaving task; E3 is the charging income of the independent energy storage power station during the charging period in the peak-shaving task; T f is the total number of discharge periods; C f,t P is the unit revenue of the independent energy storage power station discharging to the grid at time t; f,t is the discharge power of the independent energy storage power station at time t; Δt is the number of hours in each operating period of the peak load market; T c is the total number of charging periods; C c,t is the charging price at time t, P c,t is the peak-shaving charging power of the independent energy storage power station at time t; C i,t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i,t is the peak load-shaving power that the i-th new energy station bids for in the t-th period;

[0266] The frequency regulation benefit of the independent energy storage power station participating in the frequency regulation service is calculated according to the frequency regulation benefit calculation formula; wherein the frequency regulation benefit calculation formula is expressed as:

[0267]

[0268] In the formula, Q bj,t is the frequency modulation gain; C bj,t P is the winning bid price for each frequency regulation demand obtained after simulating the frequency regulation market for independent energy storage power stations; m,t is the expected winning bid frequency regulation capacity of the energy storage power station, which is numerically consistent with the winning bid frequency regulation capacity; N is the total number of time periods participating in the secondary frequency regulation;

[0269] The first expected benefit difference and the second expected benefit difference are generated according to the first expected benefit, the second expected benefit, and the frequency modulation benefit; wherein the expressions of the first expected benefit difference and the second expected benefit difference are:

[0270] Q cc,t =Q c,t -Q bj,t ;

[0271] Q fc,t =Q f,t -Q bj,t ;

[0272] In the formula, Q cc,t is the first expected return difference; Q fc,t is the second expected return difference.

[0273] In one embodiment, determining the task type of each time period in the current scheduling task according to the first expected profit difference and the second expected profit difference specifically includes:

[0274] The maximum chargeable time and the maximum dischargeable time of the energy storage power station are calculated according to the power of the energy storage power station; wherein the expression of the maximum chargeable time and the maximum dischargeable time is:

[0275]

[0276] Where, T is the maximum charging time; P n is the rated capacity of the independent energy storage power station; η is the charge-discharge conversion efficiency of the independent energy storage power station, which is generally a constant; Q max and Q min The maximum and minimum values ​​of the remaining capacity range obtained by deducting the winning frequency regulation capacity from the available capacity range of the independent energy storage power station;

[0277] The first expected profit differences are arranged in descending order, and the time period types corresponding to the first expected profit differences of the first preset number are selected as the charging time period; wherein the first preset number of accumulated time periods is consistent with the maximum chargeable time, and the expression of the charging time period is:

[0278] [t1,t2......t T +=maxQ cc,t ;

[0279] The second expected return differences are arranged in descending order, and the time period types corresponding to the second expected return differences of the second preset number are selected as the discharge time period; wherein the second preset number of accumulated time periods is consistent with the maximum dischargeable time, and the expression of the discharge time period is:

[0280]

[0281] Eliminate the charging and discharging periods in this scheduling task, and divide the task types of all other periods into non-peak-shaving charging and discharging periods.

[0282] In one embodiment, the corresponding quotation strategy is executed according to the task type in each time period to generate a real quotation of the independent energy storage power station in the frequency regulation market, specifically including:

[0283] For a first period of time in which the task type is a charging period and a discharging period, a first frequency regulation quotation strategy is executed while planning peak regulation;

[0284] For the second period where the task type is non-peak-shaving charging and discharging period, only the second frequency regulation quotation strategy is executed;

[0285] The first frequency regulation quotation strategy is to randomly select values ​​according to the frequency regulation quotation intervals provided at different stages to generate a real quotation for the independent energy storage power station in the frequency regulation market;

[0286] During the charging phase, set the frequency modulation quotation interval to In the discharge phase, the frequency modulation quotation interval is set to Where N t The expected frequency modulation equivalent number in the tth period is obtained from historical data, K cp is a constant coefficient greater than or equal to 1;

[0287] The second frequency regulation quotation strategy is to compare the frequency regulation revenue of the second period with the average frequency regulation revenue of the historical operating time, select the corresponding frequency regulation quotation range for value taking according to the comparison result, and generate the real quotation of the independent energy storage power station in the frequency regulation market.

[0288] In one embodiment, the peak-shaving market bidding model and the frequency regulation market bidding model are updated based on the real bid to obtain the updated winning frequency regulation capacity and the updated winning peak-shaving charging power of the independent energy storage power station, specifically including: updating the updated ranking price of each independent energy storage power station in the frequency regulation market based on the real bid, solving the frequency regulation market bidding model through the updated ranking price; obtaining the updated winning frequency regulation capacity according to the solution result of the frequency regulation bidding model; updating the updated available regulation power upper limit based on the updated winning frequency regulation capacity to obtain a first updated constraint condition and solving the peak-shaving market bidding model according to the first updated constraint condition, and obtaining the updated winning peak-shaving charging power according to the solution result.

[0289] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0290] The embodiment of the present invention provides a collaborative scheduling device based on independent energy storage, which generates the winning bid data of the independent energy storage power station based on the peak-shaving market bidding model and the frequency regulation market bidding model, provides basic data for subsequent profit calculation and scheduling task execution, and ensures the winning bid capacity and power of the independent energy storage power station. Based on the profit calculation model and the winning bid data, a first expected profit difference and a second expected profit difference are generated, and the economic benefits of the independent energy storage power station are determined through profit difference analysis, which provides a basis for determining the type of scheduling task and maximizes the economic benefits. According to the profit difference, the task type of each time period in this scheduling task is determined, including the charging period, the discharging period, and the non-peak-shaving charging and discharging period, and the resource allocation of the independent energy storage power station in the peak-shaving and frequency regulation services is optimized to improve the overall operation efficiency and economy. According to the task type, the corresponding quotation strategy is executed to generate a real quotation for the frequency regulation market. Through the real-time quotation strategy, the supply and demand matching and response time of the frequency regulation service are optimized, and the market competitiveness of the independent energy storage power station is improved. Based on the real bid, the peak-shaving market bidding model and the frequency regulation market bidding model are updated to obtain the updated winning frequency regulation capacity and peak-shaving charging power, dynamically reflect market changes, improve the supply and demand matching and response time of frequency regulation services, and improve the market adaptability and reliability of frequency regulation services. Based on the updated winning frequency regulation capacity and peak-shaving charging power, the tasks of each period in this dispatching task are executed, and the flexible call of independent energy storage power stations in the power system is realized, the energy storage service is optimized, and the reliability and stability of the power system are improved.

[0291] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A collaborative dispatching method based on independent energy storage, characterized in that: include: Generate winning bid data for independent energy storage power stations based on the peak load market bidding model and the frequency regulation market bidding model; Generate a first expected profit difference and a second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data; wherein the first expected profit difference is the profit difference between the expected profit during the peak-shaving charging period and the frequency regulation profit; and the second expected profit difference is the profit difference between the expected profit during the peak-shaving discharging period and the frequency regulation profit; Determine the task type of each time period in this scheduling task according to the first expected profit difference and the second expected profit difference; wherein the task type includes a charging time period, a discharging time period, and a non-peak-shaving charging and discharging time period; Execute the corresponding quotation strategy according to the task type of each time period, generate the real quotation of the independent energy storage power station in the frequency regulation market, and update the peak regulation market bidding model and the frequency regulation market bidding model based on the real quotation to obtain the updated winning frequency regulation capacity and the updated winning peak regulation charging power of the independent energy storage power station, and execute the tasks of each time period in this scheduling task based on the updated winning frequency regulation capacity and the updated winning peak regulation charging power.

2. A collaborative dispatching method based on independent energy storage as claimed in claim 1, characterized in that: The generation of the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model specifically includes: Obtain the initial peak-shaving quotation of the new energy station; wherein the expression of the initial peak-shaving quotation is: In the formula, C new，int，i，t is the initial peak load quotation, C i，max is the upper limit of the price offered by the i-th new energy station; C i，min is the lower limit of the quotation of the i-th new energy station; P i，q，t is the power abandonment power predicted by the i-th renewable energy station at time t; P i，q，max is the maximum abandoned power of the i-th renewable energy station at all times within a week; n is the number of renewable energy stations with peak load demand; S n，i is the installed capacity of the i-th new energy station; C max The maximum quotation limit stipulated by market trading rules; C i，min It is the maximum quotation lower limit stipulated by market trading rules; k is the deviation coefficient, which is used to control the floating amount of the highest quotation; Each independent energy storage power station participating in the secondary frequency regulation market transaction is input into the frequency regulation market quotation model, and a random number is taken in the quotation area provided by the frequency regulation market quotation model to obtain the frequency regulation declared price of each independent energy storage power station, and the frequency regulation declared price of each independent energy storage power station is sorted based on the frequency regulation market quotation order to obtain the sorted price of each independent energy storage power station in the frequency regulation market; wherein, the expression of the frequency regulation market quotation model is: [d tp，i ,d tp，i K ctp ]; In the formula, δ tp，i K is the average price of all frequency regulation bidding periods during the operation days of each energy storage power station in the past week, ctp is the frequency modulation quotation control coefficient, which is a constant coefficient greater than 1; The expression of the FM market quotation sequence is: In the formula, C i is the ranking price of the i-th independent energy storage power station in the frequency regulation market; is the declared price of the ith independent energy storage power station in the frequency regulation market; K i is the frequency regulation performance index of the i-th independent energy storage power station; is the response time indicator; To adjust the speed index, To adjust the accuracy index; The peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the frequency modulation market bidding model; wherein, The peak-shaving market bidding model is expressed as: Where, F is the revenue of the energy storage power station; N T is the number of discharge periods in the peak regulation process; C i，t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i，t is the winning bid for peak-shaving power proposed by the i-th new energy station in the t-th period; ΔT is the number of hours in each operating period of the peak-shaving market; The expression of the frequency modulation market bidding model is: Where P tp，i is the winning bid frequency regulation capacity of the i-th independent energy storage power station; n tp The number of independent energy storage power stations participating in the frequency regulation market bidding.

3. A coordinated dispatching method based on independent energy storage as claimed in claim 2, characterized in that: The peak-shaving market bidding model is solved based on the initial peak-shaving quotation, the frequency modulation market bidding model is solved based on the ranking result, and the winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the frequency modulation market bidding model, specifically: The solution of the peak-shaving market bidding model is constrained based on the first constraint condition; wherein the expression of the first constraint condition is: P i，t ≤P new，ini，i，t ; P c，t ≤P n，t ; Where P new，int，i，t is the peak load demand predicted by the i-th renewable energy station at time t, which is numerically equal to the power abandonment power P predicted by the i-th renewable energy station at time t i，q，t ;P c，t P is the peak-shaving charging power of the independent energy storage power station in period t; n，t is the upper limit of the available regulating power of the independent energy storage power station in period t; The solution of the frequency modulation market bidding model is constrained based on the second constraint condition; wherein the expression of the second constraint condition is: P tp，i ≤P tp，i，max ; Where P tp，i，max P is the frequency regulation power reported by the ith independent energy storage power station in the frequency regulation market; grid is the secondary frequency regulation demand capacity value of the power grid in the current period; The winning bid data is obtained according to the solution results of the peak-shaving market bidding model and the solution results of the frequency regulation market bidding model; wherein the winning bid data includes: the winning peak-shaving quotation, the winning peak-shaving power and the winning frequency regulation capacity.

4. A coordinated dispatching method based on independent energy storage as claimed in claim 3, characterized in that: The generating of the first expected profit difference and the second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data specifically includes: The first expected income of the independent energy storage power station in the peak-shaving charging period and the second expected income in the peak-shaving discharging period are generated according to the peak-shaving income calculation model; wherein the expression of the peak-shaving income calculation model is: Qc,t=E3-E2; Q f，t =E1; Where Qc,t is the first expected benefit of the independent energy storage power station during the charging period in the peak load regulation task; Q f，t is the second expected income of the independent energy storage power station during the discharge period in the peak-shaving task; E1 is the discharge income of the independent energy storage power station during the discharge period in the peak-shaving task; E2 is the charging cost of the independent energy storage power station during the charging period in the peak-shaving task; E3 is the charging income of the independent energy storage power station during the charging period in the peak-shaving task; T f is the total number of discharge periods; C f，t P is the unit revenue of the independent energy storage power station discharging to the grid at time t; f，t is the discharge power of the independent energy storage power station at time t; Δt is the number of hours in each operating period of the peak load market; T c is the total number of charging periods; C c，t is the charging price at time t, P c，t is the peak-shaving charging power of the independent energy storage power station at time t; C i，t is the winning bid for peak load regulation of the i-th renewable energy station in the t-th period; P i，t is the peak load-shaving power that the i-th new energy station bids for in the t-th period; The frequency regulation benefit of the independent energy storage power station participating in the frequency regulation service is calculated according to the frequency regulation benefit calculation formula; wherein the frequency regulation benefit calculation formula is expressed as: In the formula, Q bj，t is the frequency modulation gain; C bj，t P is the winning bid price for each frequency regulation demand obtained after simulating the frequency regulation market for independent energy storage power stations; m，t is the expected winning bid frequency regulation capacity of the energy storage power station, which is numerically consistent with the winning bid frequency regulation capacity; N is the total number of time periods participating in the secondary frequency regulation; The first expected benefit difference and the second expected benefit difference are generated according to the first expected benefit, the second expected benefit, and the frequency modulation benefit; wherein the expressions of the first expected benefit difference and the second expected benefit difference are: Q cc，t =Q c，t -Q bj，t ; Q fc，t =Q f，t -Q bj，t ; In the formula, Q cc，t is the first expected return difference; Q fc，t is the second expected return difference.

5. A coordinated dispatching method based on independent energy storage as claimed in claim 4, characterized in that: The determining the task type of each time period in the current scheduling task according to the first expected profit difference and the second expected profit difference specifically includes: The maximum chargeable time and the maximum dischargeable time of the energy storage power station are calculated according to the power of the energy storage power station; wherein the expression of the maximum chargeable time and the maximum dischargeable time is: Where, T is the maximum charging time; P n is the rated capacity of the independent energy storage power station; η is the charge-discharge conversion efficiency of the independent energy storage power station, which is generally a constant; Q max and Q min The maximum and minimum values ​​of the remaining capacity range obtained by deducting the winning frequency regulation capacity from the available capacity range of the independent energy storage power station; The first expected profit differences are arranged in descending order, and the time period types corresponding to the first expected profit differences of the first preset number are selected as the charging time period; wherein the first preset number of accumulated time periods is consistent with the maximum chargeable time, and the expression of the charging time period is: [t1,t2......t T ]=maxQ cc，t ; The second expected return differences are arranged in descending order, and the time period types corresponding to the second expected return differences of the second preset number are selected as the discharge time period; wherein the second preset number of accumulated time periods is consistent with the maximum dischargeable time, and the expression of the discharge time period is: Eliminate the charging and discharging periods in this scheduling task, and divide the task types of all other periods into non-peak-shaving charging and discharging periods.

6. A coordinated dispatching method based on independent energy storage as claimed in claim 5, characterized in that: The step of executing a corresponding quotation strategy according to the task type in each time period to generate a real quotation of the independent energy storage power station in the frequency regulation market specifically includes: For a first period of time in which the task type is a charging period and a discharging period, a first frequency regulation quotation strategy is executed while planning peak regulation; For the second period where the task type is non-peak-shaving charging and discharging period, only the second frequency regulation quotation strategy is executed; The first frequency regulation quotation strategy is to randomly select values ​​according to the frequency regulation quotation intervals provided at different stages to generate a real quotation for the independent energy storage power station in the frequency regulation market; During the charging phase, set the frequency modulation quotation interval to In the discharge phase, the frequency modulation quotation interval is set to Where N t The expected frequency modulation equivalent number in the tth period is obtained from historical data, K cp is a constant coefficient greater than or equal to 1; The second frequency regulation quotation strategy is to compare the frequency regulation revenue of the second period with the average frequency regulation revenue of the historical operating time, select the corresponding frequency regulation quotation range for value taking according to the comparison result, and generate the real quotation of the independent energy storage power station in the frequency regulation market.

7. A coordinated dispatching method based on independent energy storage as claimed in claim 6, characterized in that: The updating of the peak-shaving market bidding model and the frequency regulation market bidding model based on the real bid to obtain the updated winning frequency regulation capacity and the updated winning peak-shaving charging power of the independent energy storage power station specifically includes: Based on the real quotation, the updated ranking price of each independent energy storage power station in the frequency regulation market is updated, and the frequency regulation market bidding model is solved by the updated ranking price; according to the solution result of the frequency regulation bidding model, the updated winning frequency regulation capacity is obtained; The updated available adjustable power upper limit is updated based on the updated winning frequency regulation capacity, a first updated constraint condition is obtained, and the peak-shaving market bidding model is solved according to the first updated constraint condition, and the updated winning peak-shaving charging power is obtained according to the solution result.

8. A collaborative dispatching device based on independent energy storage, characterized in that: include: Data generation module, revenue calculation module, task determination module and scheduling execution module; The data generation module is used to generate the winning bid data of the independent energy storage power station based on the peak load market bidding model and the frequency regulation market bidding model; The profit calculation module is used to generate a first expected profit difference and a second expected profit difference of the independent energy storage power station based on the profit calculation model and the winning bid data; wherein the first expected profit difference is the profit difference between the expected profit during the peak-shaving charging period and the frequency regulation profit; the second expected profit difference is the profit difference between the expected profit during the peak-shaving discharging period and the frequency regulation profit; The task determination module is used to determine the task type of each time period in this scheduling task according to the first expected profit difference and the second expected profit difference; wherein the task type includes a charging time period, a discharging time period and a non-peak-shaving charging and discharging time period; The scheduling execution module is used to execute the corresponding bidding strategy according to the task type of each time period, generate the real quotation of the independent energy storage power station in the frequency regulation market, and update the peak regulation market bidding model and the frequency regulation market bidding model based on the real quotation, obtain the updated winning frequency regulation capacity and the updated winning peak regulation charging power of the independent energy storage power station, and execute the tasks of each time period in this scheduling task based on the updated winning frequency regulation capacity and the updated winning peak regulation charging power.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the collaborative scheduling method based on independent energy storage as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the collaborative scheduling method based on independent energy storage as described in any one of claims 1 to 7.

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