Energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity price considering demand

Abstract

The invention relates to a demand-considered energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity price. The method comprises the following steps: determining historical annual hourly load data of a user; determining day-by-day non-valley period loads; determining the load condition of each day; based on the battery capacity of the user and the daily load condition, determining a daily charging and discharging strategy of the user and new hourly load data after energy storage; determining electricity kilowatt-hour electricity charge and capacity and demand electricity charge of the user all year round before and after energy storage; determining the annual average investment cost of the battery, determining the annual average income, establishing a user side energy storage configuration and operation optimization model taking the annual average income maximization as a target function, and considering the calendar attenuation and transformer capacity limitation of an energy storage system; and determining an optimal capacity configuration and operation scheme of user energy storage. According to the application, the effects of smoothing the power utilization curve, clipping the peak and filling the valley, reducing the electric charge expenditure and the like are realized.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy storage systems, and in particular to a method for optimizing energy storage configuration and operation based on user-side multi-subject time-of-use electricity prices taking demand into account. Background Art

[0002] Policy support and the demands of the electricity market are accelerating the development of the energy storage industry. User-side energy storage, with its advantages of small size, fast response, high control precision, and flexible application, offers significant advantages in addressing power shortages and unstable power supplies caused by rapidly increasing loads. Furthermore, according to CNESA analysis, the widening gap between peak and valley prices in China has increased the economic viability of energy storage deployment for large industrial and commercial users, further stimulating demand for user-side energy storage systems.

[0003] Previously, most behind-the-meter energy storage projects considered economic viability solely based on the benefits of peak-valley price differentials from time-of-use (TOU) pricing strategies, with limited consideration given to capacity and demand protection and the limitations of user transformer capacity. While strategies that incorporate demand protection often use typical daily or short-term load data as the basis for calculations, this invention optimizes energy storage configuration and operation over a long-term, annual timeframe, ultimately achieving the optimal energy storage configuration capacity, operating strategy, and overall benefits. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a demand-based energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing. This method considers the optimization of user-side energy storage system configuration and operation scheduling based on the user's historical hourly load data and electricity pricing strategy throughout the year. While taking into account the operational constraints of the energy storage system and aiming to maximize user economic benefits, a user-side energy storage configuration and operation model is established to achieve smooth power consumption curves, peak shaving and valley filling, and reduced electricity bills.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The present application provides a method for energy storage configuration and operation optimization based on user-side multi-subject time-of-use electricity pricing, taking demand into account, including the following steps:

[0007] Step 1: Collect the user's historical hourly load data throughout the year, determine the user's two-part electricity pricing strategy and time-of-use electricity pricing rules, and simultaneously determine the unit cost and charge and discharge rate of the battery. By shifting the user's power load through low-storage and high-discharge methods, calculate the user's average annual revenue after energy storage using an energy storage model that aims to maximize annual revenue.

[0008] Step 2: Based on the collected hourly load data of the user, the daily non-valley period load of the user is determined, and the maximum value thereof is taken as the upper limit of the battery capacity optimization of the user, and based on the upper limit, the optimization variable, i.e. the capacity of the energy storage system of the user, is determined;

[0009] Step 3: Based on the historical annual hourly load data of the user and the time-of-use electricity price strategy of the user, the segmented load condition of each day is determined;

[0010] Step 4: Based on the energy storage capacity, the energy storage operation constraint condition, the transformer capacity limit of the user and the segmented load condition of each day, the charging and discharging strategy of the user each day and the new hourly load data after energy storage are determined;

[0011] Step 5: Based on the historical hourly load data of the user, the new hourly load data after energy storage, the two-part electricity price strategy, the time-of-use electricity price strategy, the calendar attenuation of the energy storage system and the transformer capacity, the annual electricity metering electricity charge and capacity demand electricity charge of the user before and after energy storage are determined, and then the income brought by the reduction of electricity charge is determined;

[0012] Step 6: Based on the battery capacity of the user, the unit cost of the battery and the expected service life of the energy storage system, the annual average investment cost of the battery and the annual electricity metering electricity charge and capacity demand electricity charge after energy storage are determined, and then the average annual income brought by the reduction of electricity charge is obtained, and the annual average income is determined, and a user-side energy storage configuration and operation optimization model with the maximum annual average income as the objective function is established;

[0013] Step 7: Based on the Scipy solver, the user-side energy storage configuration and operation optimization model is solved to determine the optimal capacity configuration and operation scheme of the user's energy storage.

[0014] The objective function of the energy storage model with the maximum annual average income as the objective is:

[0015]

[0016] Wherein, AP is the annual average income of the user's energy storage system in the life cycle; AR is the income brought by the reduction of annual electricity charge in the whole life cycle after the user's energy storage; AVC is the annual average investment cost of the user's energy storage system in the whole life cycle.

[0017] The calculation formula of the initial investment cost AVC of the user's energy storage system in the whole life cycle is as follows,

[0018]

[0019] Wherein, P battery is the rated capacity of the battery at the initial construction; I inv is the unit capacity cost of the energy storage battery system; t is the service life of the energy storage battery system;

[0020] The calculation formula of the user's annual average electricity fee reduction in the whole life cycle after energy storage is as follows,

[0021]

[0022] Wherein, C i.1 is the annual electricity fee of the user before energy storage; C i.2 is the annual electricity fee of the user after energy storage.

[0023] The annual electricity fee of the user before and after energy storage is specifically,

[0024] a) Electricity fee before energy storage

[0025]

[0026] Wherein C demand.1 is the capacity demand electricity fee before energy storage, C ele.1 is the electricity consumption electricity fee before energy storage.

[0027] b) Electricity fee after energy storage

[0028]

[0029] Wherein C demand.2 is the capacity demand electricity fee after energy storage, C ele.2 is the electricity consumption electricity fee after energy storage.

[0030] The annual capacity demand electricity fee and the electricity consumption electricity fee of the user before energy storage are specifically,

[0031] Annual capacity demand electricity fee before energy storage

[0032]

[0033] Wherein, b is the demand charge price of the user; P max.m.1 is the demand curve of the user in the mth month before energy storage; T is the transformer capacity of the user,

[0034] Annual electricity consumption electricity fee before energy storage

[0035]

[0036] Wherein, Q d.h.1 is the annual hourly load data of the user before energy storage, P h is the time-of-use electricity price of the user type at the corresponding time.

[0037] The annual capacity demand electricity fee and the electricity consumption electricity fee of the user after energy storage are specifically,

[0038] Annual capacity demand electricity fee after energy storage

[0039]

[0040] wherein b is the demand price of the user; P max.m.2 is the demand line of the user after the energy storage in the mth month; T is the transformer capacity of the user;

[0041] the total electric energy charge of the year after the energy storage

[0042]

[0043] wherein Q d.n.2 is the segmented load of the user after the energy storage, P n is the time-of-use price of the user type at the corresponding time.

[0044] The segmented load after the energy storage is the load of the user in the peak, valley and flat periods after the low storage and high discharge each day, which is obtained by comparing the segmented load before the energy storage and the energy storage capacity and adopting the corresponding charging and discharging strategy, specifically:

[0045] When the battery capacity is greater than the sum of the loads of the peak, sharp peak and flat periods of the user before the energy storage each day, the user charges the battery load in the valley period so that it can completely replace the power grid in the following peak, sharp peak and flat periods to realize power supply and meet the power demand of the user;

[0046] When the battery capacity is greater than the sum of the loads of the peak and sharp peak of the user before the energy storage each day, and is less than the sum of the loads of the peak, sharp peak and flat periods of the user before the energy storage each day, the user fully charges the battery in the valley period so that the remaining part of the battery meets the power supply in part of the flat period on the premise that it meets the power supply in the peak and sharp peak periods;

[0047] When the battery capacity is greater than the load of the sharp peak of the user before the energy storage each day, and is less than the sum of the loads of the peak and sharp peak of the user before the energy storage each day, the user fully charges the battery in the valley period so that it meets the load in the peak period as much as possible on the premise that it completely replaces the power grid to supply power in the sharp peak period, and charges in the flat period 2 after the peak period and before the sharp peak period until the remaining amount of the battery just meets the load demand in the sharp peak period;

[0048] When the battery capacity is less than the load of the sharp peak of the user before the energy storage each day, the user fully charges the battery in the valley period to meet the load in the peak period as much as possible, and charges in the flat period after the peak period and before the sharp peak period until the battery is fully charged;

[0049] After implementing the above-described charging and discharging strategy, new load data for each time period of the day is obtained. Based on the load data and the duration of each time period, the average is calculated to obtain new hourly load data. Since load data is limited by transformer capacity, and the above-described charging and discharging strategy may only exceed the transformer limit during the off-peak period and the flat period 2, the load during these two periods needs to be judged and constrained. When the hourly load data for the new off-peak period exceeds the transformer capacity limit, the energy storage system charge during the off-peak period is the difference between the transformer capacity and the original load during that period. Furthermore, based on the priority of time-of-use electricity prices, the power supply conditions during peak, peak, and flat periods are considered. If the hourly load data for the new off-peak period is less than the transformer capacity limit, but the hourly load data for the new flat period 2 is greater than the transformer capacity limit, the charge during the flat period 2 is limited by the transformer capacity, the remaining battery charge, and the peak charge, and only a portion of the energy is charged.

[0050] The calendar decay, the capacity of the battery in year i, is specifically:

[0051]

[0052] Among them, P battery is the rated capacity of the battery when it was first built; ε is the average annual attenuation rate of the battery capacity;

[0053] The segmented load before energy storage is the user's daily load during peak, valley and flat periods, specifically:

[0054]

[0055] Among them, Q d.h.1 This is the user's hourly load data for the entire year before energy storage.

[0056] Compared with existing technologies, this invention offers the following advantages: It optimizes the capacity configuration and operation scheduling of user-side energy storage, targeting the load conditions of industrial and commercial users, combined with a two-part electricity price and time-of-use pricing strategies. The optimization algorithm of this invention enables more rational energy storage configuration and guides users to implement scientific operation scheduling, thereby achieving higher economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 Schematic diagram of the energy storage configuration and operation optimization method for user-side time-of-use electricity pricing taking demand into account.

[0059] Figure 2 The principle diagram of the charging and discharging strategy of the energy storage system for a user.

[0060] Figure 3 The time-of-use electricity price diagram for a user.

[0061] Figure 4 The historical annual hourly load data diagram for a user.

[0062] Figure 5 The optimization result diagram for a user for verifying the actual effect of the application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the application will be described below with reference to the drawings in the embodiments of the application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0064] The term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusions, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article or device. Without more limitations, the element defined by the phrase "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0065] The terms "first", "second", and the like are only used to distinguish one entity or operation from another entity or operation, and cannot be understood as indicating or implying relative importance, nor can it be understood as requiring or implying any such actual relationship or order between the entities or operations.

[0066] Please refer to Figure 1 The application provides an energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity price considering demand, which is characterized in that, according to historical annual hourly load data and electricity price strategies of a user, a user-side energy storage system configuration optimization problem and an operation scheduling optimization problem are considered. Under the consideration of the operation constraints of the energy storage system, a user-side energy storage configuration and operation model is established to maximize the economic benefits of the user, so as to achieve the effects of smoothing the electricity consumption curve, shaving peak load, filling valley load, and reducing electricity charges. The specific steps include the following:

[0067] Step 1: Collect the user's historical annual hourly load data, determine the user's two-part electricity price strategy and time-of-use electricity price rules, and determine the unit cost and charging and discharging rate of the battery, and transfer the user's power load in the way of storing in low and discharging in high. Through the energy storage model with the goal of maximizing the annual average income, the user's annual average income obtained after energy storage is calculated

[0068] Step 2: Based on the collected user hourly load data, determine the user's daily non-valley period load, and take the maximum value as the upper limit of the user's battery capacity optimization. Based on the upper limit, the optimization variable, i.e. the capacity of the user's energy storage system, is determined.

[0069] Step 3: Based on the user's historical annual hourly load data and the user's time-of-use electricity price strategy, determine the segmented load of each day.

[0070] Step 4: Based on the energy storage capacity, energy storage operation constraints, user transformer capacity limit and daily segmented load, determine the user's daily charging and discharging strategy and new hourly load data after energy storage.

[0071] Step 5: Based on the user's historical hourly load data, new hourly load data after energy storage, two-part electricity price strategy, time-of-use electricity price strategy, energy storage system calendar attenuation, transformer capacity, determine the user's annual kilowatt-hour electricity fee and capacity demand electricity fee before and after energy storage, and then determine the income brought by the reduction of electricity fee.

[0072] Step 6: Based on the user's battery capacity, battery unit cost, and expected service life of the energy storage system, determine the annual average investment cost of the battery and the annual kilowatt-hour electricity fee and capacity demand electricity fee after energy storage. Based on this, the average annual income brought by the reduction of electricity fee is obtained, and then the average annual income is determined, and a user-side energy storage configuration and operation optimization model with the goal of maximizing the average annual income is established.

[0073] Step 7: Based on the Scipy solver, the user-side energy storage configuration and operation optimization model is solved to determine the optimal capacity configuration and operation scheme of the user's energy storage.

[0074] It aims to maximize the average annual income of the user's energy storage system in the whole life cycle, and the objective function of the energy storage optimization configuration and operation model is:

[0075]

[0076] Among them, AP is the average annual income of the user's energy storage system in the life cycle; AR is the income brought by the reduction of annual electricity fee after the user's energy storage in the whole life cycle; AVC is the average annual investment cost of the user's energy storage system in the whole life cycle.

[0077] The initial investment cost of the user's energy storage system in the whole life cycle, characterized in that:

[0078]

[0079] wherein P battery is the rated capacity of the battery at the time of initial construction; I inv is the unit capacity cost of the energy storage battery system; and t is the service life of the energy storage battery system, in years.

[0080] The user's annual average electricity bill reduction after energy storage in the whole life cycle brings a benefit, characterized in that:

[0081]

[0082] wherein C i.1 is the annual electricity bill of the user in the ithyear before energy storage; and C i.2 is the annual electricity bill of the user in the ithyear after energy storage.

[0083] The user's annual electricity bill in the ithyear before and after energy storage, characterized in that:

[0084] c) Electricity bill before energy storage

[0085]

[0086] wherein C demand.1 is the capacity demand electricity bill before energy storage, and C ele.1 is the electricity meter electricity bill before energy storage.

[0087] d) Electricity bill after energy storage

[0088]

[0089] wherein C demand.2 is the capacity demand electricity bill after energy storage, and C ele.2 is the electricity meter electricity bill after energy storage.

[0090] The user's annual capacity demand electricity bill and electricity meter electricity bill before energy storage, characterized in that:

[0091] a) Annual capacity demand electricity bill before energy storage

[0092]

[0093] wherein b is the demand charge price of the user; and P max.m.1 is the demand curve of the user in the mthmonth before energy storage; and T is the transformer capacity of the user.

[0094] b) Annual electricity meter electricity bill before energy storage

[0095]

[0096] wherein Q d.h.1The annual hourly load data of the user before energy storage, P h According to the time-of-use electricity price of the user type at the corresponding time.

[0097] The annual demand charge and the kilowatt-hour charge of the user after energy storage, characterized in that:

[0098] a) The annual demand charge after energy storage

[0099]

[0100] Wherein, b is the demand charge of the user; P max.m.2 The demand curve of the user in the mth month after energy storage; T is the transformer capacity of the user.

[0101] b) The annual kilowatt-hour charge after energy storage

[0102]

[0103] Wherein, Q d.n.2 The segmented load of the user after energy storage, P n According to the time-of-use electricity price of the user type at the corresponding time.

[0104] The segmented load after energy storage is the load of the user in the peak, valley and flat periods after daily low storage and high discharge, which is obtained by comparing the segmented load before energy storage and the energy storage capacity and adopting the corresponding charging and discharging strategy, characterized in that:

[0105] a) When the battery capacity is greater than the sum of the loads of the user in the peak, peak and flat periods before energy storage, the user charges the battery load in the valley period so that it can completely replace the power grid in the next peak, peak and flat periods to realize power supply and meet the user's electricity demand.

[0106] b) When the battery capacity is greater than the sum of the loads of the user in the peak and peak periods before energy storage, and less than the sum of the loads of the user in the peak, peak and flat periods before energy storage, the user fully charges the battery in the valley period, so that the remaining part of the battery can meet part of the power supply in the flat period under the premise of meeting the power supply in the peak and peak periods.

[0107] c) When the battery capacity is greater than the sum of the loads of the user in the peak period before energy storage, and less than the sum of the loads of the user in the peak and peak periods before energy storage, the user fully charges the battery in the valley period, so that it can meet the load in the peak period as much as possible under the premise of completely replacing the power grid in the peak period. And charge in the flat period 2 period after the peak period, until the remaining battery capacity just meets the load demand in the peak period.

[0108] d) When the battery capacity is less than the total load of the user's peak before energy storage on that day, the user will fully charge the battery during the off-peak period to meet the load during the peak period as much as possible, and charge the battery during the flat period after the peak period and before the peak period until the battery is fully charged.

[0109] e) After the above-mentioned charge and discharge strategy, new load data for each time period of the day is obtained, and the average is calculated based on the load data of each time period and the time length of each time period to obtain new hourly load data. Since the load data is limited by the transformer capacity, and the above-mentioned charge and discharge strategy may only exceed the transformer limit during the valley period and the flat period 2, it is necessary to judge and constrain the load of these two periods. When the hourly load data of the new valley period is greater than the transformer capacity limit, the charging amount of the energy storage system during the valley period is the difference between the transformer capacity and the original load during that period, and then the power supply conditions of peak, peak, and flat periods are considered according to the priority of the time-of-use electricity price. When the hourly load data of the new valley period is less than the transformer capacity limit, but the hourly load data of the new flat period 2 is greater than the transformer capacity limit, the charging amount during the flat period 2 is limited by the transformer capacity, the remaining battery power, and the peak power storage capacity, and only part of the power is charged.

[0110] The battery capacity will decrease over time, i.e. calendar decay. The capacity of the battery in year i is characterized by:

[0111]

[0112] Among them, P battery is the rated capacity of the battery when it was first built; ε is the average annual attenuation rate of the battery capacity.

[0113] The segmented load before energy storage is the user's load during peak, valley and flat periods of each day, and is characterized by:

[0114]

[0115] Among them, Q d.h.1 This is the user's hourly load data for the entire year before energy storage.

[0116] The segmented distribution of user time-of-use electricity prices is characterized by the following: when time h≤7, it is the off-peak period, at which the electricity price is P1; when time 7<h≤9, it is the flat period 1 period, at which the electricity price is P2; when time 9<h≤15, it is the peak period, at which the electricity price is P3; when time 15<h≤20, it is the flat period 2 period, at which the electricity price is P4; when time 20<h≤22, it is the peak period, at which the electricity price is P5; when time 22<h, it is the flat period 3 period, at which the electricity price is P6. At the same time, P1<P2=P4=P6<P3<P5.

[0117] The present invention first formulates the energy storage charging and discharging priority strategy (such asFigure 2 ), to maximize the annual average income of the user after adding the energy storage system on the user side, an energy storage configuration and operation optimization model is established; then, the user-side energy storage configuration and operation optimization model is solved based on the Scipy solver to determine the optimal capacity configuration and operation scheme of the user energy storage.

[0118] Based on the two-part electricity pricing system, the electricity bill of the user in the application includes two parts of capacity demand charge and degree-hour charge, so the income is composed of two parts of capacity demand charge income and degree-hour charge income. Among them, the capacity demand charge income is obtained by reducing the monthly maximum electricity demand through scientific and reasonable electricity consumption by energy storage or improving the electricity consumption behavior of the user, so as to obtain the income of demand reduction through demand line reduction; and the degree-hour charge is obtained by using peak-valley time-of-use electricity price, and the income of peak load shifting is obtained through the flexible throughput of the energy storage system, so as to realize peak-valley arbitrage. In order to verify the effectiveness of the optimization model, the design optimization of the energy storage is carried out based on the data of a certain power user in the application. The capacity price and demand price of the user are 26.3 yuan / (kW·month) and 42 yuan / (kW·month) respectively, and the time-of-use electricity price and load information are as shown in Figure 3 and Figure 4 In addition, the energy storage and economic parameters used in the application are shown in Table 1:

[0119] Table 1 Energy storage parameters and economic information

[0120] Technical parameters Numerical values Charging efficiency η ch ]] 92% Discharge efficiency η disch ]] 92% Charge-discharge rate C-rate 0.5 Depth of discharge DOD 90% Decay rate ξ 2% Discount rate γ 4% Maintenance rate m 1% Tax rate VAT 6% Battery unit price π 1500 yuan / kWh

[0121] After optimization calculation, the optimization result is as shown in Figure 5 The optimal energy storage capacity of the user is 4642kWh, and the corresponding annual average net income is 168,000 yuan, the payback period is 6.8 years, and the internal rate of return is 10.2%. From the perspective of the annual average net income, the method proposed in the application can effectively obtain the optimal energy storage capacity.

[0122] The above only describes the embodiments of the application and is not used to limit the protection scope of the application. For those skilled in the art, the application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for optimizing energy storage configuration and operation based on user-side multi-subject time-of-use electricity prices, taking demand into account, characterized in that: The following steps are involved: Step 1: Collect the user's historical hourly load data throughout the year, determine the user's two-part electricity pricing strategy and time-of-use electricity pricing rules, and simultaneously determine the unit cost and charge and discharge rate of the battery. By shifting the user's power load through low-storage and high-discharge methods, calculate the user's average annual revenue after energy storage using an energy storage model that aims to maximize annual revenue. Step 2: Based on the collected hourly user load data, determine the user's daily non-off-peak load, and use the maximum value as the upper limit for optimizing the user's battery capacity. Based on this upper limit, determine the optimization variable, that is, the user's energy storage system capacity; Step 3: Based on the user's historical hourly load data throughout the year and the user's time-of-use electricity price strategy, determine the daily load situation by segment; Step 4: Based on the energy storage capacity, energy storage operation constraints, user transformer capacity limitations, and daily load conditions, determine the user's daily charging and discharging strategy and the new hourly load data after energy storage; Step 5: Based on the user's historical hourly load data, the new hourly load data after energy storage, the two-part electricity pricing strategy, the time-of-use electricity pricing strategy, the calendar decay of the energy storage system, and the transformer capacity, determine the user's annual electricity charges and capacity demand charges before and after energy storage, and then determine the benefits of the electricity cost reduction; Step 6: Based on the user's battery capacity, unit battery cost, and the expected lifespan of the energy storage system, determine the average annual battery investment cost and the annual electricity and capacity-demand charges after energy storage. Based on this, calculate the benefits of the average annual electricity cost reduction, and then determine the average annual revenue. Establish a user-side energy storage configuration and operation optimization model with maximizing the average annual revenue as the objective function. Step 7: Solve the user-side energy storage configuration and operation optimization model based on the Scipy solver to determine the optimal capacity configuration and operation plan of the user energy storage.

2. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 1 is characterized in that: The objective function of the energy storage model with the goal of maximizing the average annual benefit is: ， Among them, AP is the average annual revenue of the user's energy storage system during its life cycle; AR is the revenue brought by the average annual electricity bill reduction after the user stores energy during the entire life cycle; AVC is the average annual investment cost of the user's energy storage system during the entire life cycle.

3. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 2 is characterized in that: The calculation formula for the initial investment cost AVC of the user energy storage system over its entire life cycle is as follows: ， Among them, P battery It is the rated capacity of the battery when it is first built; I inv is the unit capacity cost of the energy storage battery system; t is the service life of the energy storage battery system; The calculation formula for the benefits of reducing the average annual electricity bill over the entire life cycle after energy storage is as follows: ， Among them, C i.1 The annual electricity fee for the user in the i-th year before energy storage; C i.2 The annual electricity bill for the user in the i-th year after energy storage.

4. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 3 is characterized in that: The annual electricity fee for the user in the i-th year before and after energy storage is specifically: a) Electricity cost before energy storage ， Among them C demand.1 is the electricity fee required before energy storage, C ele.1 The electricity cost before energy storage; b) Electricity charges after energy storage ， Among them C demand.2 The electricity cost after energy storage, C ele.2 The electricity cost after energy storage.

5. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 4 is characterized in that: The user's annual capacity demand electricity fee and electricity fee before energy storage are specifically: Annual capacity electricity cost before energy storage ， Among them, b is the user's demand electricity price; P max.m.1 is the demand line of the user in the mth month before energy storage; T is the capacity of the user's transformer, Annual electricity bill before energy storage ， Among them, Q d.h.1 is the hourly load data of the user before energy storage throughout the year, P h It is the time-of-use electricity price for this user type at the corresponding moment.

6. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 5 is characterized in that: The annual capacity and electricity charges for the user after energy storage are specifically as follows: Annual electricity cost after energy storage ， Among them, b is the user's demand electricity price; P max.m.2 is the demand line of the user in the mth month after energy storage; T is the capacity of the user's transformer; Annual electricity bill after energy storage ， Among them, Q d.n.2 P is the segmented load after energy storage. n It is the time-of-use electricity price for this user type at the corresponding moment.

7. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 5 is characterized in that: The segmented load after energy storage is the load of the user in peak, valley and flat periods after low storage and high discharge every day. The segmented load after energy storage is obtained by comparing the segmented load before energy storage with the energy storage capacity and adopting corresponding charging and discharging strategies. Specifically, it is: When the battery capacity is greater than the sum of the user's load during the peak, peak, and flat periods before energy storage, the user will charge the battery load during the off-peak period, so that it can completely replace the grid during the following peak, peak, and flat periods to provide power and meet the user's electricity needs; When the battery capacity is greater than the sum of the user's peak and spike loads before energy storage on that day, and less than the sum of the user's peak, spike, and flat loads before energy storage on that day, the user will fully charge the battery during the off-peak period so that it can meet the power supply needs of both peak and spike periods, and the remaining capacity of the battery can meet part of the flat period power supply; When the battery capacity is greater than the sum of the user's peak load before energy storage on that day, and less than the sum of the user's peak load before energy storage and peak load on that day, the user will fully charge the battery during the off-peak period so that it can meet the peak load as much as possible, under the premise of completely replacing the power supply of the grid during the peak period. The battery will be charged in the flat period 2 after the peak period and before the peak period until the remaining power of the battery just meets the load demand during the peak period. If the battery capacity is less than the total load of the user's peak load before energy storage on that day, the user will fully charge the battery during the off-peak period to meet the load during the peak period as much as possible, and charge it during the flat period after the peak period and before the peak period until the battery is fully charged. After implementing the above-described charging and discharging strategy, new load data for each time period of the day is obtained. Based on the load data and the duration of each time period, the average is calculated to obtain new hourly load data. Since load data is limited by transformer capacity, and the above-described charging and discharging strategy may only exceed the transformer limit during the off-peak period and the flat period 2, the load during these two periods needs to be judged and constrained. When the hourly load data for the new off-peak period exceeds the transformer capacity limit, the energy storage system charge during the off-peak period is the difference between the transformer capacity and the original load during that period. Furthermore, based on the priority of time-of-use electricity prices, the power supply conditions during peak, peak, and flat periods are considered. If the hourly load data for the new off-peak period is less than the transformer capacity limit, but the hourly load data for the new flat period 2 is greater than the transformer capacity limit, the charge during the flat period 2 is limited by the transformer capacity, the remaining battery charge, and the peak charge, and only a portion of the energy is charged.

8. The energy storage configuration and operation optimization method for user-side multi-agent time-of-use electricity pricing taking into account demand according to claim 7 is characterized in that: The calendar decay is specifically: ， Among them, P battery is the rated capacity of the battery when it was first built; ε is the average annual attenuation rate of the battery capacity; The segmented load before energy storage is the user's daily load during peak, valley and flat periods, specifically: ， Among them, Q d.h.1 This is the user's hourly load data for the entire year before energy storage.

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