Energy storage charging and discharging control method based on multi-time scale transaction and differential clearing

Abstract

The invention relates to an energy storage charging and discharging control method based on multi-time scale transaction and differentiated clearing, which comprises a multi-time scale transaction control process, a differentiated clearing sorting process and a multi-objective optimization solving process, and is characterized in that the multi-time scale transaction control process is used for dynamically selecting an optimal transaction mode; the differentiated clearing sorting process is used for determining an energy storage clearing list, and the multi-objective optimization solving process is used for determining an optimal energy storage charging and discharging plan and clearing results and controlling the working state of each energy storage device. Compared with the prior art, the method can achieve the collaborative optimization of the energy storage benefit and the power grid benefit, and effectively solves the problems of scene collaboration deficiency, single clearing dimension and one-sided optimization target in the prior art through the fusion of multi-time scale transactions and the consideration of differential clearing of price and response capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage control, in particular to an energy storage charging and discharging control method based on multi-time scale trading and differentiated clearing. BACKGROUND

[0002] Industrial and commercial energy storage gradually develops from single scenario participation in the electricity market to multi-scenario integration as the core means of peak load shifting and energy optimization. At present, the existing technology has explored single mode of energy storage participating in spot trading, demand response or long-term contract.

[0003] For example, Chinese patent CN118589555A proposes a multi-time scale distributed energy storage aggregation peak shaving method. First, the next day's deep peak shaving and load shedding period and power are obtained in the day-ahead dispatching stage. Then, the aggregator predicts the behavior characteristics of the historical data of the distributed energy storage under its jurisdiction. The aggregator adopts a step bidding to develop a day-ahead bidding optimization strategy. The power trading center establishes a day-ahead dispatching optimization model with the minimum peak shaving dispatching cost as the target. In addition, considering the actual aggregation controllable capacity and the prediction bias dispatching cost and the energy storage battery loss cost, the aggregator establishes a real-time dispatching optimization model with the maximum benefit of the aggregator itself as the target. The optimal solution of the actual dispatching optimization model is solved to determine the output of the aggregator in each period of the dispatching period. This method essentially adopts a "day-ahead + real-time" double time scale and double target optimization, but does not involve "long-term contract - spot - demand response" three-level transaction coordination, and does not consider differentiated clearing, resulting in insufficient flexibility of resource allocation. Chinese patent CN119313109A proposes a multi-time scale energy storage configuration method for eliminating peak load. It generates a peak load segment set based on the annual load curve to construct a peak load set under different duration times. Then, the peak load values under different duration times are calculated and sorted by duration time from small to large to generate a peak load distribution graph. Then, the equivalent unit peak shaving cost of multi-time scale energy storage under different duration times is calculated. Finally, the required power of different time scale energy storage is determined according to the peak load distribution graph. This method optimizes the combination of energy storage at different time scales by constructing a peak load distribution graph to reduce the cost of energy storage configuration. However, this method only targets the peak shaving scenario and does not integrate the electricity market trading layer. There is no clearing mechanism, and the multi-objective of grid safety and energy storage life is not considered, which has poor adaptability. SUMMARY

[0004] The present application is to overcome the defects of the prior art and provide an energy storage charging and discharging control method based on multi-time scale trading and differentiated clearing to solve the problems of scene coordination missing, single clearing dimension and one-sided optimization target in the prior art.

[0005] The object of the application can be realized by the technical scheme: a kind of energy storage charging and discharging control method based on multi-time scale transaction and differential clearing, including multi-time scale transaction control process, differential clearing sequencing process and multi-objective optimization solving process, the multi-time scale transaction control process is used to dynamically select optimal transaction mode, the differential clearing sequencing process is used to determine energy storage clearing list, and the multi-objective optimization solving process is used to determine optimal energy storage charging and discharging plan and clearing result, to control the working state of each energy storage equipment.

[0006] Further, the multi-time scale transaction control process includes the following steps: A1, construct "medium and long-term contract layer + spot transaction layer + demand response layer" multi-time scale transaction system, complete medium and long-term contract layer configuration, spot transaction layer trigger threshold setting and demand response layer rule setting; A2, dynamically select optimal transaction mode by scene coordination.

[0007] Further, the medium and long-term contract layer configuration in step A1 is specifically according to the contract signed with power generation enterprise / sell electricity company, locks the basic discharge amount as 40%-60% of total energy storage capacity, and sets contract benchmark price, to hedge spot price fluctuation risk; Spot transaction layer trigger threshold setting is specifically setting "arbitrage threshold=contract benchmark price+marginal cost" and "valley charging threshold", by real-time acquisition "spot price", if "spot price≥arbitrage threshold", then release the un-locked energy storage capacity to participate in arbitrage discharge;If "spot price≤valley charging threshold", then start charging reserve; Demand response layer rule setting is specifically receiving grid dispatching center instruction, setting "basic compensation", "standard reward", and setting "demand response income=response power×(basic compensation+standard reward)", if grid issues response instruction, then adjust energy storage charging and discharging state according to instruction.

[0008] Further, the specific process of step A2 is: calculate the superposition value of "spot income+demand response income", and compare it with medium and long-term contract income;If superposition value>medium and long-term contract income, then trigger spot / demand response transaction preferentially;If superposition value≤medium and long-term contract income, then maintain medium and long-term contract operation to guarantee basic income.

[0009] Further, the differential clearing sequencing process includes the following steps: B1, execute three-level priority judgment to obtain corresponding priority basic income score; B2, calculate response capacity correction coefficient K; B3, calculate the final clearing score = each priority-based benefit score x K, rank in descending order of final score, and determine the energy storage clearing list of this transaction.

[0010] Further, the specific process of step B1 is: Primary priority judgment: detect whether both "spot price ≥ arbitrage threshold" and "demand response compensation ≥ basic compensation" are met; if so, sort by the value of "(spot price - charging cost) + demand response compensation" in descending order; if not, proceed to secondary priority judgment; Secondary priority judgment: detect whether only "spot price ≥ arbitrage threshold" or "demand response compensation ≥ basic compensation" is met; if only the spot meets the standard, sort by "spot price difference (discharge price - charging price)" in descending order; if only the demand response meets the standard, sort by "demand response compensation" in descending order; if not, proceed to tertiary priority judgment; Tertiary priority judgment: detect whether grid safety needs to be guaranteed; if so, sort by the emergency level of grid dispatching; if not, do not include it in this clearing, and directly calculate the response capacity correction coefficient K.

[0011] Further, the response capacity correction coefficient K in step B2 is specifically: K = 0.2 x response speed score + 0.3 x charging and discharging efficiency score + 0.3 x historical compliance rate score + 0.2 x equipment health score.

[0012] Further, the multi-objective optimization solving process includes the following steps: C1, construct a double-objective function including a main objective and a secondary objective, wherein the main objective is to maximize the comprehensive benefit of energy storage, and the secondary objective is to optimize the grid regulation benefit; C2, set constraint conditions including device constraints, grid constraints, and life constraints; C3, use an improved particle swarm algorithm with adaptive inertia weight to solve the double-objective optimization problem, and judge whether the solution meets all the constraint conditions: If yes, directly output the optimal energy storage charging and discharging plan and clearing result; If not, adjust the adaptive inertia weight and re-iterate the solution, and again check the constraint conditions; repeat the iteration until the result meets all the constraints, and finally output the optimal energy storage charging and discharging plan and clearing result.

[0013] Further, the main objective in step C1 is specifically:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019] wherein, is the medium and long-term contract income, is the spot income, is the demand response income, is the operation cost, is the depreciation cost; The sub-targets are specifically:

[0020] wherein, is the daily peak load, is the daily valley load, is the grid load at the moment, t is the grid load at the moment, is the grid load at the moment, t is the grid load at the moment, is the load fluctuation weight coefficient.

[0021] Further, the equipment constraints in the step C2 are specifically charge and discharge power and state of charge constraints: the charge power is less than or equal to the rated maximum charge power; the discharge power is less than or equal to the rated maximum discharge power; and the state of charge of the energy storage is within a corresponding upper and lower limit safety interval; the grid constraint is specifically a grid access power constraint, and the life constraint is specifically an energy storage equipment annual charge and discharge cycle number constraint.

[0022] Compared with the prior art, the present application has the following advantages: The present application designs a multi-time scale transaction control process, a differentiated dispatching sequencing process and a multi-objective optimization solving process, dynamically selects an optimal transaction mode by using the multi-time scale transaction control process, determines an energy storage dispatching list by using the differentiated dispatching sequencing process, and determines an optimal energy storage charge and discharge plan and dispatching result by using the multi-objective optimization solving process, so as to control the working states of the energy storage devices. Thus, a control architecture of "multi-time scale transaction layer+differentiated dispatching rule+multi-objective optimization model" is realized, the synergistic optimization of energy storage income and grid benefit can be realized, the problems of lack of scene synergy, single dispatching dimension and one-sided optimization target in the prior art can be effectively solved by fusing multi-time scale transactions and taking into account the differentiated dispatching of price and response capability.

[0023] In this invention, a multi-timescale trading system is constructed, consisting of a "medium-to-long-term contract layer (monthly / quarterly), a spot trading layer (daily / hourly), and a demand response layer (minute / hourly)". By dynamically selecting the optimal trading mode through scenario collaboration, the problem of isolated trading scenarios in existing technologies is solved, thereby reducing the volatility of energy storage revenue and avoiding losses in a single market.

[0024] This invention designs a differentiated clearing rule of "three-level priority judgment + response capability correction coefficient K", which breaks through the limitations of the existing single-price clearing and can improve the call rate of high-response energy storage and reduce resource waste.

[0025] This invention establishes a dual-objective optimization model of "maximizing the comprehensive benefits of energy storage + optimizing the regulation benefits of the power grid", incorporating triple constraints of equipment, power grid, and lifespan, and using an improved particle swarm optimization algorithm to solve the problem, which can effectively reduce the daily peak-valley difference of the power grid and suppress load fluctuations. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the core decision-making process of the multi-timescale trading model in Example 2; Figure 3 This is a schematic diagram of the clearing and ranking decision-making process for energy storage participation in transactions in Example 3; Figure 4 This is a schematic diagram of the solution execution process for the dual-objective optimization of energy storage in Example 4. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] Example 1

[0029] like Figure 1 As shown, an energy storage charging and discharging control method based on multi-timescale trading and differentiated clearing includes a multi-timescale trading control process, a differentiated clearing ranking process, and a multi-objective optimization solution process. The multi-timescale trading control process is used to dynamically select the optimal trading mode, the differentiated clearing ranking process is used to determine the energy storage clearing list, and the multi-objective optimization solution process is used to determine the optimal energy storage charging and discharging plan and clearing results, which are used to control the working status of each energy storage device.

[0030] Specifically, the multi-timescale transaction control process includes: a01) Configure the medium and long-term contract layer (monthly / quarterly) parameters: according to the contract signed with the power generation enterprise / selling company, lock the basic discharge capacity (40%-60% of the total capacity of the energy storage), and agree on the contract benchmark price (yuan / kWh) for hedging spot price fluctuation risk, and execute step a02); a02) Set the spot trading layer (daily / hourly) trigger threshold: set the "arbitrage threshold (yuan / kWh) = contract benchmark price (yuan / kWh) + marginal cost (yuan / kWh)", "valley charging threshold (yuan / kWh)", and real-time obtain "spot price", if "spot price ≥ arbitrage threshold", then release the un-locked energy storage capacity for arbitrage discharge; if "spot price ≤ valley charging threshold", then start charging reserve, and execute step a03); a03) Set the demand response layer (minute / hourly) response rules: receive the instructions from the power grid dispatching center, set the "basic compensation (yuan / kWh)", "target reward (yuan / kWh)", and the response income = response capacity × (basic compensation + target reward), if the power grid issues a response instruction, then adjust the charging and discharging state according to the instruction, and execute step a04); a04) Determine whether the superimposed value of "spot income + demand response income" is greater than the medium and long-term contract income, if yes, then trigger the spot / demand response transaction; if no, then maintain the medium and long-term contract operation to ensure the basic income.

[0031] The differentiated dispatching sorting process includes: b01) Execute the first priority judgment (step b011): detect whether "spot price ≥ arbitrage threshold" and "demand response compensation ≥ basic compensation" are met at the same time, if yes, then sort in descending order of "(spot price - charging cost) + demand response compensation"; if no, then execute the second priority judgment (step b012); b012) Detect whether only "spot price ≥ arbitrage threshold" or "demand response compensation ≥ basic compensation" is met, if only the spot meets the target, then sort in descending order of "spot price difference (discharge price - charging price)"; if only the demand response meets the target, then sort in descending order of "demand response compensation"; if no, then execute the third priority judgment (step b013); b013) Detect whether the power grid safety needs to be guaranteed, if yes, then sort according to the emergency degree of the power grid dispatching; if no, then do not include in this dispatching for the time being, and execute step b02); b02) Calculate the response capability correction coefficient K: K = 0.2 x response speed score (0-100 points, 100 points for responding within 15 minutes) + 0.3 x charge-discharge efficiency score (0-100 points, 90 points for 90% efficiency) + 0.3 x historical compliance rate score (0-100 points, 95 points for 95% compliance rate) + 0.2 x equipment health score (0-100 points, 98 points for 98% health), execute step b03); b03) Calculate the final clearing score: final score = each priority-based revenue score x K, determine the clearing list in descending order of the final score.

[0032] The multi-objective optimization solution process includes: c01) Construct the objective function: The main objective is to maximize the comprehensive benefit of energy storage , Wherein, ; ; ; ; ; The secondary objective is to optimize the grid regulation benefit , wherein, is the daily peak load, is the daily valley load, is the grid load at time t, is the grid load at time t-1, is the load fluctuation weight coefficient, execute step c02); c02) Set the constraint condition: Device constraints include: (Charging power ≤ rated maximum charging power), (Discharge power ≤ rated maximum discharge power), (Storage charge state is in the safe interval); Grid constraints: (The upper limit of grid access power); Lifetime constraint: annual charge-discharge cycle number ≤ N (N is the cycle number corresponding to the design life of the energy storage device), execute step c03); c03) Solve using improved particle swarm algorithm: introduce adaptive inertia weight to solve multi-objective optimization problems, if the solution meets all the constraints, output the optimal charge-discharge plan and clearing result; if not, reiterate until the constraints are met.

[0033] In summary, the scheme builds a control architecture of "multi-time scale transaction layer + differentiated clearing rules + multi-objective optimization model" in energy storage transactions, which can realize the collaborative optimization of energy storage benefits and grid benefits.

[0034] It should be noted that in the process of actually applying the above method, an electronic device including a central processing unit (CPU) can be used to perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0035] The various components in the device are connected to the I / O interface, including: input units such as keyboards, mice, etc.; output units such as various types of displays, speakers, etc.; storage units such as magnetic disks, optical disks, etc.; and communication units such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunications networks. The processing unit performs the various methods and processes described above, such as the method of the present application. For example, in some embodiments, the method of the present application can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the method of the present application described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method of the present application by any other appropriate means (e.g., with the help of firmware).

[0036] The functions described above in the present application can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0037] Program code for carrying out operations of the methods of the present application can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, causes the machine to perform the functions / operations specified in the flow diagrams and / or block diagrams. The program code can execute entirely on a machine, partly on a machine, as a stand-alone software package, partly on a machine and partly on a remote machine or entirely on a remote machine or server.

[0038] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable storage media can include, without limitation, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include one or more lines of electrical connections, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0039] Embodiment 2

[0040] Figure 2 The core decision-making process of the energy storage multi-time scale trading mode is shown, which is used to dynamically select the optimal trading mode through hierarchical parameter configuration and benefit comparison, to realize energy storage benefit stabilization and risk hedging, and the specific process is as follows: 1. Medium and long-term contract layer configuration (corresponding to step a01 in embodiment 1): According to the contract signed with the power generation enterprise / selling company, the basic discharge amount of 40%-60% of the total capacity of the energy storage is locked, and the contract benchmark price (unit: yuan / kWh) is agreed, to hedge the spot price fluctuation risk, and after completion, enter the spot trading layer setting link.

[0041] 2. Spot trading layer trigger threshold setting (corresponding to step a02 in embodiment 1): First, calculate the arbitrage threshold, the formula is "arbitrage threshold (yuan / kWh)=contract benchmark price (yuan / kWh)+marginal cost (yuan / kWh)", and set the valley section charging threshold (yuan / kWh); After obtaining the current spot price in real time, perform the operation in two cases: if the spot price≥arbitrage threshold, release the unlocked energy storage capacity to participate in discharge arbitrage; if the spot price≤valley section charging threshold, start energy storage charging reserve, and after completion, enter the demand response layer rule setting link.

[0042] 3. Demand response layer rule setting (corresponding to step a03 in embodiment 1): receive the instruction issued by the power grid dispatching center, set the basic compensation (yuan / kWh) and the standard reward (yuan / kWh), wherein the demand response income is calculated according to “response income = response power × (basic compensation + standard reward)”; if the power grid issues a response instruction, immediately adjust the state of charge and discharge of the energy storage according to the instruction, and after completion, enter the income comparison judgment link.

[0043] 4. Transaction mode selection judgment (corresponding to step a04 in embodiment 1): calculate the superposition value of “spot income + demand response income”, and compare it with the medium and long-term contract income; if the superposition value > medium and long-term contract income, prefer to trigger spot / demand response transaction; if the superposition value ≤ medium and long-term contract income, maintain the operation of the medium and long-term contract to guarantee the basic income, and the process ends.

[0044] Embodiment 3

[0045] Figure 3 The shown is the clearing order decision process of energy storage participating in transaction, through three-level priority judgment and response capacity correction, realizing differentiated clearing, and the specific process is as follows: 1. Three-level priority judgment (corresponding to steps b011-b013 in embodiment 1) First-level priority judgment (corresponding to step b011 in embodiment 1): detect whether “spot price ≥ arbitrage threshold” and “demand response compensation ≥ basic compensation” are met at the same time; if yes, sort in descending order according to the value of “(spot price - charging cost) + demand response compensation”; if not, enter the second-level priority judgment.

[0046] Second-level priority judgment (corresponding to step b012 in embodiment 1): detect whether only “spot price ≥ arbitrage threshold” or “demand response compensation ≥ basic compensation” is met; if only the spot meets the standard, sort in descending order according to “spot price difference (discharge price - charging price)”; if only the demand response meets the standard, sort in descending order according to “demand response compensation”; if not, enter the third-level priority judgment.

[0047] Third-level priority judgment (corresponding to step b013 in embodiment 1): detect whether the power grid safety needs to be guaranteed; if yes, sort according to the emergency degree of power grid dispatching (not included in this clearing for the time being); if not, directly not included in this clearing for the time being, and enter the response capacity correction link.

[0048] 2、Response ability correction coefficient K calculation (corresponding to step b02 in example 1): K is weighted by four indicators, the formula is "K=0.2x response speed score+0.3x charge-discharge efficiency score+0.3x historical compliance rate score+0.2x equipment health score"; wherein the score standard of each item is: response speed 100 points within 15 minutes (0-100 point interval), charge-discharge efficiency 90 points (0-100 point interval), historical compliance rate 95 points (0-100 point interval), equipment health 98 points (0-100 point interval).

[0049] 3、Final clearing list determination (corresponding to step b03 in example 1): calculate the final clearing score, the formula is "final score=each priority basic income scorex K"; arrange in descending order according to the final score to determine the energy storage clearing list of this transaction, and the process is ended.

[0050] Example 4

[0051] Figure 4 The figure shows the solution execution process of the energy storage double target optimization, which outputs the optimal charge-discharge plan and clearing result by constructing the objective function, setting the constraint conditions and improving the algorithm iteration, and the specific process is as follows: 1、Double target function construction (corresponding to step c01 in example 1) Main target: maximize the comprehensive income of energy storage; Secondary target: optimal grid regulation benefit.

[0052] 2、Set three types of constraint conditions (corresponding to step c02 in example 1) Device constraint: charge-discharge power constraint and energy storage state of charge constraint; Grid constraint: grid access power constraint; Lifetime constraint: energy storage device annual charge-discharge cycle constraint.

[0053] 3、Improved particle swarm algorithm solution (corresponding to step c03 in example 1): introduce adaptive inertia weight to solve the multi-objective optimization problem; judge whether the solution result meets all the constraint conditions: if yes, directly output the optimal charge-discharge plan and clearing result, and the process is ended; If not, adjust the adaptive inertia weight and reiterate the solution, and check the constraint conditions again; repeat the iteration until the result meets all the constraints, and then output the optimal charge-discharge plan and clearing result, and the process is ended.

Claims

1. A method for energy storage charging and discharging control based on multi-time scale trading and differentiated outages, characterized in that, The method comprises a multi-time scale transaction control process, a differentiated dispatching sequencing process and a multi-objective optimization solving process, the multi-time scale transaction control process is used for dynamically selecting an optimal transaction mode, the differentiated dispatching sequencing process is used for determining a storage energy dispatching list, and the multi-objective optimization solving process is used for determining an optimal storage energy charging and discharging plan and a dispatching result, and is used for controlling the working state of each storage energy device.

2. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 1, characterized in that, The multi-time scale transaction control process comprises the following steps: A1, a multi-time scale transaction system of a "medium and long-term contract layer + spot transaction layer + demand response layer" is constructed, medium and long-term contract layer configuration, spot transaction layer trigger threshold setting and demand response layer rule setting are completed; A2, an optimal transaction mode is dynamically selected through scene coordination judgment.

3. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 2, characterized in that, The medium and long-term contract layer configuration in the step A1 is specifically according to a signed contract with a power generation enterprise / selling power company, locking a basic discharging capacity of 40%-60% of the total capacity of the storage energy, and setting a contract benchmark price, which is used for hedging the risk of spot price fluctuation; The spot transaction layer trigger threshold setting is specifically setting a "arbitrage threshold = contract benchmark price + marginal cost" and a "valley charging threshold", by acquiring a "spot electricity price" in real time, if the "spot electricity price >= arbitrage threshold", the un-locked storage energy capacity is released to participate in discharging arbitrage, if the "spot electricity price <= valley charging threshold", charging reserve is started; The demand response layer rule setting is specifically receiving an instruction from a power grid dispatching center, setting a "basic compensation", a "standard reward", and setting a "demand response income = response electricity quantity * (basic compensation + standard reward)", if the power grid issues a response instruction, the storage energy charging and discharging state is adjusted according to the instruction.

4. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 3, characterized in that, The specific process of the step A2 is that: a superimposed value of "spot income + demand response income" is calculated, and is compared with the medium and long-term contract income; if the superimposed value > the medium and long-term contract income, the spot / demand response transaction is triggered preferentially; if the superimposed value <= the medium and long-term contract income, the medium and long-term contract operation is maintained to guarantee the basic income.

5. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 4, characterized in that, The differentiated dispatching sequencing process comprises the following steps: B1, a three-level priority judgment is performed to obtain a corresponding priority basic income score; B2, a response capacity correction coefficient K is calculated; B3, a final dispatching score = each priority basic income score * K is calculated, the final score is arranged in descending order, and a storage energy dispatching list of this transaction is determined.

6. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 5, characterized in that, The specific process of the step B1 is: The first-level priority judgment: whether "spot electricity price >= arbitrage threshold" and "demand response compensation >= basic compensation" are satisfied at the same time is detected; if yes, the value of "(spot electricity price - charging cost) + demand response compensation" is arranged in descending order; if no, the second-level priority judgment is entered; The second-level priority judgment: whether only "spot electricity price >= arbitrage threshold" or "demand response compensation >= basic compensation" is satisfied is detected; if only the spot meets the standard, the "spot price difference (discharging price - charging price)" is arranged in descending order; if only the demand response meets the standard, the "demand response compensation" is arranged in descending order; if no, the third-level priority judgment is entered; The third-level priority judgment: whether "spot electricity price < arbitrage threshold" and "demand response compensation < basic compensation" are satisfied at the same time is detected; if yes, the "basic compensation" is arranged in descending order; if no, the first-level priority judgment is entered. The third priority judgment is to detect whether the power grid safety needs to be guaranteed, if yes, the emergency degree of power grid dispatching is sorted, if no, the response capability correction coefficient K is directly calculated without being included in the current clearing.

7. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 5, characterized in that, The response capability correction coefficient K in the step B2 is specifically: K=0.2*response speed score+0.3*charge-discharge efficiency score+0.3*historical compliance rate score+0.2*equipment health score. 8.The energy storage charge and discharge control method based on multi-time scale transaction and differentiated out clearing according to claim 4, characterized in that, The multi-objective optimization solving process comprises the following steps: C1, constructing a double-target function comprising a main target and a secondary target, wherein the main target is to maximize the comprehensive benefits of energy storage, and the secondary target is to optimize the grid regulation benefits; C2, setting constraint conditions comprising equipment constraints, grid constraints and life constraints; C3, using an improved particle swarm algorithm with adaptive inertia weight to solve the double-target optimization problem, and judging whether the solving result meets all the constraint conditions: if yes, directly outputting the optimal energy storage charge-discharge plan and the clearing result; if no, re-iterating the solving after adjusting the adaptive inertia weight, and checking the constraint conditions again; repeating the iteration until the result meets all the constraints, and finally outputting the optimal energy storage charge-discharge plan and the clearing result. 9.The energy storage charge and discharge control method based on multi-time scale transaction and differentiated out clearing according to claim 8, wherein, The main target in the step C1 is specifically: ， ， ， ， ，， ， wherein, is the long-term contract revenue, is the spot revenue, is the demand response revenue, is the operating cost, is the depreciation cost; The secondary target is specifically: ， wherein, is the daily peak load, is the daily valley load, is t is the grid load at time t, is t is the grid load at time t-1, is the load fluctuation weight coefficient.

10. The energy storage charging and discharging control method based on multi-time scale transaction and differentiated out clearing according to claim 8, characterized in that, The equipment constraints in the step C2 are specifically charge-discharge power and state of charge constraints: charging power≤rated maximum charging power; discharging power≤rated maximum discharging power; the state of charge of energy storage is within the corresponding upper and lower limit safety interval; The grid constraint is specifically a grid access power constraint, and the life constraint is specifically an energy storage equipment annual charge-discharge cycle number constraint.

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