Source network load storage coordinated optimization scheduling method and system based on multiple time scales

Abstract

The invention discloses a source-network-load-storage coordinated optimization scheduling method and system based on multiple time scales, and the method comprises the steps: building a multi-time-scale optimization scheduling frame through defining scheduling stages of different time resolutions; on the basis of a multi-time-scale optimization scheduling framework, a mathematical model is established to describe an objective function and an optimization variable of each stage, and a multi-time-scale optimization scheduling model is established in combination with general constraint conditions; converting the model into a mixed integer second-order cone programming problem by utilizing a linear processing technology of convex relaxation and segmentation; solving the mixed integer second-order cone programming problem by adopting an optimization solver to obtain an optimization scheduling strategy adapted to different time scale demand changes; the method is not only suitable for a traditional power system, but also provides powerful support for development of a modern intelligent power grid, and ensures efficient, reliable and sustainable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a source-grid-load-storage coordinated optimization scheduling method and system based on multiple time scales. Background Art

[0002] Most of the existing power dispatching technologies are based on static models and empirical rules, which cannot cope with the rapid changes in renewable energy output and load demand. Without real-time data on the integration of source, grid, load and storage during the operation of the power system, it is impossible to achieve efficient energy utilization and flexible energy dispatch. At the same time, the current dispatching method only considers the overall operational safety of the system and the volatility of the system, without considering the operating characteristics of the source, grid, load and storage system. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a source-grid-load-storage coordinated optimization scheduling method and system based on multiple time scales to solve the problem of being unable to grasp the source-grid-load-storage integration related data during the operation of the power system in real time.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a source-grid-load-storage coordinated optimization scheduling method based on multiple time scales, comprising:

[0008] By defining scheduling stages with different time resolutions, a multi-time scale optimization scheduling framework is established;

[0009] Based on the multi-time scale optimization scheduling framework, a multi-time scale optimization scheduling model is constructed by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints.

[0010] By using convex relaxation and piecewise linearization techniques, the model is transformed into a mixed-integer second-order cone programming problem.

[0011] The mixed integer second-order cone programming problem is solved using an optimization solver to obtain an optimal scheduling strategy that adapts to demand changes at different time scales.

[0012] As a preferred solution of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales described in the present invention, wherein:

[0013] The scheduling stages defining different time resolutions include a day-ahead optimization scheduling stage, an intraday rolling optimization scheduling stage, and an intraday real-time feedback correction stage;

[0014] The day-ahead optimization dispatching stage includes forecasting the wind power, photovoltaic output and load in the next 24 hours every hour to generate a preliminary dispatching plan in the next 24 hours;

[0015] The intraday rolling optimization scheduling stage includes predicting the wind power, photovoltaic output and load within the next hour every 15 minutes, optimizing the scheduling plan within the current time window, and only executing the current time plan value and rolling updating, and outputting the updated scheduling plan;

[0016] The intraday real-time feedback correction stage includes predicting the wind power, photovoltaic output and load in the next 15 minutes every 5 minutes, using ultra-short-term prediction information for closed-loop control, minimizing the deviation between the distribution network output and the reference value by adjusting the output of the control equipment, and outputting the real-time adjusted control equipment output command.

[0017] As a preferred solution of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales described in the present invention, wherein:

[0018] The objective functions of each stage described by establishing a mathematical model include a day-ahead optimization scheduling model, an intraday rolling optimization scheduling model, and an intraday real-time feedback correction model;

[0019] The objective function of the day-ahead optimization scheduling model is expressed as:

[0020]

[0021] Among them, F da is the comprehensive operating cost of the power grid, is the power purchased at time t, is the electricity purchase price at time t; C DG，t is the operating cost of distributed power generation; C WT，cut and C PV，cut Penalty price per unit power for abandoned wind and solar power. is the predicted wind power output at time t, is the actual wind power output at time t, is the predicted photovoltaic output at time t, is the actual photovoltaic output at time t, C IDR Reimbursing costs for incentivized demand response.

[0022] The objective function of the intraday rolling optimization scheduling model is expressed as:

[0023]

[0024] Among them, t 0 is the current moment, and MT is the scheduling period for intraday rolling optimization;

[0025] The objective function of the intraday real-time feedback correction model is expressed as:

[0026]

[0027] Where, ΔF rt The output deviation of adjustable resources in the system at the current time and over a long time scale; x dr The output of each control resource obtained during the intraday rolling optimization phase; x max The maximum adjustment amount of resource output for long-term regulation; x rt To adjust the output of resources; x rt，real It is the actual output feedback value of resource regulation at the current moment.

[0028] As a preferred solution of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales described in the present invention, wherein:

[0029] The optimization variables in the intraday real-time feedback correction phase are expressed as:

[0030] X=[P buy , P DG , Q SVC , D q ]

[0031] Among them, P buy is the purchased power, P DG is the active power output of the distributed power source, Q SVC is the reactive power output of the static VAR compensator, D q The call power for incentive demand response.

[0032] As a preferred solution of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales described in the present invention, wherein:

[0033] The general constraints include network constraints, wind power and photovoltaic power generation operation constraints and demand response constraints;

[0034] The network constraints include active power balance constraints, reactive power balance constraints and power flow equation constraints;

[0035] The demand response constraints include constraints before implementing the demand response and constraints after implementing the demand response;

[0036] As a preferred solution of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales described in the present invention, wherein:

[0037] The model is converted into a mixed integer second-order cone programming problem by using convex relaxation and piecewise linearization processing technology, including the following steps:

[0038] Convex relaxation is performed on the power flow constraints in the distribution network to transform the nonlinear power flow equation into a convex form;

[0039] Identify the nonlinear terms that still exist in the model after convex relaxation, approximate the nonlinear terms with piecewise linear functions and add constraints;

[0040] Identify the nonlinear terms that still exist in the piecewise linearized model, introduce auxiliary variables, and use the Big M method to linearize the remaining nonlinear terms;

[0041] The linearized power flow equation constraints are further transformed into second-order cone constraints;

[0042] All the results of convex relaxation, piecewise linearization and big-M linearization are integrated into a unified mixed-integer second-order cone programming model.

[0043] As a preferred solution of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales described in the present invention, wherein:

[0044] The large-M method is used to linearize the remaining nonlinear terms and express them as follows:

[0045]

[0046] U j，t -M(1-g j，k，t )≤σ j，k，t ≤U j，t +M(1-g j，k，t )-Mg j，k，t ≤σ j，k，t ≤Mg j，k，t

[0047] Where U j，t is the voltage of node j at time step t, C j，t is the shunt capacitor at node j at time step t, is the lower bound of the parallel capacitor, s j is the step size of the parallel capacitor, is a binary variable, g j，k，t is a binary variable indicating whether a particular capacitor is enabled, σ j，k，t is an auxiliary variable;

[0048] The linearized power flow equation constraint is further transformed into a second-order cone constraint as follows:

[0049]

[0050] Among them, 2P ij，t is twice the active power of branch i, j at time step t, 2Q ij，t is twice the reactive power of branch i, j at time step t, i ij，t -U i，t is the branch current i ij，t With node voltage U i，t difference.

[0051] In a second aspect, the present invention provides a source-grid-load-storage coordinated optimization scheduling system based on multiple time scales, comprising:

[0052] The time scale partitioning module is used to establish a multi-time scale optimization scheduling framework by defining scheduling stages with different time resolutions;

[0053] Mathematical model building module, which is used to build a multi-time scale optimization scheduling model based on a multi-time scale optimization scheduling framework by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints;

[0054] A model conversion module for converting the model into a mixed integer second-order cone programming problem by using convex relaxation and piecewise linearization techniques;

[0055] The optimization solution module is used to solve the mixed integer second-order cone programming problem using an optimization solver to obtain an optimized scheduling strategy that adapts to changes in demand at different time scales.

[0056] In a third aspect, the present invention provides a computing device, comprising:

[0057] Memory, used to store programs;

[0058] A processor is used to execute the computer executable instructions, which, when executed by the processor, implements the steps of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales are implemented.

[0060] Beneficial effects of the present invention: The present invention ensures the physical feasibility of the model by performing convex relaxation on the power flow constraints in the distribution network. Convex relaxation converts the nonlinear power flow equation into a convex form, which simplifies the problem while maintaining the physical meaning; the nonlinear terms are piecewise linearized to further improve the accuracy and solution efficiency of the model. This not only reduces the computational complexity, but also ensures that the model can better approximate the operating state of the actual system. By introducing second-order cone constraints, the originally complex nonlinear optimization problem is converted into a mixed integer second-order cone programming problem. This conversion allows the problem to be quickly solved on an efficient solver, greatly improving the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0062] Figure 1 A basic flow chart of a source-grid-load-storage coordinated optimization scheduling method based on multiple time scales provided by an embodiment of the present invention;

[0063] Figure 2 A schematic diagram of multi-time scale optimization scheduling of a source-grid-load-storage coordinated optimization scheduling method based on multiple time scales is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0068] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0069] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] Example 1

[0071] Reference Figure 1-2 , is an embodiment of the present invention, and provides a source-grid-load-storage coordinated optimization scheduling method based on multiple time scales, including:

[0072] S1: Establish a multi-timescale optimization scheduling framework by defining scheduling stages with different time resolutions;

[0073] In the embodiment of the present application, the scheduling stages with different time resolutions are defined to include a day-ahead optimization scheduling stage, an intraday rolling optimization scheduling stage, and an intraday real-time feedback correction stage;

[0074] In the embodiment of the present application, the day-ahead optimization scheduling stage (resolution 1h, period 24h) includes, based on the new energy and load forecast curves of the day-ahead stage, comprehensively considering the operating constraints of controllable distributed power sources in the power grid, power grid flow constraints, energy storage power station operating constraints, and electricity price-based demand response constraints, minimizing the system's comprehensive scheduling cost as the research goal, and establishing a day-ahead optimization scheduling model.

[0075] In the embodiment of the present application, the intraday rolling optimization scheduling stage (resolution 15min, cycle 1h) includes starting an optimization scheduling calculation at every time interval MΔT, predicting the wind power output, photovoltaic output and load curve in the future MΔT period, and based on the prediction results, the research aims to minimize the comprehensive system operating cost within the time window, and master the output plan value of the control resources. At this stage, the fluctuation of energy and load should be considered, and only the planned value of the first moment in the time window is executed, which is more accurate and reliable than the day-ahead optimization stage. At the next moment t0+ΔT, the window is moved back by an interval, and then the previous process is repeated.

[0076] In the embodiment of the present application, the intraday real-time feedback correction stage (resolution 5min, cycle 15min) includes the intraday real-time feedback correction stage with Δt (Δt<ΔT) as the sampling step (the cycle is shorter than the scheduling cycle of the rolling optimization scheduling stage). Each sampling is performed to predict the current photovoltaic output, wind power output and load information. Then the control center uses the predicted ultra-short-term information, takes the output measurement value of the control equipment at the current moment as the initial state value, takes the minimum deviation between the future output of the distribution network and the reference value as the optimization goal, conducts research on the optimal scheduling plan, and feedback corrects each control output. The intraday real-time feedback correction uses the current feedback information and combines it with the latest ultra-short-term prediction to form a closed-loop optimization control, so that the goal of improving the accuracy of the optimization scheduling can be achieved.

[0077] S2: Based on the multi-time scale optimization scheduling framework, a multi-time scale optimization scheduling model is constructed by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints;

[0078] In the embodiment of the present application, by establishing a mathematical model to describe the objective function and optimization variables of each stage, including the optimization variables and objective function of the day-ahead optimization scheduling stage, the lowest comprehensive operation cost of the power grid is the day-ahead optimization scheduling objective function expressed as:

[0079]

[0080] Among them, F da is the comprehensive operating cost of the power grid, is the power purchased at time t, is the electricity purchase price at time t; C DG，t is the operating cost of distributed power generation; C WT，cut and C PV，cut The penalty price per unit power for abandoned wind and solar power is is the predicted wind power output at time t, is the actual wind power output at time t, is the predicted photovoltaic output at time t, is the actual photovoltaic output at time t, CIDR To compensate the cost of incentive-based demand response.

[0081] In the embodiments of the present application, a mathematical model is established to describe the objective functions and optimization variables at each stage, including the optimization variables and objective functions in the intraday rolling optimization scheduling stage; minimizing the comprehensive operation cost of the distribution network is the optimization objective at this stage. The solution duration is shortened from 24 hours before the day to MT (set to 1 hour), and the scheduling plans for compensating capacitors, price-based demand response, and on-load tap changers (OLTCs) are determined in the day-ahead scheduling stage. The entire optimization stage remains unchanged. The main optimization variables in the intraday rolling optimization are: active power output of distributed power sources, photovoltaic scheduling output, charge and discharge status and power of energy storage, wind power scheduling output, reactive power output of continuously variable reactive power regulation devices (SVCs), invocation status of incentive-based demand response, etc. The optimization scheduling objective function in the intraday rolling stage:

[0082]

[0083] where t 0 is the current moment, and MT is the scheduling period of the intraday rolling optimization;

[0084] In the embodiments of the present application, a mathematical model is established to describe the objective functions and optimization variables at each stage, including the optimization variables and objective functions in the intraday real-time feedback correction stage; the optimization scheduling period in the intraday real-time feedback correction stage is R (R < T, taking R = 15 min), and the resolution is set to 5 min. During this stage, the start-stop and rolling of the system are consistent, and the focus is on analyzing the planned values of the adjustable resource output in the rolling stage. In the optimization stage, the planned values of the tie-line power and the active power output of the power sources issued in the rolling optimization stage are continuously corrected. Selecting the minimum deviation between the current moment of the system and the adjustable resource output in the long time scale as the objective function, and using the system measurement values for feedback correction to make the optimization scheduling result smoother and ensure the friendly acceptance of the uncertain and volatile new energy power in the system. The objective function and optimization variables in the intraday real-time feedback correction stage are respectively expressed as:

[0085]

[0086] X = [P buy , P DG , Q SVC , D q

[0087] where ΔF rt is the deviation between the current moment of the system and the adjustable resource output in the long time scale; x dr is the output of each control resource obtained in the intraday rolling optimization stage; x max is the maximum adjustment amount of the long time scale control resource output; x rt ​To adjust the output of resources; x rt，real is the actual output feedback value of the current resource control, P buy is the purchased power, P DG is the active power output of the distributed power source, Q SVC is the reactive power output of the static VAR compensator, D q The call power for incentive demand response.

[0088] In the embodiment of the present application, the general constraints include network constraints, wind power and photovoltaic power generation operation constraints and demand response constraints;

[0089] In the embodiment of the present application, the network constraints include active power balance constraints, reactive power balance constraints and power flow equation constraints;

[0090] In the embodiment of the present application, the active power balance constraint is expressed as:

[0091]

[0092] In the formula, r ij is the branch resistance from node i to node j, The wind power output is dispatched on the day before, P ij，t is the active power flowing from node i to node j at time t, i ij，t is the branch current from node i to node j at time t, is the load active power at node j, ∑ k∈π(j) P jk，t The sum of the active powers of all other branches connected to node j at time step t.

[0093] In the embodiment of the present application, the reactive power balance constraint is expressed as:

[0094]

[0095] In the formula, Q ij is the reactive power from node i to node j, π(j) is the set of end nodes of the branch with j as the head node, is the load reactive power at node j, Reactive power generated by the continuous reactive power regulation device, is the effect of the shunt capacitor on the reactive power at node j at time step t, x ij is the reactance of branch (i, j), i ij，t is the current of branch (i, j) at time step i, is the wind power reactive power output of node j at time step t, is the load reactive power at node j, ∑ k∈π(j) Q jk，tis the sum of the reactive powers of all other branches connected to node j at time step t.

[0096] In the embodiment of the present application, the power flow equation constraint is expressed as:

[0097]

[0098] Where U i，t is the voltage at node i at time step i.

[0099] In the embodiment of the present application, the wind power and photovoltaic power generation operation constraints are expressed as:

[0100]

[0101] In the formula, To dispatch the photovoltaic power generation on the day before, To predict the output of wind power in the day ahead, Contribute to the photovoltaic output forecast.

[0102] In the embodiment of the present application, the demand response constraints include constraints before implementing the demand response and constraints after implementing the demand response;

[0103] In the embodiment of the present application, the constraints before implementing demand response are expressed as:

[0104]

[0105] In the embodiment of the present application, the constraints after implementing the demand response are expressed as:

[0106]

[0107] In the formula, is the electric load at time t after the implementation of demand response, and θ(j) is the set of all nodes in the distribution network.

[0108] S3: The model is transformed into a mixed-integer second-order cone programming problem by using convex relaxation and piecewise linearization techniques;

[0109] In the embodiment of the present application, a multi-time scale optimization scheduling model realizes multi-stage optimization. The optimization scheduling stages are interrelated but the solutions of each stage are not coupled with each other. Each optimization scheduling stage contains complex network constraints (including nonlinear terms and integer variables) and energy storage operation constraints (including 0 and 1 integer variables), so that the optimization model has a high degree of nonlinearity and a large number of integer variables, that is, the problem to be solved is a large-scale mixed integer nonlinear optimization problem.

[0110] In the embodiment of the present application, by performing convex relaxation on the power flow constraints in the distribution network and piecewise linearization on the nonlinear terms in the model, it is finally converted into a mixed integer second-order cone programming problem (there are quadratic terms in the objective function and the power flow constraints, and the remaining constraints are all mixed integer linear constraints). In the end, what needs to be solved is actually the mixed integer second-order cone programming problem.

[0111] In the embodiment of the present application, the nonlinear terms in the model are processed by piecewise linearization. Specifically, for the discrete variable C in the reactive power balance constraint j And the corresponding constraints can be expressed as:

[0112]

[0113] In the formula, is a binary variable; is the upper limit of the shunt capacitor / reactor capacity, is the lower bound; v j is an integer; s j is the step size of the parallel capacitor / reactor.

[0114] In the embodiment of the present application, for the nonlinear term U j，t C j，t It can be expressed as:

[0115]

[0116] In the embodiment of the present application, the nonlinear term is linearized using the Big M method as follows:

[0117]

[0118] U j，t -M(1-g j，k，t )≤σ j，k，t ≤U j，t +M(1-g j，k，t )-Mg j，k，t ≤σ j，k，t ≤Mg j，k，t

[0119] In the formula, is a binary variable; M is a larger integer.

[0120] In the embodiment of the present application, the linearized power flow equation constraint is further transformed into a second-order cone constraint to ensure its convexity, which is expressed as:

[0121]

[0122] In the embodiment of the present application, all the results of convex relaxation, piecewise linearization and large-M method linearization are integrated into a unified mixed-integer second-order cone programming model.

[0123] S4: The mixed integer second-order cone programming problem is solved using an optimization solver to obtain an optimized scheduling strategy that adapts to demand changes at different time scales.

[0124] In the embodiment of the present application, the solution is obtained by using Matlab modeling and CPLEX optimization solver.

[0125] This embodiment also provides a source-grid-load-storage coordinated optimization scheduling system based on multiple time scales, including:

[0126] The time scale partitioning module is used to establish a multi-time scale optimization scheduling framework by defining scheduling stages with different time resolutions;

[0127] Mathematical model building module, which is used to build a multi-time scale optimization scheduling model based on a multi-time scale optimization scheduling framework by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints;

[0128] A model conversion module for converting the model into a mixed integer second-order cone programming problem by using convex relaxation and piecewise linearization techniques;

[0129] The optimization solution module is used to solve the mixed integer second-order cone programming problem using an optimization solver to obtain an optimized scheduling strategy that adapts to changes in demand at different time scales.

[0130] Furthermore, it also includes:

[0131] Memory, used to store programs;

[0132] A processor is used to load the program to execute the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales.

[0133] This embodiment also provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales.

[0134] The storage medium proposed in this embodiment and the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0135] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0136] Example 2

[0137] This is an embodiment of the present invention, which provides a source-grid-load-storage coordinated optimization scheduling system based on multiple time scales, including a time scale division module, a mathematical model construction module, a model conversion module and an optimization solution module;

[0138] In an embodiment of the present application, the time scale division module includes establishing a multi-time scale optimization scheduling framework by defining scheduling stages with different time resolutions;

[0139] In the embodiment of the present application, the multi-time scale optimization dispatch framework is the basis of the entire system design, defining the time hierarchy of dispatch activities, including day-ahead optimization dispatch, intraday rolling optimization dispatch, and intraday real-time feedback correction. This framework ensures the continuity and consistency from long-term planning to short-term adjustment to immediate response, enabling the power system to respond more flexibly to changes on different time scales.

[0140] In the embodiment of the present application, day-ahead scheduling includes formulating a preliminary scheduling plan based on the prediction of new energy output, load demand, etc. in the next day with a resolution of 1 hour and a period of 24 hours.

[0141] In an embodiment of the present application, intra-day rolling optimization scheduling (Intra-day Rolling Scheduling) includes rolling updates of future scheduling plans based on the latest forecast information with a resolution of 15 minutes and a period of 1 hour, so as to more accurately respond to short-term changes.

[0142] In an embodiment of the present application, intraday real-time feedback correction (Real-time Feedback Correction) includes a 5-minute resolution and a 15-minute period, using actual data at the current moment and ultra-short-term forecasts to quickly adjust the scheduling strategy to ensure that the system operation is closer to the actual situation.

[0143] In the embodiment of the present application, the mathematical model construction module includes building a multi-time scale optimization scheduling model based on a multi-time scale optimization scheduling framework by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints;

[0144] In the embodiment of the present application, the multi-time scale optimization scheduling model is constructed based on the above framework and further refined, and a mathematical model is established to describe the specific objective functions (such as minimizing operating costs) and constraints (such as network constraints, wind power and photovoltaic operation restrictions, etc.) of each stage. This step provides a computable form for practical problems, takes into account the need to maximize resource utilization efficiency in different time periods, and ensures the stable operation of the system.

[0145] In the embodiment of the present application, the objective function includes setting specific optimization goals at each time scale, mainly to minimize the comprehensive operating costs of the system. These costs include power purchase costs, distributed power generation costs, energy storage operating costs, and penalty costs caused by wind and solar power abandonment.

[0146] In the embodiments of the present application, the constraints include various physical and technical limitations that need to be followed at each time scale, such as network flow constraints, wind power and photovoltaic operation constraints, demand response constraints, etc. These constraints ensure the feasibility and safety of the scheduling scheme. Through the interaction between the above-mentioned different time scales, a closed-loop control system is formed. For example, the intraday rolling optimization scheduling will be fine-tuned according to the results of the day-ahead scheduling, and the real-time feedback correction will be further refined on the basis of the rolling optimization, thereby achieving a seamless connection from long-term planning to short-term operation to immediate response.

[0147] In an embodiment of the present application, the model conversion module includes converting the model into a mixed integer second-order cone programming problem by utilizing convex relaxation and piecewise linearization processing techniques;

[0148] In an embodiment of the present application, the optimization solution module includes solving the mixed integer second-order cone programming problem using an optimization solver to obtain an optimized scheduling strategy that adapts to changes in demand at different time scales.

[0149] In the embodiment of the present application, the model solving method is the key technical means to realize the above-mentioned model. Since the original model usually contains complex nonlinear terms and integer variables, it is difficult to solve directly. Therefore, by adopting convex relaxation processing and piecewise linearization and other techniques, the problem is converted into a mixed integer second-order cone programming (MISOCP) form, so that it can be solved using existing efficient algorithms. This not only improves the solving efficiency, but also ensures the quality of the solution.

[0150] In the embodiment of the present application, through this multi-time scale optimization scheduling framework, it is possible to effectively deal with the uncertainty and volatility on different time scales, thereby improving the operating efficiency and reliability of the entire power system.

[0151] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A coordinated optimization scheduling method for source, grid, load and storage based on multiple time scales, characterized in that: include: By defining scheduling stages with different time resolutions, a multi-time scale optimization scheduling framework is established; Based on the multi-time scale optimization scheduling framework, a multi-time scale optimization scheduling model is constructed by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints. By using convex relaxation and piecewise linearization techniques, the model is transformed into a mixed-integer second-order cone programming problem. The mixed integer second-order cone programming problem is solved using an optimization solver to obtain an optimal scheduling strategy that adapts to demand changes at different time scales.

2. The source-grid-load-storage coordinated optimization scheduling method based on multiple time scales according to claim 1 is characterized by: The scheduling stages defining different time resolutions include a day-ahead optimization scheduling stage, an intraday rolling optimization scheduling stage, and an intraday real-time feedback correction stage; The day-ahead optimization dispatching stage includes forecasting the wind power, photovoltaic output and load in the next 24 hours every hour to generate a preliminary dispatching plan in the next 24 hours; The intraday rolling optimization scheduling stage includes predicting the wind power, photovoltaic output and load within the next hour every 15 minutes, optimizing the scheduling plan within the current time window, and only executing the current time plan value and rolling updating, and outputting the updated scheduling plan; The intraday real-time feedback correction stage includes predicting the wind power, photovoltaic output and load in the next 15 minutes every 5 minutes, using ultra-short-term prediction information for closed-loop control, minimizing the deviation between the distribution network output and the reference value by adjusting the output of the control equipment, and outputting the real-time adjusted control equipment output command.

3. The coordinated optimization scheduling method for source, grid, load and storage based on multiple time scales according to claim 1 or 2, characterized in that: The objective functions of each stage described by establishing a mathematical model include a day-ahead optimization scheduling model, an intraday rolling optimization scheduling model, and an intraday real-time feedback correction model; The objective function of the day-ahead optimization scheduling model is expressed as: Among them, F da is the comprehensive operating cost of the power grid, is the power purchased at time t, is the electricity purchase price at time t; C DG,t is the operating cost of distributed power generation; C WT,cut and C PV,cut The penalty price per unit power for abandoned wind and solar power is is the predicted wind power output at time t, is the actual wind power output at time t, is the predicted photovoltaic output at time t, is the actual photovoltaic output at time t, C IDR Reimbursing costs for incentivized demand response. The objective function of the intraday rolling optimization scheduling model is expressed as: Among them, t0 is the current time, and MT is the scheduling period of intraday rolling optimization; The objective function of the intraday real-time feedback correction model is expressed as: Where, ΔF rt The output deviation of adjustable resources in the system at the current time and over a long time scale; x dr The output of each control resource obtained during the intraday rolling optimization phase; x max The maximum adjustment amount of resource output for long-term regulation; x rt To adjust the output of resources; x rt,real It is the actual output feedback value of resource regulation at the current moment.

4. The coordinated optimization scheduling method for source, grid, load and storage based on multiple time scales according to claim 3 is characterized by: The optimization variables in the intraday real-time feedback correction phase are expressed as: X=[P buy ,P DG ,Q SVC ,D q ] Among them, P buy is the purchased power, P DG is the active power output of the distributed power source, Q SVC is the reactive power output of the static VAR compensator, D q The call power for incentive-based demand response.

5. The coordinated optimization scheduling method for source, grid, load and storage based on multiple time scales according to claim 4 is characterized by: The general constraints include network constraints, wind power and photovoltaic power generation operation constraints and demand response constraints; The network constraints include active power balance constraints, reactive power balance constraints and power flow equation constraints; The demand response constraints include constraints before implementing the demand response and constraints after implementing the demand response.

6. The coordinated optimization scheduling method for source, grid, load and storage based on multiple time scales according to claim 5 is characterized by: The model is converted into a mixed integer second-order cone programming problem by using convex relaxation and piecewise linearization processing technology, including the following steps: Convex relaxation is performed on the power flow constraints in the distribution network to transform the nonlinear power flow equation into a convex form; Identify the nonlinear terms that still exist in the model after convex relaxation, approximate the nonlinear terms with piecewise linear functions and add constraints; Identify the nonlinear terms that still exist in the piecewise linearized model, introduce auxiliary variables, and use the Big M method to linearize the remaining nonlinear terms; The linearized power flow equation constraints are further transformed into second-order cone constraints; All the results of convex relaxation, piecewise linearization and big-M linearization are integrated into a unified mixed-integer second-order cone programming model.

7. The coordinated optimization scheduling method for source, grid, load and storage based on multiple time scales according to claim 6 is characterized by: The large M method is used to linearize the remaining nonlinear terms and express them as follows: Where U j,t is the voltage of node j at time step t, C j,t is the shunt capacitor at node j at time step t, is the lower bound of the parallel capacitor, s j is the step size of the parallel capacitor, is a binary variable, g j,k,t is a binary variable indicating whether a particular capacitor is enabled, σ j,k,t is an auxiliary variable; The linearized power flow equation constraint is further transformed into a second-order cone constraint as follows: Among them, 2P ij,t is twice the active power of branch i, j at time step t, 2Q ij,t is twice the reactive power of branch i, j at time step t, i ij,t -U i,t is the branch current i ij,t With node voltage U i,t difference.

8. A system based on the multi-time-scale source-grid-load-storage coordinated optimization scheduling method according to claim 1, characterized in that: The time scale partitioning module is used to establish a multi-time scale optimization scheduling framework by defining scheduling stages with different time resolutions; Mathematical model building module, which is used to build a multi-time scale optimization scheduling model based on a multi-time scale optimization scheduling framework by establishing a mathematical model to describe the objective function and optimization variables of each stage and combining general constraints; A model conversion module for converting the model into a mixed integer second-order cone programming problem by using convex relaxation and piecewise linearization techniques; The optimization solution module is used to solve the mixed integer second-order cone programming problem using an optimization solver to obtain an optimized scheduling strategy that adapts to changes in demand at different time scales.

9. A computing device, characterized in that include: Memory, used to store programs; A processor is used to load the program to execute the steps of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by the processor, the steps of the source-grid-load-storage coordinated optimization scheduling method based on multiple time scales are implemented.

Images