Unconventional event post-disaster power supply capability improvement method and system based on inner and outer ring optimization, and computer readable storage medium

Abstract

The invention discloses an unconventional event post-disaster power supply capability improvement method and system based on inner and outer ring optimization, and the method comprises the following steps: combining damaged nodes into a repair sequence as a repair individual according to the current situation of a power grid system after an unconventional event; optimizing the target through an interior point method, and calculating the fitness value of each repaired individual in the population according to the optimized target; selecting an optimal restoration individual according to the fitness value, updating the remaining restoration individuals through a genetic algorithm, and combining the updated restoration individuals with the optimal restoration individual to generate a new population; the fitness value of each repaired individual in the new population is calculated again according to the step 2, so that the fitness value is updated; and repeating the steps 2-4 until the optimal repair sequence of the power grid is finally obtained. The genetic algorithm and the interior point method are combined, the repairing efficiency is remarkably improved, the voltage deviation is reduced, and the overall stability and reliability of a power grid are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system and computer-readable storage medium for improving power supply capacity after unconventional events due to internal and external loop optimization. Background Art

[0002] The optimal dispatching of power systems is an important part of ensuring the stability and reliability of power systems. Especially in post-disaster recovery scenarios, the repair strategy of power systems is crucial. In actual operation, power systems face a variety of constraints, including power balance constraints, node voltage constraints, and equipment power limits. Traditional optimization methods (such as simply using gradient descent, dynamic programming, etc.) often fall into the dilemma of local optimality or low computational efficiency when dealing with complex power system dispatching problems. For example: the power system involves a large number of generators, load nodes, transformers, and lines. The system scale is large and the variable dimension is high. When facing such complex multi-dimensional optimization problems, traditional algorithms often have low computational efficiency, and as the system scale increases, the solution time increases exponentially. Dispatching problems in power systems usually involve the optimization of discrete variables (such as the start and stop of generators, whether nodes are connected, etc.) and continuous variables (such as node voltage, line flow, etc.), which increases the complexity of the problem. Traditional optimization algorithms can often only handle a single type of variable and perform poorly when dealing with mixed integer and continuous variable problems.

[0003] It can be seen that in the post-disaster recovery process of the power system, the selection of the repair order for the damaged nodes in the power grid and the optimization of the grid status after the nodes are reconnected is a challenging multi-objective optimization problem. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a method and system for improving the power supply capacity of power grids after unconventional events based on inner and outer loop optimization, and proposes a dual-loop optimization model that combines a genetic algorithm with an interior point method, aiming to improve the performance of power grids after disasters, significantly improve repair efficiency, reduce voltage deviation, and ensure the overall stability and reliability of the power grid.

[0005] In order to solve the above technical problems, the technical solution of the present invention is:

[0006] A method for improving power supply capacity after unconventional events in a power grid based on internal and external loop optimization, comprising the following steps:

[0007] According to the current status of the power grid system after the unconventional event, the damaged nodes are combined into a repair sequence as repair individuals, and all the repair individuals constitute a population;

[0008] The target is optimized by the interior point method, and the fitness value of each repair individual in the population is calculated according to the optimization target;

[0009] The best repair individual is selected according to the fitness value, and the remaining repair individuals are updated through the genetic algorithm. The updated repair individuals are combined with the best repair individuals to generate a new population;

[0010] The fitness value of each repair individual in the new population is calculated again and the fitness value is updated; when the convergence condition is met, the fitness value update is stopped and the optimal repair sequence of the power grid is obtained.

[0011] Preferably, the damaged nodes are combined into a repair sequence as repair individuals through random combination.

[0012] Preferably, the remaining repair individuals are updated through selection, crossover and mutation operations in a genetic algorithm.

[0013] Preferably, the genetic algorithm is specifically as follows: crossover is performed on the remaining repair individuals of the population with a certain probability, and the repair sequence of some nodes in every two repair individuals is exchanged to generate new repair individuals; mutation is performed with a certain probability to change the node repair sequence in the new repair individuals.

[0014] Preferably, when optimizing the fitness value by the interior point method, the voltage deviation of each node is used as a penalty item and combined with the fitness value optimized by the interior point method as the final fitness value of each repair individual after the genetic algorithm is updated.

[0015] Preferably, the interior point method optimization model is:

[0016] The interior point method for optimizing the power grid system is to construct a Lagrangian function and iterative update algorithm, and the expression is as follows:

[0017]

[0018] st

[0019] P i =V i ∑ j∈n V j (G ij cosθ ij +B ij sinθ ij )

[0020] Q i =V i ∑ j∈n V j (G ij sinθ ij -B ij cosθ ij )

[0021] P Gi -P Li =P i

[0022] Q Gi -Q Li =Q i

[0023] P Gmin ≤p Gi <P Gmax ,i∈SG

[0024] Q Gmin ≤Q Gi <Q Gmax ,i∈S(PV)

[0025] V imin ≤V i <V imax ,i∈SN

[0026] Among them, F max (G) is the optimization target, F is the optimization target, that is, the total power generation energy of the power grid system; P iG is the generator power, P i , Q i is the power flow calculation formula for each node in the system, G ij ,B ij is the conductance and susceptance between nodes i and j, sinθ ij ,cosθ ij is the angle between nodes i and j; P Gi , Q Gi P is the system's active and reactive power; Li , Q Li is the initial fixed active and reactive load of the system; P Gmax , P Gmin is the upper and lower limits of the generator active power; Q Gmax , Q Gmin V is the upper and lower limits of the generator reactive power; max 、V min are the upper and lower limits of voltage; SG is the set of generator numbers; S(PV) is the set of PV node numbers; SN is the set of node numbers.

[0027] As a preference, the fitness value of the repair individual is calculated by the result of interior point method model optimization, and the expression is as follows:

[0028]

[0029] Fitness=F max -λ v ·penaltyv

[0030] Where G is the number of generator nodes, P iG is the generator power, v i is the voltage of the ith node, nbus is the number of all nodes, v t is the target voltage, Penalty v is the total voltage deviation, λ v is the voltage deviation penalty coefficient, f gen is the total power of the generator, and Fitness is the final fitness value after optimization.

[0031] Preferably, the fitness value of each repaired individual in the new population is calculated again. When updating the fitness value, if the fitness value of the repaired individual in the new population exceeds the fitness value of the repaired individual in the original population, it will be retained, otherwise the fitness value will be updated until the preset convergence condition is met.

[0032] Preferably, the preset convergence condition is: the final fitness value Fitness after optimization is less than a preset value ε, ε=0.001.

[0033] A system for improving power supply capacity after an unconventional disaster by realizing inner and outer loop optimization, comprising:

[0034] A population generation module, used to form a population consisting of repaired individuals;

[0035] A target optimization module, whose input end is connected to the output end of the node repair module, is used to optimize the target using the interior point method and calculate the fitness value of each repair individual in the population according to the optimization target;

[0036] A population updating module, whose input ends are respectively connected to the output ends of the target optimization module, is used to select and update the repair individuals and combine them to form a new population;

[0037] A calculation module, whose input end is connected to the output end of the population update module and the target optimization module respectively, and is used to calculate the fitness value of the new population formed by the population update module again by using the target optimization module, so as to update the fitness value;

[0038] and a logic judgment module, whose input end is connected to the output end of the calculation module, for judging whether the convergence condition is met after the last fitness value update, and stopping the fitness value update if the convergence condition is met. Preferably, the population generation module includes:

[0039] A node repair unit, used to combine damaged nodes into a repair sequence as repair individuals according to the current status of the power grid system after the unconventional event;

[0040] Population generation unit: Its input end is connected to the output end of the node repair unit, and is used to form a population from the repaired individuals obtained by the node repair unit.

[0041] Preferably, the population update module comprises:

[0042] A selection unit, whose input end is connected to the output end of the target optimization module and is used to select the optimal repair individual according to the fitness value;

[0043] An updating unit, whose input end is connected to the output end of the selection unit, is used to update the remaining repair individuals through a genetic algorithm;

[0044] And a population secondary generation module, whose input end is connected to the output end of the selection unit and the update unit, is used to combine the updated repair individuals with the optimal repair individuals to generate a new population.

[0045] A computer-readable storage medium stores computer instructions, wherein the computer instructions are used to enable a processor to implement a method for improving power supply capacity after an unconventional event based on inner and outer loop optimization when the method is executed.

[0046] The present invention has the following characteristics and beneficial effects:

[0047] By adopting the above technical solution, the dual-loop optimization model proposed in the present invention can not only perform global search to determine the optimal repair order, but also use the interior point method to perform local optimization after each repair step, thereby ensuring that the system meets various constraints and improving the system's recovery efficiency. This two-layer optimization structure overcomes the limitations of traditional algorithms in dealing with complex nonlinear constraints and multidimensional optimization problems, and has significant application prospects in post-disaster power system optimization and dispatching.

[0048] Combining the genetic algorithm with the interior point method, an optimization structure with two layers is formed. The outer layer uses the genetic algorithm to perform a global search to explore the optimal order of node repair; the inner layer uses the interior point method to perform local optimization to ensure that the system performance (such as power generation capacity) is maximized on the basis of satisfying various constraints of the power grid.

[0049] In addition, this dual-loop optimization model can not only overcome the shortcomings of genetic algorithms in terms of convergence accuracy, but also give full play to the advantages of the interior point method in local optimization, thus providing an efficient solution for the dispatch optimization of complex power grid systems. Especially in the post-disaster recovery scenario of the power system, the model can significantly improve the repair efficiency, reduce voltage deviation, and ensure the overall stability and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 It is a simple flow chart of the model of the present invention.

[0052] Figure 2 This is the fitness value variation curve of the best repair individual in each population in the embodiment of the present invention.

[0053] Figure 3 A bar chart showing the optimal repair solution found by traversing all repair sequences in the embodiment of the present invention.

[0054] Figure 4 It is the deviation curve of all node voltages and standard voltage under the optimal repair solution in the embodiment of the present invention. DETAILED DESCRIPTION

[0055] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0056] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings.

[0057] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. Those skilled in the art can fully understand the present invention without the description of these details.

[0058] The present invention provides a method for improving the power supply capacity of power grid after unconventional events based on internal and external loop optimization. Figure 1 As shown, the following steps are included:

[0059] Step 1: According to the current status of the power grid system after the unconventional event, the damaged nodes are combined into a repair sequence as repair individuals, and all the repair individuals constitute a population.

[0060] In this embodiment, multiple repair sequences are obtained by randomly combining damaged nodes.

[0061] Step 2: Calculate the fitness value of each repair individual in the population using the interior point method.

[0062] Furthermore, in this embodiment, the fitness value of the repair individuals in each generation of the population is calculated by the interior point method optimization and the risk of node voltage deviation is considered from the perspective of grid operation safety. When the fitness value is optimized by the interior point method, the voltage deviation of each node is used as a penalty item and combined with the fitness value obtained by the interior point method optimization as the final fitness value of each repair individual after the genetic algorithm is updated.

[0063] Specifically, the interior point method optimization model is: The interior point method of optimizing the power grid system is to ensure that each node of the power system meets the power balance constraints and voltage constraints by constructing Lagrangian functions and iterative update algorithms, while maximizing the system's power generation capacity. Because the system meets the power balance constraints during the optimization process, the system's power generation is used to measure the system's load recovery capacity. The optimization results of the interior point method are used to calculate the fitness value of each repair sequence. The fitness value is based on the power generation capacity of the power grid, the node voltage and the flow calculation results. The fitness value obtained by the interior point method optimization is fed back to the genetic algorithm to update the fitness value to guide the next generation of evolution of the genetic algorithm. The expression is as follows:

[0064]

[0065] st

[0066] P i =V i ∑ j∈n V j (G ij cosθ ij +B ij sinθ ij )

[0067] Q i =V i ∑ j∈n V j (G ij sinθ ij -B ij cosθ ij )

[0068] P Gi -P Li =P i

[0069] Q Gi -Q Li =Q i

[0070] P Gmin ≤p Gi <P Gmax ,i∈SG

[0071] Q Gmin ≤QGi <Q Gmax ,i∈S(PV)

[0072] V imin ≤V i <V imax ,i∈SN

[0073] Furthermore, when optimizing the fitness value by the interior point method, the voltage deviation of each node is used as a penalty term and combined with the fitness value obtained by the interior point method optimization as the final fitness value of each repair individual after the genetic algorithm is updated. The optimization model of the fitness value is as follows:

[0074]

[0075] Fitness=F max -λ v ·penalty v

[0076] Where G is the number of generator nodes, P iG is the generator power, v i is the voltage of the ith node, nbus is the number of nodes, v t is the target voltage, Penalty v is the total voltage deviation, f gen is the total power of the generator, λ v is the voltage deviation penalty coefficient. In this embodiment, the voltage deviation penalty coefficient is usually 0.1, and Fitness is the final fitness value after optimization.

[0077] Step 3: Select the best repair individual according to the fitness value, and form a new repair individual through selection, crossover and mutation operations through the genetic algorithm, so as to update the remaining repair individuals. The updated repair individuals are combined with the best repair individuals to generate a new population.

[0078] Specifically, in this embodiment, the genetic algorithm crosses the remaining repair individuals of the population with a certain probability, and exchanges the repair order of some nodes in every two repair individuals to generate new repair individuals; mutation is performed with a certain probability, so that the node repair sequence in the new repair individuals changes, and the simulation algorithm falls into a local optimum.

[0079] Step 4: According to the method in step 2, each time a new repair individual is generated, the fitness value is calculated according to the interior point method, and the fitness value is updated. If the fitness value of the new repair individual exceeds the fitness value of the old repair individual, it is retained until the preset convergence condition is met: the fitness value of the best repair individual in the population does not change significantly for several generations, and the optimal solution is reached;

[0080] The convergence condition refers to that the final fitness value Fitness after optimization is less than the preset value ε, ε = 0.001.

[0081] In this embodiment, in steps 2 and 4, the damaged nodes are repaired and reconnected to the power grid each time according to the repair individuals (repair sequence), and the interior point method optimization of the power grid system is performed once each time a node is connected, and the optimization goal is to improve the system load recovery amount.

[0082] Specifically, the interior point method for optimizing the power grid system is to construct a Lagrangian function and an iterative update algorithm, and the expression is as follows:

[0083]

[0084] st

[0085] P i =V i ∑ j∈n V j (G ij cosθ ij +B ij sinθ ij )

[0086] Q i =V i Σ j∈n V j (G ij sinθ ij -B ij cosθ ij )

[0087] P Gi -P Li =P i

[0088] Q Gi -Q Li =Q i

[0089] P G min ≤p Gi <P G max ,i∈SG

[0090] Q G min ≤Q Gi <Q G max ,i∈S(PV)

[0091] V i min ≤V i <V i max ,i∈SN

[0092] Among them, F max(G) is the optimization target, F is the optimization target, that is, the total power generation energy of the power grid system, P iG is the generator power; P i , Q i is the power flow calculation formula for each node in the system, G ij ,B ij is the conductance and susceptance between nodes i and j, sinθ ij ,cosθ ij is the angle between nodes i and j; P Gi , Q Gi P is the system's active and reactive power; Li , Q Li is the initial fixed active and reactive load of the system; P Gmax , P Gmin is the upper and lower limits of the generator active power; Q Gmax , Q Gmin V is the upper and lower limits of the generator reactive power; max 、V min are the upper and lower limits of voltage; SG is the set of generator numbers; S(PV) is the set of PV node numbers; SN is the set of node numbers.

[0093] Step 5: Repeat steps 2 to 4 until the optimal repair sequence of the power grid is finally obtained.

[0094] To make the content and practical effects of the present invention clearer and more intuitive, the IEEE-30 node network is simulated and verified in this embodiment. In this experiment, known damaged nodes (5, 8, 17, 20) are simulated, an initial population is generated according to the sequence of damaged nodes, and the optimal repair sequence is found through a double-loop optimization model.

[0095] from Figure 2 It can be seen that the fitness value of the model in finding a feasible repair sequence is gradually improving during the optimization process and finally iterates to an optimal value, indicating that the model can find the optimal repair sequence. Figure 3 It is a fitness value bar chart that traverses all combinations and finds the optimal combination. Figure 2 The results and Figure 3 The comparison of the results shows that the repair sequence found by the model is indeed the optimal repair sequence.

[0096] Figure 4 This is the deviation curve between the voltage of all nodes of the system and the reference voltage after the repair is completed according to the optimal repair sequence. It can be seen that the voltage of all nodes does not deviate much from the reference voltage, which means that the experiment ensures the safety and stability of the system voltage while finding a feasible solution.

[0097] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments including components are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

Claims

1. A method for improving power supply capacity after unconventional disasters by optimizing inner and outer loops, characterized in that: The steps include: According to the current status of the power grid system after the unconventional event, the damaged nodes are combined into a repair sequence as repair individuals, and all the repair individuals constitute a population; The target is optimized by the interior point method, and the fitness value of each repair individual in the population is calculated according to the optimization target; The best repair individual is selected according to the fitness value, and the remaining repair individuals are updated through the genetic algorithm. The updated repair individuals are combined with the best repair individuals to generate a new population; The fitness value of each repair individual in the new population is calculated again and the fitness value is updated; when the convergence condition is met, the fitness value update is stopped and the optimal repair sequence of the power grid is obtained.

2. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 1 is characterized in that: The damaged nodes are combined into a repair sequence as repair individuals through random combination.

3. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 1 is characterized in that: The remaining repair individuals are updated through selection, crossover and mutation operations in the genetic algorithm.

4. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 3 is characterized in that: The genetic algorithm is specifically: with the crossover probability P c = 0.2, cross the remaining individuals of the population, and exchange the repair order of some nodes in every two repaired individuals to generate new repaired individuals; the new individuals are based on the mutation probability P m =0.2 to mutate, causing the node repair sequence in the new individual to change.

5. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 1 is characterized in that: When optimizing the fitness value by the interior point method, the voltage deviation of each node is used as a penalty term and combined with the fitness obtained by the interior point method optimization as the final fitness value of each individual after the genetic algorithm is updated.

6. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 5 is characterized in that: The optimization model of the interior point method is: the interior point method for optimizing the power grid system is achieved by constructing a Lagrangian function and an iterative update algorithm.

7. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 6 is characterized in that: The optimization model of the interior point method is expressed as follows: Among them, F max (G) is the optimization target, F is the optimization target, that is, the total power generation energy of the power grid system; P iG is the generator power, P i , Q i is the power flow calculation formula for each node in the system, G ij ,B ij is the conductance and susceptance between nodes i and j, sinθ ij ,cosθ ij is the angle between nodes i and j; P Gi , Q Gi P is the system's active and reactive power; Li , Q Li is the initial fixed active and reactive load of the system; P Gmax , P Gmin is the upper and lower limits of the generator active power; Q Gmax , Q Gmin V is the upper and lower limits of the generator reactive power; max 、V min are the upper and lower limits of voltage; SG is the set of generator numbers; S(PV) is the set of PV node numbers; SN is the set of node numbers.

8. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 7 is characterized in that: The fitness value of the individual is calculated by the result of interior point model optimization. The expression is as follows: Where G is the number of generator nodes, P iG is the generator power, v i is the voltage of the ith node, nbus is the number of all nodes, v t is the target voltage, Penalty v is the total voltage deviation, λ v is the voltage deviation penalty coefficient, f gen is the total power of the generator, and Fitness is the final fitness value after optimization.

9. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 8, characterized in that: The fitness value of each repaired individual in the new population is calculated again. When updating the fitness value, if the fitness value of the repaired individual in the new population exceeds the fitness value of the repaired individual in the original population, it will be retained, otherwise the fitness value will be updated until the preset convergence condition is met.

10. The method for improving power supply capacity after an unconventional disaster by optimizing inner and outer loops according to claim 9, characterized in that: The preset convergence condition is: the final fitness value Fitness after optimization is less than the preset value ε, ε = 0.

001.

11. A system for improving power supply capacity after a disaster in an unconventional event based on inner and outer loop optimization, used to implement the method for improving power supply capacity after a disaster in an unconventional event based on inner and outer loop optimization according to any one of claims 1 to 10, characterized in that: include: A population generation module, used to form a population consisting of repaired individuals; A target optimization module, whose input end is connected to the output end of the node repair module, is used to optimize the target using the interior point method and calculate the fitness value of each repair individual in the population according to the optimization target; A population updating module, whose input ends are respectively connected to the output ends of the target optimization module, is used to select and update the repair individuals and combine them to form a new population; A calculation module, whose input end is connected to the output end of the population update module and the target optimization module respectively, and is used to calculate the fitness value of the new population formed by the population update module again by using the target optimization module, so as to update the fitness value; And a logic judgment module, whose input end is connected to the output end of the calculation module, is used to judge whether the convergence condition is met after the last fitness value update, and stop updating the fitness value if the convergence condition is met.

12. The system according to claim 11, characterized in that The population generation module comprises: A node repair unit, used to combine damaged nodes into a repair sequence as repair individuals according to the current status of the power grid system after the unconventional event; Population generation unit: Its input end is connected to the output end of the node repair unit, and is used to form a population from the repaired individuals obtained by the node repair unit.

13. The system according to claim 11, characterized in that The population update module includes: A selection unit, whose input end is connected to the output end of the target optimization module and is used to select the optimal repair individual according to the fitness value; An updating unit, whose input end is connected to the output end of the selection unit, is used to update the remaining repair individuals through a genetic algorithm; And a population secondary generation module, whose input end is connected to the output end of the selection unit and the update unit, is used to combine the updated repair individuals with the optimal repair individuals to generate a new population.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for improving power supply capacity after an unconventional event based on inner and outer loop optimization as described in any one of claims 1 to 10 when executed.

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