Multi-tense scheduling control optimization method and system for park optical storage direct flexible system

Abstract

The invention discloses a multi-tense scheduling control optimization method and system for a park optical storage direct-flexible system, relates to the technical field of power grid scheduling, and can improve the accuracy and response speed of a scheduling decision of the park optical storage direct-flexible system. According to the method, a system scheduling period is divided into three time scales of real time, intra-day and day-ahead, corresponding adjustable load classification models are matched according to load characteristics of different time scales, and then an integral multi-time-scale scheduling rolling optimization model is established; by solving the optimal scheduling plan obtained by the multi-time scale scheduling rolling optimization model, the response speed and the scheduling accuracy of the system coping with load changes with different characteristics under different time scales can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and particularly relates to a multi-temporal scheduling control optimization method and system for park integrated photovoltaic, energy storage, DC power distribution and flexible interaction. Background Art

[0002] With the large-scale access of renewable energy and the development of smart power grids, the park integrated photovoltaic, energy storage, DC power distribution and flexible interaction system has become an important carrier for realizing efficient energy utilization and flexible power grid dispatching. Among them, the park integrated photovoltaic, energy storage, DC power distribution and flexible interaction system is a new energy system integrating four technologies: solar photovoltaic, energy storage, DC power distribution and flexible interaction.

[0003] Data such as the power grid load and photovoltaic output of the park-level integrated photovoltaic, energy storage, DC power distribution and flexible interaction system have significant multi-temporal characteristics, that is, they show different change trends and characteristics in different time periods. In traditional scheduling technologies, most are designed based on a single time scale and do not fully consider these multi-temporal characteristics, resulting in limited accuracy and response speed of scheduling decisions when the power grid load and photovoltaic output change rapidly. With the development of the park integrated photovoltaic, energy storage, DC power distribution and flexible interaction system, the requirement for the dynamic response ability of the system is getting higher and higher, and the limited response speed will affect the reliability and stability of the system.

[0004] In view of this, a multi-temporal scheduling control optimization method and system for park integrated photovoltaic, energy storage, DC power distribution and flexible interaction are needed. Summary of the Invention

[0005] Aiming at the problems of low accuracy and slow response speed of scheduling decisions in the existing scheduling technologies, the present invention provides a multi-temporal scheduling control optimization method and system for park integrated photovoltaic, energy storage, DC power distribution and flexible interaction, which can improve the accuracy and response speed of scheduling decisions of the park integrated photovoltaic, energy storage, DC power distribution and flexible interaction system. The specific technical solutions are as follows:

[0006] In a first aspect, an embodiment of the present application provides a multi-temporal scheduling control optimization method for park integrated photovoltaic, energy storage, DC power distribution and flexible interaction, including:

[0007] Construct an adjustable load classification model, where the adjustable load classification model includes a shiftable load model, a transferable load model, and a curtailable load model. The shiftable load model is used to calculate the shiftable load scheduling cost, the transferable load model is used to calculate the transferable load scheduling cost, and the curtailable load model is used to calculate the curtailable load scheduling cost;

[0008] Based on the adjustable load classification model, a multi-time scale scheduling rolling optimization model is constructed. The multi-time scale scheduling rolling optimization model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time scheduling model. Among them, the objective function of the day-ahead scheduling model calculates the first minimum scheduling cost based on the shiftable load scheduling cost; the objective function of the intra-day scheduling model calculates the second minimum scheduling cost based on the transferable load scheduling cost and the shiftable load scheduling cost; the objective function of the real-time scheduling model calculates the third minimum scheduling cost based on the transferable load scheduling cost, the shiftable load scheduling cost, and the curtailable load scheduling cost.

[0009] Obtain the operation data of the park's PV-storage-direct-current-flexible (PV-SDF) system; based on the operation data, solve the multi-time scale scheduling rolling optimization model to obtain the optimal scheduling plan.

[0010] In a second aspect, an embodiment of the present application provides a multi-temporal scheduling control optimization system for a park's PV-SDF system, which is applied to the method in the first aspect. The system includes:

[0011] A first modeling module for constructing an adjustable load classification model, which includes a shiftable load model, a transferable load model, and a curtailable load model. The shiftable load model is used to calculate the shiftable load scheduling cost, the transferable load model is used to calculate the transferable load scheduling cost, and the curtailable load model is used to calculate the curtailable load scheduling cost.

[0012] A second modeling module for constructing a multi-time scale scheduling rolling optimization model based on the adjustable load classification model. The multi-time scale scheduling rolling optimization model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time scheduling model. Among them, the objective function of the day-ahead scheduling model calculates the first minimum scheduling cost based on the shiftable load scheduling cost; the objective function of the intra-day scheduling model calculates the second minimum scheduling cost based on the transferable load scheduling cost and the shiftable load scheduling cost; the objective function of the real-time scheduling model calculates the third minimum scheduling cost based on the transferable load scheduling cost, the shiftable load scheduling cost, and the curtailable load scheduling cost.

[0013] An acquisition module for obtaining the operation data of the park's PV-SDF system;

[0014] A calculation module for solving the multi-time scale scheduling rolling optimization model based on the operation data to obtain the optimal scheduling plan.

[0015] In a third aspect, an embodiment of the present application provides a blockchain, which includes:

[0016] A core scheduling node, configured to receive scheduling instructions from a power grid dispatching center, and based on the smart contract mechanism of the blockchain, convert the scheduling instructions into executable smart contract tasks, and distribute the smart contract tasks to execution nodes;

[0017] An energy supply node, configured to control a photovoltaic power generation unit and an energy storage system of a campus optical storage direct current flexible system to perform corresponding operations based on the smart contract task;

[0018] A load management node: configured to execute the method as described in the first aspect to obtain an optimal scheduling plan based on the smart contract task, and schedule the power grid of the campus optical storage direct current flexible system based on the optimal scheduling plan;

[0019] A market trading node: participates in power market trading, buys and sells electricity according to market prices and scheduling instructions, and provides electricity prices to the load management node so that the load management node can perform scheduling calculations.

[0020] In a fourth aspect, an embodiment of the present application provides a computing device, including: a memory, configured to store a program; a processor, configured to load the program to execute the method as described in the first aspect.

[0021] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, it controls a device where the computer-readable storage medium is located to execute the method as described in the first aspect.

[0022] Compared with the prior art, the beneficial effects of the present invention are: The multi-temporal scheduling control optimization method for campus optical storage direct current flexibility proposed by the present invention divides the system scheduling cycle into three time scales: real-time, intra-day, and day-ahead, and matches corresponding adjustable load classification models according to the load characteristics of different time scales: real-time scheduling is applicable to load that can be quickly reduced, intra-day scheduling is applicable to load that can be shifted and transferred, and day-ahead scheduling is used for long-term regulation of overall load and energy storage. Then, an overall multi-time scale scheduling rolling optimization model is established; the optimal scheduling plan obtained by solving the multi-time scale scheduling rolling optimization model can improve the response speed and scheduling accuracy of the system when dealing with load changes with different characteristics at different time scales, and solves the problems of scheduling lag and low decision-making accuracy caused by a single time scale in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to actual scale.

[0024] Figure 1 A flow chart of a multi-temporal scheduling control optimization method for a park photovoltaic storage direct-flexible system provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of multi-time scale scheduling provided in an embodiment of the present application;

[0026] Figure 3 Photovoltaic and load curve diagram provided for the embodiment of the present application;

[0027] Figure 4 The hourly load distribution diagram before scheduling provided in the embodiment of the present application;

[0028] Figure 5 An hourly load comparison chart before and after scheduling provided in an embodiment of the present application.

[0029] Figure 6 The load distribution diagram after scheduling provided by the embodiment of the present application;

[0030] Figure 7 A schematic diagram of the structure of a blockchain system provided in an embodiment of the present application;

[0031] Figure 8 A schematic diagram of the structure of a multi-temporal scheduling control optimization system for a park photovoltaic storage direct-flexible system provided in an embodiment of the present application;

[0032] Figure 9 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0034] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0035] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0036] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0037] The park integrated photovoltaic, energy storage, DC power distribution and flexible electricity utilization system is a new energy system integrating photovoltaic power generation, energy storage, DC power distribution and flexible electricity utilization, which includes a photovoltaic subsystem, an energy storage subsystem, a DC power distribution subsystem and a flexible interaction subsystem. Among them, the flexible interaction subsystem is used to monitor the electricity load in the park and the status of the photovoltaic power generation and energy storage systems in real time through an intelligent control system, and flexibly adjust the power of the electrical equipment according to factors such as the demand of the power grid and the electricity price, so as to actively respond to the power grid, optimize the electricity consumption curve and reduce the peak load.

[0038] Specifically, the flexible interaction subsystem monitors the operating status of the flexible load in real time through an intelligent control system and algorithms, and dynamically adjusts the electricity consumption strategies of these devices according to the grid signals (such as electricity price, supply and demand situation), so as to achieve optimal energy allocation. The flexible load refers to electrical equipment with flexible adjustment capabilities (such as air conditioners, electric water heaters, electric vehicle chargers, etc.), which can respond to the grid demand by means of load transfer, reduction or power adjustment.

[0039] To solve the problems of low accuracy of scheduling decisions and slow response speed for flexible loads in the existing scheduling technologies of park integrated photovoltaic, energy storage, DC power distribution and flexible electricity utilization systems, the present invention provides a multi-temporal scheduling control optimization method, system, blockchain, computing device and medium for park integrated photovoltaic, energy storage, DC power distribution and flexible electricity utilization, which can improve the accuracy and response speed of scheduling decisions of park integrated photovoltaic, energy storage, DC power distribution and flexible electricity utilization systems.

[0040] Please refer to Figure 1 and Figure 2 , Figure 1 This application embodiment provides a multi-temporal scheduling control optimization method for park integrated photovoltaic, energy storage, DC power distribution and flexible electricity utilization. This method can be applied to a computing device, such as Figure 1 shown, and this method includes the following steps:

[0041] Step 101, the computing device constructs an adjustable load classification model.

[0042] First of all, in the present invention, the scheduling period of the park integrated photovoltaic, energy storage, DC power distribution and flexible electricity utilization system is divided into different time scales: real-time, intraday and day-ahead. The following table shows the time resolution and time span of scheduling under different time scales.

[0043]

[0044]

[0045] Table 1 Multi - time - scale division of scheduling

[0046] In the present invention, three types of flexible loads are considered: shiftable loads, transferable loads, and curtailable loads. Among them, the adjustable load classification model includes a shiftable load model, a transferable load model, and a curtailable load model. The shiftable load model is used to calculate the scheduling cost of shiftable loads, the transferable load model is used to calculate the scheduling cost of transferable loads, and the curtailable load model is used to calculate the scheduling cost of curtailable loads.

[0047] 1) Shiftable load model

[0048] The power supply time of shiftable loads can be changed according to the plan (the total power supply remains unchanged, and the power supply period is variable), and their power consumption time can be flexibly adjusted to a certain extent to cooperate with the overall scheduling of the power system. Shiftable loads mainly participate in the day - ahead and intra - day scheduling plans.

[0049] Preferably, let t s and t d be the start and duration of the shiftable load respectively, and [t SL- , t SL+ be the acceptable shifting interval. Then the shifted load period τ after scheduling is:[[]]

[0050] τ ∈ [t SL- , t SL+ - t d + 1](1)

[0051] To ensure the continuity of its power consumption, the following should be satisfied:

[0052]

[0053] Among them, the Boolean variable α t = 1 indicates that the load has shifted, and α t = 0 indicates that the load has not shifted.

[0054] Assume that the power distribution vector L SL of the shiftable load before response is:

[0055]

[0056] Among them, are the shiftable loads before response at [t s , t s + t d-1] Power at each time point within the time period; the power distribution vector of the shiftable load after scheduling is:

[0057]

[0058] Among them, are respectively the powers of the shiftable load after response at each time point within the time period [τ, τ + t d -1]; the corresponding compensation cost C SL after response scheduling, that is, the scheduling cost of the shiftable load is:

[0059]

[0060] Among them, is the compensation price per unit power of the shiftable load after scheduling, and P SL,t is the shiftable load at time t.

[0061] The calculation device can obtain the shiftable load model based on the above expressions (1)-(5).

[0062] 2) Transferable load model

[0063] The transferable load has high time elasticity. Its total load remains unchanged within the transferable time period, but the power and working hours of each time period are adjustable. Through electricity price signals and incentives, users can be guided to transfer the load during peak hours to off-peak hours, thereby optimizing the load curve of the power system. The transferable load mainly participates in intraday and real-time scheduling plans.

[0064] Among them, assuming that the transferable interval of the transferable load is [t TL- , t TL+ , then the consistency invariance needs to be satisfied:

[0065]

[0066] Among them, L TL,t and P TL,t are respectively the transferable load powers before and after scheduling at time t. In addition to constraining the transfer power, it is also necessary to constrain the minimum duration of the transferable load:

[0067]

[0068] Among them, the Boolean variables β t and β t-1 respectively represent the state variables of whether the load has been transferred at time t and time t - 1. The variable being 1 indicates that the load has been transferred, and the variable being 0 indicates that the load has not been transferred; are respectively the upper and lower limits of the transfer power at this time, is the minimum continuous operation time of the transferable load. The corresponding compensation cost C after responding to the dispatch TL , that is, the dispatch cost of the transferable load is:

[0069]

[0070] Among them, is the compensation price per unit power of the transferable load after responding to the dispatch.

[0071] Among them, T is the termination time; τ is the start time of the transferable load after dispatch, is the compensation price per unit power of the transferable load after responding to the dispatch.

[0072] The computing device can obtain the transferable load model based on the above expressions (6)-(9).

[0073] 3) Curtailable load model

[0074] Curtailable load refers to the load with low requirements for power supply reliability that can be curtailed during a certain period. In real-time dispatch, when the grid load is too high or the new energy output is insufficient, this part of the load can be curtailed to balance the supply and demand relationship of the grid. Load curtailment is an important load management measure with a fast response speed, which can effectively mobilize the interactive enthusiasm of users and participate in the microgrid optimal dispatch. Curtailable load mainly participates in the real-time dispatch plan.

[0075] After the curtailable load responds to the dispatch instruction, it curtails part of the power consumption. The power P of the curtailed load at time t RL,t is:

[0076] P RL,t =(1 - u t γ t )L RL,t (10)

[0077] Among them, L RL,t is the power of the curtailable load before dispatch at time t, the boolean variables γ t and γ t-1 represent whether load curtailment occurs at time t and time t - 1. The variable being 1 indicates that load curtailment occurs, and the variable being 0 indicates that load curtailment does not occur. u t is the curtailment coefficient at time t. To ensure comfort, the load cannot be dispatched frequently, and it is necessary to restrict the number of curtailments and the continuous curtailment time:

[0078]

[0079] Among them, is the maximum and minimum continuous curtailment time, M maxLet \(N\) be the maximum number of curtailment times, \(\tau\) be the starting time of load curtailment, and \(T\) be the ending time. After responding to the dispatch, the corresponding compensation cost \(C\) is obtained. RL , that is, the scheduling cost of the load that can be curtailed is:

[0080]

[0081] Among them, is the compensation price per unit power of the load that can be curtailed after responding to the dispatch.

[0082] The computing device can obtain the load curtailment model based on the above expressions (10)-(15).

[0083] Step 102: The computing device constructs a multi-time scale scheduling rolling optimization model based on this adjustable load classification model.

[0084] After constructing the above adjustable load classification model, the computing device can match the corresponding scheduling period according to the load characteristics of different types of adjustable loads, and construct the overall multi-time scale scheduling rolling optimization model of the park's optical storage direct current flexible system.

[0085] Among them, the multi-time scale scheduling rolling optimization model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time scheduling model; among them, the objective function of minimizing the scheduling cost of the day-ahead scheduling model includes the load electricity cost, and the load electricity cost includes the scheduling cost of the shiftable load; the objective function of minimizing the scheduling cost of the intra-day scheduling model includes the scheduling cost of the transferable load and the scheduling cost of the shiftable load; the objective function of minimizing the scheduling cost of the real-time scheduling model includes the scheduling cost of the curtailable load. The specific construction idea is as follows:

[0086] 1) Day-ahead scheduling model

[0087] The optimization objective of the day-ahead scheduling model considers the first minimum scheduling cost \(F_1\) of the system, among which, it includes the load electricity cost \(F\) G , the energy storage scheduling cost \(F\) bess and the curtailment cost of light \(F\) curtail ; the objective function of the day-ahead scheduling model is:

[0088] \(\min F_1 = F\) G + \(F\) bess + \(F\) curtail (15)

[0089] Among them, the load electricity cost refers to the cost generated by the actual electricity consumption of users (park or building), the energy storage scheduling cost is the loss and operation and maintenance cost generated during the charging and discharging process of the energy storage system, and the curtailment cost of light is the economic loss corresponding to the wasted electricity due to the inability to absorb the photovoltaic power generation. The calculation formula of the load electricity cost \(F\) G is:

[0090]

[0091] λ 1,t is the electricity trading price for the day before, P 1,G,t is the electricity trading volume for the day before; the energy storage scheduling cost F bess The calculation formula is as follows:

[0092]

[0093] Among them, η ch , η dch are the charging and discharging efficiencies of the energy storage subsystem, P ch,t , P dch,t are the charging and discharging powers of the energy storage of the energy storage subsystem, π bess is the unit cost of energy storage operation; the curtailment penalty F curtail The calculation formula is as follows:

[0094]

[0095] π curtail is the unit cost of curtailment penalty, P curtail,t is the curtailment power.

[0096] The constraints that need to be satisfied for the day-ahead scheduling include:

[0097] a) Day-ahead power balance

[0098] P 1,G,t +P PV,t +P dch,t =L t +P SL,t +L TL,t +L RL,t +P ch,t +P curtail,t (19)

[0099] Among them, P PV,t is the PV output, L t is the power of the non-flexible load that does not participate in any scheduling.

[0100] b) PV output constraint

[0101]

[0102] Among them, is the predicted PV output.

[0103] c) Energy storage operation constraint

[0104]

[0105] SoCmin ≤SoC t ≤SoC max (22)

[0106] 0 ≤ P ch,t ≤ P ch,max (23)

[0107] 0 ≤ P dch,t ≤ P dch,max (24)

[0108] P ch,t P dch,t = 0 (25)

[0109] Among them, SoC t is the state of charge of the energy storage subsystem, SoC min , SoC max is the lower and upper limits of the allowable state of charge of the energy storage subsystem, P ch,max , P dch,max is the upper limit of the charging power and discharging power of the energy storage subsystem, C bess represents the capacitance of the energy storage subsystem.

[0110] d) Day-ahead power trading congestion

[0111] 0 ≤ P 1,G,t ≤ P G,max (26)

[0112] Among them, trading congestion refers to the situation where electricity cannot be smoothly transmitted from the power generation side to the power consumption side during the power transmission process, and P G,max is the maximum transmission power of the transmission line.

[0113] 2) Intra-day scheduling model

[0114] The optimization objective of the intra-day scheduling model is similar to that of the day-ahead scheduling model, but it also takes into account intra-day power trading and dispatchable load scheduling. The calculation formula for the second minimum scheduling cost F2 of the intra-day scheduling model includes:

[0115] min F2 = F G + F bess + F curtail (27)

[0116]

[0117] Among them, λ 2,t is the intra-day power trading price, and P 2,G,t is the intra-day power trading volume.

[0118] The constraint conditions of the intraday scheduling model are also similar to those of the day-ahead and intraday scheduling model, but the power balance takes into account the intraday electricity trading:

[0119] P 1,G,t +P 2,G,t +P PV,t +P dch,t =L t +P SL,t +P TL,t +L RL,t +P ch,t +P curtail,t (29)

[0120] Transaction congestion also needs to consider intraday electricity trading:

[0121] 0≤P 1,G,t +P 2,G,t ≤P G,max (30)

[0122] 3) Real-time scheduling model

[0123] The optimization objective of the real-time scheduling model is similar to that of the intraday scheduling model. The calculation formula for the third minimum scheduling cost F3 of the real-time scheduling model includes:

[0124] minF3=F G +F bess +F curtail (31)

[0125]

[0126] Among them, λ 3,t is the real-time electricity trading price, and P 3,G,t is the real-time electricity trading volume.

[0127] In the constraint conditions, the power balance takes into account the real-time electricity trading:

[0128] P 1,G,t +P 2,G,t +P 3,G,t +P PV,t +P dch,t =L t +P SL,t +P TL,t +P RL,t +P ch,t +P curtail,t (33)

[0129] In addition, transaction congestion needs to consider the impact of real-time electricity trading:

[0130] 0≤P 1,G,t +P 2,G,t +P3,G,t ≤P G,max (34)

[0131] Based on the above expressions (1)-(34), the overall multi-time scale scheduling rolling optimization model of the optical storage DC flexible system in the park can be constructed.

[0132] Step 103, the computing device obtains the operation data of the optical storage DC flexible system in the park.

[0133] Among them, the computing device can collect the data in the park in real time through sensors distributed in each subsystem of the park; specifically, it can collect the operation data of the photovoltaic power generation unit, energy storage unit, DC distribution network and flexible load unit. Preferably, the operation data includes photovoltaic power generation power, energy storage power, load power and equipment status information.

[0134] Among them, after obtaining the operation data, the computing device can preprocess the operation data. Specifically, the computing device can perform data cleaning, denoising and normalization operations on the collected real-time data to provide a unified input data source for scheduling analysis at different time scales. Exemplarily, the computing device can use interpolation method, Z-score method and other methods to preprocess the data, and this application does not make specific limitations on this.

[0135] Step 104, the computing device solves the multi-time scale scheduling rolling optimization model based on the operation data to obtain the optimal scheduling plan.

[0136] Among them, the computing device can substitute the operation data into the multi-time scale scheduling rolling optimization model for solution.

[0137] Preferably, the computing device can use the particle swarm optimization algorithm and integer programming method to solve the multi-time scale scheduling rolling optimization model based on the operation data. Specifically, the computing device can search for the optimal solution in the continuous space through the particle swarm optimization algorithm, and use the integer programming method to accurately solve the discrete variables (such as the Boolean variables in the adjustable load model) to realize the optimization of the multi-temporal scheduling control strategy.

[0138] Specifically, in the above multi-objective optimization model, the decision variables include (the independent variable time of the decision variable ):

[0139] 1) The shift Boolean variable α of the shiftable load t ;

[0140] 2) The power P after transfer of the transferable load and the transfer Boolean variable β TL,t and the transfer Boolean variable β t ;

[0141] 3) The curtailable load and the curtailment Boolean variable γt and reduction coefficient u t ;

[0142] 4) Energy storage charging power P ch,t and discharging power P dch,t ;

[0143] 5) Photovoltaic power generation P PV,t and curtailed photovoltaic power P curtail,t ;

[0144] 6) Day-ahead, intra-day, and real-time electricity trading volumes P 1,G,t , P 2,G,t , P 3,G,t .

[0145] Among them, α t , β t are discrete variables, and the rest are continuous variables. The specific solution process is as follows:

[0146] a. First, calculate the values of all decision variables of the device by the particle swarm optimization algorithm to obtain the first result; the first result includes the first continuous variable values of the continuous variables in the decision variables and the continuous values of the discrete variables.

[0147] b. Then add the following discrete variable constraint conditions and construct an integer programming problem in combination with (1) to (34):

[0148] α t ∈{0, 1} #(35)

[0149] β t ∈{0, 1} #(36).

[0150] c. Substitute the obtained first continuous variable values into (1) to (34), and use the continuous values of the discrete variables as the initial values to obtain the target values of the discrete variables by integer programming.

[0151] d. Substitute the target values into (1) to (34) and use the particle swarm optimization algorithm to solve the continuous variables to obtain the second continuous variable values.

[0152] e. When the error between the second continuous variable values obtained in two consecutive calculations is less than or equal to the preset threshold, output the target values and the second continuous variable values obtained in the last calculation as the optimal scheduling plan; when the error is greater than the preset threshold, or the number of times of calculating the second continuous variable values is less than 2, use the second continuous variable values obtained in the last calculation as the first continuous variable values and return to execute step c.

[0153] Exemplarily, the preset threshold can be 0.0001. When the error ratio between the second continuous variable value of the continuous variable solved in step d and the previous solved value does not exceed 0.0001, the computing device can determine that the solution result converges.

[0154] Among them, the particle swarm algorithm can be implemented through the PySwarm library of Python, and integer programming can be implemented through solvers such as CPLEX and Gurobi.

[0155] The multi-temporal scheduling control optimization method for campus optical storage direct-soft proposed in the embodiments of the present application divides the system scheduling period into three time scales: real-time, intraday, and day-ahead, and matches corresponding adjustable load classification models according to the load characteristics of different time scales: real-time scheduling is applicable to rapidly responsive load curtailment, intraday scheduling is applicable to shiftable and transferable loads, and day-ahead scheduling is used for long-term regulation of overall load and energy storage. Then, an overall multi-time scale scheduling rolling optimization model is established; the optimal scheduling plan obtained by solving the multi-time scale scheduling rolling optimization model can improve the response speed and scheduling accuracy of the system in coping with load changes of different characteristics at different time scales, and solves the problems of scheduling lag and low decision-making accuracy caused by a single time scale in the prior art.

[0156] Refer to Figures 3 to 6 This is another embodiment of the present invention. In order to verify the beneficial effects of the present invention, scientific demonstrations are carried out through economic benefit calculations and simulation experiments. In this embodiment, experiments are respectively carried out on the existing traditional method and the method of this embodiment. The experimental results show that the method of this embodiment can achieve the following beneficial effects:

[0157] 1) Improve the peak shaving capacity of the power grid: Through the multi-temporal scheduling control strategy, the flexible adjustment of the power grid load is realized, and the peak-valley difference of the power grid is effectively reduced.

[0158] 2) Optimize the photovoltaic accommodation efficiency: Make full use of the dual characteristics of "source-load" of the energy storage system, improve the utilization rate of photovoltaic power generation, and reduce the phenomenon of "discarded light".

[0159] 3) Reduce the system operation cost: Combine multiple market trading mechanisms, optimize the power purchase and sale strategy, reduce the system operation cost, and improve the economic benefit.

[0160] 4) Enhance the flexibility and reliability of the system: Through the dynamic rolling correction mechanism, improve the system's ability to cope with prediction errors and emergencies, and ensure the stability and reliability of the system operation.

[0161] 5) Based on blockchain technology: The introduction of blockchain technology not only improves the transparency and security of the scheduling process, but also provides strong support for realizing the trustworthy sharing of data and optimizing the trading link.

[0162] Taking the daily scheduling as an example, the effect of scheduling is simulated and analyzed. The curves of PV and load data used in the simulation are as Figure 3 shown.

[0163] Among them, the load includes three types of flexible loads: shiftable, transferable, and curtailable loads, as well as the base load that does not participate in scheduling. The distribution of each part of the load is as Figure 4 shown. The relevant technical parameters of various flexible loads are set as shown in Table 2.

[0164]

[0165] Table 2 Setting of relevant parameters of flexible loads

[0166] Figure 5 is the comparison of the load before and after scheduling. It can be seen from the figure that the maximum load after scheduling has decreased, and the electricity consumption of the load has decreased during the periods with higher electricity prices.

[0167] According to the scheduling results, the cost before and after scheduling can be compared, as shown in Table 3. According to formula (15), the cost after scheduling is the power purchase cost minus the flexible load response subsidy and the curtailment cost of PV. PV and energy storage can supply energy to the load during the periods with higher electricity prices, further reducing the electricity cost.

[0168]

[0169] Table 3 Comparison of costs before and after scheduling (unit: yuan)

[0170] Figure 6 is the load distribution after scheduling. Table 4 further statistically analyzes the hourly adjustment amount of various flexible loads and the proportion of the aggregated adjustment amount in the maximum load. Among them, the flexible load adjustment amount is the absolute value of the difference in the electricity consumption of this type of load before and after adjustment. It can be seen from the table that the maximum flexible load adjustment amount occurs at 6 o'clock. The adjustment amounts of shiftable, transferable, and curtailable loads are 27.5 kW·h, 26.7 kW·h, and 16.5 kW·h respectively, with a total of 70.7 kW·h. The maximum load at this moment before adjustment is 460 kW·h, and the proportion of the adjustment amount in the maximum load reaches 15.37%.

[0171]

[0172]

[0173] Table 4 Proportion of hourly adjustment amount and aggregated adjustment amount of various flexible loads

[0174] The method provided in the embodiments of the present application has been described above. The related devices provided in the embodiments of the present application will be described below.

[0175] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of a blockchain system provided by an embodiment of the present application. As Figure 7 shown, the blockchain system 700 includes:

[0176] A core scheduling node 701, configured to receive scheduling instructions from a power grid dispatching center, and based on the intelligent contract mechanism of the blockchain, convert the scheduling instructions into executable intelligent contract tasks, and distribute the intelligent contract tasks to execution nodes; by converting the scheduling instructions into executable intelligent contract tasks, the accuracy and timeliness of the scheduling instructions are ensured.

[0177] An energy supply node 702, configured to control the photovoltaic power generation unit and energy storage system of the park's optical storage direct current flexible system to perform corresponding operations based on the intelligent contract task.

[0178] A load management node 703: configured to execute the optimal scheduling plan obtained by the above method based on the intelligent contract task, and schedule the power grid of the park's optical storage direct current flexible system based on the optimal scheduling plan, which can improve the peak shaving ability of the power grid and the overall operation efficiency of the system. The non-adjustable load node serves as the provider of load basic data and participates in the calculation of constraint conditions such as power balance.

[0179] A market trading node 704: participates in power market trading, buys and sells electricity according to market prices and scheduling instructions, and provides electricity prices to the load management node for the load management node to perform scheduling calculations.

[0180] The coordination of multi-time scale scheduling tasks is a key step, which can integrate various technical means and strategies to ensure the efficient execution of tasks and the overall security of the system.

[0181] The scheduling tasks at different time scales are obtained by different models in the above method embodiments. The scheduling tasks at each time scale can be designed as one or more intelligent contracts, and the intelligent contracts can interact with each other to jointly execute scheduling decisions. Once the intelligent contract is deployed on the blockchain, it will be automatically executed according to the preset logic without manual intervention. This ensures the timeliness and accuracy of the scheduling tasks and reduces the errors caused by human operations.

[0182] During the task coordination process, the scheduling tasks at each time scale need to share key data such as photovoltaic output, load demand, and energy storage status. The distributed ledger feature of the blockchain enables data to be shared and synchronized across all network nodes, ensuring the transparency and immutability of the data. During the data sharing process, the consensus mechanism of the blockchain is used to verify the data to ensure that the data has not been tampered with during transmission. At the same time, the data on the blockchain is visible to all participants, but the privacy and sensitive information of the data can be protected through encryption and access control.

[0183] In the final stage of scheduling task coordination, it is necessary to convert the scheduling decision into an actual scheduling instruction and monitor the execution effect. The smart contract generates a scheduling instruction based on the scheduling decision and distributes the instruction to each execution unit (such as energy storage devices, PV inverters, load control devices, etc.) through the blockchain network. Through the real-time data feedback mechanism on the blockchain, monitor the execution status of each execution unit to ensure that the scheduling instruction is effectively executed. If an abnormal situation or deviation occurs, the smart contract can automatically trigger the corresponding adjustment mechanism or alarm mechanism.

[0184] Through the above scheduling coordination, not only the automation level and scheduling efficiency of the system are improved, but also the authenticity of the data and the overall security of the system are ensured.

[0185] In addition, in the process of task coordination and transaction optimization of the multi-time scale optimal scheduling of the park's optical storage direct current flexible (OSDCF) system, the introduction of blockchain technology not only enhances the collaboration efficiency within the system, but also greatly improves the security and credibility of transactions.

[0186] Blockchain technology provides a secure and transparent way to record transactions. The data of all transactions (such as electricity transactions, energy storage charging and discharging transactions, etc.) are recorded on the blockchain, forming an immutable transaction ledger. This can not only prevent the forgery and tampering of transaction data, but also ensure the rights and interests of both parties to the transaction are protected. The smart contract automatically executes the transaction logic on the blockchain without the participation of a third-party intermediary. When the preset transaction conditions are met (such as price, time, electricity quantity, etc.), the smart contract will automatically execute the transaction, complete the transfer of funds and the scheduling of electricity. This greatly simplifies the transaction process, reduces the transaction cost, and improves the transaction efficiency.

[0187] In the multi-time scale optimal scheduling, the market trading nodes upload the transaction data (such as electricity quantity transactions, flexible load regulation, etc.) in the park's OSDCF system to the blockchain in real time or periodically, including key information such as the identities of both parties to the transaction, transaction time, transaction electricity quantity, transaction price, etc.

[0188] When each transaction occurs, the nodes in the network will jointly verify the validity of the transaction, including the signatures of both parties to the transaction, whether the transaction amount is sufficient, whether the transaction complies with the rules of the smart contract, etc. Consensus mechanisms such as proof of work and proof of stake can be adopted to ensure that the transaction data reaches an agreement across the network. Once the transaction is verified and reaches a consensus, the transaction will be recorded on the blockchain, forming an immutable historical record. When the transaction meets the execution conditions defined in the smart contract, the smart contract will automatically execute the transaction to achieve instant settlement, ensuring the timeliness and accuracy of the transaction and reducing the settlement delay and credit risk in traditional transactions.

[0189] In addition, blockchain technology adopts a decentralized architecture, storing key data in the scheduling process (such as photovoltaic power generation, energy storage status, load demand, etc.) distributively on each node of the blockchain, rather than relying on a single central server. This storage method effectively avoids the risks of single-point failures and data tampering, improving the security and reliability of data. Before the scheduling data is uploaded to the blockchain, it must go through a strict verification process to ensure the accuracy and integrity of the data. Through consensus mechanisms such as PoW and PoS, the blockchain ensures that the majority of nodes in the network recognize the scheduling data, thereby enhancing the credibility of the data.

[0190] Deploying smart contracts on the blockchain to define the rules and logic for data sharing and automatically execute preset operations without the intervention of a third party, thus realizing the transparency and automation of data sharing. Smart contracts can automatically trigger processes such as data requests, verification, and sharing according to the system scheduling requirements, ensuring the timeliness and accuracy of data. At the same time, smart contracts can also encrypt and control the permissions of shared data to ensure data privacy and security. Advanced encryption algorithms (such as AES, RSA, etc.) are used to encrypt sensitive data to ensure the security of data during transmission and storage. The encryption feature of the blockchain makes it impossible for unauthorized users to access or tamper with the data. The permission control mechanism can also set different data access permissions for different users. Only users with corresponding permissions can access or modify relevant data, thus ensuring the legality and compliance of the data.

[0191] Blockchain technology enables the coordinated scheduling of on-chain and off-chain data. The off-chain system may include monitoring software, databases, etc. of the park's optical storage direct current flexible system. During the scheduling process on multiple time scales (such as day-ahead, intra-day, real-time), the blockchain can collect the operation status data of the off-chain system in real time and, combined with the scheduling logic of smart contracts, optimize the allocation of resources such as photovoltaics, energy storage, and load to achieve the overall efficient operation of the system.

[0192] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a multi-temporal scheduling control optimization system for a park's optical storage direct current flexible system provided by an embodiment of this application. As Figure 8 shown, this system 800 includes:

[0193] The first modeling module 801 is used to build an adjustable load classification model, which includes a shiftable load model, a transferable load model, and a curtailable load model. The shiftable load model is used to calculate the shiftable load scheduling cost, the transferable load model is used to calculate the transferable load scheduling cost, and the curtailable load model is used to calculate the curtailable load scheduling cost. Specifically, the first modeling module 801 is used to establish the classification criteria for flexible loads in the park and divide the loads into three categories: shiftable loads, transferable loads, and curtailable loads.

[0194] The second modeling module 802 is used to build a multi-time scale scheduling rolling optimization model based on the adjustable load classification model. The multi-time scale scheduling rolling optimization model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time scheduling model. Among them, the objective function of the day-ahead scheduling model calculates the first minimum scheduling cost based on the shiftable load scheduling cost; the objective function of the intra-day scheduling model calculates the second minimum scheduling cost based on the transferable load scheduling cost and the shiftable load scheduling cost; the objective function of the real-time scheduling model calculates the third minimum scheduling cost based on the transferable load scheduling cost, the shiftable load scheduling cost, and the curtailable load scheduling cost. Specifically, the second modeling module 802 is used to divide the scheduling period into three time scales: real-time scheduling, intra-day scheduling, and day-ahead scheduling, and match the corresponding adjustable load models.

[0195] The acquisition module 803 is used to acquire the operation data of the park's optical storage direct current flexible system.

[0196] The calculation module 804 is used to solve the multi-time scale scheduling rolling optimization model based on the operation data to obtain the optimal scheduling plan.

[0197] Specifically, the first modeling module 801 is used to model based on the response characteristics of various loads to generate the mathematical expressions and their regulation conditions for each load. For example, shiftable loads achieve load transfer by flexibly adjusting the power supply time; transferable loads have a constant total load during the power supply time, but can be allocated by time periods through incentive means such as price signals; curtailable loads quickly reduce the load during the peak of the grid load demand or when power generation is insufficient. Through this refined classification, specific load control strategies can be applied at each time scale during subsequent scheduling to achieve the purpose of optimizing the load distribution and balancing supply and demand.

[0198] Specifically, the second modeling module 802 is used to divide the scheduling period into three time scales: real-time scheduling, intra-day scheduling, and day-ahead scheduling, and match the corresponding adjustable load models to ensure the rationality and timeliness of scheduling control within each time scale.

[0199] Among them, based on the load classification modeling, the second modeling module 802 divides the time period and matches the corresponding load types according to the characteristics of each load model. Specifically, real-time scheduling is mainly applicable to the load curtailment with a relatively fast response speed, intraday scheduling is applicable to the shiftable and transferable loads, and day-ahead scheduling is suitable for overall planning of global factors such as photovoltaic power generation and energy storage for the next day. Through this cycle division and load matching, the scheduling system can more efficiently meet the demand changes at different time scales and achieve reasonable resource allocation.

[0200] The acquisition module 803 is specifically used to collect real-time operation data through distributed sensors and perform preprocessing to provide unified data input for the multi-time scale scheduling rolling optimization model.

[0201] Among them, the acquisition module 803 is responsible for obtaining real-time data from the photovoltaic power generation units, energy storage units, and flexible load units in the park, including power generation power, energy storage status, current load power, and equipment status, etc. The collected data is subjected to cleaning, denoising, and normalization processing to ensure the accuracy and consistency of the data. After data preprocessing, it can be directly used for real-time scheduling, intraday scheduling, and day-ahead scheduling, providing accurate real-time input for subsequent rolling optimization scheduling.

[0202] The calculation module 804 is specifically used to comprehensively consider grid demand, photovoltaic power generation prediction, energy storage status, and load characteristics, and generate the optimal scheduling plan at multiple time scales through an optimization algorithm, and perform output and execution.

[0203] Among them, the calculation module 804 uses the particle swarm algorithm and integer programming to perform rolling optimization on the scheduling models at each time scale to achieve the minimization of the total cost and the optimal allocation of resources. During the solution process, the optimization algorithm will perform iterative solution between continuous variables and discrete variables to ensure the accuracy and stability of the scheduling results. The final optimization result generates the scheduling plan of the park at different time scales, realizing the dynamic balance within the system.

[0204] The multi-temporal scheduling control optimization system for the park's optical storage direct flexibility provided by the embodiments of the present application can be understood by referring to the corresponding content in the foregoing method embodiment part, and will not be repeated here.

[0205] As Figure 9 shown, Figure 9 is a possible logical structure diagram of the computing device provided by the embodiments of the present application. The computing device 900 includes: a processor 901, a communication interface 902, a memory 903, and a bus 904. The processor 901, the communication interface 902, and the memory 903 are interconnected through the bus 904. In the embodiments of the present application, the processor 901 is used to control and manage the actions of the computing device 900. For example, the processor 901 is used to execute Figure 1The steps in the embodiments and / or other processes for the technologies described herein. The communication interface 902 is used to support the communication of the computing device 900. The memory 903 is used to store the program code and data of the computing device 900.

[0206] Wherein, the processor 901 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in connection with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 904 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 9 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0207] In another embodiment of the present application, a computer-readable storage medium is further provided. The computer-readable storage medium includes instructions that, when run on a computer, cause the computer to execute the above Figure 1 methods described in the embodiments.

[0208] Those of ordinary skill in the art can realize that the units of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0209] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0210] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0211] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0212] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0213] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs, etc., which can store program codes.

[0214] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and 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, and they should all be covered by the scope of the claims and the specification of the present invention.

Claims

1. A multi-temporal scheduling control optimization method for park integrated photovoltaic, energy storage, DC power distribution and building direct current injection, characterized in that Including: Construct an adjustable load classification model, where the adjustable load classification model includes a shiftable load model, a transferable load model, and a curtailable load model. The shiftable load model is used to calculate the shiftable load scheduling cost, the transferable load model is used to calculate the transferable load scheduling cost, and the curtailable load model is used to calculate the curtailable load scheduling cost; Based on the adjustable load classification model, construct a multi-time scale scheduling rolling optimization model, where the multi-time scale scheduling rolling optimization model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time scheduling model. Among them, the objective function of the day-ahead scheduling model calculates the first minimum scheduling cost based on the shiftable load scheduling cost; the objective function of the intra-day scheduling model calculates the second minimum scheduling cost based on the transferable load scheduling cost and the shiftable load scheduling cost; the objective function of the real-time scheduling model calculates the third minimum scheduling cost based on the transferable load scheduling cost, the shiftable load scheduling cost, and the curtailable load scheduling cost; Obtain the operation data of the park's photovoltaic-storage-direct-current flexible system; Based on the operation data, solve the multi-time scale scheduling rolling optimization model to obtain an optimal scheduling plan.

2. The method according to claim 1, characterized in that, The expression of the shiftable load model includes: τ ∈ [t SL- , t SL+ - t d + 1] Among them, τ is the time period of the shiftable load after scheduling, and t SL- is the lower bound of the acceptable shift interval of the shiftable load, and t SL+ is the upper bound of the acceptable shift interval of the shiftable load, and t s and t d are the start and duration of the shiftable load respectively, and t represents time; α t is a Boolean variable. When the variable is 1, it means the load has shifted, and when the variable is 0, it means the load has not shifted; L SL is the power distribution vector of the shiftable load before response, and are the powers of the shiftable load before response at each time point in the time period [t s , t s +t d -1], P SL is the power distribution vector of the shiftable load after scheduling, and P τ , P τ+1 , …, P τ+td-1 are the powers of the shiftable load after response at each time point in the time period [τ, τ + t d -1], C SL is the corresponding compensation cost after responding to the scheduling, is the compensation price per unit power of the shiftable load after scheduling, and P SL,t is the shiftable load at time t; The expression of the transferable load model includes: Among them, t TL- is the lower bound of the transferable interval of the transferable load, and t TL+ is the upper bound of the transferable interval of the transferable load. L TL,t and P TL,t are the transferable load powers before and after scheduling at time t respectively. T is the termination time; The Boolean variables β t and β t-1 are the state variables indicating whether the load has been transferred at time t and time t - 1 respectively. The variable being 1 indicates that the load has been transferred, and the variable being 0 indicates that the load has not been transferred; are the upper and lower limits of the transfer power at the current moment respectively, is the minimum continuous operation time of the transferable load. τ is the starting time of the transferable load after scheduling, is the compensation price per unit power of the transferable load after responding to the scheduling. C TL is the corresponding compensation cost after responding to the scheduling; The expression of the curtailable load model is: P RL,t = (1 - u t γ t )L RL,t Among them, P RL,t is the reduced load power at time t, L RL,t is the load shedding power that can be reduced before scheduling at time t, the Boolean variable γ t and γ t-1 indicate whether load shedding occurs at time t and time t - 1. The variable being 1 indicates that load shedding occurs, and the variable being 0 indicates that load shedding does not occur; u t is the reduction coefficient at time t, τ is the starting time of load shedding, is the maximum and minimum continuous reduction time, M max is the maximum number of reduction times, T represents the termination time, C RL is the corresponding compensation cost obtained after responding to the scheduling, is the compensation price per unit power of the load shedding that can be reduced after responding to the scheduling.

3. The method according to claim 1 or 2, characterized in that The operation data includes photovoltaic power generation, energy storage power, load power, and equipment status information.

4. The method according to claim 1 or 2, characterized in that, The calculation items of the objective function of the day-ahead scheduling model further include energy storage scheduling cost and curtailment cost, and the constraint conditions of the day-ahead scheduling model include day-ahead power balance, photovoltaic output constraint, energy storage operation constraint, and day-ahead power trading congestion; The calculation items of the objective function of the intra-day scheduling model further include the energy storage scheduling cost, the curtailment cost, and the intra-day power trading cost, and the constraint conditions of the intra-day scheduling model include intra-day power balance and intra-day power trading congestion; The calculation items of the objective function of the real-time scheduling model further include the energy storage scheduling cost, the curtailment cost, the intra-day power trading cost, and the real-time power trading cost, and the constraint conditions of the real-time scheduling model include real-time power balance and real-time power trading congestion.

5. The method according to claim 1 or 2, characterized in that, The step of based on the operation data, solving the multi-time scale scheduling rolling optimization model to obtain an optimal scheduling plan includes: Using the particle swarm optimization algorithm and the integer programming method, based on the operation data, solve the multi-time scale scheduling rolling optimization model to obtain the optimal scheduling plan.

6. The method according to claim 5, wherein The step of using the particle swarm optimization algorithm and the integer programming method, based on the operation data, solving the multi-time scale scheduling rolling optimization model to obtain the optimal scheduling plan includes: a. Solve the decision variables in the multi-time scale scheduling rolling optimization model through the particle swarm optimization algorithm to obtain a first result, where the first result includes the continuous values of the first continuous variables and the continuous values of the discrete variables; b. Add the constraint conditions of the discrete variables, and construct an integer programming problem based on the multi-time scale scheduling rolling optimization model; c. Substitute the first continuous variable value into the multi-time scale scheduling rolling optimization model, use the continuous value of the discrete variable as the initial value, and obtain the target value of the discrete variable by using integer programming; d. Substitute the target value into the multi-time scale scheduling rolling optimization model, and use the particle swarm algorithm to solve the continuous variable to obtain the second continuous variable value; e. When the error between the second continuous variable values calculated continuously twice is less than or equal to the preset threshold, output the target value and the second continuous variable value calculated last time as the optimal scheduling plan; when the error is greater than the preset threshold, or when the number of times of calculating the second continuous variable value is less than 2, use the second continuous variable value calculated last time as the first continuous variable value, and return to execute step c.

7. A multi-temporal scheduling control optimization system for optical storage, direct current, and flexible power in a park, characterized in that Applied to the method according to any one of claims 1 to 6, the system includes: The first modeling module is used to construct an adjustable load classification model, the adjustable load classification model includes a shiftable load model, a transferable load model and a curtailable load model, the shiftable load model is used to calculate the shiftable load scheduling cost, the transferable load model is used to calculate the transferable load scheduling cost, and the curtailable load model is used to calculate the curtailable load scheduling cost; The second modeling module is used to construct a multi-time scale scheduling rolling optimization model based on the adjustable load classification model, the multi-time scale scheduling rolling optimization model includes a day-ahead scheduling model, an intra-day scheduling model and a real-time scheduling model; wherein, the objective function of the day-ahead scheduling model calculates the first minimum scheduling cost based on the shiftable load scheduling cost; the objective function of the intra-day scheduling model calculates the second minimum scheduling cost based on the transferable load scheduling cost and the shiftable load scheduling cost; the objective function of the real-time scheduling model calculates the third minimum scheduling cost based on the transferable load scheduling cost, the shiftable load scheduling cost and the curtailable load scheduling cost; The acquisition module is used to acquire the operation data of the park's optical storage direct current flexible system; The calculation module is used to solve the multi-time scale scheduling rolling optimization model based on the operation data to obtain the optimal scheduling plan.

8. A blockchain system, characterized in that, The blockchain includes: The core scheduling node is used to receive the scheduling instruction from the grid dispatching center, and based on the intelligent contract mechanism of the blockchain, convert the scheduling instruction into an executable intelligent contract task, and distribute the intelligent contract task to the execution node; The energy supply node is used to control the photovoltaic power generation unit and the energy storage system of the park's optical storage direct current flexible system to perform corresponding operations based on the intelligent contract task; The load management node: is used to execute the method according to any one of claims 1 to 6 to obtain the optimal scheduling plan based on the intelligent contract task, and schedule the power grid of the park's optical storage direct current flexible system based on the optimal scheduling plan; The market trading node: participates in the electricity market trading, buys and sells electricity according to the market price and the scheduling instruction, and provides the electricity price to the load management node so that the load management node can perform scheduling calculations.

9. A computing device, characterized in that, Includes: A memory for storing a program; A processor for loading the program to execute the method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method according to any one of claims 1-6.

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