Wind-fire load storage local capacity optimal configuration method capable of meeting autonomous peak regulation requirement

Abstract

The invention discloses a wind-fire load storage local capacity optimal configuration method meeting an autonomous peak regulation requirement, and the method comprises the steps: building a target function with the minimum net load fluctuation after wind power consumption as a target through combining the total power load of all production lines of large users, which can translate at any time, and obtaining an optimized large user comprehensive load curve; and according to the power balance constraint and the autonomous peak regulation boundary condition of the local wind-fire storage capacity planning unit, a wind-fire storage local capacity configuration optimization model is established, and a wind-fire storage bundling configuration result is obtained, so that optimal configuration of the wind-fire storage local capacity is realized, and the peak regulation burden of the system is reduced. According to the method, for a large user load configuration wind power application scene, the wind-fire load storage local capacity is subjected to optimal configuration, the large user load optimization model and the wind-fire load storage local capacity optimal configuration model are optimized separately by adjusting the operation interval and the time sequence of the large user production line, and the difficulty of common optimization solution of the wind-fire load storage is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation, and in particular to a method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak regulation. Background Art

[0002] Wind power has the characteristics of uncertainty, intermittency, and reverse peak load. With the continuous increase of wind power installed capacity in the power system, the system peak load regulation capacity and the absorption of wind power are facing severe challenges. Therefore, a development model of self-load matching wind power has emerged. This model needs to solve the problem of flexible resource optimization and autonomous peak load regulation. Most of the existing new energy planning methods jointly plan new energy and flexible resources such as thermal storage, and jointly reduce the operating burden of new energy on the system by increasing the use of new flexible resources and the original flexible resources of the system. However, the existing planning methods use new flexible resources and the original flexible resources of the system to jointly deal with the fluctuation and absorption of new energy, resulting in the new energy generation occupying the original flexible resources of the system and increasing the peak load of the power grid. In the context of large-scale access to new energy, it is not conducive to the synchronous planning of new energy at different nodes of the power grid, and the different order of access to the system will cause differences in the allocation of flexible resources.

[0003] The production cycle of large user loads such as graphite electrodes is relatively fixed. The operation intervals and timings of each production line can be arranged to adjust the load curve, and the peak-shaving cost can be reduced by coordinated optimization with wind, fire and storage. However, the optimization of the start and stop timing of the production line belongs to combined optimization. The number of production lines directly affects the optimization scale. Optimizing together with wind, fire and storage will increase the difficulty of solving. Summary of the invention

[0004] The present invention discloses a method for optimizing the local capacity configuration of wind, fire, load and storage that meets the requirements of autonomous peak regulation, so as to overcome the above technical problems.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] A method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak regulation includes the following steps:

[0007] S1: Obtain the power load curve of a single production line of a large user with time-shiftable structure to obtain the total power load l of all production lines at time t t ;

[0008] S2: Obtain the historical output characteristics of local wind power resources to obtain the annual wind power output curve, and then obtain the wind power simulation scenario output curve P w,s,t ;

[0009] S3: Based on the total power load l of all production lines at time t tAnd wind power simulation scenario output curve P w,s,t , establishing an objective function with the goal of minimizing the net load fluctuation after absorbing wind power, solving the objective function with the goal of minimizing the net load fluctuation after absorbing wind power, and obtaining an optimized large user comprehensive load curve;

[0010] S4: Establish the autonomous peak-shaving boundary conditions of the local wind-fire storage capacity planning unit;

[0011] S5: establishing a power balance constraint according to the optimized large user comprehensive load curve;

[0012] S6: According to the power balance constraint, bundled thermal power output constraint, minimum wind curtailment rate constraint, energy storage operation constraint and autonomous peak-shaving boundary conditions of the local wind-fire-load-storage capacity planning unit, a local capacity configuration optimization model of wind-fire-load-storage is established to obtain a wind-fire-load-storage bundled configuration result, so as to achieve optimal configuration of the local capacity of wind-fire-load-storage according to the wind-fire-load-storage bundled configuration result;

[0013] The wind-fire-load-storage bundling configuration results include the average bundled thermal power capacity, the planned wind power capacity, and the planned maximum unit power of energy storage.

[0014] Furthermore, in S3, the objective function with the goal of minimizing the net load fluctuation after absorbing wind power is expressed as follows:

[0015]

[0016] l′ s,t = l t -C w P w,s,t

[0017] Where: ob. represents the objective function; t represents the time; T represents the total number of hours in a year; s represents the wind power output scenario index; S represents the total number of wind power output scenarios; l′ s,t represents the net load power at time t; l′ s,t-1 represents the net load power at time t-1; C w represents the planned wind power capacity; P w,s,t Represents the output curve of wind power simulation scenario; l t Represents the total power load of all production lines at time t;

[0018] in,

[0019]

[0020] Where: τ represents the start time of the production line; T l Represents the production cycle of a single production line; Pl ,t Represents the power consumption of a single production line at time t; nl Indicates the total number of production lines; s τ A 0-1 variable indicating the start of a single production line at time τ within the production cycle.

[0021] Furthermore, in S3, the objective function with the goal of minimizing the net load fluctuation after absorbing wind power needs to satisfy the following constraints:

[0022]

[0023] Where: η cut represents the wind abandonment rate; p w,c,s,t represents the wind power abandoned in scenario s and period t; C w represents the planned wind power capacity; P w,s,t Represents the output curve of wind power simulation scenario.

[0024] Furthermore, in S4, the autonomous peak-shaving boundary conditions of the local wind-fire storage capacity planning unit are established as follows:

[0025]

[0026] Where: p s1,t Indicates the power purchased from the grid by the newly added wind, fire, load and storage bundling unit; p s2,t Indicates the power delivered by the bundling unit to the grid; T P represents the set of peak load periods of the power system; T V Represents the set of off-peak load periods in the power system.

[0027] Furthermore, in S6, the wind, fire, load and storage local capacity configuration optimization model is established as follows:

[0028]

[0029] Where: Represents the annualized net income of the new unit; U w is the annualized net income per unit of wind power installed capacity; C w Planning capacity for wind power; U es,1 U is the annual cost of energy storage per unit of electricity; es,2 is the annual cost of energy storage unit power capacity; Q es,max Indicates the maximum unit power of energy storage planning; p es,max represents the annual planning variable of the maximum power capacity of the energy storage unit; b u,t A "0-1" flag variable indicating that there is an upward peak adjustment in the system during period t; p s1,t represents the power purchased from the grid by the newly added wind-fire-load-storage bundled unit; k1, k2, k are all penalty coefficients; b d,t A "0-1" flag variable indicating that there is downward peak regulation in the system during period t; p s2,tIndicates the power delivered by the bundling unit to the grid; b c,t is a "0-1" flag variable indicating the system has wind curtailment burden during period t; c g is the unit power generation cost of bundled thermal power; p g,t represents the bundled thermal power capacity at time t; c se is the electricity purchase price of the local bundled unit; c bu The electricity selling price for the local bundled unit.

[0030] Furthermore, in S5, the power balance constraint is established as follows:

[0031] p g,t +C w P w,t -p w,c,t +p dch,t -p ch,t = l t -p s1,t +p s2,t

[0032] Where: p g,t represents the bundled thermal power capacity at time t; C w represents the planned wind power capacity; P w,t Indicates the output size of unit wind power installed capacity; p w,c,t Indicates the abandoned wind power; p dch,t Indicates discharge power; l t Represents the total power load of all production lines at time t after optimization.

[0033] Beneficial effects: The method for optimizing the configuration of the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak-shaving of the present invention, by combining the total power load of all production lines of large users that can be shifted at a certain time, establishes an objective function with the goal of minimizing the net load fluctuation after absorbing wind power, and obtains the optimized comprehensive load curve of large users; and according to the power balance constraint and the autonomous peak-shaving boundary conditions of the local wind, fire, load and storage capacity planning unit, establishes a local capacity configuration optimization model of wind, fire, load and storage, obtains the bundling configuration result of wind, fire, load and storage, and realizes the optimization configuration of the local capacity of wind, fire, load and storage according to the bundling configuration result of wind, fire, load and storage, so as to reduce the peak-shaving burden on the system. The present invention is oriented to the application scenario of large user load configuration of wind power, optimizes the configuration of the local capacity of wind, fire, load and storage, and optimizes the large user load optimization model and the local capacity optimization configuration model of wind, fire, load and storage by adjusting the operating interval and timing of the large user production line, thereby reducing the difficulty of solving the joint optimization of wind, fire, load and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 It is a flow chart of the method for optimizing the configuration of the local capacity of wind, fire, load and storage of the present invention;

[0036] Figure 2 It is a schematic diagram of the optimal annual load curve for large users in an embodiment of the present invention;

[0037] Figure 3 It is a schematic diagram of annual power balance results in an embodiment of the present invention;

[0038] Figure 4 A schematic diagram of monthly bundled thermal power capacity and its minimum output in an embodiment of the present invention;

[0039] Figure 5 A schematic diagram of bundled thermal power capacity configured with wind and thermal storage per month in an embodiment of the present invention;

[0040] Figure 6 It is a schematic diagram of the bundling configuration result of wind, fire and load storage in an embodiment of the present invention;

[0041] Figure 7 It is a schematic diagram of energy storage charging and discharging power of the bundled configuration of wind, fire and load storage in an embodiment of the present invention;

[0042] Figure 8 The present invention is a flow chart of the method for optimizing the local capacity configuration of wind, fire, load and storage in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] This embodiment introduces a method for optimizing the local capacity configuration of wind, fire, load and storage that meets the requirements of autonomous peak load regulation. Figure 1 and Figure 8 As shown,

[0045] S1: Obtain the power load curve of a single production line of a large user with time-shiftable structure to obtain the total power load l of all production lines at time t t ;

[0046] Specifically, in the power system, time-shiftable large users refer to large user loads whose load supply time can be changed according to plan. The characteristic of this type of load is that their power demand can be shifted in different time periods, while the total power consumption remains unchanged. In China, under normal circumstances, industrial large users with a maximum load of no less than 1,000 kilowatts can be regarded as large user loads.

[0047] Specifically, the large user load that can be shifted in this embodiment is a plurality of power production lines, for example, graphite electrodes. The plurality of production lines are started in a staggered manner at different times to minimize load fluctuation.

[0048] Specifically, the load characteristics of a large user with multiple production lines whose timing can be shifted are obtained, including the start and stop characteristics of a single production line, the production cycle, the single-cycle load curve, and the timing requirements between multiple production lines.

[0049] S2: Obtain the historical output characteristics of local wind power resources to obtain the annual wind power output curve, and then obtain the wind power simulation scenario output curve P w,s,t ;

[0050] Specifically, the historical output characteristics of local wind power resources, including volatility and annual utilization hours, are obtained to obtain the annual wind power output curve. The K-means clustering algorithm is used to conduct clustering research on wind power scenarios. The first initial cluster center is obtained based on the density idea, and the remaining initial cluster centers are obtained based on the maximum and minimum distance principle. The typical wind power output scenario is obtained through the synchronous back-substitution elimination algorithm based on the Kantorovich distance.

[0051] Specifically, a wind power simulation scenario is equivalent to a wind power output curve, and the wind power output curve is the output data of the wind turbine in a certain period of time, which is plotted as a curve for display. Therefore, the wind power output data can be obtained through the wind power output curve; specifically, in this embodiment, the wind power output curve is obtained by using a computer to generate random numbers by referring to the historical output characteristics of the historical wind power resources, so it is called a wind power simulation scenario output curve.

[0052] S3: According to the total power load l of the integrated production line at time t t And wind power simulation scenario output curve P w,s,t , establishing an objective function with the goal of minimizing the net load fluctuation after absorbing wind power, solving the objective function with the goal of minimizing the net load fluctuation after absorbing wind power, and obtaining an optimized large user comprehensive load curve;

[0053] Specifically, this embodiment proposes a two-dimensional optimization method for the combined optimization problem of the power loads of multiple production lines of a large user with a time sequence that can be shifted, that is, the optimization problem of a large user with a time sequence that can be shifted for the start and stop time of each production line, with the goal of minimizing the net load fluctuation after absorbing wind power, to optimize the load operation mode of the large user, so as to obtain the optimized load curve of the large user load, which is as follows:

[0054] Preferably, the objective function aiming at minimizing the net load fluctuation after absorbing wind power is expressed as follows:

[0055]

[0056] l′ s,t = l t -C w P w,s,t

[0057] Where: ob. represents the objective function; t represents the time; T represents the total number of hours in a year; s represents the wind power output scenario index; S represents the total number of wind power output scenarios; l′ s,t represents the net load power at time t; l′ s,t-1 represents the net load power at time t-1; C w represents the planned wind power capacity; P w,s,t Represents the output curve of wind power simulation scenario; l t represents the total power load of the integrated production line at time t;

[0058] In this implementation, graphite electrodes are used as an example, and it is assumed that the number of large users whose timing can be shifted is n. l The production line consists of 2 production lines. The first start-up time can be arranged arbitrarily, and after start-up, continuous production is carried out according to a fixed cycle.

[0059] Among them, the total power load of the optimized integrated production line at time t is expressed as:

[0060]

[0061] Where: τ represents the start time of the production line; T l Represents the production cycle of a single production line; P l,t Represents the power consumption of a single production line at time t; n l Indicates the total number of production lines; s τ A 0-1 variable indicating that a single production line is started at time τ within the production cycle;

[0062] Preferably, the objective function with the goal of minimizing the net load fluctuation after absorbing wind power needs to satisfy the following constraints:

[0063] In order to ensure that the optimized load curve can absorb wind power to the greatest extent, the total wind abandonment rate in all scenarios should not be higher than η cut :

[0064]

[0065] Where: η cut represents the wind abandonment rate; p w,c,s,t represents the wind power abandoned in scenario s and period t; C w represents the planned wind power capacity; P w,s,t Represents the output curve of wind power simulation scenario;

[0066] Specifically, this embodiment solves the objective function of minimizing the net load fluctuation after absorbing wind power to obtain the optimized large-user comprehensive load curve, which is actually a mixed integer programming problem. This embodiment uses the existing cplex commercial solver and adopts conventional methods in the field to solve the objective function, so it is not described in detail here.

[0067] S4: Establish the autonomous peak-shaving boundary conditions of the local wind-fire storage capacity planning unit;

[0068] Specifically, in order not to increase the system's paid peak-shaving burden, that is, after the net load power of the newly added bundled unit is connected to the grid, the total load power of the system will not be increased during the peak period of system power consumption, thereby not affecting the number of startups; during the low-load period of the system, power will not be transmitted to the grid, thereby not increasing the deep peak-shaving pressure of thermal power units. The autonomous peak-shaving boundary conditions of the local planning unit are constructed as follows;

[0069]

[0070] Where: p s1,t Indicates the power purchased from the grid by the newly added wind, fire, load and storage bundling unit; p s2,t Indicates the power delivered by the bundling unit to the grid; T P represents the set of peak load periods of the power system; T V Represents the set of off-peak load periods in the power system.

[0071] S5: establishing a power balance constraint according to the optimized large user comprehensive load curve;

[0072] Preferably, the power balance constraint is established as follows:

[0073] p g,t +C w P w,t -p w,c,t +p dch,t -p ch,t = l t -p s1,t +ps2,t

[0074] Where: p g,t represents the bundled thermal power capacity at time t; C w represents the planned wind power capacity; P w,t Indicates the output size of unit wind power installed capacity; p w,c,t Indicates the abandoned wind power; p dch,t Indicates discharge power; l t It represents the total power load of the optimized comprehensive production line at time t, that is, the optimized comprehensive load curve of large users;

[0075] S6: According to the power balance constraint, bundled thermal power output constraint, minimum wind curtailment rate constraint, energy storage operation constraint and autonomous peak-shaving boundary conditions of the local wind-fire-load-storage capacity planning unit, a local capacity configuration optimization model of wind-fire-load-storage is established to obtain a wind-fire-load-storage bundled configuration result, so as to achieve optimal configuration of the local capacity of wind-fire-load-storage according to the wind-fire-load-storage bundled configuration result;

[0076] The wind-fire-load-storage bundle configuration results include the average bundled thermal power capacity, the wind power planning capacity C w 、Maximum unit power of energy storage planning Q es,max , annual planning variable p of maximum power capacity es,max .

[0077] Specifically, in order to optimize the operation mode of large user loads that can be shifted in time sequence, maximize the planned benefits of wind power, and bundle a certain amount of thermal power capacity and energy storage to achieve autonomous peak load regulation of bundled units. With the goal of maximizing the difference between the annualized net income of wind power and the annualized investment cost of energy storage, a local capacity optimization configuration model for wind, thermal, load and storage is established;

[0078] Preferably, the wind, fire, load and storage local capacity configuration optimization model is established as follows:

[0079]

[0080] Where: Represents the annualized net income of the new unit; U w is the annualized net income per unit of wind power installed capacity; C w Planning capacity for wind power; U es,1 U is the annual cost of energy storage per unit of electricity; es,2 is the annual cost of energy storage unit power capacity; Q es,max Indicates the maximum unit power of energy storage planning; p es,max represents the annual planning variable of the maximum power capacity of the energy storage unit; b u,t A "0-1" flag variable indicating that there is an upward peak adjustment in the system during period t; p s1,trepresents the power purchased from the grid by the newly added wind-fire-load-storage bundled unit; k1, k2, k are all penalty coefficients; b d,t A "0-1" flag variable indicating that there is downward peak regulation in the system during period t; p s2,t Indicates the power delivered by the bundling unit to the grid; b c,t is a "0-1" flag variable indicating the system has wind curtailment burden during period t; c g is the unit power generation cost of bundled thermal power; p g,t represents the bundled thermal power capacity at time t; c se is the electricity purchase price of the local bundled unit; c bu The electricity sales price for local bundled units;

[0081] Specifically, the bundled thermal power output constraints in this embodiment are as follows:

[0082] η min P g,i ≤P g,t ≤P g,i , t∈{time period within i day}

[0083] Where: η min is the minimum output ratio coefficient of bundled thermal power; P g,i is the bundled thermal power capacity on the i-th day; P g,t is the bundled thermal power capacity at time t;

[0084] Specifically, the minimum wind abandonment rate constraint in this embodiment is as follows:

[0085]

[0086] P w,c,t ≤C w P w,t

[0087] Where: t represents the time; P w,t Indicates the output size of unit wind power installed capacity; p w,c,t Indicates the abandoned wind power; C w Indicates the planned wind power capacity.

[0088] Specifically, the energy storage operation constraints in this embodiment are as follows:

[0089] p ch,t ≤min{p es,max ,(Q es,max -q es,t )η ch}

[0090] p dch,t ≤min{p es,max ,(q es,t -Q es,min )ηdch}

[0091] q es,t =q es,t-1 +p ch,t η ch -p dch,t / η dch

[0092] Q es,min ≤q es,t ≤Q es,max

[0093] Where: p ch,t Indicates charging power; p dch,t Indicates discharge power; q es,t represents the state of charge of the energy storage at time t; p es,max The annual planning variable representing the maximum power capacity of the energy storage unit; Q es,max Indicates the maximum unit power of energy storage planning; η ch Represents the energy storage charging efficiency; η dch Indicates the energy storage discharge efficiency; Q es,min Indicates the minimum unit of electricity in energy storage planning;

[0094] Specifically, this embodiment solves the local capacity configuration optimization model of wind, fire, load and storage, which is a mixed integer programming problem, and uses the commercial solver cplex to solve it. The solution process is not described in detail here.

[0095] A specific implementation example of the present invention is as follows:

[0096] Based on the historical output data of a wind farm in Jilin Province, a wind farm with an annual output of 100MW was simulated by scaling the installed capacity of the wind farm in proportion. The annual utilization hours of the wind farm are 3690 hours. The optimal annual load curve obtained by the large user load optimization model is as follows: Figure 2 As shown in Figure 1, the optimal time shift interval and start time interval are 1 hour and 19 hours respectively. The peak and valley power of the load curve are 214MW and 161MW respectively, and the annual electricity consumption is 1.61 billion kWh.

[0097] The optimal configuration capacity of wind power under the wind-fire-load configuration mode is as follows:

[0098] Based on the optimization results of large user loads, assuming that the wind curtailment rate is 10%, when the monthly bundled thermal power capacity is variable, the planned total wind power capacity is 137MW. The annual power balance curve under this configuration mode is as follows: Figure 3 The large user load first consumes the minimum output of the bundled thermal power units, and then follows the net load fluctuation to avoid increasing the peak load burden of the system.

[0099] The other part of the load is used to consume wind power. The annual wind power consumption is 450 million kWh, accounting for 29.75% of the annual load. In some cases, some wind power is abandoned because the wind power output exceeds the demand of the remaining load.

[0100] The monthly bundled thermal power capacity and its minimum output are as follows: Figure 4 As shown in the figure, the average bundled thermal power capacity is 212MW. The minimum output is consumed by large user loads, and its adjustable capacity is used to track the remaining load after consuming wind power. The results show that the utilization rate of bundled thermal power is 62.69%.

[0101] The optimal wind power configuration capacity under the wind-fire-load-storage configuration mode of this embodiment is as follows:

[0102] In this embodiment, under the optimal configuration of wind power, thermal power, load and storage, the optimal wind power configuration capacity is 159MW, and the energy storage configuration capacity is 15.9MW / 60MWh. Figure 5 As shown, the average capacity is 194MW and the utilization rate of bundled thermal power capacity is 64.89%.

[0103] The power balance results under the optimal configuration of wind, fire, load and storage bundling are as follows: Figure 6 and Figure 7 As shown in the figure, the annual charging and discharging power of the energy storage is 8575.58MWh and 7739.46MWh respectively.

[0104] Through the above two configurations, it can be seen that in the wind-fire-load-storage optimization configuration mode, the configured wind power capacity is increased to 159MW compared with 137MW in the wind-fire-load configuration mode. The utilization rate of thermal power units is also increased from 62.69% to 64.89%.

[0105] The present invention provides a method for optimizing the local capacity configuration of wind, fire, load and storage that meets the requirements of autonomous peak-shaving. By combining the total power load of all production lines of large users that can be shifted at different times, an objective function with the goal of minimizing the net load fluctuation after absorbing wind power is established to obtain the optimized comprehensive load curve of large users. Based on the optimization of large user loads, according to power balance constraints and autonomous peak-shaving boundary conditions of local wind, fire, load and storage capacity planning units, an optimization model for the local capacity configuration of wind, fire, load and storage that takes into account power transmission restrictions during peak and valley periods is established to obtain the bundling configuration results of wind, fire, load and storage, so as to optimize the local capacity of wind, fire, load and storage according to the bundling configuration results of wind, fire, load and storage, so as to reduce the peak-shaving burden on the system and achieve the purpose of not occupying the peak-shaving resources of the system. This embodiment is aimed at the application scenario of wind power configuration for large user loads, and combines wind power scenario generation and reduction technology to carry out uncertainty planning: optimize the configuration of local capacity of wind, fire, load and storage, and separately optimize the large user load optimization model and the local capacity optimization configuration model of wind, fire, load and storage by adjusting the operating interval and timing of the large user production line, thereby reducing the difficulty of solving the joint optimization of wind, fire, load and storage, and at the same time solving the problem of existing new energy planning methods occupying the original peak-shaving resources of the system.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the local capacity of wind, fire, load and storage to meet the requirements of autonomous peak load regulation, characterized in that: The steps include: S1: Obtain the power load curve of a single production line of a large user with time-shiftable structure to obtain the total power load l of all production lines at time t t ; S2: Obtain the historical output characteristics of local wind power resources to obtain the annual wind power output curve, and then obtain the wind power simulation scenario output curve P w,s,t ; S3: Based on the total power load l of all production lines at time t t And wind power simulation scenario output curve P w,s,t , establishing an objective function with the goal of minimizing the net load fluctuation after absorbing wind power, solving the objective function with the goal of minimizing the net load fluctuation after absorbing wind power, and obtaining an optimized large user comprehensive load curve; S4: Establish the autonomous peak-shaving boundary conditions of the local wind-fire storage capacity planning unit; S5: establishing a power balance constraint according to the optimized large user comprehensive load curve; S6: According to the power balance constraint, bundled thermal power output constraint, minimum wind curtailment rate constraint, energy storage operation constraint and autonomous peak-shaving boundary conditions of the local wind-fire-load-storage capacity planning unit, a local capacity configuration optimization model of wind-fire-load-storage is established to obtain a wind-fire-load-storage bundled configuration result, so as to achieve optimal configuration of the local capacity of wind-fire-load-storage according to the wind-fire-load-storage bundled configuration result; The wind-fire-load-storage bundling configuration results include the average bundled thermal power capacity, the planned wind power capacity, and the planned maximum unit power of energy storage.

2. According to claim 1, a method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak load regulation is characterized in that: In S3, the objective function with the goal of minimizing the net load fluctuation after absorbing wind power is expressed as follows: l′ s,t =l t -C w P w,s,t Where: ob. represents the objective function; t represents the time; T represents the total number of hours in a year; s represents the wind power output scenario index; S represents the total number of wind power output scenarios; l′ s,t represents the net load power at time t; l′ s,t-1 represents the net load power at time t-1; C w represents the planned wind power capacity; P w,s,t Represents the output curve of wind power simulation scenario; l t Represents the total power load of all production lines at time t; in, Where: τ represents the start time of the production line; T l Represents the production cycle of a single production line; P l,t Represents the power consumption of a single production line at time t; n l Indicates the total number of production lines; s τ A 0-1 variable indicating the start of a single production line at time τ within the production cycle.

3. The method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak load regulation according to claim 1 is characterized in that: In S3, the objective function with the goal of minimizing the net load fluctuation after absorbing wind power needs to satisfy the following constraints: Where: η cut represents the wind abandonment rate; p w,c,s,t represents the wind power abandoned in scenario s and period t; C w represents the planned wind power capacity; P w,s,t Represents the output curve of wind power simulation scenario.

4. The method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak load regulation according to claim 1 is characterized in that: In S4, the autonomous peak-shaving boundary conditions of the local wind-fire storage capacity planning unit are established as follows: Where: p s1,t Indicates the power purchased from the grid by the newly added wind, fire, load and storage bundling unit; p s2,t Indicates the power delivered by the bundling unit to the grid; T P represents the set of peak load periods of the power system; T V Represents the set of off-peak load periods in the power system.

5. The method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak load regulation according to claim 1 is characterized in that: In S6, the wind, fire, load and storage local capacity configuration optimization model is established as follows: Where: Represents the annualized net income of the new unit; U w is the annualized net income per unit of wind power installed capacity; C w Planning capacity for wind power; U es,1 U is the annual cost of energy storage per unit of electricity; es,2 is the annual cost of energy storage unit power capacity; Q es,max Indicates the maximum unit power of energy storage planning; p es,max represents the annual planning variable of the maximum power capacity of the energy storage unit; b u,t "0-1" flag variable indicating that there is upward peak regulation in the system during period t; p s1,t represents the power purchased from the grid by the newly added wind-fire-load-storage bundled unit; k1, k2, k are all penalty coefficients; b d,t "0-1" flag variable indicating that there is downward peak regulation in the system during period t; p s2,t Indicates the power delivered by the bundling unit to the grid; b c,t is a "0-1" flag variable indicating the system has wind curtailment burden during period t; c g is the unit power generation cost of bundled thermal power; p g,t represents the bundled thermal power capacity at time t; c se is the electricity purchase price of the local bundled unit; c bu The electricity selling price for the local bundled unit.

6. The method for optimizing the local capacity of wind, fire, load and storage that meets the requirements of autonomous peak load regulation according to claim 1 is characterized in that: In S5, the power balance constraint is established as follows: p g,t +C w P w,t -p w,c,t +p dch,t -p ch,t =l t -p s1,t +p s2,t Where: p g,t represents the bundled thermal power capacity at time t; C w represents the planned wind power capacity; P w,t Indicates the output size of unit wind power installed capacity; p w,c,t Indicates the abandoned wind power; p dch,t Indicates discharge power; l t Represents the total power load of all production lines at time t after optimization.

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