Power system multi-element resource scheduling method, system, device and storage medium

Abstract

The application belongs to the technical field of power systems, and discloses a power system multi-element resource scheduling method, system, device and storage medium, wherein the method comprises the following steps: setting constraint conditions of each link of power system multi-element resource scheduling, and limiting the change range between adjacent two scheduling time periods in the operation process of each link of multi-element resource scheduling, so as to establish a multi-element resource scheduling model of the power system; reconstructing the multi-element resource scheduling model by scene reduction, introduction of scheduling safety domain and real-time scheduling constraint, and establishing a multi-element resource scheduling model based on safety domain foresight; setting the safety domain by a fixed time window rolling foresight mode, forming a multi-element resource scheduling model based on safety domain foresight rolling, and performing multi-element resource scheduling of the power system. The application can fully mobilize various regulation resources, realize advanced optimal scheduling of multi-element resource collaborative interaction, better cope with the adequacy risk in system operation, and guarantee the safety of system operation.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, device and storage medium for multi-resource scheduling in power systems. Background Technology

[0002] The proportion of new energy sources such as wind and solar power in the power system continues to rise. However, due to the randomness and volatility of new energy sources, large-scale integration of new energy sources poses challenges to the power balance problem of the power system, especially after extreme events, which exacerbates the problem and places higher demands on the power system dispatching methods. To address this, scholars both domestically and internationally have conducted extensive research and proposed a series of dispatching methods oriented towards system adequacy. These methods are mainly based on stochastic optimization and robust optimization.

[0003] Power system dispatching facing sufficiency risks needs to achieve comprehensive feasibility while considering the unpredictability of dispatching decisions. However, existing technologies still have many shortcomings in addressing system sufficiency risks, such as:

[0004] 1. Scenario-based stochastic optimization methods require generating a large number of typical scenarios, and the safety and economy of scheduling are related to the number of scenarios. However, a large number of scenarios will bring a huge computational burden, which cannot meet the real-time operation requirements of the power system.

[0005] 2. An inherent problem with robust optimization is that it fails to consider the unpredictability of random variables (such as renewable energy output, line outages caused by extreme external environments, etc.) that affect the realization of the current decision period in subsequent decision periods. This fundamental flaw means that the scheduling, operation, and maintenance arrangements formulated by robust optimization may not meet the "extreme ramp-up scenarios" that may occur in the actual operation of the power system, thereby impairing the economy of the original scheduling and operation plan, and even leading to situations where the original plan fails to meet operational requirements. Summary of the Invention

[0006] This invention provides a method, system, device, and storage medium for multi-resource scheduling in power systems to address the problems of insufficient system operation security and robustness in existing technologies. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0007] According to a first aspect of the present invention, a method for multi-resource scheduling in a power system is provided, comprising:

[0008] To establish a multi-resource scheduling model for the power system, constraints are set for each stage of the multi-resource scheduling process, and the range of variation between two adjacent scheduling periods is limited.

[0009] Based on implicit decision rules, the multi-resource scheduling model is reconstructed by reducing scenarios, introducing scheduling security domains and real-time scheduling constraints, and a multi-resource scheduling model based on security domain look-ahead is established.

[0010] By setting the safety domain using a fixed-time-window rolling look-ahead approach, and updating the scope of the safety domain within the look-ahead period, a multi-resource scheduling model based on the rolling look-ahead of the safety domain is formed. This model is then used for multi-resource scheduling of the power system.

[0011] In one embodiment, the method further includes the steps of setting constraints on each stage of the multi-resource scheduling of the power system and limiting the range of variation between two adjacent scheduling periods during the operation of each stage of multi-resource scheduling, in order to establish a multi-resource scheduling model for the power system:

[0012] The multi-resource scheduling process includes at least one of the following constraints: new energy output constraints, electrochemical energy storage constraints, temperature-controlled load cluster constraints, and electric vehicle cluster constraints.

[0013] In one embodiment, the method further includes the steps of setting constraints on each stage of the multi-resource scheduling of the power system and limiting the range of variation between two adjacent scheduling periods during the operation of each stage of multi-resource scheduling, in order to establish a multi-resource scheduling model for the power system:

[0014] The output constraint of new energy sources is determined by the following formula:

[0015]

[0016]

[0017] in, The actual output of new energy sources obtained during the current time period. To maximize the contribution of new energy sources and These represent the upper and lower limits of the uncertain range for new energy output, respectively. Uncertain sets of boxes that contribute to new energy sources.

[0018] In one embodiment, the method, based on implicit decision rules, reconstructs the multi-resource scheduling model by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints. The steps for establishing a multi-resource scheduling model based on safety domain look-ahead further include:

[0019] Introducing a power safety domain to the output Reconstructing the constraints on new energy output:

[0020]

[0021]

[0022]

[0023] in, Let t be the output of the thermal power unit to be dispatched at time t. and These are the minimum technical output and the maximum output of a thermal power unit, respectively. This represents the maximum climbing power of the thermal power unit. and These are the lower and upper limits of the unit's output safety domain, respectively.

[0024] In one embodiment, the method further includes the steps of setting constraints on each stage of the multi-resource scheduling of the power system and limiting the range of variation between two adjacent scheduling periods during the operation of each stage of multi-resource scheduling, in order to establish a multi-resource scheduling model for the power system:

[0025] Electrochemical energy storage constraints include the constraints that simulate the operation of energy storage devices using the following formulas, and introduce auxiliary functions. To establish the relationship between charging and discharging power and the state of charge of energy storage devices:

[0026]

[0027]

[0028]

[0029] in, This is the upper limit of the charging / discharging power of energy storage devices. E represents the actual charging / discharging power of the energy storage devices during the current time period, obtained through scheduling. e,ini It is the initial state of charge of the energy storage device, E e and These are the lower and upper limits of the State of Charge (SOC) for energy storage, respectively. and These represent the charging efficiency and discharging efficiency of energy storage, respectively, where v refers to several moments before time t. It refers to the actual charging / discharging power at a certain moment before time t.

[0030] In one embodiment, the method, based on implicit decision rules, reconstructs the multi-resource scheduling model by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints. The steps for establishing a multi-resource scheduling model based on safety domain look-ahead further include:

[0031] Introducing charging and discharging safety domains for energy storage devices Reconstructing the constraints of electrochemical energy storage:

[0032]

[0033]

[0034]

[0035]

[0036] in, E represents the State of Charge (SOC) of the energy storage device to be dispatched at time t. e,t and These are the minimum and maximum SOC (State of Charge) levels for energy storage devices. and These represent the lower and upper limits of the charging and discharging safety domains for energy storage devices, respectively. This refers to the maximum charging and discharging power of the energy storage device.

[0037] In one embodiment, the method further includes the steps of setting constraints on each stage of the multi-resource scheduling of the power system and limiting the range of variation between two adjacent scheduling periods during the operation of each stage of multi-resource scheduling, in order to establish a multi-resource scheduling model for the power system:

[0038] The temperature-controlled load cluster constraint is modeled using a virtual energy storage model with the following formula:

[0039]

[0040]

[0041]

[0042]

[0043] in, Let be the equivalent energy of the virtual energy storage at time t. The injected power for virtual energy storage at time t. As the baseline load, C n R n Here are the thermodynamic parameters corresponding to the nth temperature-controlled load; T n,t T represents the temperature of the nth individual temperature-controlled load during time period t.set For the ideal ambient temperature; Q n,t For the nth individual unit temperature control load power, Let t be the ambient temperature at time t;

[0044] The equivalent energy of virtual energy storage is limited by the upper and lower limits of indoor temperature, and the constraints are as follows:

[0045]

[0046] in, T and These are the upper and lower limits of indoor temperature, respectively.

[0047] The injection power of virtual energy storage is limited by the power of the individual compressors of each temperature-controlled load, and the constraints are as follows:

[0048]

[0049] Where n is the number of temperature-controlled loads, Q max This is the maximum power of a single temperature-controlled load.

[0050] In one embodiment, the method, based on implicit decision rules, reconstructs the multi-resource scheduling model by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints. The steps for establishing a multi-resource scheduling model based on safety domain look-ahead further include:

[0051] Introducing a temperature safety domain to temperature-controlled load clusters Reconstruct the constraints of the temperature-controlled load cluster:

[0052]

[0053] make

[0054]

[0055]

[0056]

[0057] in, The SOC state of the energy storage device during the time period t to be determined by scheduling. and These represent the lower and upper limits of the temperature safety range for the temperature-controlled load cluster during time period t, respectively. and These represent the lower and upper limits of the charging and discharging safety domain for the converted temperature-controlled load virtual energy storage model during time period t.

[0058] In one embodiment, the method further includes the steps of setting constraints on each stage of the multi-resource scheduling of the power system and limiting the range of variation between two adjacent scheduling periods during the operation of each stage of multi-resource scheduling, in order to establish a multi-resource scheduling model for the power system:

[0059] The electric vehicle cluster constraint is modeled using a virtual energy storage model with the following formula:

[0060] The probability distribution of charging start time is represented as a piecewise normal distribution:

[0061]

[0062] Where T is the user's charging start time, μ T The mean of the start time for charging users, σ T The standard deviation of the user's charging start time. The variance of the user's charging start time;

[0063] The probability density function of the charging stop time follows a log-normal distribution, and its distribution function is expressed as:

[0064]

[0065] Among them, T s The user's charging start time, The average time to start charging for users. The standard deviation of the user's charging start time. The variance of the user's charging start time;

[0066] The charging power demand of electric vehicles is expressed in terms of the user's daily driving mileage as follows:

[0067] R = ω EV d

[0068] Where R is the charging power requirement, ω EV denoted as the average energy consumption coefficient of electric vehicles, and d represents the user's daily mileage.

[0069] By sampling the probability distributions of the charging start time, charging duration, and target SOC state of individual electric vehicles, a virtual energy storage model for each electric vehicle is obtained. The energy storage models of the individual electric vehicles in the cluster are then summed to obtain the virtual energy storage model of the electric vehicle cluster.

[0070]

[0071]

[0072] in The actual charging / discharging power of the electric vehicle cluster to be scheduled. The maximum discharge power of the electric vehicle cluster during time period t. Let m represent the maximum charging power of the electric vehicle cluster during time period t, where m refers to several time periods before time t. Refers to the actual charging / discharging power at a certain moment before time t. R t EV and R represents the minimum SOC capacity and maximum capacity of the virtual energy storage model of the electric vehicle cluster at time t, respectively. EV,ini The initial charge of the virtual energy storage is determined by an auxiliary function. Establish the relationship between charging and discharging power and the equivalent energy state of virtual energy storage:

[0073]

[0074] in, To improve the charging efficiency of electric vehicle clusters. The discharge efficiency of the electric vehicle cluster.

[0075] In one embodiment, the method, based on implicit decision rules, reconstructs the multi-resource scheduling model by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints. The steps for establishing a multi-resource scheduling model based on safety domain look-ahead further include:

[0076] Introducing an adjustable safety domain for electric vehicle clusters Reconstruct the constraints of the electric vehicle cluster:

[0077]

[0078]

[0079]

[0080] in, Let be the virtual energy storage capacity of the electric vehicle cluster to be scheduled during time period t. and These represent the lower and upper limits of the schedulable safety domain for the electric vehicle cluster during time period t, respectively.

[0081] In one embodiment, the method, based on implicit decision rules, reconstructs the multi-resource scheduling model by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints. The steps for establishing a multi-resource scheduling model based on safety domain look-ahead further include:

[0082] A DC power flow model is used as the system power flow constraint.

[0083] In one embodiment, the method, based on implicit decision rules, reconstructs the multi-resource scheduling model by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints. The steps for establishing a multi-resource scheduling model based on safety domain look-ahead further include:

[0084] The expected value of new energy output constraints is used as the basic scenario, and the upper limit and lower limit scenarios of new energy output constraints are used as the vertex scenarios. The intersection of the upper limit scenario and the lower limit scenario is used to form the reduced scenario set.

[0085] In one embodiment, the method further includes setting a safety domain using a fixed-time-window rolling look-ahead approach, updating the scope of the safety domain within the look-ahead period to form a multi-resource scheduling model based on the rolling look-ahead of the safety domain, and performing multi-resource scheduling of the power system using the multi-resource scheduling model based on the rolling look-ahead of the safety domain.

[0086] At time t, a safety region is defined for a rolling time window of length nΔt, and a unified optimization solution is performed to solve the scheduling problem. The scheduling result is executed only within a time interval Δt.

[0087] At time t+Δt, the rolling time window is moved forward by one time interval Δt. Within the rolling time window, the safety region is redefined and optimized.

[0088] Repeat the rolling process within the first time interval Δt until the optimized scheduling for all time intervals is completed.

[0089] According to a second aspect of the present invention, a multi-resource dispatching system for a power system is provided.

[0090] In one embodiment, a power system multi-resource dispatching system includes:

[0091] The basic model building module is used to set the constraints of each link in the multi-resource scheduling of the power system and limit the range of change between two adjacent scheduling periods during the operation of each link in the multi-resource scheduling, so as to establish a multi-resource scheduling model of the power system.

[0092] The forward-looking safety domain formulation module is used to reconstruct the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing scheduling safety domains and real-time scheduling constraints, to formulate scheduling safety domains, and to establish a multi-resource scheduling model based on forward-looking safety domains.

[0093] The rolling scheduling module is used to set the safety domain by rolling forward using a fixed time window, and to update the range of the safety domain within the forward period, forming a multi-resource scheduling model based on rolling forward of the safety domain. The multi-resource scheduling of the power system is then carried out through the multi-resource scheduling model based on rolling forward of the safety domain.

[0094] According to a third aspect of the present invention, a computer device is provided.

[0095] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method as described in the first aspect.

[0096] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0097] In some embodiments, a computer-readable storage medium stores a computer program; the computer program, when executed by a processor, implements the steps of the method as described in the first aspect.

[0098] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0099] This paper proposes the concept of a scheduling safety domain for multiple regulatory resources. Addressing the unpredictability of new energy output, a multi-resource scheduling model based on a look-ahead rolling safety domain is established. The model is transformed into a mixed-integer linear programming problem using implicit decision rules, enabling it to be solved by commonly used commercial solvers. This scheduling strategy, through look-ahead rolling of the safety domain, incorporates the unpredictability of random variables affecting the next decision period from the current one. This allows for comprehensive feasibility while considering the unpredictability of scheduling decisions, making the scheduling decisions more closely aligned with actual operational requirements. This strategy can fully mobilize various regulatory resources to achieve proactive optimization scheduling through collaborative interaction among multiple resources, effectively addressing sufficiency risks in system operation and ensuring system safety.

[0100] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0101] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0102] Figure 1 This is a flowchart of the power system multi-resource scheduling method of the present invention;

[0103] Figure 2 This is a schematic diagram of the multi-stage decision-making process of the power system multi-resource scheduling method of the present invention;

[0104] Figure 3 This is a schematic diagram illustrating the reconstructed formation method of the multi-resource scheduling model of the present invention;

[0105] Figure 4This is a schematic diagram of dynamic rolling optimization scheduling for the multi-resource scheduling method of the power system of the present invention;

[0106] Figure 5 This is a structural diagram of the multi-resource dispatching system for power systems according to the present invention;

[0107] Figure 6 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment of the present invention. Detailed Implementation

[0108] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Throughout this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0109] In this document, unless otherwise stated, the term "multiple" means two or more.

[0110] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0111] Figure 1 A flowchart of the power system multi-resource scheduling method of the present invention is shown, as follows: Figure 1 As shown:

[0112] S1: Set constraints for each stage of the multi-resource scheduling of the power system and limit the range of change between two adjacent scheduling periods during the operation of each stage of multi-resource scheduling, so as to establish a multi-resource scheduling model for the power system.

[0113] In some embodiments of the present invention, the various stages of multi-resource scheduling include at least one of new energy output constraints, electrochemical energy storage constraints, temperature-controlled load cluster constraints, and electric vehicle cluster constraints.

[0114] In practical implementation, a constraint model for new energy output is constructed:

[0115]

[0116]

[0117] in, The actual output of new energy sources obtained during the current time period. To maximize the contribution of new energy sources and Let X represent the upper and lower limits of the uncertainty interval of new energy output, respectively, and let X be the box-shaped uncertainty set of new energy output.

[0118] In practical implementation, further electrochemical energy storage constraint model construction is required:

[0119] Electrochemical energy storage constraints include the constraints that simulate the operation of energy storage devices using the following formulas, and introduce auxiliary functions. To establish the relationship between charging and discharging power and the state of charge of energy storage devices:

[0120]

[0121]

[0122]

[0123] in, This is the upper limit of the charging / discharging power of energy storage devices. E represents the actual charging / discharging power of the energy storage devices during the current time period, obtained through scheduling. e,ini It is the initial state of charge of the energy storage device, E e and E e These are the lower and upper limits of the State of Charge (SOC) for energy storage, respectively. and Let represent the charging efficiency and discharging efficiency of energy storage, respectively, and v refer to several moments before time t. It refers to the actual charging / discharging power at a certain moment before time t.

[0124] In the specific implementation, further constraints for the temperature-controlled load cluster are constructed:

[0125] Specifically, temperature-controlled loads have a relatively large thermodynamic time constant, and adjustments to the load's power consumption will not cause sudden temperature changes. Therefore, the load power can be adjusted reasonably while keeping the temperature within the allowable range.

[0126] For single-unit temperature-controlled loads, a first-order thermodynamic model is commonly used for description:

[0127]

[0128] in This indicates the indoor temperature for the next time period. Indicates the outdoor ambient temperature for the next time period; Δt represents the indoor ambient temperature at the current time; Δt represents the time step between adjacent time periods; R, C, and Q are all thermodynamic parameters.

[0129] Because the number of parameters in this model increases rapidly with the number of temperature-controlled loads, it becomes difficult to solve. Therefore, for temperature-controlled load clusters, a virtual energy storage model is used for lumped modeling. Assuming that the indoor temperature of each temperature-controlled load is consistent, the overall dynamic characteristics can be obtained:

[0130] The temperature-controlled load cluster constraint is modeled using a virtual energy storage model with the following formula:

[0131]

[0132]

[0133]

[0134]

[0135] in, Let be the equivalent energy of the virtual energy storage at time t. The injected power for virtual energy storage at time t. As the baseline load, C n R n Here are the thermodynamic parameters corresponding to the nth temperature-controlled load; T n,t T represents the temperature of the nth individual temperature-controlled load during time period t. set For the ideal ambient temperature; Q n,t For the nth individual unit temperature control load power, Let t be the ambient temperature at time t;

[0136] The equivalent energy of virtual energy storage is limited by the upper and lower limits of indoor temperature, and the constraints are as follows:

[0137]

[0138] in, T and These are the upper and lower limits of indoor temperature, respectively.

[0139] The injection power of virtual energy storage is limited by the power of the individual compressors of each temperature-controlled load, and the constraints are as follows:

[0140]

[0141] Where n is the number of temperature-controlled loads, Q max This is the maximum power of a single temperature-controlled load.

[0142] In the specific implementation, further constraints for electric vehicle clusters are constructed:

[0143] Specifically, for electric vehicle clusters, a virtual energy storage model is used for lumped modeling.

[0144] Specifically, existing research indicates that the probability distribution of a user's daily driving mileage can be approximately represented by a log-normal distribution:

[0145]

[0146] Where d represents the user's daily mileage; μ d α represents the average daily mileage driven by the user. d The standard deviation of the user's daily mileage; This represents the variance of the user's daily mileage.

[0147] The probability distribution of charging start time is represented as a piecewise normal distribution:

[0148]

[0149] Where T is the user's charging start time, μ T The mean of the start time for charging users, σ T The standard deviation of the user's charging start time. The variance of the user's charging start time;

[0150] The probability density function of the charging stop time follows a log-normal distribution, and its distribution function is expressed as:

[0151]

[0152] Among them, T s The user's charging start time, The average time to start charging for users. The standard deviation of the user's charging start time. The variance of the user's charging start time;

[0153] The charging power demand of electric vehicles is expressed in terms of the user's daily driving mileage as follows:

[0154] R = ω EV d (16)

[0155] Where R is the charging power requirement, ω EV denoted as the average energy consumption coefficient of electric vehicles, and d represents the user's daily mileage.

[0156] By sampling the probability distributions of the charging start time, charging duration, and target SOC state of individual electric vehicles, a virtual energy storage model for each electric vehicle is obtained. The energy storage models of the individual electric vehicles in the cluster are then summed to obtain the virtual energy storage model of the electric vehicle cluster.

[0157]

[0158]

[0159] in The actual charging / discharging power of the electric vehicle cluster to be scheduled. The maximum discharge power of the electric vehicle cluster during time period t. Let m represent the maximum charging power of the electric vehicle cluster during time period t, where m refers to several time periods before time t. Refers to the actual charging / discharging power at a certain moment before time t. R t EV and R represents the minimum SOC capacity and maximum capacity of the virtual energy storage model of the electric vehicle cluster at time t, respectively. EV,ini The initial charge of the virtual energy storage is determined by an auxiliary function. Establish the relationship between charging and discharging power and the equivalent energy state of virtual energy storage:

[0160]

[0161] in, To improve the charging efficiency of electric vehicle clusters. The discharge efficiency of the electric vehicle cluster.

[0162] In some embodiments of the present invention, when scheduling the aforementioned flexible resources, the information in the scheduling model can be divided into two categories: the first category is the upper and lower limits of the new energy output range. and The first type can be determined at the start of scheduling; the second type is the uncertainty that occurs successively during the scheduling process, namely the actual output of new energy sources. Therefore, the scheduling model is a multi-stage decision-making process, such as... Figure 2 As shown. The complete decision-making process includes the decisions made in each scheduling period, that is, the thermal power output, new energy output, and energy storage charging and discharging status determined by optimization at each scheduling time.

[0163] Please continue reading Figure 1 :

[0164] S2: Based on implicit decision rules, the multi-resource scheduling model is reconstructed by reducing scenarios, introducing scheduling security domains and real-time scheduling constraints, and establishing a multi-resource scheduling model based on security domain look-ahead.

[0165] In practical implementation, due to the randomness of new energy output, the scheduling model for the aforementioned flexible resources is a multi-stage stochastic programming problem with unexpected constraints, which cannot be solved using common commercial solvers. Based on implicit decision rules, and through scenario reduction, the introduction of a scheduling safety domain, and real-time scheduling update constraints, the established multivariate resource scheduling model based on safety domain look-ahead is a mixed-integer linear programming problem. The reconstruction method of the multivariate resource scheduling model based on safety domain look-ahead proposed in this invention is as follows: Figure 3 As shown.

[0166] In practical implementation, the power output is introduced into the power output safety domain. Reconstructing the constraints on new energy output:

[0167]

[0168]

[0169]

[0170] in, Let t be the output of the thermal power unit to be dispatched at time t. and These are the minimum technical output and the maximum output of a thermal power unit, respectively. This represents the maximum climbing power of the thermal power unit. and These are the lower and upper limits of the unit's output safety domain, respectively.

[0171] In practical implementation, charging and discharging safety domains are introduced for energy storage devices. Reconstructing the constraints of electrochemical energy storage:

[0172]

[0173]

[0174]

[0175]

[0176] in, E represents the State of Charge (SOC) of the energy storage device to be dispatched at time t. e,t and E e,tThese are the minimum and maximum SOC (State of Charge) levels for energy storage devices. and These represent the lower and upper limits of the charging and discharging safety domains for energy storage devices, respectively. This refers to the maximum charging and discharging power of the energy storage device.

[0177] In practical implementation, a temperature safety domain is introduced for temperature-controlled load clusters. Reconstruct the constraints of the temperature-controlled load cluster:

[0178]

[0179] make

[0180]

[0181]

[0182]

[0183] in, The SOC state of the energy storage device during the time period t to be determined by scheduling. and These represent the lower and upper limits of the temperature safety range for the temperature-controlled load cluster during time period t, respectively. and These represent the lower and upper limits of the charging and discharging safety domain for the converted temperature-controlled load virtual energy storage model during time period t.

[0184] In practical implementation, an adjustable safety domain is introduced for electric vehicle clusters. Reconstruct the constraints of the electric vehicle cluster:

[0185]

[0186]

[0187]

[0188] in, Let be the virtual energy storage capacity of the electric vehicle cluster to be scheduled during time period t. and These represent the lower and upper limits of the schedulable safety domain for the electric vehicle cluster during time period t, respectively.

[0189] In some embodiments of the present invention, for the current scheduling period, the decision depends on realized uncertainties, namely the decision information of the current scheduling period and the decision scheme of the previous scheduling period, thereby transforming the unit and energy storage operation constraints introduced into the safety domain into the following form:

[0190]

[0191]

[0192]

[0193]

[0194] in, These are the lower and upper limits of the safety domain for thermal power units, energy storage devices, temperature-controlled load clusters, and electric vehicle clusters, respectively, determined based on realized uncertainties.

[0195] For constraints between different scheduling periods, the update rule is to replace the safe operating area variable with the actual scheduling scheme of the previous scheduling period, and the specific form is as follows:

[0196]

[0197]

[0198]

[0199]

[0200] For electric vehicle clusters and temperature-controlled load clusters, there are similar forms to equations (40) to (41), which will not be elaborated here.

[0201] In some embodiments of the present invention, since the multi-resource scheduling strategy based on security domain forward rolling proposed in the present invention is mainly aimed at large power grids, the DC power flow model is selected as the system power flow constraint.

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208] Where π(j) is the set of paths originating from node j, δ j Let P be the set of routes ending at node j. l P represents the active power flowing through line l. G,j For the output of the generator located at node j, PL,j For the load of node j, B ij Let be the susceptance of the line between node i and node j. Let q be the capacity of line l. l Let θ represent the state of line l. i This represents the phase angle of node i. and P represents the minimum and maximum output of generator g, respectively; shed,j Reduce the load power at node j; N b N is the set of system nodes; l For the system circuit set; N g For the system's generator set set; θ l,i and θ l,j The phase angles of the nodes at both ends of line l are represented; constraint (40) is a power balance constraint; constraint (41) represents the relationship between the power flowing through the line and the phase angles at both ends of the line; constraint (42) restricts the power flowing through the line; constraint (43) restricts the load shedding amount from being greater than the load amount of the node; constraint (46) restricts the generator output; and constraint (47) restricts the phase angle difference between the two ends of the line.

[0209] In some embodiments of the present invention, to ensure the feasibility of the scheduling scheme across all scenarios, basic scenarios and vertex scenarios must be selected to characterize the scenario range. The selection of basic scenarios is based on economic considerations, while vertex scenarios are chosen to fully represent the set of uncertainties. Therefore, the expected value of new energy output is selected as the basic scenario, and the upper limit scenario and the lower limit scenario of new energy output are selected as vertex scenarios. The intersection of these two scenarios constitutes the reduced scenario set.

[0210] Based on the selected scene set The original objective function can be transformed into the following form:

[0211]

[0212] in, The weight coefficient C for each scenario T,t C C,t C E,t These represent the system's thermal power generation cost, renewable energy generation cost, and energy storage (including virtual energy storage) operating cost, respectively, under the corresponding scenarios; P C,t and P LS,t These represent the power of renewable energy curtailment and the power of load reduction, respectively; K1 and K2 represent the cost of renewable energy curtailment per unit power and the cost of load reduction per unit power, respectively; ΔT represents the dispatch period.

[0213] In this embodiment, the established multivariate resource scheduling model based on security domain look-ahead is a mixed-integer linear programming problem, which can be solved using commonly used commercial solvers.

[0214] Please continue reading Figure 1 :

[0215] S3: The safety domain is set by a fixed time window rolling look-ahead method, and the range of the safety domain within the look-ahead period is updated on a rolling basis to form a multi-resource scheduling model based on the rolling look-ahead of the safety domain. The multi-resource scheduling of the power system is carried out through the multi-resource scheduling model based on the rolling look-ahead of the safety domain.

[0216] In practical implementation, the aforementioned scheduling model is a multi-time interval optimization problem, requiring a rolling approach to solve it over consecutive scheduling time intervals. The dynamic rolling optimization scheduling method is as follows: Figure 3 As shown. At time t, a safety region is defined for a rolling time window of length nΔt, and a unified optimization solution is performed to solve the scheduling problem, but the scheduling result is executed only within one time interval Δt. At time t+Δt, the rolling window moves forward by one time interval Δt. At this time, a new safety region is defined within the rolling time window, and an optimization solution is performed, again executing the scheme within the first time interval Δt. This rolling process is repeated until the optimized scheduling for all time intervals is completed.

[0217] In summary, this invention first establishes a mathematical model of multiple schedulable resources in a power system; secondly, based on implicit decision rules, it establishes a multi-resource scheduling model based on a safety domain-based forward rolling mechanism by reducing scenarios and introducing a scheduling safety domain. With the safety domain as an operational constraint, it formulates a scheduling strategy for the current time period in response to potential uncertainties; finally, it describes the rolling method of the scheduling safety domain, which ensures the safety of system operation through rolling scheduling.

[0218] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0219] Please see Figure 5 One embodiment of the present invention provides a multi-resource dispatching system for power systems, comprising:

[0220] The basic model construction module 10 is used to set the constraints of each link of the multi-resource scheduling of the power system and limit the range of change between two adjacent scheduling periods during the operation of each link of the multi-resource scheduling, so as to establish a multi-resource scheduling model of the power system.

[0221] The forward-looking safety domain formulation module 20 is used to reconstruct the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing scheduling safety domains and real-time scheduling constraints, to formulate scheduling safety domains, and to establish a multi-resource scheduling model based on forward-looking safety domains.

[0222] The rolling scheduling module 30 is used to set the safety domain through a fixed time window rolling look-ahead method, and to update the range of the safety domain within the look-ahead period in a rolling manner, forming a multi-resource scheduling model based on the rolling look-ahead of the safety domain, and to perform multi-resource scheduling of the power system through the multi-resource scheduling model based on the rolling look-ahead of the safety domain.

[0223] For specific limitations regarding the aforementioned power system multi-resource dispatching system, please refer to the limitations of the power system multi-resource dispatching method mentioned above, which will not be repeated here. Each module in the aforementioned power system multi-resource dispatching system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0224] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0225] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer device to which the present invention is applied. Specifically, the computer device may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0226] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the method embodiments described above.

[0227] 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, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0228] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for multi-resource scheduling in a power system, characterized in that, include: Constraints are set for each stage of the multi-resource scheduling of the power system, and the range of variation between two adjacent scheduling periods during the operation of each stage of the multi-resource scheduling is limited, so as to establish a multi-resource scheduling model of the power system. Each stage of the multi-resource scheduling includes at least one of the following: new energy output constraints, electrochemical energy storage constraints, temperature-controlled load cluster constraints, and electric vehicle cluster constraints. The constraint on the output of the new energy source is determined by the following formula: in, The actual output of new energy sources obtained during the current time period. To maximize the contribution of new energy sources and These represent the upper and lower limits of the uncertain range for new energy output, respectively. Uncertain sets of boxes that contribute to new energy sources; The electrochemical energy storage constraints include the constraints for simulating the operation of the energy storage device using the following formula, and introduce auxiliary functions. To establish the relationship between charging and discharging power and the state of charge of energy storage devices: in, This is the upper limit of the charging / discharging power of energy storage devices. The actual charging / discharging power of the energy storage devices during the current time period is obtained through scheduling. It is the initial state of charge of the energy storage device. and These represent the lower and upper limits of the State of Charge (SOC) for energy storage, respectively. and These represent the charging efficiency and discharging efficiency of energy storage, respectively. Refers to several moments before time t. Refers to the actual charging / discharging power at a certain moment before time t; The temperature-controlled load cluster constraint is modeled using a virtual energy storage model with the following formula: in, Let be the equivalent energy of the virtual energy storage at time t. The injected power for virtual energy storage at time t. As the baseline load, , For the corresponding thermodynamic parameters of the nth temperature-controlled load; Let be the temperature of the nth individual temperature-controlled load during time period t; For the ideal ambient temperature; For the nth individual unit temperature control load power, Let t be the ambient temperature at time t; The equivalent energy of virtual energy storage is limited by the upper and lower limits of indoor temperature, and the constraints are as follows: in, and These are the upper and lower limits of indoor temperature, respectively. The injection power of virtual energy storage is limited by the power of the individual compressors of each temperature-controlled load, and the constraints are as follows: in, For the number of temperature-controlled loads, This is the maximum power of a single temperature-controlled load. The electric vehicle cluster constraint is modeled using a virtual energy storage model with the following formula: The probability distribution of charging start time is represented as a piecewise normal distribution: Where T represents the user's charging start time. The average time to start charging for users. The standard deviation of the user's charging start time. The variance of the user's charging start time; The probability density function of the charging stop time follows a log-normal distribution, and its distribution function is expressed as: in, The user's charging start time, The average time to start charging for users. The standard deviation of the user's charging start time. The variance of the user's charging start time; The charging power demand of electric vehicles is expressed in terms of the user's daily driving mileage as follows: in, To meet charging power requirements, denoted as the average energy consumption coefficient of electric vehicles, and d represents the user's daily mileage. By sampling the probability distributions of the charging start time, charging duration, and target SOC state of individual electric vehicles, a virtual energy storage model for each electric vehicle is obtained. The energy storage models of the individual electric vehicles in the cluster are then summed to obtain the virtual energy storage model of the electric vehicle cluster. in The actual charging / discharging power of the electric vehicle cluster to be scheduled. The maximum discharge power of the electric vehicle cluster during time period t. Let m represent the maximum charging power of the electric vehicle cluster during time period t, where m refers to several time periods before time t. Refers to the actual charging / discharging power at a certain moment before time t. and These represent the minimum and maximum SOC capacity of the virtual energy storage model for the electric vehicle cluster at time t, respectively. The initial charge of the virtual energy storage is determined by an auxiliary function. Establish the relationship between charging and discharging power and the equivalent energy state of virtual energy storage: in, To improve the charging efficiency of electric vehicle clusters. The discharge efficiency of the electric vehicle cluster; Based on implicit decision rules, the multi-resource scheduling model is reconstructed by reducing scenarios, introducing scheduling security domains and real-time scheduling constraints, and a multi-resource scheduling model based on security domain look-ahead is established. By setting a safety domain using a fixed-time-window rolling look-ahead method, and rolling updates the range of the safety domain within the look-ahead period, a multi-resource scheduling model based on the rolling look-ahead of the safety domain is formed, and the multi-resource scheduling of the power system is performed through the multi-resource scheduling model based on the rolling look-ahead of the safety domain.

2. The power system multi-resource dispatching method according to claim 1, characterized in that, The steps of reconstructing the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints, and establishing a multi-resource scheduling model based on safety domain look-ahead, further include: Introducing a power safety domain for power output [ The power output constraints of the new energy source will be reconstructed: in, Let be the output of the thermal power unit to be dispatched at time t. and These are the minimum technical output and the maximum output of a thermal power unit, respectively. This represents the maximum ramping power of the thermal power unit. and These are the lower and upper limits of the unit's output safety domain, respectively.

3. The power system multi-resource dispatching method according to claim 1, characterized in that, The steps of reconstructing the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints, and establishing a multi-resource scheduling model based on safety domain look-ahead, further include: Introducing charging and discharging safety domains for energy storage devices The electrochemical energy storage constraint is reconstructed as follows: in, Let SOC state of the energy storage device to be scheduled be determined at time t. and These are the minimum and maximum SOC (State of Charge) levels for energy storage devices. and These represent the lower and upper limits of the charging and discharging safety domains for energy storage devices, respectively. This refers to the maximum charging and discharging power of the energy storage device.

4. The power system multi-resource dispatching method according to claim 1, characterized in that, The steps of reconstructing the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints, and establishing a multi-resource scheduling model based on safety domain look-ahead, further include: Introducing a temperature safety domain for temperature-controlled load clusters [ The constraints of the temperature-controlled load cluster will be reconstructed. make , : in, The virtual energy storage SOC state is to be determined for time period t. and These represent the lower and upper limits of the temperature safety range for the temperature-controlled load cluster during time period t, respectively. and These represent the lower and upper limits of the charging and discharging safety domain for the converted temperature-controlled load virtual energy storage model during time period t.

5. The power system multi-resource dispatching method according to claim 1, characterized in that, The steps of reconstructing the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints, and establishing a multi-resource scheduling model based on safety domain look-ahead, further include: Introducing an adjustable safety domain for electric vehicle clusters The constraints of the electric vehicle cluster are reconstructed as follows: in, Let be the virtual energy storage capacity of the electric vehicle cluster to be scheduled during time period t. and These represent the lower and upper limits of the schedulable safety domain for the electric vehicle cluster during time period t, respectively.

6. The power system multi-resource dispatching method according to any one of claims 1-5, characterized in that, The steps of reconstructing the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints, and establishing a multi-resource scheduling model based on safety domain look-ahead, further include: A DC power flow model is used as the system power flow constraint.

7. The power system multi-resource dispatching method according to claim 6, characterized in that, The steps of reconstructing the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing a scheduling safety domain, and incorporating real-time scheduling constraints, and establishing a multi-resource scheduling model based on safety domain look-ahead, further include: The expected value of the new energy output constraint is used as the basic scenario, and the upper limit scenario and the lower limit scenario of the new energy output constraint are used as the vertex scenarios. The intersection of the upper limit scenario and the lower limit scenario is used to form the reduced scenario set.

8. The power system multi-resource dispatching method according to claim 7, characterized in that, The step of setting a safety domain through a fixed-time-window rolling look-ahead method, updating the range of the safety domain within the look-ahead period, forming a multi-resource scheduling model based on the rolling look-ahead of the safety domain, and performing multi-resource scheduling of the power system through the multi-resource scheduling model based on the rolling look-ahead of the safety domain further includes: At time t, a safety region is defined for a rolling time window of length nΔt, and a unified optimization solution is performed to solve the scheduling problem. The scheduling result is executed only within a time interval Δt. At time t+Δt, the rolling time window is moved forward by one time interval Δt, and the safety region is redefined within the rolling time window and optimized. Repeat the rolling process within the first time interval Δt until the optimized scheduling for all time intervals is completed.

9. A multi-resource dispatching system for a power system, characterized in that, include: The basic model construction module is used to set the constraints of each link in the multi-resource scheduling of the power system and limit the range of change between two adjacent scheduling periods during the operation of each link in the multi-resource scheduling, so as to establish a multi-resource scheduling model of the power system. Each link in the multi-resource scheduling includes at least one of the following: new energy output constraints, electrochemical energy storage constraints, temperature-controlled load cluster constraints, and electric vehicle cluster constraints. The constraint on the output of the new energy source is determined by the following formula: in, The actual output of new energy sources obtained during the current time period. To maximize the contribution of new energy sources and These represent the upper and lower limits of the uncertain range for new energy output, respectively. Uncertain sets of boxes that contribute to new energy sources; The electrochemical energy storage constraints include the constraints for simulating the operation of the energy storage device using the following formula, and introduce auxiliary functions. To establish the relationship between charging and discharging power and the state of charge of energy storage devices: in, This is the upper limit of the charging / discharging power of energy storage devices. The actual charging / discharging power of the energy storage devices during the current time period is obtained through scheduling. It is the initial state of charge of the energy storage device. and These represent the lower and upper limits of the State of Charge (SOC) for energy storage, respectively. and These represent the charging efficiency and discharging efficiency of energy storage, respectively. Refers to several moments before time t. Refers to the actual charging / discharging power at a certain moment before time t; The temperature-controlled load cluster constraint is modeled using a virtual energy storage model with the following formula: in, Let be the equivalent energy of the virtual energy storage at time t. The injected power for virtual energy storage at time t. As the baseline load, , For the corresponding thermodynamic parameters of the nth temperature-controlled load; Let be the temperature of the nth individual temperature-controlled load during time period t; For the ideal ambient temperature; For the nth individual unit temperature control load power, Let t be the ambient temperature at time t; The equivalent energy of virtual energy storage is limited by the upper and lower limits of indoor temperature, and the constraints are as follows: in, and These are the upper and lower limits of indoor temperature, respectively. The injection power of virtual energy storage is limited by the power of the individual compressors of each temperature-controlled load, and the constraints are as follows: in, For the number of temperature-controlled loads, This is the maximum power of a single temperature-controlled load. The electric vehicle cluster constraint is modeled using a virtual energy storage model with the following formula: The probability distribution of charging start time is represented as a piecewise normal distribution: Where T represents the user's charging start time. The average time to start charging for users. The standard deviation of the user's charging start time. The variance of the user's charging start time; The probability density function of the charging stop time follows a log-normal distribution, and its distribution function is expressed as: in, The user's charging start time, The average time to start charging for users. The standard deviation of the user's charging start time. The variance of the user's charging start time; The charging power demand of electric vehicles is expressed in terms of the user's daily driving mileage as follows: in, To meet charging power requirements, denoted as the average energy consumption coefficient of electric vehicles, and d represents the user's daily mileage. By sampling the probability distributions of the charging start time, charging duration, and target SOC state of individual electric vehicles, a virtual energy storage model for each electric vehicle is obtained. The energy storage models of the individual electric vehicles in the cluster are then summed to obtain the virtual energy storage model of the electric vehicle cluster. in The actual charging / discharging power of the electric vehicle cluster to be scheduled. The maximum discharge power of the electric vehicle cluster during time period t. Let m represent the maximum charging power of the electric vehicle cluster during time period t, where m refers to several time periods before time t. Refers to the actual charging / discharging power at a certain moment before time t. and These represent the minimum and maximum SOC capacity of the virtual energy storage model for the electric vehicle cluster at time t, respectively. The initial charge of the virtual energy storage is determined by an auxiliary function. Establish the relationship between charging and discharging power and the equivalent energy state of virtual energy storage: in, To improve the charging efficiency of electric vehicle clusters. The discharge efficiency of the electric vehicle cluster; The forward-looking safety domain formulation module is used to reconstruct the multi-resource scheduling model based on implicit decision rules by reducing scenarios, introducing scheduling safety domains and real-time scheduling constraints, to formulate scheduling safety domains, and to establish a multi-resource scheduling model based on forward-looking safety domains. The rolling scheduling module is used to set the safety domain by rolling the look-ahead method with a fixed time window, and to update the range of the safety domain within the look-ahead period to form a multi-resource scheduling model based on rolling the look-ahead of the safety domain. The multi-resource scheduling of the power system is then performed through the multi-resource scheduling model based on rolling the look-ahead of the safety domain.

10. The power system multi-resource dispatching system according to claim 9, characterized in that, The forward-looking security domain designation module also includes: Introducing a power safety domain for power output [ The power output constraints of the new energy source will be reconstructed: in, Let be the output of the thermal power unit to be dispatched at time t. and These are the minimum technical output and the maximum output of a thermal power unit, respectively. This represents the maximum ramping power of the thermal power unit. and These are the lower and upper limits of the unit's output safety domain, respectively.

11. The power system multi-resource dispatching system according to claim 9, characterized in that, The forward-looking security domain designation module also includes: Introducing charging and discharging safety domains for energy storage devices The electrochemical energy storage constraint is reconstructed as follows: in, Let SOC state of the energy storage device to be scheduled be determined at time t. and These are the minimum and maximum SOC (State of Charge) levels for energy storage devices. and These represent the lower and upper limits of the charging and discharging safety domains for energy storage devices, respectively. This refers to the maximum charging and discharging power of the energy storage device.

12. The power system multi-resource dispatching system according to claim 9, characterized in that, The forward-looking security domain designation module also includes: Introducing a temperature safety domain for temperature-controlled load clusters [ The constraints of the temperature-controlled load cluster will be reconstructed. make , : in, The virtual energy storage SOC state is to be determined for time period t. and These represent the lower and upper limits of the temperature safety range for the temperature-controlled load cluster during time period t, respectively. and These represent the lower and upper limits of the charging and discharging safety domain for the converted temperature-controlled load virtual energy storage model during time period t.

13. The power system multi-resource dispatching system according to claim 9, characterized in that, The forward-looking security domain designation module also includes: Introducing an adjustable safety domain for electric vehicle clusters The constraints of the electric vehicle cluster are reconstructed as follows: in, Let be the virtual energy storage capacity of the electric vehicle cluster to be scheduled during time period t. and These represent the lower and upper limits of the schedulable safety domain for the electric vehicle cluster during time period t, respectively.

14. The power system multi-resource dispatching system according to any one of claims 9-13, characterized in that, The forward-looking security domain designation module also includes: A DC power flow model is used as the system power flow constraint.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

16. A computer-readable storage medium, characterized in that, It stores a computer program thereon; when the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

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