Source-grid-load coordinated scheduling optimization methods, devices, equipment and media

Abstract

This application relates to the field of microgrid dispatching technology, and proposes a source-grid-load coordinated dispatching optimization method, device, equipment, and medium. The method includes: acquiring various unit start-up and shutdown modes and unit tie-line plans of the interconnected power grid; calling a pre-constructed source-grid-load coordinated dispatching optimization model, which includes an upper-level optimization model and a lower-level optimization model. The upper-level optimization model is used for upper-level optimization of unit start-up and shutdown modes and unit tie-line plans, while the lower-level optimization model is used for lower-level optimization of lower-level economic dispatching under feasible unit start-up and shutdown modes and unit tie-line plans; and substituting the source-grid-load coordinated dispatching optimization model for each unit start-up and shutdown mode and unit tie-line plan to solve for dispatching optimization, thereby determining the optimal source-grid-load coordinated dispatching optimization scheme. This technical solution improves optimization efficiency, and by solving for economic dispatching separately for specific start-up and shutdown and tie-line plans, the accuracy of the optimization results can also be improved.

Description

Technical Field

[0001] This application relates to the field of microgrid dispatching technology, and in particular to a source-grid-load coordinated dispatching optimization method, device, equipment and medium. Background Technology

[0002] With the rapid development of information technology and energy technology, "Internet + smart energy," represented by 5G, big data, cloud platforms, artificial intelligence, the Internet of Things, distributed energy, energy storage technology, energy internet, and self-sustaining energy systems, has brought tremendous convenience to the building energy industry. It empowers and reshapes traditional building energy systems with a brand-new concept and architecture, bringing a safe, stable, efficient, green, energy-saving, balanced, autonomous, convenient, and intelligent new energy consumption experience.

[0003] A microgrid is a low-voltage power distribution system that can operate in grid-connected or islanded mode. Current microgrid optimization and dispatching technologies only consider some fixed influencing factors of microgrid dispatching, resulting in energy waste in microgrids. Summary of the Invention

[0004] This application provides a source-grid-load coordinated scheduling optimization method, apparatus, equipment, and medium, aiming to solve the technical problems existing in related technologies.

[0005] In a first aspect, embodiments of this application provide a source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions, including:

[0006] Obtain various unit start-up and shutdown methods and unit tie-line plans for the interconnected power grid;

[0007] The pre-built source-grid-load coordinated scheduling optimization model is invoked. The source-grid-load coordinated scheduling optimization model includes an upper-level optimization model and a lower-level optimization model. The upper-level optimization model is used to perform upper-level optimization for unit start-up and shutdown methods and unit tie-line plans. The lower-level optimization model is used to perform lower-level optimization for lower-level economic scheduling under feasible unit start-up and shutdown methods and unit tie-line plans.

[0008] For each unit's start-up and shutdown method and unit tie line plan, the source-grid-load coordinated scheduling optimization model is substituted into the solution to determine the optimal source-grid-load coordinated scheduling optimization scheme.

[0009] In one embodiment, optionally, the optimal source-grid-load coordinated scheduling optimization scheme includes: optimal unit start-up and shutdown mode, optimal unit tie line plan, and optimal output plan.

[0010] In one embodiment, optionally, the upper-level optimization model includes an upper-level objective function aimed at minimizing the overall operating cost of the power grid after the execution of the source-grid-load coordination plan;

[0011] The lower-level optimization model includes a first objective function aimed at minimizing the cost of curtailing renewable energy, a second objective function aimed at minimizing the cost of peak-valley difference, and a third objective function aimed at minimizing operating costs.

[0012] In one embodiment, optionally, the constraints of the lower-level optimization model include: regional power grid power balance constraints, flexible load dispatch constraints, regional power grid spinning reserve constraints, thermal power unit output range, processing and maintenance time, and ramp-up constraints.

[0013] In one embodiment, optionally, the first objective function include:

[0014]

[0015] in, Describe the first objective function The corresponding value at confidence level τ1; Pr{.} represents the probability that the condition is satisfied. faw This indicates the cost of wind power abandonment across the entire network. fav This represents the cost of abandoning light across the entire network;

[0016] in,

[0017]

[0018]

[0019] in, T Indicates the total scheduling period. Indicates the number of wind turbine units at the sending end. , i (.) indicates a wind turbine generator. i The cost function of wind curtailment, Indicates the first i The actual maximum output of each wind turbine during time period t. Indicates the first i The planned scheduling volume of a wind turbine unit in time period t. Indicates the number of photovoltaic units at the sending end. Indicates photoelectric unit j The cost function of light abandonment, Indicates the first j The actual maximum output of each photovoltaic unit during time period t. Indicates the first j The planned scheduling amount of each photovoltaic unit in time period t.

[0020] In one embodiment, optionally, the second objective function include:

[0021]

[0022] in, Describing the second objective function The corresponding value at confidence level τ2; Pr{.} represents the probability that the condition is satisfied. , These represent the actual peak and valley values ​​of the receiving-end load during the scheduling period, respectively. Represents the peak-to-valley difference cost function;

[0023]

[0024] in, This indicates the actual load at the receiving end. Indicates the number of receiving-end loads. Indicates the receiving end n The actual demand of each load. Indicates the number of flexible loads. This represents the planned scheduling amount of flexible load during time period t. This represents the actual maximum output of the flexible load during time period t.

[0025] In one embodiment, optionally, the third objective function include:

[0026]

[0027]

[0028]

[0029] in, fes , fesu , fel These represent the costs of purchasing electricity for thermal power units, starting up thermal power units, and flexible load dispatching, respectively. and Representing the sending and receiving ends respectively k Dispatch cost function for each thermal power unit; and Thermal power units in the sending and receiving ends of the power grid respectively k At any moment t The plan is to contribute; , These represent the number of thermal power units at the sending and receiving ends, respectively. and These represent the sending-end and receiving-end thermal power units, respectively. k The start / stop state at time t , These represent the sending-end and receiving-end thermal power units, respectively.k At time t-1, the start / stop status is 0 for off and 1 for on; Mu This indicates the unit startup cost. T Indicates the total scheduling period.

[0030] Secondly, embodiments of this application provide a source-grid-load coordinated scheduling optimization device for interconnected power grids under stochastic conditions, comprising:

[0031] The acquisition module is used to acquire various unit start-up and shutdown methods and unit tie-line plans of the interconnected power grid;

[0032] The calling module is used to call a pre-built source-grid-load coordinated scheduling optimization model, wherein the source-grid-load coordinated scheduling optimization model includes an upper-level optimization model and a lower-level optimization model. The upper-level optimization model is used to perform upper-level optimization for unit start-up and shutdown methods and unit tie line plans, and the lower-level optimization model is used to perform lower-level optimization for lower-level economic scheduling under feasible unit start-up and shutdown methods and unit tie line plans.

[0033] The determination module is used to input the source-grid-load coordinated scheduling optimization model into the start-up and shutdown methods and tie-line plans of each unit to perform scheduling optimization solutions, so as to determine the optimal source-grid-load coordinated scheduling optimization scheme.

[0034] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions.

[0035] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions.

[0036] In the scheme implemented by the above-mentioned source-grid-load coordinated scheduling optimization method, device, equipment, and medium, various unit start-up and shutdown modes and unit tie-line plans of the interconnected power grid are obtained; a pre-constructed source-grid-load coordinated scheduling optimization model is invoked, wherein the source-grid-load coordinated scheduling optimization model includes an upper-level optimization model and a lower-level optimization model, wherein the upper-level optimization model is used to perform upper-level optimization for unit start-up and shutdown modes and unit tie-line plans, and the lower-level optimization model is used to perform lower-level optimization for lower-level economic scheduling under feasible unit start-up and shutdown modes and unit tie-line plans; for each unit start-up and shutdown mode and unit tie-line plan, the source-grid-load coordinated scheduling optimization model is substituted to perform scheduling optimization solution to determine the optimal source-grid-load coordinated scheduling optimization scheme. In this invention, the coordinated scheduling problem under study is constructed as a hierarchical optimization problem, namely, an upper-level optimization problem for optimizing unit start-up and shutdown modes and tie-line plans, and a lower-level optimization problem for system economic scheduling under given start-up and shutdown modes and tie-line plans. In the upper-level optimization process, for each feasible unit start-up / shutdown and tie-line plan, corresponding lower-level economic scheduling optimization is performed to obtain the optimal unit output and other plans under that unit start-up / shutdown and tie-line plan. Then, the corresponding cost components and overall cost are calculated, and the optimal plan and cost are returned to the upper level for evaluation of the current upper-level plan and the optimization solution of the upper-level problem. Finally, the optimal unit start-up / shutdown and tie-line plan and its corresponding optimal output plan obtained from the upper-level solution are taken as the optimal scheduling plan for the system. This hierarchical solution mode can quickly avoid infeasible upper-level unit start-up / shutdown and tie-line plans by considering constraints such as start-up / shutdown time, improving optimization efficiency. Furthermore, solving for specific start-up / shutdown and tie-line plans separately through economic scheduling can also improve the accuracy of the optimization results. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Picture 1 A schematic flowchart of a source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions, according to an embodiment of this application, is shown.

[0039] Picture 2 A block diagram of a source-grid-load coordinated scheduling optimization device for an interconnected power grid under a random environment, according to an embodiment of this application, is shown. Detailed Implementation

[0040] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0041] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0042] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0043] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0044] Please see Picture 1 , Picture 1 A schematic flowchart illustrating a source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions, according to an embodiment of this application, is shown. This source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions addresses the technical problem in related technologies where the optimal scheduling scheme cannot be determined under stochastic conditions, thus causing resource waste.

[0045] Considering the Direct Load Control (DLC) flexible load in the incentive-based flexible load category, its scheduling cost can be expressed as:

[0046]

[0047] In the formula: For flexible loads i The scheduling cost function; for t Planned scheduling of flexible loads during specific time periods; T This represents the total number of time periods; This represents the number of flexible loads.

[0048] A stochastic environment refers to stochastic chance-constrained programming. Stochastic chance-constrained programming is primarily used to solve planning problems where constraints contain random variables, and decisions must be made before these random variables occur. Chance-constrained programming addresses the impact of uncertainty on system constraints by setting constraint confidence levels. Its basic form can be expressed as:

[0049]

[0050] In the formula, x is the decision vector; ξ is the random vector; {.} represents the probability that the condition is satisfied; gj represents constraint j; ηj is the constraint confidence level of constraint j. A constraint with a constraint confidence level means that the selected decision vector x needs to ensure that the probability of satisfying the constraint is not less than the set constraint confidence level ηj. In this case, under the combined effect of the random vector and the decision vector, the probability that the system constraint is valid is not less than the corresponding confidence level.

[0051] Considering that source-load uncertainty can affect actual renewable energy consumption targets, the objective function needs to reflect the impact of uncertainty. For renewable energy consumption, the actual amount of renewable energy curtailment is related not only to the planned output of renewable energy units but also to the actual maximum power generation. When the actual maximum power generation is greater or less than the planned output, the amount of renewable energy curtailment and the consumption level will change. Therefore, traditional opportunity-constrained programming models are insufficient to describe the coordinated scheduling optimization problem studied in this chapter. To address the problem of the objective function being affected by uncertainties, an optimization model is established based on stochastic opportunity-constrained programming. Stochastic opportunity-constrained programming is a type of opportunity-constrained programming. Unlike conventional opportunity-constrained programming, stochastic opportunity-constrained programming provides two types of confidence levels to address the impact of random variables on system constraints and objectives: one is the constraint confidence level. ηj Its definition is consistent with the constraint confidence level in opportunity-constrained programming; another type is the target confidence level. τm In other words, the decision to be made requires that the probability of the objective function being greater than (or less than) a certain value be no less than the target confidence level. τm The basic form of stochastic chance-constrained programming can be expressed as:

[0052]

[0053] In the formula: For the objective function At confidence level τ m The corresponding value is F(·), which is a function of the overall operating cost when considering multiple objectives. The definitions of the other variables are shown in Formula 3.2.

[0054] like Picture 1 As shown, a source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions, according to an embodiment of this application, includes:

[0055] Step S101: Obtain the various unit start-up and shutdown methods and unit tie line plans of the interconnected power grid;

[0056] Step S102: Invoke the pre-built source-grid-load coordinated scheduling optimization model, wherein the source-grid-load coordinated scheduling optimization model includes an upper-level optimization model and a lower-level optimization model. The upper-level optimization model is used to perform upper-level optimization for unit start-up and shutdown methods and unit tie-line plans, and the lower-level optimization model is used to perform lower-level optimization for lower-level economic scheduling under feasible unit start-up and shutdown methods and unit tie-line plans.

[0057] Step S103: For each unit's start-up and shutdown mode and unit tie line plan, substitute the source-grid-load coordinated scheduling optimization model to perform scheduling optimization solution, so as to determine the optimal source-grid-load coordinated scheduling optimization scheme.

[0058] In this embodiment, the advantage of particle swarm optimization (PSO) in handling both discrete and continuous variables can be utilized to directly solve the basic model. Specifically, discrete and continuous variables are combined to form optimization particles, and different parts of the particles are updated using two different particle optimization formulas (one for continuous variables and one for discrete variables). Based on this, a nested hierarchical scheduling optimization model is proposed: an upper-level optimization model for tie-line planning and unit start-up / shutdown, and a lower-level optimization model for system economic scheduling given start-up / shutdown methods and tie-line plans. Considering that unit start-up / shutdown states and tie-line plans are fundamental conditions for system collaborative optimization and economic scheduling, and that the optimization results of economic scheduling reflect the merits of unit combinations and tie-line plans to a certain extent, this paper, combining this characteristic and the different properties of the optimization variables, constructs the collaborative scheduling problem under study as a hierarchical optimization problem: an upper-level optimization problem for optimizing unit start-up / shutdown methods and tie-line plans, and a lower-level optimization problem for system economic scheduling under given start-up / shutdown methods and tie-line plans.

[0059] In one embodiment, optionally, the optimal source-grid-load coordinated scheduling optimization scheme includes: optimal unit start-up and shutdown mode, optimal unit tie line plan, and optimal output plan.

[0060] In this invention, during the upper-level optimization process, for each feasible unit start-up / shutdown and tie-line plan, a corresponding lower-level economic scheduling optimization solution is performed to obtain the optimal unit output plan under that unit start-up / shutdown and tie-line plan. Then, the corresponding cost components and overall cost are calculated, and the obtained optimal plan and cost are returned to the upper level for evaluation of the current upper-level plan and the optimization solution of the upper-level problem. Finally, the optimal unit start-up / shutdown and tie-line plan and its corresponding optimal output plan obtained from the upper-level solution are taken as the optimal scheduling plan for the system. This hierarchical solution mode can quickly avoid infeasible upper-level unit start-up / shutdown and tie-line plans by considering constraints such as start-up / shutdown time, improving optimization efficiency. Furthermore, solving for specific start-up / shutdown and tie-line plans separately through economic scheduling can also improve the accuracy of the optimization results.

[0061] In one embodiment, optionally, the upper-level optimization model includes an upper-level objective function aimed at minimizing the overall operating cost of the power grid after the execution of the source-grid-load coordination plan;

[0062] The upper-level optimization problem is the unit start-up and shutdown and tie-line planning optimization problem. This invention incorporates the tie-line planning power as part of the decision-making quantity, i.e. Udc =[ Udc 1…, Udc T Specifically, the transmission power range of the tie line is divided into... N The power transmission line is divided into several levels, and each level is represented by a 0 / 1 encoding. This discretization process ensures stable operation of the connection line, facilitates control by the dispatch center, and meets actual operational needs. Furthermore, discretizing the transmission power effectively avoids violations of constraints such as power flipping and operating range, simplifies the optimization process, and improves computational efficiency.

[0063] The goal of the upper-level optimization problem is to find the optimal unit start-up and shutdown and tie-line scheduling plan, which minimizes the corresponding lower-level optimal objective function value, i.e., minimizes the overall network scheduling cost under this plan, expressed as:

[0064]

[0065] In the formula, P S PR provides feasible planning space for lower levels. For a given Us , UR , Udc The optimal lower-level scheduling objective function value is determined by the time-of-use time. The objective function optimized at the upper level reflects the overall operating cost of the power grid after the execution of the source-grid-load coordination plan. This cost includes not only economic efficiency but also cleanliness and load peak-valley difference.

[0066] The lower-level optimization model includes a first objective function aimed at minimizing the cost of curtailing renewable energy, a second objective function aimed at minimizing the cost of peak-valley difference, and a third objective function aimed at minimizing operating costs.

[0067] In one embodiment, optionally, the first objective function include:

[0068]

[0069] in, Describe the first objective function The corresponding value at confidence level τ1; Pr{.} represents the probability that the condition is satisfied. faw This indicates the cost of wind power abandonment across the entire network. fav This represents the cost of abandoning light across the entire network;

[0070] in,

[0071]

[0072]

[0073] in, T Indicates the total scheduling period. Indicates the number of wind turbine units at the sending end. , i (.) indicates a wind turbine generator. i The cost function of wind curtailment, Indicates the first i The actual maximum output of each wind turbine during time period t. Indicates the first i The planned scheduling volume of a wind turbine unit in time period t. Indicates the number of photovoltaic units at the sending end. Indicates photoelectric unit j The cost function of light abandonment, Indicates the first j The actual maximum output of each photovoltaic unit during time period t. Indicates the first j The planned scheduling amount of each photovoltaic unit in time period t.

[0074] In one embodiment, optionally, the second objective function include:

[0075]

[0076] in, Describing the second objective function The corresponding value at confidence level τ2; Pr{.} represents the probability that the condition is satisfied. , These represent the actual peak and valley values ​​of the receiving-end load during the scheduling period, respectively. Represents the peak-to-valley difference cost function;

[0077]

[0078] in, This indicates the actual load at the receiving end. Indicates the number of receiving-end loads. Indicates the receiving end n The actual demand of each load. Indicates the number of flexible loads. This represents the planned scheduling amount of flexible load during time period t. This represents the actual maximum output of the flexible load during time period t.

[0079] In one embodiment, optionally, the third objective function include:

[0080]

[0081]

[0082]

[0083] in, fes , fesu , fel These represent the costs of purchasing electricity for thermal power units, starting up thermal power units, and flexible load dispatching, respectively. and Representing the sending and receiving ends respectively k Dispatch cost function for each thermal power unit; and Thermal power units in the sending and receiving ends of the power grid respectively k At any moment t The plan is to contribute; , These represent the number of thermal power units at the sending and receiving ends, respectively. and These represent the sending-end and receiving-end thermal power units, respectively. k The start / stop state at time t , These represent the sending-end and receiving-end thermal power units, respectively. k At time t-1, the start / stop status is 0 for off and 1 for on; Mu This indicates the unit startup cost. T Indicates the total scheduling period.

[0084] In the above embodiments, the lower-level optimization model mainly considers three scheduling benefits, as follows:

[0085] (a) Increasing the absorption of new energy sources can relatively reduce the use of fossil fuels, which helps improve the cleanliness of the power grid. Therefore, reducing the curtailment of new energy is one of the goals to improve the new energy absorption capacity of the large power grid. Since this goal is affected by the stochasticity of wind and solar power, a confidence level τ1 is given to describe its function, i.e.

[0086]

[0087] In the formula, faw This indicates the cost of wind power curtailment across the entire network, specifically:

[0088]

[0089] in, TFor the total scheduling period, NSwind This refers to the number of wind turbine units at the sending end. (.) represents a wind turbine. i The cost function of wind curtailment, fav This indicates the cost of abandoning broadband across the entire network, specifically...

[0090] (3.6)

[0091] It should be noted that due to the inherent uncertainty of photovoltaic and wind power output, the actual maximum output may be less than the set dispatch plan, meaning that the actual output cannot meet the planned output. In such cases, the actual output can only be given according to the actual maximum output. Therefore, it is necessary to reserve sufficient reserves to prevent power shortages caused by insufficient wind and solar power.

[0092] (b) Currently, the peak-to-valley load difference and peak-shaving pressure are increasing year by year. Therefore, a corresponding objective function is set from the perspective of reducing the peak-to-valley load difference under heavy load. Since load uncertainty will affect the peak-to-valley values, a target confidence level is given to describe this objective function. The peak-to-valley load difference objective function is expressed as:

[0093]

[0094] In the formula: , These represent the actual peak and valley values ​​of the receiving-end load during the scheduling period, respectively, and are derived from the actual load volume at different times of the day.

[0095] (3.8)

[0096] in, This indicates the actual load at the receiving end. Indicates the number of receiving-end loads. Indicates the receiving end n The actual demand of each load.

[0097] (c) With reducing scheduling costs as one of the objectives, the corresponding function is expressed as:

[0098] (3.9)

[0099] (3.10)

[0100] (3.11)

[0101] in, fes , fesu , fel These represent the costs of purchasing electricity for thermal power units, starting up thermal power units, and flexible load dispatching, respectively. and Representing the sending and receiving ends respectively k Dispatch cost function for each thermal power unit; and Thermal power units in the sending and receiving ends of the power grid respectively k At any moment t The plan is to contribute; , These represent the number of thermal power units at the sending and receiving ends, respectively. and These represent the sending-end and receiving-end thermal power units, respectively. k The start / stop state at time t , These represent the sending-end and receiving-end thermal power units, respectively. k At time t-1, the start / stop status is 0 for off and 1 for on; Mu This indicates the unit startup cost. T This represents the total scheduling period. The cost of flexible load scheduling is as described in formula (3.1), and will not be repeated here.

[0102] The constraints include power balance constraints of interconnected power grids, tie line operation constraints, unit operation constraints, reserve constraints, and flexible load adjustment constraints.

[0103] (a) Power balance constraints

[0104] Power balance constraints include three aspects: sending-end power balance, receiving-end power balance, and overall grid power balance. This paper considers the matching between the generation plan and the load forecast. In actual operation, the impact of source-load uncertainties on the system's power supply and demand is described in the form of reserve and power difference adjustment costs. The sending-end and receiving-end power balance constraints are expressed as follows:

[0105]

[0106] In the formula: for t Time-of-use tie line power; for flexible loads i exist t The dispatchable capacity for a given time period includes the normal electricity consumption of the portion of flexible load that is not subject to dispatch. , These are the number of conventional loads at the sending and receiving ends, respectively. The other variables have already been defined in previous chapters and will not be repeated here.

[0107] (b) Flexible load scheduling constraints

[0108] The planned flexible load dispatch amount should not exceed the reported flexible load dispatchable capacity, that is:

[0109]

[0110] (c) Tie line constraints

[0111] Since the scheduling plan includes tie-line planning, rather than treating it as a power balance slack, tie-line constraints, including power reversal, are given in the form of deterministic constraints, as shown below:

[0112]

[0113] The above formulas respectively represent the constraints on power reversal of tie line transmission, ramping constraint, operating range constraint, and output maintenance constraint; and The upper and lower limits of the power ramp-up of the tie line; , These represent the upper and lower limits of the permissible operating range for the connecting line, respectively. To maintain the planned power of the tie line for the shortest possible time.

[0114] (d) Constraints of thermal power units

[0115] Taking the receiving-end power grid as an example, the output range of thermal power units is constrained as follows:

[0116]

[0117] In the formula: , and thermal power units i During the scheduling period t Output limits and start / stop status. Minimum start / stop time constraints for thermal power units:

[0118]

[0119] In the formula: for t Time-of-use units i Start-up (downtime); , These represent the shortest startup / shutdown durations, respectively.

[0120] Unit ramp-up constraints:

[0121]

[0122] In the formula, , thermal power units i Uphill / downhill speed limits.

[0123] (e) Rotational Spare Constraint

[0124] Source load uncertainty will cause the dispatch plan to fail to fully match the actual load. Therefore, thermal power units need to provide corresponding reserve capacity to cope with source load fluctuations, and a constraint confidence level should be given. η 1, η 2. To describe the system's spinning reserve. In particular, since wind and solar power can theoretically provide a large amount of negative reserve, only the system's positive spinning reserve is considered.

[0125] Sending-end power grid is rotating and ready for standby

[0126] 3.22

[0127] In the formula, =max{0, pi, ,tw_ i , ,tw} and =max{0, pi, ,tv_ i , ,tv} respectively represent the first i The difference between the actual output of the typhoon-generated solar turbine and the planned output; the definitions of the other variables have been given above and will not be repeated here.

[0128] Receiving end power grid is rotating and ready for standby

[0129] 3.23

[0130] In the formula: .

[0131] Sampling simulation technology is used to simulate uncertainties on both the source and load sides, thereby generating system operation samples for evaluating indicators.

[0132] The objective function of the lower-level economic dispatch problem includes the cost of renewable energy curtailment, peak-valley difference cost, operating cost, unit ramp-up / ramp-down constraint violation cost, and power difference adjustment cost, which can be expressed as:

[0133]

[0134] In the formula, PS , PR This indicates the unit output and flexible load scheduling plan at both the sending and receiving ends; US , UR , Udc The upper-level selection indicates the given start-up and shutdown plan of the sending and receiving units and the tie line plan, respectively. The weighting coefficients are set artificially for three objectives: peak-valley difference, renewable energy consumption, and economic efficiency. The multiple sub-objective functions are weighted, and the final weighted overall benefit value reflects the overall benefit of power grid operation.

[0135] The constraints of the lower-level model include: regional power grid power balance constraints, flexible load dispatch constraints, regional power grid spinning reserve constraints, thermal power unit output range, processing and maintenance time, and ramp-up constraints, as shown in equations 3.12~3.14 and 3.19~3.23. It is important to note that the regional power grid power balance constraints and spinning reserve are affected not only by unit start-up and shutdown plans but also by unit output plans. The lower-level optimization is an economic dispatch plan optimization under given unit start-up and shutdown and tie-line plans, encompassing information from both types of start-up and shutdown and output plans. Therefore, power balancer and reserve constraints need to be considered in the lower-level particles.

[0136] Picture 2 A block diagram of a source-grid-load coordinated scheduling optimization device for an interconnected power grid under a random environment, according to an embodiment of this application, is shown.

[0137] like Picture 2 As shown, in a second aspect, embodiments of this application provide a source-grid-load coordinated scheduling optimization device 20 for interconnected power grids under stochastic conditions, comprising:

[0138] The acquisition module 21 is used to acquire various unit start-up and shutdown methods and unit tie line plans of the interconnected power grid;

[0139] Module 22 is invoked to invoke a pre-built source-grid-load coordinated scheduling optimization model, wherein the source-grid-load coordinated scheduling optimization model includes an upper-level optimization model and a lower-level optimization model. The upper-level optimization model is used to perform upper-level optimization for unit start-up and shutdown methods and unit tie-line plans, and the lower-level optimization model is used to perform lower-level optimization for lower-level economic scheduling under feasible unit start-up and shutdown methods and unit tie-line plans.

[0140] The determination module 23 is used to perform scheduling optimization by substituting the start-up and shutdown methods of each unit and the unit tie line plan into the source-grid-load coordinated scheduling optimization model to determine the optimal source-grid-load coordinated scheduling optimization scheme.

[0141] In one embodiment, optionally, the optimal source-grid-load coordinated scheduling optimization scheme includes: optimal unit start-up and shutdown mode, optimal unit tie line plan, and optimal output plan.

[0142] In one embodiment, optionally, the upper-level optimization model includes an upper-level objective function aimed at minimizing the overall operating cost of the power grid after the execution of the source-grid-load coordination plan;

[0143] The lower-level optimization model includes a first objective function aimed at minimizing the cost of curtailing renewable energy, a second objective function aimed at minimizing the cost of peak-valley difference, and a third objective function aimed at minimizing operating costs.

[0144] In one embodiment, optionally, the constraints of the lower-level optimization model include: regional power grid power balance constraints, flexible load dispatch constraints, regional power grid spinning reserve constraints, thermal power unit output range, processing and maintenance time, and ramp-up constraints.

[0145] In one embodiment, optionally, the first objective function include:

[0146]

[0147] in, Describe the first objective function The corresponding value at confidence level τ1; Pr{.} represents the probability that the condition is satisfied. faw This indicates the cost of wind power abandonment across the entire network. fav This represents the cost of abandoning light across the entire network;

[0148] in,

[0149]

[0150]

[0151] in, T Indicates the total scheduling period. Indicates the number of wind turbine units at the sending end. , i (.) indicates a wind turbine generator. i The cost function of wind curtailment, Indicates the first i The actual maximum output of each wind turbine during time period t. Indicates the first i The planned scheduling volume of a wind turbine unit in time period t. Indicates the number of photovoltaic units at the sending end. Indicates photoelectric unit j The cost function of light abandonment, Indicates the first j The actual maximum output of each photovoltaic unit during time period t. Indicates the first j The planned scheduling amount of each photovoltaic unit in time period t.

[0152] In one embodiment, optionally, the second objective function include:

[0153]

[0154] in, Describing the second objective function The corresponding value at confidence level τ2; Pr{.} represents the probability that the condition is satisfied. , These represent the actual peak and valley values ​​of the receiving-end load during the scheduling period, respectively. Represents the peak-to-valley difference cost function;

[0155]

[0156] in, This indicates the actual load at the receiving end. Indicates the number of receiving-end loads. Indicates the receiving end n The actual demand of each load. Indicates the number of flexible loads. This represents the planned scheduling amount of flexible load during time period t. This represents the actual maximum output of the flexible load during time period t.

[0157] In one embodiment, optionally, the third objective function include:

[0158]

[0159]

[0160]

[0161] in, fes , fesu , fel These represent the costs of purchasing electricity for thermal power units, starting up thermal power units, and flexible load dispatching, respectively. and Representing the sending and receiving ends respectively k Dispatch cost function for each thermal power unit; and Thermal power units in the sending and receiving ends of the power grid respectively k At any moment t The plan is to contribute; , These represent the number of thermal power units at the sending and receiving ends, respectively. and These represent the sending-end and receiving-end thermal power units, respectively. k The start / stop state at time t , These represent the sending-end and receiving-end thermal power units, respectively. k At time t-1, the start / stop status is 0 for off and 1 for on; Mu This indicates the unit startup cost. T Indicates the total scheduling period.

[0162] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions.

[0163] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described source-grid-load coordinated scheduling optimization method for interconnected power grids under stochastic conditions.

[0164] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0165] It should be understood that although the terms "first," "second," etc., may be used to describe the setting units in the embodiments of this application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of this application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0166] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0167] In the several embodiments provided in this application, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between systems or units may be electrical, mechanical, or other forms.

[0168] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0169] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0170] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A source-network-load coordinated scheduling optimization method for interconnected power grids in a stochastic environment, characterized in that, The method comprises the following steps: acquiring a plurality of unit start-stop modes and unit tie-line plans of the interconnected power grid; calling a pre-constructed source-grid-load coordination scheduling optimization model, wherein the source-grid-load coordination scheduling optimization model comprises an upper optimization model and a lower optimization model, wherein the upper optimization model is used for upper optimization for the unit start-stop modes and the unit tie-line plans, and the lower optimization model is used for lower optimization for lower economic scheduling under the feasible unit start-stop modes and the unit tie-line plans; for each unit start-stop mode and unit tie-line plan, substituting the source-grid-load coordination scheduling optimization model into the scheduling optimization model to determine an optimal source-grid-load coordination scheduling optimization scheme; the upper optimization model comprises an upper objective function aiming to minimize the overall operation cost of the power grid after the execution of the source-grid-load coordination plan; the lower optimization model comprises a first objective function aiming to minimize the cost of new energy curtailment, a second objective function aiming to minimize the cost of peak-valley difference, and a third objective function aiming to minimize the cost of operation; the first objective function comprises: wherein, denotes the first objective function the corresponding value at a confidence level τ1; Pr{.} denotes the probability that the condition is fulfilled, faw denotes the total wind curtailment cost, fav denotes the total light curtailment cost; wherein wherein, T denotes the total scheduling period, denotes the number of wind power generators at the sending end, , i (.) denotes the wind power generator i abandonment cost function, denotes the actual maximum output of the i th wind power generator at the t period, denotes the planned scheduling amount of the i th wind power generator at the t period, denotes the number of photovoltaic generators at the sending end, denotes the photovoltaic generator j abandonment cost function, denotes the actual maximum output of the j th photovoltaic generator at the t period, denotes the planned scheduling amount of the j th photovoltaic generator at the t period.

2. The method of claim 1, wherein, the optimal source-grid-load coordination scheduling optimization scheme comprises an optimal unit start-stop mode, an optimal unit tie-line plan, and an optimal output plan.

3. The method of claim 1, wherein, The constraint conditions of the lower optimization model comprise regional power grid power balance constraints, flexible load scheduling constraints, regional power grid spinning reserve constraints, thermal power unit output intervals, handling maintenance time, and ramping constraints.

4. The method of claim 1, wherein, the second objective function comprises: wherein, denotes the second objective function the corresponding value at the confidence level τ2; Pr{.} denotes the probability that the condition is fulfilled, , denote the actual peak and valley values of the demand at the receiving end within the dispatching period, respectively, denotes the peak-valley difference cost function; wherein, represents the actual load amount of the receiving end, represents the number of loads of the receiving end, represents the actual demand amount of the first n load of the receiving end, represents the number of flexible loads, represents the planned scheduling amount of the flexible load at the t period, represents the actual maximum output of the flexible load at the t period.

5. The method of claim 1, wherein, the third objective function comprises: in, fes , fesu , fel These represent the costs of purchasing electricity for thermal power units, starting up thermal power units, and flexible load dispatching, respectively. and Representing the sending and receiving ends respectively k Dispatch cost function for each thermal power unit; and Thermal power units in the sending and receiving ends of the power grid respectively k At any moment t The plan is to contribute; , These represent the number of thermal power units at the sending and receiving ends, respectively. and These represent the sending-end and receiving-end thermal power units, respectively. k The start / stop state at time t , These represent the sending-end and receiving-end thermal power units, respectively. k At time t-1, the start / stop status is 0 for off and 1 for on; Mu This indicates the unit startup cost. T Indicates the total scheduling period.

6. A source-network-load coordinated scheduling optimization device for interconnected power grids in a random environment, characterized in that, The method comprises the following steps: an acquiring module is configured to acquire a plurality of unit start-stop modes and unit tie-line plans of the interconnected power grid; a calling module is configured to call a pre-constructed source-grid-load coordination scheduling optimization model, wherein the source-grid-load coordination scheduling optimization model comprises an upper optimization model and a lower optimization model, wherein the upper optimization model is used for upper optimization for the unit start-stop modes and the unit tie-line plans, and the lower optimization model is used for lower optimization for lower economic scheduling under the feasible unit start-stop modes and the unit tie-line plans; a determining module is configured to, for each unit start-stop mode and unit tie-line plan, substitute the source-grid-load coordination scheduling optimization model into the scheduling optimization model to determine an optimal source-grid-load coordination scheduling optimization scheme; the upper optimization model comprises an upper objective function aiming to minimize the overall operation cost of the power grid after the execution of the source-grid-load coordination plan; the lower optimization model comprises a first objective function aiming to minimize the cost of new energy curtailment, a second objective function aiming to minimize the cost of peak-valley difference, and a third objective function aiming to minimize the cost of operation; the first objective function comprises: wherein, denotes the first objective function the corresponding value at a confidence level τ1; Pr{.} denotes the probability that the condition is fulfilled, faw denotes the total wind curtailment cost, fav denotes the total light curtailment cost; wherein wherein, T denotes the total scheduling period, denotes the number of wind power generators at the sending end, i (.) denotes the wind power generator i abandonment cost function, denotes the actual maximum output of the i th wind power generator at the t period, denotes the planned scheduling amount of the i th wind power generator at the t period, denotes the number of photovoltaic generators at the sending end, denotes the photovoltaic generator j abandonment cost function, denotes the actual maximum output of the j th photovoltaic generator at the t period, denotes the planned scheduling amount of the j th photovoltaic generator at the t period.​ 7. A computer device, comprising: The method comprises the following steps: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer executable instructions are used to execute the method of any one of claims 1 to 5.

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