Energy storage layered optimization peak regulation method and system considering demand side response

Abstract

The invention discloses an energy storage hierarchical optimization peak regulation method considering demand side response, and the method comprises the following steps: analyzing the price type demand response characteristics based on time-of-use electricity price, introducing an electricity price elastic matrix, and obtaining the electricity consumption of a user in three different time periods of peak, flat and valley; establishing an energy storage hierarchical model, and determining decision variables, objective functions and constraint conditions of upper and lower models; by taking an improved IEEE30 node system as an example, simulation calculation analysis is performed to obtain an energy storage charging and discharging plan and a scheduling scheme of a thermal power generating unit, and simulation results of considering and not considering demand side response are compared. The method has the advantages that the price-type demand side response and the energy storage system are introduced to assist peak regulation cooperatively, the peak-valley difference can be improved, the net load curve can be optimized, meanwhile, the peak regulation pressure of the thermal power generating unit can be effectively relieved, the peak regulation cost is reduced, and certain support is provided for stable and economical operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and system for optimizing peak load regulation by energy storage stratification taking into account demand-side response. Background Art

[0002] With the development of modern power systems, the peak load regulation of power systems has increasingly become a key challenge to ensure stable, efficient and sustainable power supply.

[0003] First, due to the significant changes in the composition and characteristics of power load characteristics, the growth and diversification of industrial, commercial and residential electricity demand has led to a continuous increase in the peak-to-valley difference of the power load curve. For example, a large number of commercial activities are concentrated in the daytime, and residential electricity consumption peaks during the evening peak hours, while the load is at a low point at night. This expansion of the peak-to-valley difference has put pressure on the stable operation of the power system, and traditional power generation and dispatching methods are difficult to meet the needs of such complex load changes.

[0004] Secondly, with the large-scale access of renewable energy (such as wind power and solar energy) to the power system, its intermittency and uncertainty have further aggravated the difficulty of peak load regulation of the power system. For example, wind power may generate a large amount of electricity when the wind is strong at night, which may be the low period of power load; solar power generation generates electricity when the sun is abundant during the day, which may not fully match the peak load during some daytime hours. This makes it difficult to maintain the power balance of the power system at different times, and more effective peak load regulation methods are needed.

[0005] However, traditional thermal power units currently have certain limitations in terms of peak load regulation. The start-up and shutdown and load adjustment speed of thermal power units are relatively slow. Frequent start-up and shutdown and large-scale load adjustments will also affect the service life of the units, increase operating costs and emit pollutants. Although the application of energy storage technology in power systems provides a new way to regulate peak loads, single energy storage peak load regulation also faces challenges. If only energy storage is used to assist peak load regulation, it may be difficult to achieve the optimal peak load regulation effect.

[0006] Demand response (DR) has great potential as an emerging way of power resource management. By guiding users to adjust their electricity consumption behavior according to electricity price signals, the shape of the power load curve can be effectively changed and the peak-valley difference can be reduced. Price-based demand response based on time-of-use electricity prices is one of the important ways. By analyzing the elastic response of users to electricity prices, users' electricity consumption in different periods of peak, flat and valley can be reasonably arranged to achieve optimal allocation of power resources.

[0007] In summary, in this context, the research on energy storage stratified optimization peak-shaving methods and systems taking into account demand-side response has important theoretical and practical significance. It aims to comprehensively utilize the advantages of demand-side response and energy storage coordinated peak-shaving, solve the peak-shaving problem of the power system, and improve the operating efficiency and stability of the power system. Summary of the invention

[0008] In view of the defects of the prior art, the present invention provides a method and system for peak-shaving by optimizing energy storage layering and taking demand-side response into account, so as to solve the peak-shaving problem of the new power system.

[0009] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0010] A method for optimizing peak load regulation by energy storage stratification taking into account demand-side response comprises the following steps:

[0011] S1: Analyze the price-based demand response characteristics based on time-of-use electricity prices, introduce the electricity price elasticity matrix, and obtain the user's electricity consumption in three different time periods: peak, flat, and valley;

[0012] S2: Establish a stratified energy storage optimization peak-shaving model and determine the decision variables, objective functions and constraints of the upper and lower models;

[0013] S3: Perform simulation calculation and analysis to obtain the energy storage charging and discharging plan and the scheduling plan for thermal power units, and compare the simulation results with and without considering the demand-side response.

[0014] Preferably, the price-based demand response characteristic analysis based on time-of-use electricity prices in step S1 is specifically determined as follows:

[0015] S11: The user's response to electricity prices is described by the demand price elasticity coefficient:

[0016]

[0017] Where: P t 0 , are the load and electricity price in period t before price-based demand response; ΔP t , ΔD t are the change in load and electricity price after price-type demand response, respectively; ε is the demand price elasticity coefficient.

[0018] S12: Divide users into three time periods: peak, flat, and valley, and introduce the electricity price elasticity matrix E to obtain the electricity consumption in each time period after price-based demand response. The load electricity price response model can be expressed as:

[0019]

[0020]

[0021] Where: and They are the electricity consumption in each period before and after price-based demand response; and ΔD f , ΔD p , ΔD g They are the electricity prices and changes in each period before and after price-type demand response; E is the demand price elasticity matrix, whose main diagonal is the self-elasticity coefficient and the secondary diagonal is the mutual elasticity coefficient.

[0022] Preferably, the energy storage layered optimization peak-shaving model modeling method in step S2 is as follows:

[0023] S21: The upper model mainly considers the optimal improvement of the net load peak-valley difference by energy storage power stations and demand-side response. The objective function is:

[0024]

[0025]

[0026]

[0027] Where: P glt is the net load value at time t; P load,t is the load value at time t after the demand side response; is the actual grid-connected power of wind farm l at time t; is the actual grid-connected power of PV m at time t; P St is the discharge power of the energy storage device at time t, P St >0 means energy storage discharge, P St <0 means energy storage charging; P glt,av is the average net load in a dispatching cycle; N W is the total number of wind farms; N S is the total number of photovoltaic power stations.

[0028] The constraints are:

[0029] 1) Wind power output constraints:

[0030]

[0031] In the formula, is the maximum output of the wind farm at time t.

[0032] 2) Output constraints of photovoltaic power stations:

[0033]

[0034] In the formula, is the maximum output of the photovoltaic power station at time t.

[0035] 3) Energy storage power station operation constraints:

[0036] The operation constraints of energy storage power stations include: charging and discharging power range constraints, charging and discharging state constraints, and charge state constraints, as follows:

[0037] ①Charge and discharge power range constraints:

[0038]

[0039] Where: P ch,t , P dis,t They are respectively the energy storage charging and discharging power at time t; is the maximum charging power of the energy storage station, is the maximum discharge power of the energy storage power station; U ch,t , U dis,t They represent the charging and discharging states of the energy storage at time t respectively.

[0040] ②Charge and discharge state constraints:

[0041]

[0042] ③ State of Charge Constraint (SOC):

[0043]

[0044] Where: SOC(t) is the state of charge of the energy storage power station at time t, γ ch and γ dis They represent the charging efficiency and discharging efficiency of the energy storage power station respectively.

[0045] SOC min ≤SOC(t)≤SOC max (12)

[0046] Where: SOC min , SOC max It is the upper and lower limits of the charge state of the energy storage power station.

[0047] In addition, to ensure that the battery energy storage system returns to the initial state of charge after an operation cycle, the initial state of charge must be set equal to the final state of charge constraint as follows:

[0048] SOC(0)=SOC(T) (13)

[0049] 4) Price-based demand-side response constraints:

[0050] ① The demand side adopts the time-of-use electricity price strategy for response adjustment, and the total demand electricity before and after the response needs to remain unchanged, that is, to meet:

[0051]

[0052] Where: It responds to the load in the previous period t.

[0053] ② The load power that can be responded to in any period is limited, that is, it satisfies:

[0054]

[0055] Where: δ is the upper limit of the response load proportion in each period.

[0056] ③ The electricity purchase cost of users after the response shall not be higher than the electricity purchase cost before the response, that is, it meets the following requirements:

[0057]

[0058] Where: Δp t They are respectively the electricity price in period t before the implementation of time-of-use electricity prices and the change in electricity price in period t after the implementation of time-of-use electricity prices.

[0059] S22: The equivalent load curve transmitted by the lower model through the upper model mainly considers the lowest peak load regulation cost of thermal power units, and the objective function is:

[0060] f=f mh +f qt (17)

[0061]

[0062]

[0063] Where: f mh and f qt are the coal consumption cost and start-up and shutdown cost of thermal power units respectively; a i 、b i 、c i are the consumption coefficients of thermal power unit i respectively.

[0064] The constraints are:

[0065] 1) Power balance constraints:

[0066]

[0067] Where P i,t is the active power output of the i-th thermal power unit at time t; is the actual grid-connected power of wind farm l at time t; is the actual grid-connected power of PV m at time t; P St is the discharge power of the energy storage device at time t; P load.tis the load power during period t.

[0068] 2) Constraints on thermal power units:

[0069] ① Unit power constraints:

[0070] u i,t P i,min <P i,t ≤u i,t P i,max (twenty one)

[0071] ② Unit climbing constraints:

[0072]

[0073] Where: Indicates the climbing and descending speed of the unit.

[0074] ③ Unit start and stop constraints:

[0075]

[0076]

[0077] Where: u i,t represents the operating status of the i-th unit at time t (u i,t =1 means running, u i,t =0 means shutdown), T on (i) is the minimum continuous operation time of the i-th unit, T off (i) is the minimum continuous downtime of the i-th unit.

[0078] 3) Line transmission capacity constraints:

[0079] P l,min ≤P l,t ≤P l,max (25)

[0080] Where: P l,t represents the transmission power of line l at time t; P l,max Represents the maximum transmission capacity of line l.

[0081] The present invention also provides a storage layered optimization peak-shaving system taking into account demand-side response, which can be used to implement the above-mentioned storage layered optimization peak-shaving method taking into account demand-side response, and specifically comprises: a demand-side response analysis module, a model building module and a simulation solution module;

[0082] Demand-side response analysis module: Analyzes the price-based demand response characteristics based on time-of-use electricity prices, introduces the electricity price elasticity matrix, and obtains the user's electricity consumption in three different time periods: peak, flat, and valley;

[0083] Model building module: Establish a storage layered optimization peak-shaving model to determine the decision variables, objective functions and constraints of the upper and lower layer models;

[0084] Simulation solution module: Perform simulation calculation and analysis to obtain the energy storage charging and discharging plan and the scheduling plan of thermal power units, and compare the simulation results with and without considering the demand-side response.

[0085] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned energy storage layered optimization peak-shaving method taking into account demand-side response is implemented.

[0086] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned energy storage layered optimization peak-shaving method taking into account demand-side response.

[0087] Compared with the prior art, the advantages of the present invention are:

[0088] By introducing price-based demand response based on time-of-use electricity prices in the upper-level model, taking full account of users' sensitivity to electricity price fluctuations, and using the electricity price elasticity matrix, users' electricity consumption in different periods of peak, flat and valley can be dynamically adjusted. This method can effectively guide users to shift peaks and fill valleys, reducing the load pressure of the power grid during peak hours. It can not only coordinate with the energy storage system to optimize the net load curve and improve the peak-valley difference, but also alleviate the peak-shaving pressure of thermal power units, reducing the dispatching burden of thermal power units during peak hours, thereby improving the overall operation efficiency of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a schematic flow chart of a method for optimizing peak load regulation by energy storage layering taking into account demand-side response according to an embodiment of the present invention;

[0090] Figure 2 It is an improved IEEE30 node topology diagram adopted in the embodiment of the present invention;

[0091] Figure 3 It is a load curve diagram of wind power, photovoltaic power, initial load, and load after demand-side response in an embodiment of the present invention;

[0092] Figure 4 is a diagram showing the optimization scheduling results of each unit considering the demand side response in an embodiment of the present invention;

[0093] Figure 5 is a charging and discharging power diagram of an energy storage power station considering demand-side response in an embodiment of the present invention;

[0094] Figure 6 This is a diagram showing the optimization scheduling results of each unit without considering the demand side response in an embodiment of the present invention;

[0095] Figure 7 This is a charging and discharging power diagram of an energy storage power station without considering the demand side response in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0097] The present invention provides a method for optimizing peak load regulation by energy storage stratification taking into account the response of the demand side. The specific process is as follows: Figure 1 shown.

[0098] The specific application steps are as follows:

[0099] The improved IEEE30-node example is used for simulation. The system includes 5 thermal power units, and the basic parameters of the units are shown in Table 1; 1 wind farm with a total capacity of 300MW, 1 photovoltaic power station with a total capacity of 50MW; and 1 energy storage power station, whose specific parameters are shown in Table 2. The time-of-use electricity price parameter table is shown in Table 3, and the topology diagram is shown in Figure 2 As shown, the initial load curves of wind power, photovoltaic power and Figure 3 shown.

[0100] Step 1: Analyze the price-based demand response characteristics based on time-of-use electricity prices, introduce the electricity price elasticity matrix, and obtain the user's electricity consumption in three different time periods: peak, flat, and valley;

[0101] Step 2: Establish a storage layered optimization peak-shaving model and determine the decision variables, objective functions and constraints of the upper and lower layer models;

[0102] Step 3: Taking the improved IEEE30-node system as an example, simulation calculation and analysis are carried out to obtain the energy storage charging and discharging plan and the scheduling plan of the thermal power unit, and compare the simulation results with and without considering the demand-side response.

[0103] Table 1 shows the parameters of the thermal power unit involved in the present invention.

[0104] Table 1 Thermal power unit parameters

[0105]

[0106] Table 2 shows the parameters of the energy storage power station involved in the present invention.

[0107] Table 2 Energy storage power station parameters

[0108]

[0109] Table 3 is a table of time-of-use electricity price parameters involved in the present invention.

[0110] Table 3 Time-of-use electricity price parameters

[0111]

[0112]

[0113] The simulation results with and without considering the demand-side response are compared, such as Figure 4-7 As shown. From the simulation results, it can be concluded that when the regulatory effect of demand-side response on the load curve is not considered, when wind power, photovoltaic power and energy storage are integrated into the grid as a whole for joint operation, the net load fluctuation is 102.1842, and the equivalent load peak-to-valley difference is 556.5MW; when the demand-side response is considered, the net load fluctuation is 60.5374, and the equivalent load peak-to-valley difference is 406.5MW. After the demand-side response guides the user's load curve, both the net load fluctuation and the load peak-to-valley difference are relatively reduced. At the same time, considering the demand-side response, the peak-shaving cost of thermal power units is reduced by 39.53 million yuan compared with the original. From the perspective of peak-shaving effect, considering the demand-side response can not only work together with energy storage to improve the load peak-to-valley difference, but also effectively assist the peak-shaving of thermal power units, reduce the peak-shaving cost, and achieve better comprehensive benefits.

[0114] 2. In another embodiment of the present invention, a system for optimizing peak load regulation by energy storage stratification taking into account demand-side response is provided. The system can be used to implement the above-mentioned method for optimizing peak load regulation by energy storage stratification taking into account demand-side response, and specifically comprises: a demand-side response analysis module, a model building module and a simulation solution module;

[0115] Demand-side response analysis module: Analyzes the price-based demand response characteristics based on time-of-use electricity prices, introduces the electricity price elasticity matrix, and obtains the user's electricity consumption in three different time periods: peak, flat, and valley;

[0116] Model building module: Establish a storage layered optimization peak-shaving model to determine the decision variables, objective functions and constraints of the upper and lower layer models;

[0117] Simulation solution module: Taking the improved IEEE30-node system as an example, simulation calculation and analysis are carried out to obtain the energy storage charging and discharging plan and the scheduling plan of the thermal power unit, and compare the simulation results with and without considering the demand-side response.

[0118] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or 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. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the energy storage layered optimization peak-shaving method taking into account the demand side response, including the following steps:

[0119] S1: Analyze the price-based demand response characteristics based on time-of-use electricity prices, introduce the electricity price elasticity matrix, and obtain the user's electricity consumption in three different time periods: peak, flat, and valley;

[0120] S2: Establish a stratified energy storage optimization peak-shaving model and determine the decision variables, objective functions and constraints of the upper and lower models;

[0121] S3: Taking the improved IEEE30-node system as an example, simulation calculation and analysis are carried out to obtain the energy storage charging and discharging plan and the scheduling plan of the thermal power unit, and compare the simulation results with and without considering the demand-side response.

[0122] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0123] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the energy storage layered optimization peak-shaving method taking into account demand-side response in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0124] S1: Analyze the price-based demand response characteristics based on time-of-use electricity prices, introduce the electricity price elasticity matrix, and obtain the user's electricity consumption in three different time periods: peak, flat, and valley;

[0125] S2: Establish a stratified energy storage optimization peak-shaving model and determine the decision variables, objective functions and constraints of the upper and lower models;

[0126] S3: Taking the improved IEEE30-node system as an example, simulation calculation and analysis are carried out to obtain the energy storage charging and discharging plan and the scheduling plan of the thermal power unit, and compare the simulation results with and without considering the demand-side response.

[0127] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0128] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method and system for optimizing peak load regulation by energy storage stratification taking into account demand-side response, characterized in that: The following steps are involved: S1: Analyze the price-based demand response characteristics based on time-of-use electricity prices, introduce the electricity price elasticity matrix, and obtain the user's electricity consumption in three different time periods: peak, flat, and valley; S2: Establish a stratified energy storage optimization peak-shaving model and determine the decision variables, objective functions and constraints of the upper and lower models; S3: Perform simulation calculation and analysis to obtain the energy storage charging and discharging plan and the scheduling plan for thermal power units, and compare the simulation results with and without considering the demand-side response.

2. A method for calculating the new energy consumption capacity of a large power grid according to claim 1, characterized in that: The user's response to electricity prices in step S1 can be described by the demand price elasticity coefficient: Where: P t 0 , are the load and electricity price in period t before price-based demand response; ΔP t , ΔD t are the load change and electricity price change after price-based demand response, respectively; ε is the demand price elasticity coefficient; The users are divided into three time periods: peak, flat and valley. The electricity price elasticity matrix E is introduced to obtain the electricity consumption in each time period after the price-based demand response. The load electricity price response model can be expressed as: Where: and They are the electricity consumption in each period before and after price-based demand response; and ΔD f , ΔD p , ΔD g They are the electricity prices and changes in each period before and after price-type demand response; E is the demand price elasticity matrix, whose main diagonal is the self-elasticity coefficient and the secondary diagonal is the mutual elasticity coefficient.

3. The energy storage layered optimization peak-shaving method taking into account demand-side response according to claim 1 is characterized in that: The energy storage layered optimization peak load model in step S2 is as follows: S21: The upper model mainly considers the optimal improvement of the net load peak-valley difference by energy storage power stations and demand-side response. The objective function is: Where: P glt is the net load value at time t; P load,t is the load value at time t after the demand side response; is the actual grid-connected power of wind farm l at time t; is the actual grid-connected power of PV m at time t; P St is the discharge power of the energy storage device at time t, P St >0 means energy storage discharge, P St <0 means energy storage charging; P glt,av is the average net load in a dispatching cycle; N W is the total number of wind farms; N S is the total number of photovoltaic power stations. The constraints are: 1) Wind power output constraints: In the formula, is the maximum output of the wind farm at time t. 2) Output constraints of photovoltaic power stations: In the formula, is the maximum output of the photovoltaic power station at time t. 3) Energy storage power station operation constraints: The operation constraints of energy storage power stations include: charging and discharging power range constraints, charging and discharging state constraints, and charge state constraints, as follows: ①Charge and discharge power range constraints: Where: P ch,t , P dis,t They are respectively the energy storage charging and discharging power at time t; is the maximum charging power of the energy storage station, is the maximum discharge power of the energy storage power station; U ch,t , U dis,t They represent the charging and discharging states of the energy storage at time t respectively. ②Charge and discharge state constraints: ③ State of Charge Constraint (SOC): Where: SOC(t) is the state of charge of the energy storage power station at time t, γ ch and γ dis They represent the charging efficiency and discharging efficiency of the energy storage power station respectively. SOC min ≤SOC(t)≤SOC max (12) Where: SOC min , SOC max It is the upper and lower limits of the charge state of the energy storage power station. In addition, to ensure that the battery energy storage system returns to the initial state of charge after an operation cycle, the initial state of charge must be set equal to the final state of charge constraint as follows: SOC(0)=SOC(T) (13) 4) Price-based demand-side response constraints: ① The demand side adopts the time-of-use electricity price strategy for response adjustment, and the total demand electricity before and after the response needs to remain unchanged, that is, to meet: Where: It responds to the load in the previous period t. ② The load power that can be responded to in any period is limited, that is, it satisfies: Where: δ is the upper limit of the response load proportion in each period. ③ The electricity purchase cost of users after the response shall not be higher than the electricity purchase cost before the response, that is, it meets the following requirements: Where: Δp t They are respectively the electricity price in period t before the implementation of time-of-use electricity prices and the change in electricity price in period t after the implementation of time-of-use electricity prices. S22: The equivalent load curve transmitted by the lower model through the upper model mainly considers the lowest peak load regulation cost of thermal power units, and the objective function is: f=f mh +f qt (17) Where: f mh and f qt are the coal consumption cost and start-up and shutdown cost of thermal power units respectively; a i , b i 、c i are the consumption coefficients of thermal power unit i respectively. The constraints are: 1) Power balance constraints: Where P i,t is the active power output of the i-th thermal power unit at time t; is the actual grid-connected power of wind farm l at time t; is the actual grid-connected power of PV m at time t; P St is the discharge power of the energy storage device at time t; P load.t is the load power during period t. 2) Constraints on thermal power units: ① Unit power constraints: u i,t P i,min <P i,t ≤u i,t P i,max (21) ② Unit climbing constraints: Where: Indicates the climbing and descending speed of the unit. ③ Unit start and stop constraints: Where: u i,t represents the operating status of the i-th unit at time t (u i,t =1 means running, u i,t =0 means shutdown), T on (i) is the minimum continuous operation time of the i-th unit, T off (i) is the minimum continuous downtime of the i-th unit. 3) Line transmission capacity constraints: P l,min ≤P l,t ≤P l,max (25) Where: P l,t represents the transmission power of line l at time t; P l,max Represents the maximum transmission capacity of line l.

4. An energy storage layered optimization peak-shaving system taking into account demand-side response, characterized in that: The system can be used to implement the energy storage layered optimization peak-shaving method taking into account demand-side response as described in any one of claims 1 to 3, specifically comprising: a demand-side response analysis module, a model building module and a simulation solution module; Demand-side response analysis module: Analyzes the price-based demand response characteristics based on time-of-use electricity prices, introduces the electricity price elasticity matrix, and obtains the user's electricity consumption in three different time periods: peak, flat, and valley; Model building module: Establish a storage layered optimization peak-shaving model to determine the decision variables, objective functions and constraints of the upper and lower layer models; Simulation solution module: Taking the improved IEEE30-node system as an example, simulation calculation and analysis are carried out to obtain the energy storage charging and discharging plan and the scheduling plan of the thermal power unit, and compare the simulation results with and without considering the demand-side response.

5. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the energy storage layered optimization peak-shaving method taking into account demand-side response as described in one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that: A computer program is stored on a computer-readable storage medium, and when the program is executed by a processor, the energy storage layered optimization peak-shaving method taking into account demand-side response as described in one of claims 1 to 3 is implemented.

Images