Power grid prevention-emergency collaborative control method and system considering dynamic allocation of resources

Abstract

The application belongs to the technical field of power grid operation risk prevention and control, and discloses a power grid prevention-emergency collaborative control method and system considering resource dynamic allocation, which comprises the following steps: determining a prevention control risk boundary and an emergency control risk boundary respectively; on the basis of the prevention control risk boundary and the emergency control risk boundary, a prevention-emergency collaborative control model is established with the least sum of prevention control stage cost and emergency control stage cost as the target; and the prevention-emergency collaborative control model is iteratively solved to obtain a system resource allocation strategy and a prevention-emergency collaborative control strategy with the lowest collaborative control cost. The application can realize risk-driven resource dynamic allocation to solve the problem that prevention control measures in a power system cannot dynamically match resource intensity according to operation risk.

Description

Technical Field

[0001] This invention relates to the field of power grid operation risk prevention and control technology, and in particular to a power grid prevention-emergency coordinated control method and system that considers dynamic resource allocation. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rigid growth in demand for power supply and the intensification of risks and challenges, large-scale load disturbances and extreme weather have become significant challenges to power grid operation. The traditional "prevention-oriented, emergency backup" control strategy is increasingly showing its shortcomings in adaptability. For example, "prevention and control" still relies on offline static analysis of the power grid and lacks the ability to allocate resources in a differentiated manner based on risk classification. "Emergency control," as the last line of defense, has long been disconnected from the prevention process in terms of its action logic and cost model, forming "strategy silos." In scenarios where uncertainties on both the power source and load sides are superimposed, the risks to power grid operation are amplified exponentially. Therefore, it is urgent to build a dynamically adaptable optimization technology system.

[0004] The current power dispatching system mainly relies on static reserve allocation and rule-based safety verification. Core strategies such as safety reservation settings and reserve capacity allocation are mostly based on historical experience and remain static throughout the dispatching cycle. This fails to detect changes in risk levels caused by factors such as fluctuations in renewable energy output, load migration, and redistribution of network power flow in real time. It is also unable to adjust the intensity of resource input in real time according to changes in risk levels. This "static defense" model makes it difficult to achieve a dynamic balance between safety margin and operating costs, resulting in a "uniform quota" characteristic for prevention and control. This may lead to redundant resource waste in low-risk scenarios and difficulty in providing sufficient regulation capacity in high-risk scenarios. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a power grid prevention-emergency coordinated control method and system that considers dynamic resource allocation. It introduces a two-stage structured decision model, processing discrete start-up / switching decisions and continuous adjustment variables in stages. By identifying the most unfavorable operating scenario, it achieves risk-driven dynamic resource allocation.

[0006] In some implementations, the following technical solutions are adopted: A power grid prevention-emergency coordinated control method considering dynamic resource allocation includes: Construct a set of system operation scenarios under multi-source uncertainty to form a prevention and control risk table and an emergency control risk table; Determine the risk boundaries for prevention and control and the risk boundaries for emergency control respectively; Based on the aforementioned prevention and control risk boundaries and emergency control risk boundaries, a prevention-emergency collaborative control model is established with the goal of minimizing the sum of the costs of the prevention and control phase and the emergency control phase. The objective function for the cost of the prevention and control phase includes two-stage optimization: the first stage optimizes the operating cost of the binary decision variables, and the second stage optimizes the operating cost of the continuous variables under the first risk boundary and the optimized binary decision variables; the objective function for the cost of the emergency control phase optimizes the operating costs of the binary decision variables and the continuous variables under the second risk boundary. The prevention-emergency coordinated control model is iteratively solved to obtain the system resource allocation strategy and the prevention-emergency coordinated control strategy that minimize the cost of coordinated control.

[0007] As a further solution, the aforementioned prevention and control risk table and emergency control risk table refer to the risks under the actual operating conditions in the region. They are derived from fault-triggered risks or uncertain power imbalance risks, and are constructed according to specific indicators of the probability and cost of different risks in each region.

[0008] As a further step, the risk boundaries for prevention and control will be defined, specifically as follows: Based on the feasible region of power grid operation constraints and dispatch adjustment strategies, determine the dispatchable set for preventing and controlling risks. By constructing an objective function, the critical condition under the most extreme case is measured when the risk of prevention and control changes; To maximize risk prevention and control, the objective function is optimized. Introducing Type I Special Ordered Set Variable Vectors and The constraints are linearized, and a penalty term is added to the objective function to obtain the penalized optimized objective function; Solving the penalty-optimized objective function yields the boundary of risk prevention and control. .

[0009] As a further measure, the emergency control risk boundary is specifically defined as follows: Remove prevention and control risks from the prevention and control risk table The remaining risks, along with the risks in the emergency control risk table, together constitute the emergency control risk boundary.

[0010] As a further option, the objective function for the cost of the prevention and control phase is specifically as follows: ; in, For the binary decision variable vector in the prevention and control phase; For the continuous scheduling variable vector in the prevention and control phase; For the cost function of the prevention and control phase, This indicates a defined allocation of prevention and control resources. Indicates a definite risk to be prevented and controlled; , These are cost coefficient vectors; This is the coefficient matrix of the first-stage variables. The constraint constant vector; This is the feasible region for the second phase. For the prevention and control risks identified within the feasible domain, To prevent and control resources, This represents the feasible region in the first phase. To prevent and control risk sets, Let be the constant vector of the constraints. This is a coefficient matrix that reflects the degree of impact of risk on physical constraints.

[0011] As a further solution, the objective function for the cost of the emergency control phase is specifically as follows: ; in, For the emergency control phase, a binary decision variable vector is used. For the emergency control phase, it is a continuous variable; For the cost function of the emergency control phase, This is the feasible domain for the emergency control phase. Resources for the identified emergency control phase. To identify and control the emergency risks, In order to urgently control resources, In order to urgently control the risk, , These are cost coefficient vectors.

[0012] As a further solution, the iterative solution process for the aforementioned prevention-emergency coordinated control model is as follows: The objective function of cost in the prevention and control stage is solved based on the column constraint generation algorithm: the optimization of operating cost of binary decision variables is taken as the main problem, and the optimization of operating cost of continuous variables is taken as the sub-problem. The main problem is solved first, and the sub-problems are solved based on the optimal solution. Based on the optimal control strategy obtained from the binary decision variables and continuous scheduling variables in the prevention and control phase, the emergency control strategy under the remaining emergency control resource constraints is solved under the risk boundary in the emergency control phase.

[0013] In other embodiments, the following technical solutions are adopted: A power grid prevention-emergency coordinated control system considering dynamic resource allocation includes: The risk construction module is configured to build a set of system operation scenarios under multi-source uncertainty, forming a prevention and control risk table and an emergency control risk table. The risk boundary determination module is configured to determine the prevention and control risk boundary and the emergency control risk boundary, respectively. The collaborative control model construction module is configured to establish a prevention-emergency collaborative control model based on the prevention and control risk boundary and the emergency control risk boundary, with the goal of minimizing the sum of the costs of the prevention and control phase and the emergency control phase. The objective function for the cost of the prevention and control phase includes two-stage optimization: the first stage optimizes the operating cost of the binary decision variables, and the second stage optimizes the operating cost of the continuous variables under the first risk boundary and the optimized binary decision variables; the objective function for the cost of the emergency control phase optimizes the operating costs of the binary decision variables and the continuous variables under the second risk boundary. The collaborative control model solving module is configured to iteratively solve the prevention-emergency collaborative control model to obtain the optimal system resource allocation strategy and the prevention-emergency collaborative control strategy.

[0014] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions adapted to be loaded and executed by the processor of the above-described power grid prevention-emergency coordinated control method considering dynamic resource allocation.

[0015] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described power grid prevention-emergency coordinated control method considering dynamic resource allocation.

[0016] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention regards the entire resource of “source, grid, load and storage” as a dynamic and adjustable object. In the prevention and control stage, a two-stage structured decision model is introduced to process discrete start-stop / switching decisions and continuous adjustment variables in stages. The C&CG algorithm is used to identify the most unfavorable operating scenario and realize risk-driven dynamic resource allocation to solve the problem that prevention and control measures in the power system cannot dynamically match resource intensity according to operating risks.

[0017] (2) This invention constructs a two-stage optimization framework for “prevention-emergency” collaboration, introduces binary decision variables to describe the emergency control triggering mechanism, and dynamically corrects the prevention cost by identifying the worst loss scenario, thereby solving the problems of lack of interconnection between prevention and emergency control strategies, inability to form a collaborative optimal, and difficulty in balancing overall economy and safety.

[0018] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] Figure 1 This is a flowchart of a power grid prevention-emergency coordinated control method considering dynamic resource allocation in an embodiment of the present invention; Figure 2 This is a schematic diagram of prevention-emergency coordinated control in an embodiment of the present invention; Figure 3 A schematic diagram illustrating the iterative solution process of the main problem and its subproblems. Detailed Implementation

[0020] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0022] Example 1 In one or more embodiments, a power grid prevention-emergency coordinated control method considering dynamic resource allocation is disclosed, combined with... Figure 1 Specifically, it includes the following process: S101: Construct a set of system operation scenarios under multi-source uncertainty, and form a prevention and control risk table and an emergency control risk table.

[0023] Specifically, a set of system operation scenarios under multi-source uncertainty is constructed to describe the impact of uncertain factors such as load, power output, and grid topology disturbances on system operation. Among them, multi-source uncertainty refers to random disturbance factors from multiple different sources that affect the operation of the power system, such as power output fluctuations, load fluctuations, etc. The set of system operation scenarios under multi-source uncertainty can be generated using methods such as Monte Carlo sampling.

[0024] A risk table is used to characterize the probability of a fault occurring during the actual operation of a power grid in a certain region, as well as the cost (loss) of such a fault.

[0025] The prevention and control risk table and emergency control risk table in this embodiment refer to the risks under the actual operating conditions of the region. They can be derived from fault-triggered risks (such as short-circuit faults, critical equipment shutdowns, cascading failures, etc.) or uncertain power imbalance risks (such as renewable energy output fluctuations, sudden load fluctuations, energy storage system failures, etc.). They are constructed according to the specific indicators of the probability and cost (loss) of different risks in each region.

[0026] S102: Determine the risk boundaries for prevention and control and the risk boundaries for emergency control, respectively.

[0027] First, define the variables for the prevention and control phase: Let the generator set be denoted as The busbar set is The line set is Energy storage is a collection of Time period set is Risk set as ,resource The values ​​are fixed, where prevention and control resources are variables. Emergency control resources are variables ,and .

[0028] The generator sets produce electrical energy, which is fed into the bus. The electrical energy is then transmitted from the high-voltage bus to the low-voltage bus through lines of different voltage levels, and then distributed to the power loads in various areas. Excess electrical energy is sent to energy storage for storage.

[0029] In this embodiment, the risk index is used to quantify risk, which refers to the failure probability multiplied by the failure cost. Resources refer to system resources such as generator sets, energy storage equipment, or compensation devices. The scheduling strategy is a decision on how to allocate resources to achieve the goal.

[0030] The risk set refers to the set of anticipated incidents or uncertainties, quantifying the potential disturbances the system may face. The resource set refers to the available means within the system for maintaining power balance and stability margin.

[0031] The constraints of the entire power system mainly consider power balance constraints, unit output constraints, minimum rise and fall time constraints, and energy storage constraints, as detailed below: (1) Output constraints of thermal power units: ; In the formula: Indicates the first Taiwan thermal power units at time Those who have made meritorious contributions; and This represents the minimum and maximum active power output. Indicates the time of the unit The start / stop status (1 indicates running, 0 indicates stopping).

[0032] (2) Gradient constraints of thermal power units: ; In the formula: and These are the unit's power reduction ramp limit and power increase ramp limit, respectively.

[0033] (3) Time constraints for the ascent and descent of thermal power units: ; ; In the formula: For the unit The startup decision variable (1 represents startup, 0 represents shutdown); For the unit The shutdown decision variable (1 represents shutdown, 0 represents startup); and These are the minimum continuous running time and the minimum continuous downtime, respectively. express The shutdown decision variable at any given time. express The startup decision variables at any given moment.

[0034] (4) Energy storage constraints: ; ; ; ; In the formula: Indicates energy storage In time The state of charge; and These are the minimum and maximum energy storage limits, respectively. Indicates energy storage In time Power output; and They represent energy storage In time The charging power and discharging power; and Indicates energy storage Maximum charging and discharging power; Indicates energy storage Rated capacity (MWh); and Indicates energy storage In time The charge and discharge efficiency; The initial state of charge. Let T be the state of charge at time T. For time step.

[0035] (5) Renewable energy constraints: ; In the formula: Indicates the time of renewable energy generator sets 'output power' Predict its power.

[0036] (6) System load and reserve constraints: ; ; In the formula: Indicates the total number of thermal power units; Indicates the total number of energy storage systems; Indicates the number of renewable energy generator sets; Indicates the load quantity; Indicates time The load power; Indicates time Load shedding power; Indicates time The system's backup power requirements.

[0037] (7) Load shedding constraint: ; (8) Transmission line constraints: ; In the formula: and For the first Minimum and maximum transmission power limits for each transmission line; , , and These are thermal power units, energy storage systems, renewable energy units, and load nodes relative to the first The power transmission distribution factor of a transmission line.

[0038] Current power system risk optimization problems typically involve load fluctuations, generator failures, and renewable energy fluctuations. To better address these issues, this embodiment proposes a novel power system risk optimization model. This model minimizes the total operating cost of the power system by dynamically allocating preventive and emergency control resources, and generates and updates constraints under different risk scenarios.

[0039] The risk boundaries for the prevention and control phase and the emergency control phase are defined below: S1021: The process of determining the risk boundaries for prevention and control is as follows: (1) Based on the feasible region of power grid operation constraints and dispatch adjustment strategies, determine the dispatchable set of risk prevention and control; The magnitude of system risk is related to the system's scheduling strategy. Because risk fluctuates at each stage, the risk during the prevention and control phase... It can be represented as ,in To prevent and control risks at present; The prediction error is to be determined.

[0040] When observed At that time, the system's prevention and control resources will be adjusted to Its generator output power will be adjusted to To restore system balance. During the scheduling and adjustment phase, the output increment of each generator must not exceed its reserve capacity.

[0041] The schedulable area problem aims to identify and prevent risks. The fluctuation range allows for the existence of effective scheduling and adjustment schemes. .

[0042] Therefore, the constraint can be expressed as: ; In the formula: The binary decision variable vector for the prevention and control phase includes the unit start-up / shutdown state and the on / off state; The continuous scheduling variable vector (representing the strategy corresponding to the continuous variables of prevention and control) in the prevention and control phase includes unit output power, energy storage charging and discharging power, etc. For the coefficients in the aforementioned constraints (1) and (5), The coefficients in the aforementioned constraints (2), (3), (4), (6), (7), and (8) represent the technical parameters of the equipment, such as maximum output, etc. The coefficient matrix is ​​a continuous variable matrix, and the load matrix is ​​a load matrix. The coefficient matrix, This indicates the impact of risk on system constraints.

[0043] Therefore, in a given and At that time, the continuous variable vector of prevention and control The feasible region is defined as: (1) Its prevention and control risks are a schedulable set. ,satisfy , and , .

[0044] Therefore, the set It can be represented as: (2) The schedulable region can be expressed by the linear inequality as follows: ; This represents the linear inequality matrix describing the schedulable region. It is a constant vector.

[0045] (2) By constructing an objective function, the infeasibility of the worst-case scenario of the constraints (i.e. the critical condition in the most extreme case) is measured.

[0046] Specifically, the core issue of schedulable regions is when exist When the set changes, verify the set. The non-emptiness of.

[0047] This embodiment is for determining fixation. hour Is it an empty set? Introduce a positive relaxation vector. and We can obtain: (3) like ,but ;like ,but .

[0048] like It is an uncertain quantity and belongs to a set. ,but The necessary and sufficient condition is .

[0049] (4) The objective function in the above equation measures the performance of a given condition. In the set When the constraints change, the worst-case scenario is infeasible.

[0050] (3) Maximize the prevention and control of risks by optimizing the objective function.

[0051] In this embodiment, risk is maximized. (The consequences are the most severe), and the optimal strategy is deployed accordingly. (Minimum loss), which can be expressed as Linear minima problem: (5) like Then the set All scenarios in the process are schedulable, that is... , yes The largest set that holds true.

[0052] Replacing the minimization problem of the inner layer in the above equation with its dual linear programming problem, we obtain the bilinear programming (BLP) problem: (6) in, As dual variables; The feasible region of the dual variable is defined as: ; at this time, and The constraints are separable, therefore the above equation can be viewed as a two-stage optimization problem: (7) in, For the dual variable of the inner maximization problem.

[0053] According to the strong duality theorem of linear programming, the inner-level problem satisfies the following relationship at the optimal solution: (8) Based on the above formula, we can obtain the objective function of a linear programming problem with complementary constraints, where the variables are... and The middle part is in linear form: (9) in, express .

[0054] The core idea of ​​the algorithm for solving the above formula (9) is to construct a sufficiently large region that contains schedulable areas. The initial set is then used to gradually remove unschedulable elements by generating a boundary hyperplane until all remaining elements are schedulable. The specific process is as follows: (3-1) Choose a sufficiently large set B and error The schedulable region is approximately constructed as ; (3-2) Solve the current The corresponding linear programming problem with complementary constraints has the optimal solution as follows: and The optimal value is .like Proceed to step (3-3); if The algorithm terminates at this point. This is the schedulable area.

[0055] (3-3) on the line segment Looking for points , making .at this time It is the optimal value of the following linear programming problem: ; (3-4) generates a linear equation, which is: The boundary hyperplane (the hyperplane is used to approximate the schedulable region): ; Update matrix and vector Return to step (3-2).

[0056] (4) Introduce type 1 special ordered set variable vectors and The constraints are linearized, and a penalty term is added to the objective function to obtain the penalized optimized objective function.

[0057] Due to the aforementioned schedulable area The computation process requires solving a linear programming problem with complementary constraints. Due to the existence of complementary relaxation conditions, this problem is non-convex and computationally difficult. Therefore, this embodiment employs two linearization techniques to solve the problem by finding equivalent mixed-integer linear programming problems. Specifically: Replace the constraints in equation (9) with linear constraints: ; in, A vector of binary decision variables; It is a sufficiently large constant. Therefore, if ,but , ,vice versa, express The i-th component.

[0058] Therefore, equation (9) can be equivalent to: (10) Furthermore, while solving mixed-integer linear programming problems can yield the global optimal solution of bilinear programming, this method has two potential problems: 1) When the uncertainty dimension is high, the difficulty of solving mixed-integer linear programming problems increases. 2) In some cases, choosing an appropriate constant... If the value is too small, the constraints cannot be correctly transformed and will impose stricter boundaries on the original variables; if the value is too large, it will lead to computational difficulties.

[0059] To overcome the above problems, this embodiment introduces a technique that combines a penalty method with a type 1 special ordered set (SOS1) variable, by introducing a variable vector. and Linearizing the constraints, we get: ; in, and For SOS1 variable.

[0060] SOS1 Variable Allowed Set At most one variable in the equation takes a strictly positive value, and the other variable is forced to take a value of 0. Some mixed-integer linear programming solvers can directly call SOS1 variables, or they can be represented by binary variables. To reduce the total number of SOS1 variables or binary variables, a penalty term is added to formula (9), so the optimization problem after penalty is as follows: (11) in, This represents the penalty term of the objective function.

[0061] The aforementioned linearization method requires setting a large constant. Different, the constants in the formula The value may be very small. If the optimal solution of the linear programming problem fails to satisfy all complementary constraints, the remaining complementarity will be enforced through type 1 special ordered set (SOS1) variables and related constraints. The advantage of this method is that the branch and bound algorithm corresponding to SOS1 variables is faster than the branch and bound algorithm for traditional binary variables.

[0062] (5) Solve the objective function after penalty optimization to obtain the boundary of risk prevention and control. .

[0063] Specifically, by using the methods described in steps (3-1) to (3-4) above, the risk boundary for prevention and control can be obtained. .

[0064] In the preceding text, the boundary of the worst-case scenario for prevention and control has been defined, which delineates the scope of all possible risks; however, in actual working conditions, it is necessary to introduce t to solve for the specific risks. ; t is fluctuating, t It is the definite value obtained by solving.

[0065] Specifically, once an unschedulable scenario is found... line segment It will inevitably happen at some point Passing through the schedulable area The boundary. We need to find the exit point. ,in express The boundary. A boundary hyperplane is then generated to cull unschedulable scenarios, because... This point can be represented as ,in (It fluctuates). And It satisfies the following properties: ; Furthermore, the strong duality condition for linear programming can be obtained as follows: ; Therefore, scalar The solution is as follows: ; Obtain scalar After that, the risks can be prevented and controlled. Emergency control of risks The table consists of an emergency control risk table and a prevention control failure risk table. It consists of two parts.

[0066] This embodiment determines the upper limit of prevention and control by solving the risk boundaries between the prevention and control phase and the emergency control phase, and how much risk can be covered under the current prevention and control measures. Based on this, emergency control measures are then used as a safety net.

[0067] S103: Based on the risk boundaries of prevention and control and emergency control, establish a prevention-emergency collaborative control model with the goal of minimizing the sum of costs in the prevention and control phase and the emergency control phase.

[0068] In the existing technological system, preventive control mainly sets a fixed cost ceiling from a pre-emptive safety perspective, while emergency control, as a post-event remedial measure, is often triggered independently. The lack of a unified cost-risk trade-off mechanism between the two leads to two extreme states of "over-prevention" or "over-emergency control" in actual operation of the scheduling strategy.

[0069] Based on this, combined Figure 2 This embodiment constructs a prevention-emergency collaborative control model, specifically as follows: ; in, To prevent and control costs, Costs incurred during the emergency control phase; Total system resources; Resources for the prevention and control phase; Resources for the emergency control phase; For a set of systemic risks; For the risk set in the prevention and control phase; This is a risk set for the emergency control phase.

[0070] In this embodiment, the objective function for the cost of the prevention and control phase includes two-stage optimization. The first stage optimizes the operating cost of the binary decision variables, and the second stage optimizes the operating cost of the continuous variables under the first risk boundary and the optimized binary decision variables. The specific objective function for the prevention and control phase is as follows: ; in, The binary decision variable vector for the prevention and control phase includes the unit start-up / shutdown state and the on / off state; This is a continuous scheduling variable vector for the prevention and control phase, including unit output power, energy storage charging and discharging power, etc. For the cost function of the prevention and control phase, This indicates a defined allocation of prevention and control resources. Indicates a definite risk to be prevented and controlled; , These are cost coefficient vectors; This is the coefficient matrix of the first-stage variables. The constraint constant vector; This is the feasible region for the second phase. For the prevention and control risks identified within the feasible domain, To prevent and control resources, This represents the feasible region in the first phase. To prevent and control risk sets, Let be the constant vector of the constraints. This is a coefficient matrix that reflects the degree of impact of risk on physical constraints.

[0071] The objective function for the cost of the emergency control phase aims to optimize the operating costs of binary decision variables and continuous variables under the second risk boundary. Specifically, the objective function for the emergency control phase is as follows: ; in, This is a binary decision variable vector for the emergency control phase, containing the unit's emergency stop status and load shedding flag; These are continuous variables during the emergency control phase, including unit output power and energy storage charging and discharging power. For the cost function of the emergency control phase, This is the feasible domain for the emergency control phase. Resources for the identified emergency control phase. To identify and control the emergency risks, In order to urgently control resources, In order to urgently control the risk, , These are cost coefficient vectors.

[0072] By constructing a two-stage optimization framework for "prevention-emergency" collaboration, and introducing binary decision variables to describe the emergency control triggering mechanism, the core technical challenges of the lack of interconnection between prevention and emergency control strategies and the difficulty in balancing overall economy and security are addressed.

[0073] S104: Iteratively solve the prevention-emergency coordinated control model to obtain the system resource allocation strategy and the prevention-emergency coordinated control strategy that minimizes the cost of coordinated control.

[0074] Among them, the system resource allocation strategy refers to the use of resources, such as adjusting generator output, to achieve greater coverage of risks at a lower cost when facing different risks; the prevention and emergency coordinated control is to determine the control strategy with the lowest cost among the two measures of prevention control and emergency control.

[0075] The iterative solution process for the prevention-emergency coordinated control model in this embodiment is as follows: S1041: Solving the objective function of cost in the prevention and control stage based on the column constraint generation algorithm: taking the optimization of the operating cost of the binary decision variable as the main problem and the optimization of the operating cost of the continuous variable as the sub-problem, first solving the main problem, and then solving the sub-problem based on the optimal solution.

[0076] Specifically, the optimization problem in the prevention and control phase can be viewed as a mixed-integer linear programming problem, and its nonlinear constraints can be transformed into mixed-integer linear constraints. This model is solved based on the column constraint generation algorithm (C&CG), combined with... Figure 3 The algorithm flow is as follows: (1) Initialize the lower and upper bounds of the feasible solution to . and and initialize the number of iterations to ; (2) Solve the following problem, which is defined as the principal problem: ; The optimal solution is denoted as And the lower bound is updated to ; in, This represents the vector of continuous variables for prevention and control in the k-th iteration. This represents the solution for a given binary decision variable. This represents the value of the second-stage cost. , These are the optimal strategies for each historical scenario.

[0077] (3) Solve the subproblems: ; The optimal solution is The upper boundary has been updated to ;in, This represents the worst-case risk generated in the current iteration. The criterion for stopping the algorithm's iteration is usually an extremely small positive number.

[0078] (4) If ,return The solution is found to be optimal; the solution process stops there. Otherwise, perform the following steps a) or b): a) If the subproblem has a solution, then create a variable. And add constraints: ; b) If the solution to the subproblem is not feasible, then create a variable. And add the following constraints: ; (5) Update Return to step (2).

[0079] In this embodiment, the prevention and control phase determines the basic operating mode through fixed structural decisions; continuous variables are adaptively adjusted in the second phase according to different scenarios to reflect the system's ability to cope with risks; and the emergency control mechanism handles the risk allocation of risk sets and the risk of prevention and control failure.

[0080] By iteratively solving the main problem and sub-problems, the system can automatically identify the most dangerous scenarios and iteratively correct prevention strategies, ensuring that the final solution simultaneously meets cost constraints and safety risk constraints, significantly improving the adaptability and robustness of the scheduling strategy under uncertain conditions. Simultaneously, by identifying the worst-case loss scenario, the prevention cost threshold (the current optimal investment cost calculated through optimization) is dynamically adjusted to ensure that the cost of prevention and control is minimized without system collapse.

[0081] S1042: Binary decision variables obtained from the prevention and control phase. Optimal control strategy for continuous scheduling variables Under the risk boundary of the emergency control phase, solve for the emergency control strategy under the constraint of remaining emergency control resources. .

[0082] Obtain the optimal solution and Next, determine whether the coordination cost is the lowest. If so, output the optimal resource allocation and prevention-emergency control coordination strategy; otherwise, return to reallocate control resources.

[0083] use Figure 3 The alternating iterations shown are used to optimize resource allocation and control decision variables in the prevention and emergency control phases, and to determine the optimal resource allocation and prevention-emergency control synergistic strategy.

[0084] The core of preventive-emergency coordinated control of power systems lies in the fact that the system needs to simultaneously consider operating costs, backup configurations, and the safety of extreme scenarios under uncertain disturbances. It achieves coordination between different time scales and control types through two-stage modeling, and achieves the optimal trade-off between risk and cost by iteratively identifying the most unfavorable scenario and dynamically updating the constraint set.

[0085] Example 2 In one or more embodiments, a power grid prevention-emergency coordinated control system considering dynamic resource allocation is disclosed, comprising: The risk construction module is configured to build a set of system operation scenarios under multi-source uncertainty, forming a prevention and control risk table and an emergency control risk table. The risk boundary determination module is configured to determine the prevention and control risk boundary and the emergency control risk boundary, respectively. The collaborative control model construction module is configured to establish a prevention-emergency collaborative control model based on the prevention and control risk boundary and the emergency control risk boundary, with the goal of minimizing the sum of the costs of the prevention and control phase and the emergency control phase. The objective function for the cost of the prevention and control phase includes two-stage optimization: the first stage optimizes the operating cost of the binary decision variables, and the second stage optimizes the operating cost of the continuous variables under the first risk boundary and the optimized binary decision variables; the objective function for the cost of the emergency control phase optimizes the operating costs of the binary decision variables and the continuous variables under the second risk boundary. The collaborative control model solving module is configured to iteratively solve the prevention-emergency collaborative control model to obtain the optimal system resource allocation strategy and the prevention-emergency collaborative control strategy.

[0086] It should be noted that the specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.

[0087] Example 3 In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions adapted to be loaded by the processor and executed by the processor for the power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in Embodiment 1.

[0088] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0089] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0090] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0091] Example 4 In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in Embodiment 1.

[0092] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A power grid prevention-emergency coordinated control method considering dynamic resource allocation, characterized in that, include: Construct a set of system operation scenarios under multi-source uncertainty to form a prevention and control risk table and an emergency control risk table; Determine the risk boundaries for prevention and control and the risk boundaries for emergency control respectively; Based on the aforementioned prevention and control risk boundaries and emergency control risk boundaries, a prevention-emergency collaborative control model is established with the goal of minimizing the sum of the costs of the prevention and control phase and the emergency control phase. The objective function for the cost of the prevention and control phase includes two-stage optimization: the first stage optimizes the operating cost of the binary decision variables, and the second stage optimizes the operating cost of the continuous variables under the first risk boundary and the optimized binary decision variables; the objective function for the cost of the emergency control phase optimizes the operating costs of the binary decision variables and the continuous variables under the second risk boundary. The prevention-emergency coordinated control model is iteratively solved to obtain the system resource allocation strategy and the prevention-emergency coordinated control strategy that minimize the cost of coordinated control.

2. The power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in claim 1, characterized in that, The aforementioned prevention and control risk table and emergency control risk table refer to the risks under actual operating conditions in the region. They originate from fault-triggered risks or uncertain power imbalance risks and are constructed according to specific indicators of the probability and cost of different risks in each region.

3. The power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in claim 1, characterized in that, Determine the risk boundaries for prevention and control, specifically as follows: Based on the feasible region of power grid operation constraints and dispatch adjustment strategies, determine the dispatchable set for preventing and controlling risks. By constructing an objective function, the critical condition under the most extreme case is measured when the risk of prevention and control changes; To maximize risk prevention and control, the objective function is optimized. Introducing Type I Special Ordered Set Variable Vectors and The constraints are linearized, and a penalty term is added to the objective function to obtain the penalized optimized objective function; Solving the penalty-optimized objective function yields the boundary of risk prevention and control. .

4. A power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in claim 1 or 3, characterized in that, The specific emergency control risk boundary is as follows: Remove prevention and control risks from the prevention and control risk table The remaining risks, along with the risks in the emergency control risk table, together constitute the emergency control risk boundary.

5. A power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in claim 1, characterized in that, The objective function for the cost of the prevention and control phase is specifically as follows: ； in, For the binary decision variable vector in the prevention and control phase; For the continuous scheduling variable vector in the prevention and control phase; For the cost function of the prevention and control phase, This indicates a defined allocation of prevention and control resources. Indicates a definite risk to be prevented and controlled; , These are cost coefficient vectors; This is the coefficient matrix of the first-stage variables. The constraint constant vector; This is the feasible region for the second phase. For the prevention and control risks identified within the feasible domain, To prevent and control resources, This represents the feasible region in the first phase. To prevent and control risk sets, Let be the constant vector of the constraints. This is a coefficient matrix that reflects the degree of impact of risk on physical constraints.

6. The power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in claim 1, characterized in that, The objective function for the cost of the emergency control phase is specifically as follows: ； in, For the emergency control phase, a binary decision variable vector is used. For the emergency control phase, it is a continuous variable; For the cost function of the emergency control phase, This is the feasible domain for the emergency control phase. Resources for the identified emergency control phase. To identify and control the emergency risks, In order to urgently control resources, In order to urgently control the risk, , These are cost coefficient vectors.

7. A power grid prevention-emergency coordinated control method considering dynamic resource allocation as described in claim 1, characterized in that, The iterative solution process for the aforementioned prevention-emergency coordinated control model is as follows: The objective function of cost in the prevention and control stage is solved based on the column constraint generation algorithm: the optimization of operating cost of binary decision variables is taken as the main problem, and the optimization of operating cost of continuous variables is taken as the sub-problem. The main problem is solved first, and the sub-problems are solved based on the optimal solution. Based on the optimal control strategy obtained from the binary decision variables and continuous scheduling variables in the prevention and control phase, the emergency control strategy under the remaining emergency control resource constraints is solved under the risk boundary in the emergency control phase.

8. A power grid prevention-emergency coordinated control system considering dynamic resource allocation, characterized in that, include: The risk construction module is configured to build a set of system operation scenarios under multi-source uncertainty, forming a prevention and control risk table and an emergency control risk table. The risk boundary determination module is configured to determine the prevention and control risk boundary and the emergency control risk boundary, respectively. The collaborative control model construction module is configured to establish a prevention-emergency collaborative control model based on the prevention and control risk boundary and the emergency control risk boundary, with the goal of minimizing the sum of the costs of the prevention and control phase and the emergency control phase. The objective function for the cost of the prevention and control phase includes two-stage optimization: the first stage optimizes the operating cost of the binary decision variables, and the second stage optimizes the operating cost of the continuous variables under the first risk boundary and the optimized binary decision variables; the objective function for the cost of the emergency control phase optimizes the operating costs of the binary decision variables and the continuous variables under the second risk boundary. The collaborative control model solving module is configured to iteratively solve the prevention-emergency collaborative control model to obtain the optimal system resource allocation strategy and the prevention-emergency collaborative control strategy.

9. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed as described in any one of claims 1-7, the power grid prevention-emergency coordinated control method considering dynamic resource allocation.

10. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded by the processor of the terminal device and executed as described in any one of claims 1-7, the power grid prevention-emergency coordinated control method considering dynamic resource allocation.

Images