Multi-time-scale collaborative optimization scheduling method, system, equipment and medium for power system

Abstract

The invention relates to the technical field of power system scheduling optimization, and discloses a power system multi-time scale collaborative optimization scheduling method, system and device and a medium, and the method comprises the steps: obtaining a target parameter of a target power system, and building a line heat balance model according to the target parameter; calculating the global real-time transmission capacity of the power transmission line of the target power system according to the output of the line heat balance model; establishing a day-ahead optimal scheduling model, and establishing an intra-day optimal scheduling model; through optimal scheduling of day-ahead and intra-day time scales, fine control and optimization of the operation state of the power system are realized. Through calculation of the global real-time transmission capacity, the scheduling model can fully consider the actual bearing capacity and the heat balance state of the power transmission line, and line damage or power failure accidents caused by overload are avoided. Meanwhile, the first constraint condition set and the second constraint condition set are set, so that the operation efficiency and the stability of the power system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatch optimization, and in particular to a method, system, equipment and medium for collaborative optimization dispatch of a power system at multiple time scales. Background Art

[0002] Traditional power system dispatching methods under extreme weather conditions mainly formulate unit output plans based on the next day's load forecast data and the physical damage to the power grid lines caused by extreme events. They do not consider the impact of environmental factors such as the next day's wind speed and temperature on the capacity of the transmission lines. As a result, the dispatching plan is difficult to ensure that the line transmission power is within the normal safety range under extremely high temperature conditions. When the transmission capacity allowed by the line in extremely high temperature weather is significantly reduced with changes in the external ambient temperature, how to reasonably arrange the start and stop of the unit and the output plan to achieve safe power transmission is an urgent problem that needs to be solved. Some existing technologies have proposed a two-layer robust optimization dispatching method for the day ahead to improve the resilience of the power system to storm weather events, taking into account the failure of lines and towers under the influence of storms. Others have proposed a two-layer coordinated elastic dispatching model taking into account the unified power flow controller under typhoon weather, making the optimal decision based on the system operating status under the influence of the typhoon to avoid chain failures of the power grid lines.

[0003] In summary, existing optimization scheduling methods focus on the direct physical damage caused by extreme disasters to transmission lines, but do not consider the dynamic impact of extreme high temperatures on the transmission capacity of transmission lines. The time-varying transmission capacity of transmission lines is not modeled in detail and comprehensively in the optimization model. Therefore, the scheduling schemes formulated do not fully consider the actual transmission capacity of the lines. When applied to actual power grids, individual lines may trip due to current overload, thereby inducing cascading failures. Summary of the Invention

[0004] In view of the above existing problems, this application is proposed.

[0005] Therefore, the present application provides a method, system, equipment and medium for collaborative optimization scheduling of multi-time scales in power systems, which can solve the problem of lack of coordination of scheduling strategies at different time scales in existing power systems.

[0006] To solve the above technical problems, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for multi-time-scale collaborative optimization scheduling of a power system, comprising:

[0008] Acquiring target parameters of a target power system and establishing a line heat balance model according to the target parameters;

[0009] The line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line;

[0010] calculating the global real-time transmission capacity of the target power system transmission line based on the output of the line heat balance model;

[0011] Establishing a day-ahead optimization scheduling model, wherein the day-ahead scheduling optimization model includes a first objective function and a first constraint condition set;

[0012] The first set of constraints includes a real-time transmission capacity constraint obtained from a global real-time transmission capacity;

[0013] Establishing an intraday optimization scheduling model, wherein the intraday optimization scheduling model includes a second objective function and a second constraint condition set;

[0014] The second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

[0015] As a preferred solution of the multi-time-scale collaborative optimization scheduling method for the power system described in this application, wherein: the global real-time transmission capacity of the target power system transmission line is calculated based on the output of the line heat balance model, including:

[0016] Obtaining local real-time transmission capacity in different areas of the transmission line according to the line heat balance model;

[0017] Obtain the minimum value of local real-time transmission capacity under different weather conditions;

[0018] Taking the minimum value of the local real-time transmission capacity as the actual transmission capacity value of the transmission lines in different areas;

[0019] The actual transmission capacity values in all regions are integrated as the global real-time transmission capacity of the target power system transmission lines.

[0020] As a preferred solution of the multi-time-scale collaborative optimization scheduling method for the power system described in this application, the establishment of the day-ahead optimization scheduling model includes:

[0021] Establishing a prediction data curve according to the target parameters;

[0022] Using the expression of the forecast data curve as a day-ahead optimization scheduling model;

[0023] The first objective function of the day-ahead optimization scheduling model is to minimize the sum of the power purchase cost, start-up and shutdown cost, standby cost, and wind curtailment penalty cost of conventional thermal power units and fast start-up and shutdown units.

[0024] As a preferred solution of the multi-time-scale collaborative optimization scheduling method for the power system described in this application, the establishment of the intraday optimization scheduling model includes:

[0025] Establishing an update mechanism, the update mechanism is used to update the prediction data curve established according to the target parameter;

[0026] The expression of the forecast data curve including the updating mechanism is used as the intraday optimization scheduling model;

[0027] The second objective function of the intraday optimization scheduling model is to minimize the sum of the electricity purchase cost of conventional thermal power units, the electricity purchase cost of rapid start-up and shutdown units, the start-up and shutdown cost and the standby cost, and the wind power curtailment penalty cost during the remaining period of the day.

[0028] As an optimal solution for the multi-time-scale collaborative optimization scheduling method of the power system described in this application, the intraday optimization scheduling model is used to determine the start-up and shutdown status of the rapid start-up and shutdown units and the rotating reserve capacity purchase plan, and at the same time update the output plan of the conventional thermal power units.

[0029] As an optimal solution of the multi-time-scale collaborative optimization scheduling method for the power system described in this application, the boundary condition constraints include constraints related to the start-up and shutdown status of conventional thermal power units on the next day and constraints related to the spinning reserve capacity purchase plan.

[0030] As an optimal solution for the multi-time-scale collaborative optimization scheduling method of the power system described in this application, the first set of constraints includes line dynamic transmission capacity constraints, active power flow balance constraints, unit output upper and lower limit constraints, unit climbing constraints, unit start-up and shutdown status constraints, and energy storage constraints.

[0031] In a second aspect, the present application provides a multi-timescale collaborative optimization scheduling system for a power system, comprising:

[0032] A first model building module is used to obtain target parameters of the target power system and build a line heat balance model according to the target parameters;

[0033] The line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line;

[0034] a capacity determination module, configured to calculate the global real-time transmission capacity of the target power system transmission line based on the output of the line heat balance model;

[0035] A second model building module is used to establish a day-ahead optimization scheduling model, wherein the day-ahead scheduling optimization model includes a first objective function and a first constraint condition set;

[0036] The first set of constraints includes a real-time transmission capacity constraint obtained from a global real-time transmission capacity;

[0037] A third model building module is used to establish an intraday optimization scheduling model, wherein the intraday optimization scheduling model includes a second objective function and a second constraint condition set;

[0038] The second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

[0039] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0041] Compared with the prior art, the beneficial effects of the present application are as follows: the present application proposes a multi-time-scale collaborative optimization scheduling method for a power system, obtains the target parameters of the target power system, establishes a line thermal balance model based on the target parameters; calculates the global real-time transmission capacity of the target power system's transmission lines based on the output of the line thermal balance model; establishes a day-ahead optimization scheduling model, which includes a first objective function and a first set of constraints; establishes an intraday optimization scheduling model, which includes a second objective function and a second set of constraints; through optimization scheduling at both day-ahead and intraday time scales, the present application achieves fine control and optimization of the power system's operating state. The calculation of the global real-time transmission capacity enables the scheduling model to fully consider the actual carrying capacity and thermal balance state of the transmission line, avoiding line damage or power outages caused by overload. At the same time, the setting of the first and second constraint sets ensures that the scheduling scheme improves the operating efficiency and stability of the power system while meeting multiple requirements such as safety, economy, and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 A flowchart of a method for multi-time-scale collaborative optimization scheduling of a power system provided in one embodiment of the present application.

[0044] Figure 2A schematic diagram of a day-ahead and intraday optimization scheduling mode of a multi-time-scale collaborative optimization scheduling method for a power system provided in one embodiment of the present application.

[0045] Figure 3 A schematic diagram of a transmission line spanning multiple regions with different temperatures in a multi-time-scale collaborative optimization scheduling method for a power system provided in one embodiment of the present application.

[0046] Figure 4 A schematic diagram of a line heat balance model of a multi-time-scale collaborative optimization scheduling method for a power system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the following detailed description of the specific embodiments of this application is given in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of this application.

[0048] Example 1, with reference to Figures 1-4 , which is the first embodiment of the present application, provides a multi-time-scale collaborative optimization scheduling method for a power system, comprising:

[0049] Existing technologies present several challenges. For example, traditional power system dispatch methods often only consider factors at a single timescale, ignoring the interactions and synergies across different timescales. This can lead to irrational energy planning and allocation over long timescales and imbalances in power supply and demand over short timescales. Furthermore, existing dispatch methods lack sufficient flexibility and adaptability in the complex and ever-changing power system environment, making it difficult to effectively respond to various emergencies and uncertainties.

[0050] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to elaborate on how to implement the multi-time scale collaborative optimization scheduling method of the power system;

[0051] Figure 1 A method flow chart of a multi-time-scale collaborative optimization scheduling method for a power system is shown, including:

[0052] S101, obtaining target parameters of a target power system and establishing a line heat balance model according to the target parameters;

[0053] It should be noted that in order to achieve multi-timescale collaborative optimization of power system scheduling, it is necessary to obtain relevant parameters of the target power system. These parameters can be used to rationally implement multi-timescale collaborative optimization scheduling. These parameters can include the power system's load demand, generator output limits, line transmission capacity limits, predicted output of renewable energy, the status of energy storage devices, and electricity market price information. These parameters are the foundation for building a multi-timescale collaborative optimization scheduling model, and they can fully reflect the operating status and constraints of the power system.

[0054] It should be noted that through the precise acquisition and in-depth analysis of these parameters, it is possible to achieve optimized scheduling of the power system at different time scales, thereby solving the problems existing in traditional scheduling methods and improving the operating efficiency and stability of the power system.

[0055] In an optional embodiment, the aforementioned parameters can be obtained through real-time monitoring of the power system's operating status, statistical analysis using historical data, and the adoption of advanced data prediction algorithms. Real-time monitoring ensures that the acquired parameters are up-to-date and accurate, reflecting the current state of the power system; historical data analysis helps identify the power system's operating patterns and trends, providing an important reference for optimized scheduling; and advanced data prediction algorithms can predict key parameters such as the power system's load demand and renewable energy output over a period of time, further improving the foresight and accuracy of scheduling decisions. These combined approaches can provide comprehensive support and assurance for the coordinated optimization of multi-timescale scheduling of the power system.

[0056] In the embodiment of the present application, the target parameters include the rated capacity and location of thermal power, wind power, photovoltaic power, and energy storage; technical parameters such as the topology of the transmission network, the unit ramp rate, and the maximum and minimum output; economic parameters such as unit operation and maintenance cost, load shedding penalty cost, and energy abandonment cost; hourly wind and solar load time-series output forecast data under extreme high temperatures the next day, meteorological data such as wind speed and temperature, and line parameters such as the latitude of the line location and surface radiation coefficient.

[0057] In an optional embodiment, the time scale of day-ahead and intraday scheduling is shown in Table 1. Day-ahead scheduling is based on short-term forecast data to formulate the scheduling plan for the next day (next 24 hours), and intraday scheduling is based on extended short-term forecast data to formulate the scheduling plan for the remaining time of each day. The flow chart is shown in the attached figure. Figure 2 shown.

[0058] Table 1: Indicators of planning schemes that consider frequency safety in extreme scenarios

[0059] index Day-ahead scheduling Intraday scheduling Execution cycle / h 24 1 Scheduling step / h 1 0.25 Total scheduling time / h 24 Remaining period Scheduling times 1 24

[0060] In an embodiment of the present application, the line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line.

[0061] In an optional embodiment, the local real-time transmission capacity of the transmission line is calculated every hour of the next day. Figure 3 Based on the data of ambient temperature, ambient wind speed, operation time and latitude of the line in each area, the line heat balance model is established as shown in formula (1). Figure 4 , that is, the heat dissipated by the transmission line is equal to the sum of the heat absorbed by the line and the heat generated by the line.

[0062] Q dis =Q abs +Q gen (1)

[0063] Where: Q dis , Q abs With Q gen These are the heat dissipation, heat absorption, and heat generation of transmission lines. Transmission line heat dissipation includes convection, radiation, and evaporation. Transmission line heat absorption is generally sunlight absorption. Conductor heat generation includes corona heating, hysteresis loss, and Joule heating.

[0064] In an optional embodiment, referring to IEEE Std.738, the transient form of the heat balance equation of the transmission line is as shown in equations (2) to (3):

[0065]

[0066]

[0067] Where: Q c,t Indicates the convection heat dissipation rate of the circuit at time t, expressed as the ambient temperature T a,t , circuit surface temperature T b,t and ambient wind speed v t The function of the three; Q d,t It represents the radiation heat dissipation rate of the circuit at time t, which is a function of the ambient temperature and the conductor surface temperature; C h,t is the total heat capacity of the circuit at time t; Q p,t is the solar radiation heat absorption rate of the line, which is expressed as a function of latitude Ψ; R is the line resistance value, which is affected by the line surface temperature; It is the transmission capacity of the line at time t.

[0068] In an optional embodiment, the convection heat dissipation of the line conductor can be divided into forced convection and natural convection. Forced convection refers to the heat dissipation process of the line under the action of wind. The IEEE Std.738 standard uses different Reynolds number models to calculate the heat dissipation rate Q' under low wind speed and high wind speed conditions according to the wind speed difference. c,t and Q" c,t , as shown in equations (4) to (5). The natural convection heat dissipation of the line occurs in a windless environment, and its heat dissipation rate is Q'' c,t The calculation is as shown in formula (6).

[0069] Q' c,t =K w [1.01+1.35N R (D c ,v t ,ρ t ,μ t ) 0.52 ]K a (T a,t -T b,t ) (4)

[0070] Q” c,t =0.754K w N R (D c ,v t ,ρ t ,μ t ) 0.6 K a (T a,t -T b,t ) (5)

[0071]

[0072] In formulas (4) to (6): t is the air density of the line environment at time t; Ka is the thermal conductivity of air; Kw is the wind direction factor; NR is the Reynolds number, which is expressed as the line diameter Dc and the air viscosity μ t , wind speed vt and air density ρ t The function is shown in formula (7).

[0073]

[0074] In an optional embodiment, according to IEEE Std.738, Q' may be taken c,t , Q” c,t and Q"' c,t The maximum value is the best estimate of the final circuit convective heat dissipation rate, as shown in formula (8).

[0075] Q' c,t=max{Q' c,t ,Q” c,t ,Q”' c,t} (8)

[0076] The radiation heat dissipation of the circuit mainly depends on the temperature difference between the circuit and its surrounding environment. d,t The calculation of is shown in formula (9).

[0077]

[0078] Where: α is the radiation coefficient of the line surface, which is generally in the range of 0.23 to 0.91.

[0079] In an optional embodiment, the solar radiation absorption of the line depends on the sunshine angle, the geographical orientation of the line and the characteristics of the outer layer. p,t As shown in formula (10).

[0080] Q p,t =βQ s sin(θ s )A (10)

[0081] Where: β is the heat absorption coefficient of the line to solar radiation, which is within the range of 0.23 to 0.91; A is the projected area of the line; Q s is the total solar radiation absorption intensity corrected by altitude; θ s is the effective angle of incidence of the sun.

[0082] The resistance value of the circuit is calculated as shown in formula (11).

[0083]

[0084] Where: R(T b,t ) is the resistance value of the circuit at time t; With R(T b ) are the known higher temperatures With lower temperature T b The estimated resistance of the transmission line in IEEE Std.738 is a linear function of the conductor temperature.

[0085] It should be noted that although the transient heat balance equations described by equations (2) to (3) can accurately characterize the dynamic heat balance process of the transmission line, their calculation process is somewhat complex. From the analysis of equation (3), it can be seen that the conductor temperature shows an approximately exponential growth characteristic as the current increases, and its dynamic response speed is determined by the conductor thermal time constant. Taking the typical Drake-type steel-core aluminum stranded wire as an example, the thermal balance process usually reaches a steady state within 60 minutes, at which time the conductor temperature tends to be stable. Research shows that although there is a difference in the real-time current carrying capacity of the transmission line calculated based on the steady-state and transient heat balance equations, the difference is within the engineering allowable range.

[0086] Therefore, to reduce the computational complexity within the accuracy allowed, the steady-state heat balance equation is adopted according to IEEE Std. 738, as shown in Equation (12). Based on this, the real-time current carrying capacity limit Imax of the transmission line affected by the local operating ambient temperature, wind speed, etc. is calculated under the given maximum allowable conductor temperature Tb,max, as shown in Equation (13).

[0087]

[0088] It should be noted that obtaining the target parameters of the target power system and establishing a line thermal balance model based on these parameters can more accurately reflect the power system's operating status at different timescales, particularly regarding line temperature fluctuations, which is crucial for the stability and security of the power system. By establishing a line thermal balance model, line temperature fluctuations can be monitored in real time, thereby predicting and avoiding possible overheating and reducing the risk of power outages caused by line faults.

[0089] S102, calculating the global real-time transmission capacity of the target power system transmission line according to the output of the line heat balance model;

[0090] It should be noted that the above process introduces the maximum transmission capacity of a local line. The entire line may span multiple geographical areas with different temperatures. The transmission capacity of the entire line should be determined by the transmission capacity of the smallest local line.

[0091] In an embodiment of the present application, calculating the global real-time transmission capacity of the target power system transmission line according to the output of the line heat balance model includes:

[0092] Obtain local real-time transmission capacity in different areas of the transmission line based on the line heat balance model;

[0093] Obtain the minimum value of local real-time transmission capacity under different weather conditions;

[0094] The minimum value of the local real-time transmission capacity is used as the actual transmission capacity value of the transmission lines in different areas;

[0095] The actual transmission capacity values in all regions are integrated as the global real-time transmission capacity of the target power system transmission lines.

[0096] In an optional embodiment, the local line current carrying capacity limit at time t accounts for the line current carrying capacity limit under the reference environment proportion for:

[0097]

[0098] Where: and Represent the reference ambient temperature and reference ambient wind speed respectively.

[0099] Therefore, the transmission capacity of the entire transmission line is F max for:

[0100]

[0101] Where: Ξ is the set of different temperature zones spanned by the line; is the line transmission capacity under the baseline environment.

[0102] It should be noted that calculating the global real-time transmission capacity of the target power system's transmission lines based on the output of the line heat balance model can improve the accuracy and flexibility of power system scheduling. By accurately calculating the global real-time transmission capacity, the dispatch system can more rationally allocate power resources, avoiding line overloads and energy waste. This also helps improve the stability and reliability of the power system, ensuring stable power supply under various environmental conditions and meeting social electricity demand.

[0103] S103, establishing a day-ahead optimization scheduling model, where the day-ahead optimization scheduling model includes a first objective function and a first constraint condition set;

[0104] In an optional embodiment, the day-ahead optimization scheduling model can be solved using a genetic algorithm. As a heuristic search algorithm, the genetic algorithm can simulate natural selection and genetic mechanisms, finding the global optimal solution through iterative optimization. In the day-ahead optimization scheduling model, the genetic algorithm can be used to search for the optimal power scheduling solution—that is, one that minimizes power system operating costs and maximizes energy efficiency while satisfying various constraints. This approach can further enhance the intelligence level of power system scheduling and achieve more refined power management.

[0105] In an optional embodiment, the day-ahead dispatch model can also be solved using a particle swarm optimization algorithm. Particle swarm optimization is a swarm intelligence-based optimization algorithm that simulates the foraging behavior of a flock of birds, searching for the optimal solution through information sharing and collaboration among particles. In the day-ahead dispatch model, the particle swarm optimization algorithm can efficiently search for the optimal power dispatch strategy, ensuring that while meeting power system stability and security requirements, it maximizes the use of renewable energy, reduces carbon emissions, and achieves green, low-carbon power dispatch.

[0106] However, the above two methods cannot well fit the actual operating characteristics and future change trends of the target power system, which may lead to inaccuracy and infeasibility of the scheduling plan. Therefore, this application proposes the following.

[0107] In an embodiment of the present application, the first set of constraints includes a real-time transmission capacity constraint obtained from a global real-time transmission capacity;

[0108] In the embodiment of the present application, establishing a day-ahead optimization scheduling model includes:

[0109] Establish a prediction data curve based on target parameters;

[0110] The expression of the forecast data curve is used as the day-ahead optimization scheduling model;

[0111] The first objective function of the day-ahead optimization dispatch model is to minimize the sum of the power purchase cost, start-up and shutdown cost, standby cost, and wind curtailment penalty cost of conventional thermal power units and fast start-up and shutdown units.

[0112] In an optional embodiment, based on equations (1) to (15), after determining the line transmission capacity for the next day, a day-ahead optimization dispatch model for the power system is constructed. Taking the minimization of the total system operating cost as the first objective function, multiple cost factors for conventional thermal power units and rapid start-stop units are comprehensively considered, including: power purchase cost, unit start-up and shutdown costs, system backup capacity cost, and penalty costs for insufficient wind and solar energy consumption, as shown in equation (16).

[0113]

[0114]

[0115] Where: The superscripts "c" and "q" represent conventional thermal power units and rapid start-stop units, respectively. The cost calculation methods for the two types of units are the same. Here, conventional thermal power units are used as an example. and are the operating cost, reserve capacity cost, start-up and shutdown cost, and penalty cost of renewable energy abandonment of conventional thermal power units; Nc is the number of conventional thermal power units; ξ i,ζ i is the electricity purchase cost coefficient of conventional thermal power unit i; is the output of conventional thermal power unit i at time t; Δt is the scheduling time resolution of the day-ahead optimization scheduling stage, which is 1 hour; is the start / stop status of conventional thermal power unit i during period t, 1 indicates the unit is on, and 0 indicates the unit is off; represent the cost coefficients of positive and negative spinning reserves of conventional thermal power unit i respectively; and are the positive and negative spinning reserve capacities of conventional thermal power unit i at time t, respectively; and are the start-up and shutdown cost coefficients of conventional thermal power unit i; c r represents the energy abandonment cost coefficient; Indicates the actual output of the wind power / photovoltaic station at time t.

[0116] In an embodiment of the present application, the first constraint condition set includes line dynamic transmission capacity constraint, active power flow balance constraint, unit output upper and lower limit constraints, unit climbing constraint, unit start and stop status constraint and energy storage constraint.

[0117] In an optional embodiment, the day-ahead transmission capacity of the line and the operation of conventional thermal power units are used as constraints, and a day-ahead optimization scheduling model is constructed based on the predicted data curves of wind and solar load time series output, wind speed, and temperature. The specific first set of constraints is as follows:

[0118] (1) Dynamic transmission capacity constraints of lines.

[0119] |F ij,t |≤F ij,t,max (18)

[0120] Where: F ij,t 、F ij,t,max are respectively the active power flow and dynamic transmission capacity of line ij at time t, F ij,t,max It is determined by the above formulas (1) to (15) and will not be repeated here.

[0121] (2) Active power flow balance constraint.

[0122]

[0123] Where: To quickly start and stop the unit's output; and are the charging and discharging powers of the energy storage at node i; i Represents the collection of nodes connected to the node.

[0124] (3) Upper and lower limit constraints on unit output.

[0125]

[0126] Where: and are the upper and lower limits of the output of the conventional unit at node i respectively; and They are the upper and lower limits of the output of the fast start and stop unit at node i; It is the start and stop status of the rapid start and stop unit i in period t, 1 indicates the start state, and 0 indicates the shutdown state.

[0127] (4) Unit climbing constraints.

[0128]

[0129] Where: and They are the upper and lower limits of the ramp rate of conventional thermal power units respectively; and They are the upper and lower limits of the ramp rate of the fast start and stop unit respectively.

[0130] (5) Constraints on the start and stop status of the unit.

[0131]

[0132] Where: and They are respectively the start-up and shutdown actions of the conventional unit at time t, and the action time value is 1; and They are respectively the start-up and shutdown actions of the rapid start-up and shutdown group at time t, and the action time value is 1.

[0133] (6) Energy storage constraints

[0134]

[0135] In formulas (26) to (29), and are the upper and lower limits of the energy storage charging power at node i respectively; and are the upper and lower limits of energy storage discharge power at node i; E t,i is the energy storage capacity at node i; and are the energy storage charging and discharging efficiency; and They are the upper and lower limits of energy storage capacity respectively.

[0136] It should be noted that the establishment of a day-ahead optimal dispatch model, including the first objective function and the first set of constraints, fully considers the dynamic transmission capacity of the line, ensuring the stable operation of the power system over different time periods. By accurately predicting and optimizing line transmission capacity, problems such as overloads and voltage fluctuations can be effectively avoided, improving the reliability and security of the power system. Furthermore, the model can flexibly adjust dispatch strategies based on actual needs, achieving optimal allocation of power resources and economical operation.

[0137] S104, establishing an intraday optimization scheduling model, the intraday optimization scheduling model including a second objective function and a second constraint condition set;

[0138] In an embodiment of the present application, the second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

[0139] In the embodiment of the present application, establishing an intraday optimization scheduling model includes:

[0140] Establishing an update mechanism, the update mechanism is used to update the prediction data curve established according to the target parameters;

[0141] The expression of the forecast data curve including the updating mechanism is used as the intraday optimization scheduling model;

[0142] The second objective function of the intraday optimization scheduling model is to minimize the sum of the electricity purchase cost of conventional thermal power units, the electricity purchase cost of rapid start-up and shutdown units, the start-up and shutdown cost and the reserve cost, as well as the wind power curtailment penalty cost during the remaining period of the day.

[0143] In an embodiment of the present application, the intraday optimization scheduling model is used to determine the start and shutdown status of the rapid start and shutdown units and the spinning reserve capacity purchase plan, while updating the output plan of the conventional thermal power units.

[0144] In the embodiment of the present application, the boundary condition constraints include constraints related to the start-up and shutdown states of conventional thermal power units on the next day and constraints related to the spinning reserve capacity purchase plan.

[0145] In summary, the present application proposes a multi-time-scale collaborative optimization scheduling method for a power system, obtains the target parameters of the target power system, establishes a line thermal balance model based on the target parameters; calculates the global real-time transmission capacity of the target power system transmission line based on the output of the line thermal balance model; establishes a day-ahead optimization scheduling model, which includes a first objective function and a first set of constraints; establishes an intraday optimization scheduling model, which includes a second objective function and a second set of constraints; through the optimization scheduling of the two time scales of day-ahead and intraday, the present application realizes the fine control and optimization of the operating state of the power system. The calculation of the global real-time transmission capacity enables the scheduling model to fully consider the actual carrying capacity and thermal balance state of the transmission line, avoiding line damage or power outages caused by overload. At the same time, the setting of the first set of constraints and the second set of constraints ensures that the scheduling scheme achieves the improvement of the operating efficiency and stability of the power system under the premise of meeting multiple requirements such as safety, economy, and environmental protection.

[0146] Example 2: In a preferred embodiment, after determining the start-up and shutdown status of conventional thermal power units and the spinning reserve capacity purchase plan for the next day, the day-ahead dispatcher forms an intraday dispatch plan. The intraday optimization dispatch model aims to minimize the sum of the electricity purchase cost of conventional thermal power units, the electricity purchase cost of rapid start-up and shutdown units, the unit start-up and shutdown costs and the reserve capacity cost, as well as the penalty cost for curtailing renewable energy such as wind and solar power. The second objective function is as follows:

[0147]

[0148] The various costs in formula (30) are calculated in the same way as the day-ahead optimization scheduling model and will not be described here.

[0149] The forecast curves for wind and solar load time series output, wind speed, and temperature are updated every hour. The dynamic transmission capacity of the line is updated based on equations (1) to (15). An intraday optimization scheduling model is constructed. The second set of constraints is equations (18) to (23) and (25) to (29). The intraday scheduling is responsible for the start-up and shutdown status of the rapid start-up and shutdown units and the purchase plan of spinning reserve capacity, while also updating the output plan of conventional thermal power units.

[0150] The above-mentioned day-ahead optimization scheduling model and intraday optimization scheduling model are both mixed integer linear programming models, which can be efficiently solved using commercial solvers such as Gurobi.

[0151] Embodiment 3: This embodiment further provides a multi-time-scale collaborative optimization dispatching system for a power system, including:

[0152] A first model building module is used to obtain target parameters of the target power system and build a line heat balance model according to the target parameters;

[0153] The line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line;

[0154] a capacity determination module for calculating the global real-time transmission capacity of the target power system transmission line based on the output of the line heat balance model;

[0155] A second model building module is used to establish a day-ahead optimization scheduling model, where the day-ahead scheduling optimization model includes a first objective function and a first constraint condition set;

[0156] The first constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity;

[0157] A third model building module is used to establish an intraday optimization scheduling model, where the intraday optimization scheduling model includes a second objective function and a second constraint condition set;

[0158] The second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

[0159] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0160] This embodiment also provides an electronic device, which may be a terminal. The electronic device includes a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal via wired or wireless communication. The wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for multi-timescale collaborative optimization scheduling of a power system. The display of the electronic device may be a liquid crystal display or an electronic ink display. The input device of the electronic device may be a touch layer covering the display, or may be buttons, a trackball, or a touchpad provided on the electronic device housing, or may be an external keyboard, touchpad, or mouse.

[0161] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0162] Obtain target parameters of the target power system and establish a line heat balance model based on the target parameters;

[0163] The line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line;

[0164] Calculating the global real-time transmission capacity of the target power system transmission line based on the output of the line heat balance model;

[0165] Establishing a day-ahead optimization scheduling model, the day-ahead scheduling optimization model includes a first objective function and a first constraint condition set;

[0166] The first constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity;

[0167] Establishing an intraday optimization scheduling model, the intraday optimization scheduling model includes a second objective function and a second constraint condition set;

[0168] The second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

[0169] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application 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 application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and all of these should be included in the scope of the claims of the present application.

[0170] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0171] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0174] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0175] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A multi-time-scale collaborative optimization scheduling method for a power system, characterized in that: include: Acquiring target parameters of a target power system and establishing a line heat balance model according to the target parameters; The line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line; calculating the global real-time transmission capacity of the target power system transmission line based on the output of the line heat balance model; Establishing a day-ahead optimization scheduling model, wherein the day-ahead scheduling optimization model includes a first objective function and a first constraint condition set; The first set of constraints includes a real-time transmission capacity constraint obtained from a global real-time transmission capacity; Establishing an intraday optimization scheduling model, wherein the intraday optimization scheduling model includes a second objective function and a second constraint condition set; The second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

2. The multi-time-scale collaborative optimization scheduling method for a power system according to claim 1, characterized in that: Calculating the global real-time transmission capacity of the target power system transmission line according to the output of the line heat balance model includes: Obtaining local real-time transmission capacity in different areas of the transmission line according to the line heat balance model; Obtain the minimum value of local real-time transmission capacity under different weather conditions; Taking the minimum value of the local real-time transmission capacity as the actual transmission capacity value of the transmission lines in different areas; The actual transmission capacity values in all regions are integrated as the global real-time transmission capacity of the target power system transmission lines.

3. The multi-time-scale collaborative optimization scheduling method for a power system according to claim 2, characterized in that: The establishment of the day-ahead optimization scheduling model includes: Establishing a prediction data curve according to the target parameters; Using the expression of the forecast data curve as a day-ahead optimization scheduling model; The first objective function of the day-ahead optimization scheduling model is to minimize the sum of the power purchase cost, start-up and shutdown cost, standby cost, and wind curtailment penalty cost of conventional thermal power units and fast start-up and shutdown units.

4. A multi-time-scale collaborative optimization scheduling method for a power system according to claim 3, characterized in that: The establishment of the intraday optimization scheduling model includes: Establishing an update mechanism, the update mechanism is used to update the prediction data curve established according to the target parameter; The expression of the forecast data curve including the updating mechanism is used as the intraday optimization scheduling model; The second objective function of the intraday optimization scheduling model is to minimize the sum of the electricity purchase cost of conventional thermal power units, the electricity purchase cost of rapid start-up and shutdown units, the start-up and shutdown cost and the standby cost, and the wind power curtailment penalty cost during the remaining period of the day.

5. The multi-time-scale collaborative optimization scheduling method for a power system according to claim 4, characterized in that: The intraday optimization scheduling model is used to determine the start and stop status of the rapid start and stop units and the spinning reserve capacity purchase plan, while updating the output plan of the conventional thermal power units.

6. A multi-time-scale collaborative optimization scheduling method for a power system according to claim 5, characterized in that: The boundary condition constraints include constraints related to the start-up and shutdown states of conventional thermal power units on the next day and constraints related to the spinning reserve capacity purchase plan.

7. A multi-time-scale collaborative optimization scheduling method for a power system according to claim 6, characterized in that: The first constraint condition set includes line dynamic transmission capacity constraint, active power flow balance constraint, unit output upper and lower limit constraint, unit climbing constraint, unit start and stop state constraint and energy storage constraint.

8. A multi-time-scale collaborative optimization dispatching system for a power system, applying the method according to any one of claims 1 to 7, characterized in that: include: A first model building module is used to obtain target parameters of the target power system and build a line heat balance model according to the target parameters; The line heat balance model is used to calculate the local real-time transmission capacity of the target power system transmission line; a capacity determination module, configured to calculate the global real-time transmission capacity of the target power system transmission line based on the output of the line heat balance model; A second model building module is used to establish a day-ahead optimization scheduling model, wherein the day-ahead scheduling optimization model includes a first objective function and a first constraint condition set; The first set of constraints includes a real-time transmission capacity constraint obtained from a global real-time transmission capacity; A third model building module is used to establish an intraday optimization scheduling model, wherein the intraday optimization scheduling model includes a second objective function and a second constraint condition set; The second constraint condition set includes a real-time transmission capacity constraint obtained from the global real-time transmission capacity and a boundary condition constraint obtained by updating the intraday optimization scheduling model.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a multi-time-scale collaborative optimization scheduling method for a power system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-time-scale collaborative optimization scheduling method for a power system according to any one of claims 1 to 7 are implemented.

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