A coordination optimization method and system for energy storage power station participating in multi-type peak regulation transaction

Abstract

A kind of energy storage power station participates in the coordination optimization method and system of multiple type peak regulation transaction, comprising: based on the optimal model of pre-constructed transaction electricity price and transaction power of energy storage power station, the best power of energy storage power station participating in different time scale peak regulation auxiliary service transaction is obtained by iterative calculation;Based on the best power of energy storage power station participating in different time scale peak regulation auxiliary service transaction, the peak regulation transaction of energy storage power station is coordinated and optimized;Wherein, transaction power is determined based on the peak regulation auxiliary service transaction variety used by energy storage power station;Optimization model is based on the relationship between the income of different time scale peak regulation auxiliary service transaction variety used by energy storage power station and daily penalty cost and daily loss cost of energy storage power station, and is constructed to maximize daily net income of energy storage power station as target.This application realizes the optimal arrangement of energy storage power station participating in multiple time scale peak regulation auxiliary service different transaction variety, prolongs the operating life of energy storage power station, and improves the overall income level of energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage, specifically to a coordinated optimization method and system for energy storage power stations participating in multiple types of peak-shaving transactions. Background Technology

[0002] To ensure the safe and economical transmission of electricity from the generation side to the user side, services such as frequency regulation, voltage regulation, peak shaving, and black start are provided by generation, grid, or demand-side resources. Peak shaving services can be divided into basic peak shaving services and paid peak shaving services. Paid peak shaving refers to both deep peak shaving services provided by generating units exceeding the prescribed peak shaving depth and start-up / shutdown peak shaving services provided by thermal power units in accordance with dispatch instructions to complete unit start-up and shutdown within a specified time. Currently, energy storage power stations can enter the peak shaving ancillary service market as independent market entities. Peak shaving ancillary services have various trading varieties, and the corresponding trading rules, such as trading cycles and trading prices, vary depending on the different trading varieties of energy storage peak shaving ancillary services.

[0003] Typically, when a peak-shaving power source cannot be deployed due to its own reasons or fails to meet the lower limit of its regulation performance, it is subject to assessment. Therefore, the net revenue of an energy storage power station participating in peak-shaving ancillary service transactions consists of three parts: compensation received for participating in peak shaving, penalties for failing to meet peak-shaving targets, and operating losses incurred by the power source itself. As peak-shaving compensation revenue increases, energy storage losses will also increase, and its net revenue may not necessarily be at its maximum.

[0004] In summary, existing energy storage peak-shaving operation strategies cannot meet the operational needs of energy storage power stations participating in different peak-shaving ancillary service trading products across multiple time scales. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides an optimized operation method for energy storage power stations participating in peak-shaving ancillary service transactions, comprising:

[0006] Obtain the traded electricity from energy storage power station transactions, and the traded electricity price determined based on the peak-shaving ancillary service trading products adopted by the energy storage power station;

[0007] Based on the transaction price and transaction power, the optimal power volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales is obtained through iterative calculation using a pre-built optimization model.

[0008] The participation of the energy storage power station in peak shaving transactions is coordinated and optimized based on the optimal electricity volume for participating in peak shaving ancillary service transactions at different time scales.

[0009] The optimization model is constructed with the goal of maximizing the daily net profit of the energy storage power station, based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by the energy storage power station and the daily penalty cost and daily loss cost of the energy storage power station.

[0010] Preferably, the peak-shaving ancillary service transactions adopted by the energy storage power station include: monthly bilateral negotiated transactions, deep peak-shaving bidding transactions, start-stop peak-shaving bidding transactions, and grid dispatch unilateral transactions.

[0011] Preferably, the construction of the optimization model includes:

[0012] Based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by energy storage power stations and the daily penalty cost and daily loss cost of energy storage power stations, an optimization operation objective function for energy storage power stations is established with the goal of maximizing the daily net revenue of energy storage power stations.

[0013] Set constraints on the power relationship of peak-shaving ancillary service transactions and the state of charge of energy storage power stations for the objective function.

[0014] Preferably, the objective function is as follows:

[0015] max I day =I B +I D +I S +I R -C P -C L

[0016] In the formula, I day For the daily net revenue of energy storage power stations; I B The average daily revenue from bilateral negotiated transactions; I D For the average daily revenue of deep peak shaving transactions; I S The average daily revenue from starting and stopping peak-shaving transactions; I R C is the average daily revenue from unilateral transactions involving power grid access. P The daily penalty cost of the energy storage power station; C L This refers to the daily loss cost of the energy storage power station.

[0017] Preferably, the revenue I from the monthly bilateral negotiated transaction B As shown in the following formula:

[0018]

[0019] In the formula, The electricity price for each time interval is negotiated bilaterally; P t B The electricity traded in each time interval is negotiated and traded bilaterally; Δt is the time interval.

[0020] The revenue from the deep peak shaving auction transaction I D As shown below:

[0021]

[0022] In the formula, The trading price for each time interval in deep peak shaving trading; P t D For deep peak shaving trading, the traded electricity for each time interval;

[0023] The revenue from the peak-shaving bidding transaction is I. S As shown below:

[0024] I S =nρ S P S

[0025] In the formula, n represents the number of times peak regulation is started and stopped; ρ S The transaction price for peak-shaving trading; P S The electricity traded for starting and stopping peak shaving transactions;

[0026] The revenue from the unilateral transaction of the power grid dispatch I R As shown below:

[0027]

[0028] In the formula, The transaction price for each time interval of the unilateral transaction for grid access; P t R Electricity traded through unilateral transactions for grid access.

[0029] Preferably, the daily penalty cost C of the energy storage power station P As shown in the following formula:

[0030]

[0031] In the formula, k is the penalty coefficient, and ΔP t Let ρ be the output deviation at time t. t P The penalty electricity price at time t; the daily loss cost C of the energy storage power station. L As shown in the following formula:

[0032]

[0033] In the formula, C tot For the total cost of the energy storage power station, f[min(S) tThe minimum state of charge of an energy storage power station in a day is the number of cycles corresponding to the depth of discharge. The relationship curve between the number of cycles and the depth of discharge is usually provided by the energy storage manufacturer.

[0034] Preferably, the power relationship constraint for the peak-shaving ancillary service transaction is as follows:

[0035] 0≤P t B +P t D +P t R ≤P rat -P S

[0036] In the formula, P rat This refers to the rated power of the energy storage power station;

[0037] The state of charge constraint of the energy storage power station is shown in the following formula:

[0038]

[0039] In the formula, S(t) represents the state of charge of the energy storage power station; S max S min The upper and lower limits of the state of charge of the energy storage power station are given; ΔS(t) is the increase in the amount of electricity generated by the energy storage power station in the t-th time period; E rat This refers to the rated capacity of the energy storage power station.

[0040] Based on the same inventive concept, this invention provides a coordinated optimization system for energy storage power stations to participate in multiple types of peak shaving transactions, including: an acquisition module, a calculation module and an optimization module;

[0041] The acquisition module acquires the traded electricity of the energy storage power station and the traded electricity price determined based on the peak-shaving ancillary service trading product adopted by the energy storage power station;

[0042] The calculation module, based on the transaction price and the transaction power, uses a pre-built optimization model to iteratively calculate the optimal amount of electricity for the energy storage power station to participate in peak-shaving ancillary service transactions at different time scales.

[0043] The optimization module coordinates and optimizes the participation of the energy storage power station in peak shaving transactions based on the optimal electricity volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales.

[0044] Preferably, it also includes: a model building module;

[0045] The model building module is constructed based on the relationship between the revenue of peak-shaving ancillary service trading products at different time scales used by energy storage power stations and the daily penalty cost and daily loss cost of energy storage power stations, with the goal of maximizing the daily net revenue of energy storage power stations.

[0046] Preferably, the model building module includes:

[0047] Objective function construction submodule and constraint construction submodule;

[0048] The objective function construction submodule establishes an optimized operation objective function for the energy storage power station based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by the energy storage power station and the daily penalty cost and daily loss cost of the energy storage power station, with the goal of maximizing the daily net profit of the energy storage power station.

[0049] The constraint construction submodule sets constraints on the power relationship of peak-shaving ancillary service transactions and the state of charge of energy storage power stations for the objective function.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] A coordinated optimization method for energy storage power stations participating in multiple types of peak-shaving transactions includes: acquiring the traded electricity of the energy storage power station and the traded electricity price determined based on the peak-shaving ancillary service trade varieties adopted by the energy storage power station; using the traded electricity price and traded electricity, iteratively calculating the optimal electricity volume for the energy storage power station to participate in peak-shaving ancillary service transactions at different time scales using a pre-built optimization model; and coordinating and optimizing the energy storage power station's participation in peak-shaving transactions based on the optimal electricity volume for participating in peak-shaving ancillary service transactions at different time scales. The optimization model is constructed with the objective of maximizing the daily net profit of the energy storage power station, based on the relationship between the revenue of the peak-shaving ancillary service trade varieties adopted by the energy storage power station at different time scales and the daily penalty cost and daily loss cost of the energy storage power station. This method achieves the optimal arrangement for energy storage power stations to participate in different types of peak-shaving ancillary services at multiple time scales, extends the operating life of the energy storage power station, and improves the overall profitability of the energy storage power station. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the steps of the optimized operation method for an energy storage power station to participate in peak-shaving ancillary service transactions according to the present invention.

[0053] Figure 2 A chart showing the trading products for peak-shaving ancillary services of energy storage power stations;

[0054] Figure 3 This is a flowchart of the optimized operation method for energy storage power stations to participate in peak shaving ancillary service transactions according to the present invention. Detailed Implementation

[0055] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0056] Example 1:

[0057] An optimized operation method for energy storage power stations participating in peak-shaving ancillary service transactions, such as... Figure 1 As shown, it includes:

[0058] Step 1: Obtain the traded electricity from the energy storage power station and the traded electricity price determined based on the peak-shaving ancillary service trading product adopted by the energy storage power station;

[0059] Step 2: Based on the transaction price and transaction power, the optimal power volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales is obtained through iterative calculation using a pre-built optimization model;

[0060] Step 3: Based on the optimal electricity volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales, coordinate and optimize the participation of the energy storage power station in peak shaving transactions;

[0061] The optimization model is constructed with the goal of maximizing the daily net profit of the energy storage power station, based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by the energy storage power station and the daily penalty cost and daily loss cost of the energy storage power station.

[0062] A coordinated optimization method for energy storage power stations participating in multiple types of peak-shaving transactions is as follows: Figure 3 As shown,

[0063] Step 1, obtaining the traded electricity from the energy storage power station and the traded electricity price determined based on the peak-shaving ancillary service trading product adopted by the energy storage power station, specifically includes:

[0064] Obtain trading products for peak-shaving ancillary services of energy storage power stations;

[0065] like Figure 2 As shown, the peak-shaving ancillary service trading products for energy storage power stations include four types: monthly bilateral negotiated transactions, deep peak-shaving bidding transactions, start-stop peak-shaving bidding transactions, and grid dispatch unilateral transactions.

[0066] 1) Monthly bilateral negotiated transactions refer to transactions in which energy storage power stations negotiate with wind power and photovoltaic new energy power stations to determine the peak-shaving trading period, trading price, trading power and trading volume, and are approved and executed by the dispatching agency;

[0067] 2) Deep peak shaving competitive bidding refers to the process where energy storage power stations need to submit their trading intentions, including the trading period, trading price, trading power, and trading volume, to the peak shaving auxiliary service trading platform for centralized market competitive bidding. After the power dispatching agency conducts safety verification, the transaction is confirmed and executed through a market-based competitive bidding clearing mechanism.

[0068] 3) Start-stop peak shaving bidding transaction refers to the transaction in which energy storage power stations need to submit a transaction intention to the peak shaving auxiliary service platform, including the transaction period, transaction price, transaction power and transaction volume, and the transaction is confirmed and executed through the market-based bidding clearing mechanism after the power dispatching agency conducts safety verification.

[0069] 4) Grid dispatch unilateral transaction refers to the direct grid dispatch unilateral market clearing if there is still peak-shaving demand after market bidding clearing;

[0070] Step 2: Based on the transaction price and the transaction power, the optimal power volume for the energy storage power station to participate in peak-shaving ancillary service transactions at different time scales is obtained through iterative calculation using a pre-built optimization model. The construction of the optimization model in this step includes:

[0071] Constructing the optimal operation objective function of the energy storage power station

[0072] The objective function for optimizing the operation of the energy storage power station is as follows:

[0073] max I day =I B +I D +I S +I R -C P -C L

[0074] In the formula, I day C represents the daily net revenue of the energy storage power station. P The daily penalty cost of the energy storage power station; C L This refers to the daily loss cost of the energy storage power station.

[0075] Among them, the revenue of energy storage power stations participating in peak shaving ancillary service transactions comes from four categories: monthly bilateral negotiated transactions, deep peak shaving bidding transactions, start-stop peak shaving bidding transactions, and grid dispatch unilateral transactions.

[0076] 1) The revenue from monthly bilateral negotiated transactions is as follows:

[0077]

[0078] In the formula, I B The average daily revenue from bilateral negotiated transactions; The electricity price for each time interval is negotiated bilaterally; P t B The electricity traded in each time interval is negotiated and traded bilaterally; Δt is the time interval.

[0079] 2) The revenue from deep peak shaving auction trading is as follows:

[0080]

[0081] In the formula, I D The average daily revenue from deep peak shaving transactions; The trading price for each time interval in deep peak shaving trading; P t D The traded electricity for each time interval of deep peak shaving trading.

[0082] 3) The revenue from start / stop peak shaving auction transactions is as follows:

[0083] I S =nρ S P S

[0084] In the formula, I S ρ represents the average daily revenue from peak shaving and shutdown transactions; n represents the number of peak shaving and shutdown transactions; ρ S The transaction price for peak-shaving trading; P S The electricity traded for starting and stopping peak shaving transactions.

[0085] 4) The revenue from unilateral transactions by the power grid is as follows:

[0086]

[0087] In the formula, I R The average daily revenue from one-sided transactions involving power grid access; The transaction price for each time interval of the unilateral transaction for grid access.

[0088] The losses of energy storage power stations include:

[0089] 1) The daily penalty cost of an energy storage power station is as follows:

[0090]

[0091] In the formula, k is the penalty coefficient, and ΔP t Let ρ be the output deviation at time t. t P Let t be the penalty price at time t.

[0092] 2) The daily loss cost of the energy storage power station is as follows:

[0093]

[0094] In the formula, C tot For the total cost of the energy storage power station, f[min(S) t The minimum state of charge (SOC) of an energy storage power station within a day represents the number of cycles corresponding to the depth of discharge. The relationship curve between the number of cycles and the depth of discharge is usually provided by the energy storage manufacturer.

[0095] The constraints are as follows: The power transaction relationships of the energy storage power station participating in monthly bilateral negotiated transactions, deep peak-shaving bidding transactions, start-stop peak-shaving bidding transactions, and grid dispatch unilateral transactions at various times are as follows:

[0096] 0≤P t B +P t D +P t R ≤P rat -P S

[0097] In the formula, P rat This refers to the rated power of the energy storage power station.

[0098] The state of charge constraints for energy storage power stations are as follows:

[0099]

[0100] In the formula, S(t) represents the state of charge of the energy storage power station; S max S min The upper and lower limits of the state of charge of the energy storage power station; ΔS(t) is the increase in the amount of electricity generated by the energy storage power station in the t-th time period; E rat This refers to the rated capacity of the energy storage power station.

[0101] Step 2 uses iterative model calculations until the optimal solution is obtained, thus determining the optimal electricity volume for the energy storage power station to participate in peak-shaving ancillary service transactions at different time scales.

[0102] Step 3: Based on the optimal electricity volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales, coordinate and optimize the participation of the energy storage power station in peak shaving transactions.

[0103] This invention provides a coordinated optimization method for energy storage power stations to participate in multiple types of peak-shaving transactions. It considers participation in different types of peak-shaving ancillary service transactions across multiple time scales, improving the overall revenue and utilization efficiency of the energy storage power station. It also considers the loss costs of the energy storage power station, extending its operational lifespan. This method can optimize the peak-shaving operation strategy of the energy storage power station and enhance its overall peak-shaving revenue.

[0104] Example 2:

[0105] Based on the same inventive concept, this invention also provides an optimized operation system for energy storage power stations participating in peak-shaving ancillary service transactions, comprising:

[0106] Acquisition module, calculation module, and optimization module;

[0107] The acquisition module acquires the traded electricity of the energy storage power station and the traded electricity price determined based on the peak-shaving ancillary service trading product adopted by the energy storage power station;

[0108] The calculation module, based on the transaction price and the transaction power, uses a pre-built optimization model to iteratively calculate the optimal amount of electricity for the energy storage power station to participate in peak-shaving ancillary service transactions at different time scales.

[0109] The optimization module coordinates and optimizes the participation of the energy storage power station in peak shaving transactions based on the optimal electricity volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales.

[0110] Preferably, it also includes: a model building module;

[0111] The model building module is constructed based on the relationship between the revenue of peak-shaving ancillary service trading products at different time scales used by energy storage power stations and the daily penalty cost and daily loss cost of energy storage power stations, with the goal of maximizing the daily net revenue of energy storage power stations.

[0112] Preferably, the model building module includes:

[0113] Objective function construction submodule and constraint construction submodule;

[0114] The objective function construction submodule establishes an optimized operation objective function for the energy storage power station based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by the energy storage power station and the daily penalty cost and daily loss cost of the energy storage power station, with the goal of maximizing the daily net profit of the energy storage power station.

[0115] The constraint construction submodule sets constraints on the power relationship of peak-shaving ancillary service transactions and the state of charge of energy storage power stations for the objective function.

[0116] Preferably, the objective function is as follows:

[0117] max I day =I B +I D +I S +I R -C P -C L

[0118] In the formula, I day For the daily net revenue of the energy storage power station; I B The average daily revenue from bilateral negotiated transactions; I D For the average daily revenue of deep peak shaving transactions; I S The average daily revenue from starting and stopping peak-shaving transactions; I R C is the average daily revenue from unilateral transactions involving power grid access. P The daily penalty cost of the energy storage power station; C L This refers to the daily loss cost of the energy storage power station.

[0119] Preferably, the revenue I from the monthly bilateral negotiated transaction B As shown in the following formula:

[0120]

[0121] In the formula, The electricity price for each time interval is negotiated bilaterally; P t B The electricity traded in each time interval is negotiated and traded bilaterally; Δt is the time interval.

[0122] The revenue from the deep peak shaving auction transaction I D As shown below:

[0123]

[0124] In the formula, The trading price for each time interval in deep peak shaving trading; P t D The traded electricity for each time interval of deep peak shaving trading.

[0125] The revenue from the peak-shaving bidding transaction is I. S As shown below:

[0126] I S =nρ S P S

[0127] In the formula, n represents the number of times peak regulation is started and stopped; ρ S The transaction price for peak-shaving trading; P S The electricity traded for starting and stopping peak shaving transactions.

[0128] The revenue from the unilateral transaction of the power grid dispatch I R As shown below:

[0129]

[0130] In the formula, The transaction price for each time interval of the unilateral transaction for grid access; P t R Electricity traded through unilateral transactions for grid access.

[0131] Preferably, the daily penalty cost C of the energy storage power station P As shown in the following formula:

[0132]

[0133] In the formula, k is the penalty coefficient, and ΔP t Let ρ be the output deviation at time t.t P Let be the penalty electricity price at time t.

[0134] The daily loss cost C of the energy storage power station L As shown in the following formula:

[0135]

[0136] In the formula, C tot For the total cost of the energy storage power station, f[min(S) t The minimum state of charge (SOC) of an energy storage power station within a day represents the number of cycles corresponding to the depth of discharge. The relationship curve between the number of cycles and the depth of discharge is usually provided by the energy storage manufacturer.

[0137] Preferably, the power relationship constraint for the peak-shaving ancillary service transaction is as follows:

[0138] 0≤P t B +P t D +P t R ≤P rat -P S

[0139] In the formula, P rat This refers to the rated power of the energy storage power station.

[0140] The state of charge constraint of the energy storage power station is shown in the following formula:

[0141]

[0142] In the formula, S(t) represents the state of charge of the energy storage power station; S max S min The upper and lower limits of the state of charge of the energy storage power station are given; ΔS(t) is the increase in the amount of electricity generated by the energy storage power station in the t-th time period; E rat This refers to the rated capacity of the energy storage power station.

[0143] The specific functions implemented by each module in this embodiment are the same as those in Embodiment 1, and will not be repeated here.

[0144] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0145] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0147] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0149] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A coordinated optimization method for energy storage power stations participating in multiple types of peak-shaving transactions, characterized in that, include: Obtain the traded electricity from energy storage power station transactions, and the traded electricity price determined based on the peak-shaving ancillary service trading products adopted by the energy storage power station; Based on the transaction price and transaction power, the optimal power volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales is obtained through iterative calculation using a pre-built optimization model. The participation of the energy storage power station in peak shaving transactions is coordinated and optimized based on the optimal electricity volume for participating in peak shaving ancillary service transactions at different time scales. The optimization model is constructed with the goal of maximizing the daily net profit of the energy storage power station, based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by the energy storage power station and the daily penalty cost and daily loss cost of the energy storage power station. The peak-shaving ancillary service transactions adopted by the energy storage power station include: monthly bilateral negotiated transactions, deep peak-shaving bidding transactions, start-stop peak-shaving bidding transactions, and grid dispatch unilateral transactions; The revenue from the monthly bilateral negotiated transactions As shown in the following formula: In the formula, The transaction price for each time interval of the bilaterally negotiated transaction; The electricity traded between the two parties is negotiated for each time interval. For time intervals; The revenue from the deep peak shaving auction transaction As shown below: In the formula, The transaction price for each time interval of deep peak shaving trading; For deep peak shaving trading, the traded electricity for each time interval; The revenue from the peak shaving and peak-shaving auction transaction As shown below: In the formula, n represents the number of times peak regulation is started and stopped; The transaction price for peak-shaving transactions; The electricity traded for starting and stopping peak shaving transactions; The revenue from the unilateral transaction of the power grid As shown below: In the formula, The transaction price for each time interval of the unilateral transaction for grid access; Electricity traded through unilateral transactions for grid access; The daily penalty cost of the energy storage power station As shown in the following formula: In the formula, The penalty coefficient is... Let be the output deviation at time t. Let be the penalty electricity price at time t; Daily loss cost of the energy storage power station As shown in the following formula: In the formula, The total cost of an energy storage power station, The minimum state of charge of an energy storage power station in a day, i.e., the number of cycles corresponding to the depth of discharge, is usually given by the energy storage manufacturer.

2. The optimization method as described in claim 1, characterized in that, The construction of the optimization model includes: Based on the relationship between the revenue of peak-shaving ancillary service trading products with different time scales adopted by energy storage power stations and the daily penalty cost and daily loss cost of energy storage power stations, an optimal operation objective function for energy storage power stations is established with the goal of maximizing the daily net revenue of energy storage power stations. Set constraints on the power relationship of peak-shaving ancillary service transactions and the state of charge of energy storage power stations for the objective function.

3. The optimization method as described in claim 2, characterized in that, The objective function is shown in the following equation: In the formula, This represents the daily net revenue of the energy storage power station; This represents the average daily revenue from monthly bilateral negotiated transactions. The average daily revenue from deep peak shaving transactions; The average daily revenue from starting and stopping peak-shaving transactions; The average daily revenue from one-sided transactions involving power grid access; The daily penalty cost of energy storage power stations; This refers to the daily loss cost of the energy storage power station.

4. The optimization method as described in claim 1, characterized in that, The power relationship constraints for peak-shaving ancillary services transactions are shown in the following formula: In the formula, This refers to the rated power of the energy storage power station; The state of charge constraint of the energy storage power station is shown in the following formula: In the formula, This refers to the state of charge of the energy storage power station. , These are the upper and lower limits of the state of charge of the energy storage power station; This represents the increase in electricity consumption of the energy storage power station during the t-th time period. This refers to the rated capacity of the energy storage power station.

5. A coordinated optimization system for energy storage power stations participating in multiple types of peak-shaving transactions, used to implement the method as described in claim 1, characterized in that, include: Acquisition module, calculation module, and optimization module; The acquisition module acquires the traded electricity of the energy storage power station and the traded electricity price determined based on the peak-shaving ancillary service trading product adopted by the energy storage power station; The calculation module, based on the transaction price and the transaction power, uses a pre-built optimization model to iteratively calculate the optimal amount of electricity for the energy storage power station to participate in peak-shaving ancillary service transactions at different time scales. The optimization module coordinates and optimizes the participation of the energy storage power station in peak shaving transactions based on the optimal electricity volume for the energy storage power station to participate in peak shaving ancillary service transactions at different time scales.

6. The optimization system as described in claim 5, characterized in that, It also includes: a model building module; The model building module is constructed based on the relationship between the revenue of peak-shaving ancillary service trading products at different time scales used by energy storage power stations and the daily penalty cost and daily loss cost of energy storage power stations, with the goal of maximizing the daily net revenue of energy storage power stations.

7. The optimization system as described in claim 6, characterized in that, The model building module includes: Objective function construction submodule and constraint construction submodule; The objective function construction submodule establishes an optimized operation objective function for the energy storage power station based on the relationship between the revenue of peak-shaving ancillary service trading varieties with different time scales adopted by the energy storage power station and the daily penalty cost and daily loss cost of the energy storage power station, with the goal of maximizing the daily net profit of the energy storage power station. The constraint construction submodule sets constraints on the power relationship of peak-shaving ancillary service transactions and the state of charge of energy storage power stations for the objective function.

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