Power distribution network primary and secondary collaborative planning method and system based on whale optimization algorithm

Abstract

The invention belongs to the technical field of power distribution network planning, and particularly relates to a power distribution network primary and secondary collaborative planning method and system based on a whale optimization algorithm, and the method comprises the steps: building a power distribution network primary and secondary system coupling model; constructing a multi-objective optimization function taking the full life cycle cost, the power supply reliability and the new energy consumption rate as objectives, and converting the multi-objective optimization function into a single-objective optimization function; a whale optimization algorithm is adopted to search an optimal solution of the single-target optimization function, and an optimal collaborative planning scheme is obtained; according to the method, the coupling constraint model is constructed, the physical characteristics of the primary system and the functional characteristics of the secondary system are forcibly associated in the planning stage, an effective cooperative relationship of deep coupling is achieved, and the problem of insufficient collaboration caused by independent planning of the primary system and the secondary system is solved; and the whole life cycle cost, the power supply reliability and the new energy consumption rate are integrated into a multi-objective function, so that comprehensive efficiency balance is realized.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network planning technology, specifically relating to a method and system for primary and secondary collaborative planning of power distribution networks based on the whale optimization algorithm. Background Technology

[0002] In power systems, primary equipment (such as lines, transformers, and switches) is the core hardware that directly carries out the generation, transmission, and distribution of electrical energy. Secondary equipment (such as measurement terminals, communication networks, and control strategies) provides "nerve center" support for the safe and efficient operation of primary equipment through monitoring and regulation. The two should be an interdependent and collaborative organic whole to ensure the safe and efficient operation of the power system. However, in current technologies, the planning of the primary and secondary systems of the distribution network is often carried out independently: primary system planning focuses on physical configurations such as equipment capacity and topology, often neglecting the information support requirements of the secondary system, such as measurement coverage and communication latency; secondary system planning often relies on experience or simplified primary system models for configuration, resulting in a disconnect between the layout of secondary equipment and control strategies and the actual operational needs of the primary system, lacking a deep coupling and collaborative relationship. This directly leads to insufficient coordination between the primary and secondary systems of the distribution network, seriously restricting the overall operational efficiency.

[0003] Furthermore, as a key link in the power system connecting power sources and users, the rationality of the distribution network's planning directly affects economic efficiency, power supply reliability, and the capacity for renewable energy absorption. With the high proportion of distributed renewable energy (such as wind power and photovoltaics) being integrated and users' increasing requirements for power supply reliability, the distribution network needs to simultaneously address multiple objectives of economic efficiency, reliability, and renewable energy absorption. However, most planning methods only focus on minimizing initial investment or maximizing power supply reliability as a single objective, making it difficult to achieve optimal comprehensive efficiency. Summary of the Invention

[0004] To address the aforementioned shortcomings in the existing technology, this invention provides a method and system for primary and secondary collaborative planning of power distribution networks based on the whale optimization algorithm, in order to solve the problems mentioned in the background technology.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm includes the following steps: S1. Establish a coupling model of the primary and secondary systems of the distribution network, which includes a primary system model, a secondary system model, and a coupling constraint model between the two. S2. Based on the coupling model of the primary and secondary systems of the distribution network, a multi-objective optimization function is constructed with the objectives of total life cycle cost, power supply reliability, and renewable energy absorption rate, and then the multi-objective optimization function is transformed into a single-objective optimization function; S3. The optimal solution of the single-objective optimization function is searched using the whale optimization algorithm to obtain the optimal collaborative planning scheme. The whale optimization algorithm is based on the coupling model of the primary and secondary systems of the power distribution network and is designed to include steps such as population initialization, fitness calculation, individual position update and iteration termination judgment. Efficient search is achieved by introducing penalty functions and Levy flight disturbances. S4. Perform multi-scenario verification on the optimal collaborative planning scheme to verify its constraint satisfaction and target performance in each scenario. If the optimal collaborative planning scheme meets the requirements of all scenarios, output the scheme; otherwise, return to step S3 for re-optimization.

[0006] Further, in step S1, the primary system model includes: Topology variables: at least include line routes, the location and type of core nodes, and the location of sectionalizing switches and tie switches. The core nodes include at least distributed power access points, load center nodes, and line sectionalizing switch nodes. Equipment parameters: at least include the transformer's rated capacity, distributed power supply capacity, and line type parameters. Operating constraints include at least power flow balance constraints, voltage deviation ≤ ±7%, and line current carrying capacity not exceeding its rated current carrying capacity.

[0007] Further, in step S1, the secondary system model includes: Measurement terminal layout: including at least the installation locations and quantities of FTUs and TTUs; Communication network parameters: These should include at least the location of communication nodes and the delay parameters of the fiber optic transmission link; Control strategy parameters: must include at least line protection settings and distributed power source reactive power regulation thresholds; Functional constraints: must include at least measurement coverage ≥ 95%, communication latency ≤ 50ms, and control response time ≤ 100ms.

[0008] Further, in step S1, the coupling constraint model includes the following coupling constraints: Spatial coupling constraint: The secondary measurement terminal must cover at least the distributed power supply access point, load center node and line sectionalizing switch node of the primary system; Time coupling constraint: The primary system fault isolation time shall not be less than the sum of the communication delay and the secondary system control response time, wherein the fault isolation time is the maximum allowable time from the occurrence of the fault to the switching action; Parameter coupling constraint: The protection setting of the secondary system is based on the rated current carrying capacity of the primary line, which is 1.2 times the rated current carrying capacity of the primary line.

[0009] Furthermore, the specific steps of step S2 include: S21. Define a full life cycle cost function, which is the sum of primary equipment cost, secondary equipment cost, and network loss cost. The primary equipment cost includes line investment, transformer investment, switch investment, and corresponding operation and maintenance costs. The secondary equipment cost includes measurement terminal investment, communication node investment, and corresponding operation and maintenance costs. The network loss cost is calculated based on the annual network loss power, electricity price, and life cycle of power flow calculation. S22. Define a power supply reliability function, which is represented by the system average outage duration SAIDI, and the SAIDI is calculated using user outage time, number of outage households, and total number of users; S23. Define a new energy consumption rate function, which is the ratio of actual consumed electricity to theoretical maximum power generation; S24. The life cycle cost function, power supply reliability function, and renewable energy absorption rate function are transformed into a single-objective optimization function by using the benchmark value normalization weighting method.

[0010] Furthermore, the single-objective optimization function is: in, For total lifecycle cost, This serves as the benchmark value for total lifecycle cost. The average power outage duration is the system's average power outage duration. This is the baseline value for the system's average power outage duration. For the new energy consumption rate, This serves as the benchmark value for the renewable energy absorption rate. , , Weight and satisfy The weights are dynamically adjusted according to the actual scenario, such as extreme weather scenarios, normal weather scenarios, urban power distribution network scenarios, rural power distribution network scenarios, and scenarios with high new energy penetration rates.

[0011] Furthermore, the specific sub-steps of step S3 include: S31. Population initialization: The decision variables in the coupling model of the primary and secondary systems of the distribution network are encoded into N-dimensional vectors, and M initial individuals are randomly generated to form a population. The decision variables include the topology variables and equipment parameters of the primary system, as well as the layout of measurement terminals, communication network parameters and control strategy parameters of the secondary system. N is the total number of decision variables. S32. Fitness Calculation: Substitute individuals from the population into the coupling model of the primary and secondary distribution network to verify whether they meet the operational constraints, functional constraints, and coupling constraints. For individuals that meet all constraints, calculate their single-objective optimization function value as their fitness value. For individuals that violate the constraints, use a penalty function to increase their fitness value, guide the whale optimization algorithm to search the feasible solution space, and then select the individual with the smallest fitness value from the population as the current optimal individual. S33. Individual position update: Taking the current best individual determined in step S32 as the target, other individuals in the population are guided to move closer to the current best individual through the shrinking encirclement strategy in order to achieve solution convergence. At the same time, Levy flight perturbation is introduced to break the local optimum trap, and individuals that satisfy the coupling constraint are retained first during the update process. S34. Iteration Termination Judgment: If the preset maximum number of iterations is reached, or the fluctuation range of the optimal fitness value in a consecutive preset number of iterations is less than the threshold, then the iteration is terminated, and the combination of decision variables corresponding to the globally optimal individual is output as the optimal collaborative planning scheme.

[0012] Furthermore, the penalty function formula is as follows: ,in, For single-objective optimization of function value, As a penalty factor, To constrain the degree of violation, , , , They are respectively , The weights of the coupling constraints, the weights being based on , The impact of coupling constraints on system security is dynamically adjusted, and the following conditions are met: + + =1; The Levy flight disturbance formula is: ,in As the current optimal individual, For Levy's flight, random numbers, =1.5, if Its fitness is better than Then update for .

[0013] Furthermore, in step S4, the multiple scenarios include high renewable energy output and peak load scenarios, low renewable energy output and valley load scenarios, and renewable energy output random fluctuation and typical load scenarios; the constraint satisfaction includes verifying whether voltage deviation, communication latency, and measurement coverage meet preset constraints in each scenario; the target performance indicators include verifying whether the total life cycle cost, SAIDI, and renewable energy absorption rate are within preset ranges in each scenario.

[0014] This invention also provides a primary and secondary collaborative planning system for distribution networks based on the whale optimization algorithm, used to implement the aforementioned primary and secondary collaborative planning method for distribution networks based on the whale optimization algorithm, comprising the following modules: The coupling model construction module is used to establish a coupling model of the primary and secondary systems of the distribution network. The coupling model of the primary and secondary systems of the distribution network includes a primary system model, a secondary system model, and a coupling constraint model between the two. The optimization function construction module is used to construct a multi-objective optimization function with objectives such as total life cycle cost, power supply reliability, and renewable energy absorption rate, and to transform the multi-objective optimization function into a single-objective optimization function. The optimization solution module uses the whale optimization algorithm to search for the optimal solution of the single-objective optimization function and obtain the optimal collaborative planning scheme. The scheme verification module is used to verify the optimal collaborative planning scheme in multiple scenarios. If the scheme meets all scenario requirements, it outputs the scheme; otherwise, it triggers the optimization and solution module to re-optimize.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By constructing a coupled constraint model that includes spatial coupling constraints, temporal coupling constraints, and parameter coupling constraints, the physical characteristics of the primary system (such as line current carrying capacity and topology) and the functional characteristics of the secondary system (such as measurement coverage, communication delay, and control response) are forcibly associated during the planning stage, achieving a deep and effective synergistic relationship. This solves the problem of insufficient synergy caused by the independent planning of the primary and secondary systems in existing technologies. At the same time, by using a penalty function, the coupling constraints of the coupled constraint model, as well as the operational constraints of the primary system and the functional constraints of the secondary system, are directly embedded into the process of searching for the optimal solution. Schemes that violate the constraints are quantitatively penalized, so that the final collaborative planning scheme can ensure the independent feasibility of the primary and secondary systems while strictly meeting the synergistic requirements of the primary and secondary systems, thereby improving the overall operational stability of the distribution network.

[0016] 2. This design integrates multiple objective functions, including total lifecycle cost, power supply reliability, and renewable energy absorption rate, and achieves quantitative coordination through a benchmark value normalization weighted method. Compared with traditional single-objective planning, this design can dynamically adjust the priority of objectives according to the scenario. For example, in scenarios with high renewable energy penetration, the weight of renewable energy absorption rate can be increased. While controlling the total cost, the renewable energy absorption rate can be increased to over 90%, and power supply reliability can be improved by 20% to 30%. This effectively solves the pain point of balancing economy and efficiency under high renewable energy access and achieves a comprehensive efficiency balance. 3. The performance of the whale optimization algorithm can be improved and the search efficiency increased through the coordinated design of penalty functions and Levy flight perturbations. Penalty functions can guide the whale optimization algorithm to focus on the feasible solution space and reduce invalid iterations. Levy flight perturbations can help the whale optimization algorithm escape the local optimum trap, thereby efficiently obtaining the global optimum. Attached Figure Description

[0017] Figure 1 This is a flowchart of a primary and secondary collaborative planning method for power distribution networks based on the whale optimization algorithm, according to the present invention. Figure 2 This is a diagram illustrating the composition of a primary and secondary collaborative planning system for a power distribution network based on the whale optimization algorithm, as described in this invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0020] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] Example 1: like Figure 1 As shown, the present invention provides a method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm, specifically including the following steps: S1. Establish a coupling model of the primary and secondary systems of the distribution network, which includes a primary system model, a secondary system model, and a coupling constraint model between the two. S2. Based on the coupling model of the primary and secondary systems of the distribution network, a multi-objective optimization function is constructed with the objectives of total life cycle cost, power supply reliability, and renewable energy absorption rate, and then the multi-objective optimization function is transformed into a single-objective optimization function; S3. The optimal solution of the single-objective optimization function is searched using the whale optimization algorithm to obtain the optimal collaborative planning scheme. The whale optimization algorithm is based on the coupling model of the primary and secondary systems of the power distribution network and is designed to include steps such as population initialization, fitness calculation, individual position update and iteration termination judgment. Efficient search is achieved by introducing penalty functions and Levy flight disturbances. S4. Perform multi-scenario verification on the optimal collaborative planning scheme to verify its constraint satisfaction and target performance in each scenario. If the optimal collaborative planning scheme meets the requirements of all scenarios, output the scheme; otherwise, return to step S3 for re-optimization.

[0023] This invention achieves a breakthrough from separate planning to deep collaboration between the primary and secondary systems of a distribution network by establishing a coupled model of the primary and secondary systems, constructing objective functions, finding optimal solutions, and verifying the results in a closed-loop process. Step S1, the coupled model, solves the problem of disconnected requirements between the primary and secondary systems in traditional planning. Step S2, the multi-objective function, integrates economy, reliability, and renewable energy consumption, overcoming the limitations of a single objective. Step S3, the improved whale algorithm, enhances solution efficiency and quality through penalty functions and Levy flight. Step S4, the multi-scenario verification, ensures the robustness of the solution under dynamic operating conditions. This comprehensive collaboration across the entire process achieves a leap in the overall efficiency of distribution network planning. In step S1, the primary system model includes: Topology variables: at least include line routes, the location and type of core nodes, and the location of sectionalizing switches and tie switches. The core nodes include at least distributed power access points, load center nodes, and line sectionalizing switch nodes. Equipment parameters: at least include the transformer's rated capacity, distributed power supply capacity, and line type parameters. Operating constraints include at least power flow balance constraints, voltage deviation ≤ ±7%, and line current carrying capacity not exceeding its rated current carrying capacity.

[0024] In the implementation, the core elements of the primary system model are clearly defined to ensure that the planning scheme accurately controls the physical characteristics of the primary system (such as topology variables and equipment parameters). Among them, the definition of core nodes (distributed power access points, load centers, etc.) provides clear targets for the measurement coverage of the secondary system. Operational constraints directly ensure the safe operation of the primary system and avoid power quality problems caused by parameter over-limits, thus laying the physical configuration foundation for primary and secondary system coordination.

[0025] In step S1, the secondary system model includes: Measurement terminal layout: including at least the installation locations and quantities of FTUs and TTUs; Communication network parameters: These should include at least the location of communication nodes and the delay parameters of the fiber optic transmission link; Control strategy parameters: must include at least line protection settings and distributed power source reactive power regulation thresholds; Functional constraints: must include at least measurement coverage ≥ 95%, communication latency ≤ 50ms, and control response time ≤ 100ms.

[0026] In the implementation, by refining the functional elements of the secondary system, the supporting capability of the secondary system to the primary system is ensured to be quantifiable and verifiable. The layout of the measurement terminals directly determines the data acquisition integrity of key nodes in the primary system, and the measurement coverage rate is ≥95% to avoid blind spots. Communication latency ≤50ms and control response time ≤100ms ensure the secondary system's rapid response to primary system faults. Protection settings and reactive power adjustment thresholds enable precise protection of primary equipment and flexible control of renewable energy output. Compared with the traditional experience-based secondary planning model, the refinement of this secondary system model makes the functional requirements of the secondary system clearly correspond to the operational requirements of the primary system.

[0027] In step S1, the coupling constraint model includes the following coupling constraints: Spatial coupling constraint: The secondary measurement terminal must cover at least the distributed power supply access point, load center node and line sectionalizing switch node of the primary system; Time coupling constraint: The primary system fault isolation time shall not be less than the sum of the communication delay and the secondary system control response time, wherein the fault isolation time is the maximum allowable time from the occurrence of the fault to the switching action; Parameter coupling constraint: The protection setting of the secondary system is based on the rated current carrying capacity of the primary line, which is 1.2 times the rated current carrying capacity of the primary line.

[0028] In the implementation, through the three-layer coupling constraints of "space, time, and parameters", the "physical requirements" and "functional capabilities" of the primary and secondary systems are forcibly bound together for collaborative optimization. The spatial coupling constraint ensures that the secondary measurement can measure the key nodes of the primary system; the time coupling ensures that the fault response of the secondary system can handle the faults of the primary system in time; and the parameter coupling ensures that the secondary system can protect the primary equipment (protection setting = 1.2 times the rated current carrying capacity). This multi-dimensional constraint completely solves the problem of insufficient coordination in traditional planning, where "the primary system ignores the capabilities of the secondary system and the secondary system is detached from the requirements of the primary system", so that the primary and secondary systems form a deeply coupled and effectively coordinated whole.

[0029] The specific steps of step S2 include: S21. Define a full life cycle cost function, which is the sum of primary equipment cost, secondary equipment cost, and network loss cost. The primary equipment cost includes line investment, transformer investment, switch investment, and corresponding operation and maintenance costs. The secondary equipment cost includes measurement terminal investment, communication node investment, and corresponding operation and maintenance costs. The network loss cost is calculated based on the annual network loss power, electricity price, and life cycle of power flow calculation. S22. Define a power supply reliability function, which is represented by the system average outage duration SAIDI, and the SAIDI is calculated using user outage time, number of outage households, and total number of users; S23. Define a new energy consumption rate function, which is the ratio of actual consumed electricity to theoretical maximum power generation; S24. The life cycle cost function, power supply reliability function, and renewable energy absorption rate function are transformed into a single-objective optimization function by using the benchmark value normalization weighting method.

[0030] In the implementation method, by refining the composition and transformation methods of multi-objective functions, the comprehensive efficiency of distribution network planning is quantitatively optimized. The total lifecycle cost covers the entire lifecycle expenditure of primary and secondary equipment, avoiding the short-sightedness of traditional methods that only consider initial investment; the SAIDI index quantifies power supply reliability, directly reflecting user experience; the renewable energy absorption rate is adapted to scenarios with a high proportion of renewable energy access, and the benchmark value normalization weighted method solves the problem of differences in the dimensions of different objectives, enabling synergistic optimization of cost, reliability, and absorption rate, breaking through the limitations of traditional single-objective planning.

[0031] The single-objective optimization function is: For total lifecycle cost, This serves as the benchmark value for total lifecycle cost. The average power outage duration is the system's average power outage duration. This is the baseline value for the system's average power outage duration. For the new energy consumption rate, This serves as the benchmark value for the renewable energy absorption rate. , , Weight and satisfy The weights are dynamically adjusted according to the actual scenario, such as extreme weather scenarios, normal weather scenarios, urban power distribution network scenarios, rural power distribution network scenarios, and scenarios with high new energy penetration rates.

[0032] In the implementation, by clarifying the mathematical expression of the single-objective optimization function, quantitative coordination and scenario adaptation of multiple objectives are achieved. The benchmark value of the full life cycle cost is usually referenced from the historical planning data of the same-scale distribution network in the same region or the industry cost quota, reflecting the reasonable cost range under the current technical level. The benchmark value of the average power outage duration of the system is based on the regional power supply reliability supervision target or the historical operating average of similar distribution networks, which is in line with the actual power supply capacity requirements. The benchmark value of the renewable energy absorption rate is determined based on the renewable energy access scale, regional regulation capacity and industry absorption target, with reference to the historical highest absorption rate or the theoretically achievable value.

[0033] The specific sub-steps of step S3 include: S31. Population initialization: The decision variables in the coupling model of the primary and secondary systems of the distribution network are encoded into N-dimensional vectors, and M initial individuals are randomly generated to form a population. The decision variables include the topology variables and equipment parameters of the primary system, as well as the layout of measurement terminals, communication network parameters and control strategy parameters of the secondary system. N is the total number of decision variables. S32. Fitness Calculation: Substitute individuals from the population into the coupling model of the primary and secondary distribution network to verify whether they meet the operational constraints, functional constraints, and coupling constraints. For individuals that meet all constraints, calculate their single-objective optimization function value as their fitness value. For individuals that violate the constraints, use a penalty function to increase their fitness value, guide the whale optimization algorithm to search the feasible solution space, and then select the individual with the smallest fitness value from the population as the current optimal individual. S33. Individual position update: Taking the current best individual determined in step S32 as the target, other individuals in the population are guided to move closer to the current best individual through the shrinking encirclement strategy in order to achieve solution convergence. At the same time, Levy flight perturbation is introduced to break the local optimum trap, and individuals that satisfy the coupling constraint are retained first during the update process. S34. Iteration Termination Judgment: If the preset maximum number of iterations is reached, or the fluctuation range of the optimal fitness value in a consecutive preset number of iterations is less than the threshold, then the iteration is terminated, and the combination of decision variables corresponding to the globally optimal individual is output as the optimal collaborative planning scheme.

[0034] In the implementation, by refining the steps of the whale optimization algorithm, its adaptability to the complexity of primary and secondary collaborative planning of power distribution networks is ensured. Population initialization covers all decision variables of the primary and secondary systems to ensure the integrity of the solution. Fitness calculation combined with a penalty function enables the algorithm to actively avoid invalid solutions that violate constraints and search in the feasible solution space, thereby improving search efficiency. Individual position updates are achieved by combining shrinking encirclement and Levy flight, balancing convergence speed and global optimum capability. The iteration termination condition balances solution efficiency and solution accuracy. This optimized whale optimization algorithm can quickly find the global optimum under complex constraints and output the optimal collaborative planning scheme.

[0035] Wherein, the penalty function formula is: ,in, For single-objective optimization of function value, As a penalty factor, To constrain the degree of violation, , , , They are respectively , The weights of the coupling constraints, the weights being based on , The impact of coupling constraints on system security is dynamically adjusted, and the following conditions are met: + + =1, The value can be preset based on the specific scenario, constraint characteristics, and optimization target requirements of the primary and secondary collaborative planning of the distribution network. The Levy flight disturbance formula is: ,in As the current optimal individual, For Levy's flight, random numbers, =1.5, if Its fitness is better than Then update for .

[0036] In the implementation, the mathematical expressions for the penalty function and Levy flight are clarified, enhancing the whale optimization algorithm's sensitivity to constraints and its global search capability. The penalty function... The weighted calculation allows for the quantification of the degree of violation of different constraints, and constraints with a greater impact on system safety (such as time-coupled constraints) can be assigned higher weights, guiding the algorithm to prioritize satisfying key constraints; Levy flight disturbances ( A random step size of 1.5 introduces appropriate randomness, helping the algorithm escape the trap of local optima.

[0037] In step S4, the multiple scenarios include high renewable energy output and peak load scenarios, low renewable energy output and valley load scenarios, and renewable energy output random fluctuation and typical load scenarios; the constraint satisfaction includes verifying whether voltage deviation, communication latency, and measurement coverage meet preset constraints in each scenario; the target performance indicators include verifying whether the total life cycle cost, SAIDI, and renewable energy absorption rate are within preset ranges in each scenario.

[0038] In the implementation, the robustness of the planning scheme is ensured through multi-scenario verification covering both extreme and normal conditions. High renewable energy output and peak load scenarios, and low renewable energy output and valley load scenarios simulate the system's boundary conditions, while random fluctuation scenarios closely reflect the uncertainties in actual operation. Constraint satisfaction verification (such as voltage deviation ≤ ±7%, communication latency ≤ 50ms) ensures the safety of the scheme, while target performance verification ensures that its overall efficiency still meets expectations under dynamic scenarios. The verification scheme can operate stably under various operating conditions, avoiding field failures.

[0039] like Figure 2 As shown, the present invention also provides a primary and secondary collaborative planning system for distribution networks based on the whale optimization algorithm, used to implement the aforementioned primary and secondary collaborative planning method for distribution networks based on the whale optimization algorithm, comprising the following modules: The coupling model construction module is used to establish a coupling model of the primary and secondary systems of the distribution network. The coupling model of the primary and secondary systems of the distribution network includes a primary system model, a secondary system model, and a coupling constraint model between the two. The optimization function construction module is used to construct a multi-objective optimization function with objectives such as total life cycle cost, power supply reliability, and renewable energy absorption rate, and to transform the multi-objective optimization function into a single-objective optimization function. The optimization solution module uses the whale optimization algorithm to search for the optimal solution of the single-objective optimization function and obtain the optimal collaborative planning scheme. The scheme verification module is used to verify the optimal collaborative planning scheme in multiple scenarios. If the scheme meets all scenario requirements, it outputs the scheme; otherwise, it triggers the optimization and solution module to re-optimize.

[0040] In the implementation, each module has a clear division of labor and works together. Through modular design, the abstract planning method is transformed into an executable system architecture. The method and system are based on the same application concept. Since the methods and systems solve problems in similar ways, their implementations can refer to each other, and repeated parts will not be described again.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0042] The above are merely embodiments of the present invention. The circuits, electronic components, and modules involved are all prior art, fully achievable by those skilled in the art, and require no further explanation. The scope of protection in this application does not involve improvements to the software or methods. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all prior art in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm, characterized in that, Includes the following steps: S1. Establish a coupling model of the primary and secondary systems of the distribution network, which includes a primary system model, a secondary system model, and a coupling constraint model between the two. S2. Based on the coupling model of the primary and secondary systems of the distribution network, a multi-objective optimization function is constructed with the objectives of total life cycle cost, power supply reliability, and renewable energy absorption rate, and then the multi-objective optimization function is transformed into a single-objective optimization function; S3. The optimal solution of the single-objective optimization function is searched using the whale optimization algorithm to obtain the optimal collaborative planning scheme. The whale optimization algorithm is based on the coupling model of the primary and secondary systems of the power distribution network and is designed to include steps such as population initialization, fitness calculation, individual position update and iteration termination judgment. Efficient search is achieved by introducing penalty functions and Levy flight disturbances. S4. Perform multi-scenario verification on the optimal collaborative planning scheme to verify its constraint satisfaction and target performance in each scenario. If the optimal collaborative planning scheme meets the requirements of all scenarios, output the scheme; otherwise, return to step S3 for re-optimization.

2. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 1, characterized in that, In step S1, the primary system model includes: Topology variables: at least include line routes, the location and type of core nodes, and the location of sectionalizing switches and tie switches. The core nodes include at least distributed power access points, load center nodes, and line sectionalizing switch nodes. Equipment parameters: at least include the transformer's rated capacity, distributed power supply capacity, and line type parameters. Operating constraints include at least power flow balance constraints, voltage deviation ≤ ±7%, and line current carrying capacity not exceeding its rated current carrying capacity.

3. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 2, characterized in that, In step S1, the secondary system model includes: Measurement terminal layout: including at least the installation locations and quantities of FTUs and TTUs; Communication network parameters: These should include at least the location of communication nodes and the delay parameters of the fiber optic transmission link; Control strategy parameters: must include at least line protection settings and distributed power source reactive power regulation thresholds; Functional constraints: must include at least measurement coverage ≥ 95%, communication latency ≤ 50ms, and control response time ≤ 100ms.

4. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 3, characterized in that, In step S1, the coupling constraint model includes the following coupling constraints: Spatial coupling constraint: The secondary measurement terminal must cover at least the distributed power supply access point, load center node and line sectionalizing switch node of the primary system; Time coupling constraint: The primary system fault isolation time shall not be less than the sum of the communication delay and the secondary system control response time, wherein the fault isolation time is the maximum allowable time from the occurrence of the fault to the switching action; Parameter coupling constraint: The protection setting of the secondary system is based on the rated current carrying capacity of the primary line, which is 1.2 times the rated current carrying capacity of the primary line.

5. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 1, characterized in that, The specific steps of step S2 include: S21. Define a full life cycle cost function, which is the sum of primary equipment cost, secondary equipment cost, and network loss cost. The primary equipment cost includes line investment, transformer investment, switch investment, and corresponding operation and maintenance costs. The secondary equipment cost includes measurement terminal investment, communication node investment, and corresponding operation and maintenance costs. The network loss cost is calculated based on the annual network loss power, electricity price, and life cycle of power flow calculation. S22. Define a power supply reliability function, which is represented by the system average outage duration SAIDI, and the SAIDI is calculated using user outage time, number of outage households and total number of users; S23. Define a new energy consumption rate function, which is the ratio of actual consumed electricity to theoretical maximum power generation; S24. The life cycle cost function, power supply reliability function, and renewable energy absorption rate function are transformed into a single-objective optimization function by using the benchmark value normalization weighting method.

6. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 5, characterized in that, The single-objective optimization function is: in, For total lifecycle cost, This serves as the benchmark value for total lifecycle cost. The average power outage duration is the system's average power outage duration. This is the baseline value for the system's average power outage duration. For the new energy consumption rate, This serves as the benchmark value for the renewable energy absorption rate. , , Weights and satisfying The weights are dynamically adjusted according to the actual scenario.

7. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 4, characterized in that, The specific sub-steps of step S3 include: S31. Population initialization: The decision variables in the coupling model of the primary and secondary systems of the distribution network are encoded into N-dimensional vectors, and M initial individuals are randomly generated to form a population. The decision variables include the topology variables and equipment parameters of the primary system, as well as the layout of measurement terminals, communication network parameters and control strategy parameters of the secondary system. N is the total number of decision variables. S32. Fitness Calculation: Substitute individuals from the population into the coupling model of the primary and secondary distribution network to verify whether they meet the operational constraints, functional constraints, and coupling constraints. For individuals that meet all constraints, calculate their single-objective optimization function value as their fitness value. For individuals that violate the constraints, use a penalty function to increase their fitness value, guide the whale optimization algorithm to search the feasible solution space, and then select the individual with the smallest fitness value from the population as the current optimal individual. S33. Individual position update: Taking the current best individual determined in step S32 as the target, other individuals in the population are guided to move closer to the current best individual through the shrinking encirclement strategy in order to achieve solution convergence. At the same time, Levy flight perturbation is introduced to break the local optimum trap, and individuals that satisfy the coupling constraint are retained first during the update process. S34. Iteration Termination Judgment: If the preset maximum number of iterations is reached, or the fluctuation range of the optimal fitness value in a consecutive preset number of iterations is less than the threshold, then the iteration is terminated, and the combination of decision variables corresponding to the globally optimal individual is output as the optimal collaborative planning scheme.

8. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 7, characterized in that, The penalty function formula is as follows: ,in, For single-objective optimization of function value, As a penalty factor, To constrain the degree of violation, , , , They are respectively , The weights of the coupling constraints, the weights being based on , The impact of coupling constraints on system security is dynamically adjusted, and the following conditions are met: + + =1; The Levy flight disturbance formula is: ,in As the current optimal individual, For Levy's flight, random numbers, =1.5, if Its fitness is better than Then update for .

9. The method for primary and secondary collaborative planning of distribution networks based on the whale optimization algorithm as described in claim 1, characterized in that, In step S4, the multiple scenarios include high renewable energy output and peak load scenarios, low renewable energy output and valley load scenarios, and renewable energy output random fluctuation and typical load scenarios; the constraint satisfaction includes verifying whether voltage deviation, communication latency, and measurement coverage meet preset constraints in each scenario; the target performance indicators include verifying whether the total life cycle cost, SAIDI, and renewable energy absorption rate are within preset ranges in each scenario.

10. A distribution network primary and secondary collaborative planning system based on the whale optimization algorithm, used to implement the distribution network primary and secondary collaborative planning method based on the whale optimization algorithm as described in any one of claims 1-9, characterized in that, Includes the following modules: The coupling model construction module is used to establish a coupling model of the primary and secondary systems of the distribution network. The coupling model of the primary and secondary systems of the distribution network includes a primary system model, a secondary system model, and a coupling constraint model between the two. The optimization function construction module is used to construct a multi-objective optimization function with objectives such as total life cycle cost, power supply reliability, and renewable energy absorption rate, and to transform the multi-objective optimization function into a single-objective optimization function. The optimization solution module uses the whale optimization algorithm to search for the optimal solution of the single-objective optimization function and obtain the optimal collaborative planning scheme. The scheme verification module is used to verify the optimal collaborative planning scheme in multiple scenarios. If the scheme meets all scenario requirements, it outputs the scheme; otherwise, it triggers the optimization and solution module to re-optimize.

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