Reconfigurable distribution network energy optimization method and device and storage medium
By introducing power constraints and population complementarity rules into reconfigurable distribution networks, and combining the bald eagle algorithm to optimize the population, the power balance problem between distributed resources and loads is solved, achieving efficient energy optimization and rational allocation, and avoiding the problems of power imbalance and insufficient iteration speed.
Patent Information
- Application Number
- CN202511224729.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-29
- Publication Date
- 2025-12-02
AI Technical Summary
Existing intelligent algorithms struggle to achieve power balance between distributed resources and loads in reconfigurable distribution networks, leading to power imbalance problems, increasing the energy storage system's input and output costs, and potentially causing adverse phenomena such as power backfeeding. Furthermore, the algorithm's iteration speed is insufficient to meet the real-time requirements of load changes.
When constructing a reconfigurable distribution network energy optimization model, power constraints are introduced, population complementarity rules and random population generation methods are designed, and the vulture algorithm is combined to optimize the population through regional search, prey search and hunting sprint stages, so as to ensure the balance between wind power and photovoltaic power output and load power and avoid power imbalance.
It accelerates the time complexity of energy optimization, realizes the rational allocation of distributed resources, avoids power backflow, improves the iteration speed and solution accuracy, and meets the energy optimization needs of distribution networks under the high penetration rate of new energy.
Smart Images
Figure CN121055484A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of reconfigurable distribution network technology, and particularly relates to a reconfigurable distribution network energy optimization method, device and storage medium. Background Technology
[0002] In recent years, my country's installed capacity of new energy sources, mainly wind and solar power, has increased rapidly. The penetration rate of new energy power generation in the distribution network is also rising, making the operation and control of the distribution system more complex. The practical significance of studying the power generation operation of reconfigurable distribution networks with high new energy penetration is growing. Achieving energy optimization management of distributed resources in reconfigurable distribution networks can save costs, realize the full utilization of wind and solar power, avoid power imbalance problems in multi-modal operation of reconfigurable distribution networks, avoid adverse phenomena such as power backfeeding, and solve the problem of high-proportion new energy supply and consumption.
[0003] In recent years, many scholars both domestically and internationally have primarily utilized intelligent algorithms to solve energy optimization problems, including genetic algorithms, particle swarm optimization, and gray wolf optimization algorithms. Intelligent algorithms offer advantages such as fast convergence and simple models. However, due to the randomness of population generation in general heuristic and metaheuristic algorithms, the optimal solution obtained upon convergence may not necessarily meet the power balance requirements of distributed resources and loads. This increases the throughput cost of energy storage systems. When power imbalance exceeds the capacity of the energy storage system, problems such as power backfeeding can occur, adversely affecting the power grid. Considering the randomness of load changes, the real-time performance of energy optimization is also a key consideration, and the iteration speed of the algorithm has a significant impact on the energy optimization of reconfigurable distribution networks. Summary of the Invention
[0004] The purpose of this invention is to provide a reconfigurable distribution network energy optimization method, device and storage medium. When constructing the energy optimization model, power constraints are introduced and population generation rules are designed according to the characteristics of distributed resources. In the process of solving the energy optimization model, the time complexity of energy optimization is accelerated and the power balance problem in the optimization process is solved.
[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:
[0006] In a first aspect, the present invention provides a reconfigurable distribution network energy optimization model based on distributed resources accessed by the reconfigurable distribution network;
[0007] Based on the power constraints of distributed resources, a population complementarity rule and a random population generation method are constructed, wherein each individual in the population represents a candidate reconfigurable distribution network energy optimization scheme.
[0008] The reconfigurable distribution network energy optimization model is solved using the population complementarity rule and the random population generation method to obtain a reconfigurable distribution network energy optimization scheme that satisfies the maximum benefit objective.
[0009] Optionally, a reconfigurable distribution network energy optimization model is constructed based on the distributed resources accessed by the reconfigurable distribution network, including:
[0010] Distributed resources connected to a reconfigurable distribution network are defined as including multiple wind power generation systems and photovoltaic power generation systems with the same unit operating cost. Based on power balance constraints, the total load power required by these distributed resources is expressed as follows:
[0011] ,
[0012] in, This represents the total load power required by the distributed resources, where m represents the number of wind power generation systems. This represents the output active power of the i-th wind power generation system, and n represents the number of photovoltaic power generation systems. This represents the output active power of the i-th photovoltaic power generation system. and Subject to the following constraints: , and These represent the minimum output active power of wind power generation and photovoltaic power generation systems, respectively. and These represent the maximum active power output of the wind power generation and photovoltaic power generation systems, respectively;
[0013] Based on the total load power required by distributed resources, a distribution network energy optimization model is obtained, with the output active power of wind power generation systems and photovoltaic power generation systems as planning variables and minimizing the operating cost of distributed resources as the maximum benefit objective. The operating cost of distributed resources is expressed as:
[0014] ,
[0015] Where C represents the operating cost of the distributed resource. and All are weighting coefficients, and satisfy the following conditions: , and These represent the operating cost coefficients for wind power generation and photovoltaic power generation, respectively. This indicates the total load power required by the wind power generation system. This indicates the total load power required by the photovoltaic power generation system.
[0016] Optionally, the constraints of the reconfigurable distribution network energy optimization model include:
[0017] ,
[0018] ,
[0019] ,
[0020] .
[0021] Optionally, the population complementarity rule includes: obtaining the other half of the individual values based on a determined half of the individual values, as expressed below:
[0022]
[0023] Where k is the number of individuals in the population. The expected sum of the values of all individuals in the population. The mean value of individuals in the population. Let represent the i-th individual value, which includes the output active power of distributed resources in the corresponding reconfigurable distribution network energy optimization scheme, the expected value of all individual values, and the total load power required by distributed resources in all candidate reconfigurable distribution network energy optimization schemes.
[0024] Optionally, the random population generation method includes:
[0025] According to the population complementarity rule, half of the individual values are generated, and the remaining individual values are supplemented based on the generated half of the individual values. The expression is as follows:
[0026] ,
[0027] in, and These are the upper and lower limits of the individual value, respectively. To generate the variation amplitude of random individuals, It is a range Random variations within.
[0028] Optionally, the step of solving the reconfigurable distribution network energy optimization model according to the population complementarity rule and the random population generation method to obtain a reconfigurable distribution network energy optimization scheme that satisfies the maximum benefit objective includes:
[0029] An initial population is generated based on the population complementarity rule and the random population generation method.
[0030] Using the vulture algorithm, the search area of the initial population is narrowed during the area search phase. During the prey search phase, the optimal search position is determined within the narrowed search area. During the hunting sprint phase, candidate individuals are iterated at the optimal search position until the reconfigurable distribution network energy optimization scheme converges to meet the objective of minimizing the operating cost of distributed resources, thus obtaining the optimal solution of the reconfigurable distribution network energy optimization model. In the area search phase, prey search phase, and hunting sprint phase, the updated population is modified according to the population complementarity rule.
[0031] Optionally, the expression for narrowing the search area of the initial population during the region search phase is as follows:
[0032] ,
[0033] in, This is the optimal result from the previous iteration. For control parameters, For range Random numbers within, The mean of the elements of the vector represented by each bald eagle. To update the information of the j-th vulture, This represents the information of the j-th vulture before the update. Each bald eagle represents an individual.
[0034] Optionally, the expression for determining the optimal search location within the narrowed search area during the prey search phase is as follows:
[0035] ,
[0036] ,
[0037] in, and Let x and y represent the position information of the j-th bald eagle on the horizontal and vertical axes, respectively. and Let x and y represent the unit-level horizontal and vertical positions of the j-th bald eagle, respectively. and Describe the polar radius and polar angle in the spiral path respectively. and These are the parameters that determine the spiral path.
[0038] Optionally, the expression for iterating over candidate individuals at the optimal search location during the hunting sprint phase is as follows:
[0039] ,
[0040] ,
[0041] in, and These represent the horizontal and vertical position information of the prey after the unit mark, respectively. and These represent motion parameters.
[0042] In a second aspect, the present invention provides a reconfigurable distribution network energy optimization device, comprising:
[0043] Reconfigurable distribution network energy optimization model construction module: used to construct a reconfigurable distribution network energy optimization model based on the distributed resources accessed by the reconfigurable distribution network;
[0044] Population generation rule construction module: used to construct population complementarity rules and random population generation methods based on the power constraints of distributed resources, wherein each individual in the population represents a candidate reconfigurable distribution network energy optimization scheme;
[0045] The module for obtaining reconfigurable distribution network energy optimization schemes is used to solve the reconfigurable distribution network energy optimization model based on the population complementarity rule and the random population generation method, so as to obtain the reconfigurable distribution network energy optimization scheme that satisfies the maximum benefit objective.
[0046] Thirdly, the present invention provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the reconfigurable distribution network energy optimization method as described in any of the first aspects.
[0047] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: Based on the power constraints of the distributed resources themselves, the total power is allocated; a population complementarity rule and a random population generation method are designed; the individual dimension in the optimization algorithm is halved, accelerating the iteration speed; population complementarity correction is added to the traditional vulture algorithm; this ensures a balance between wind and solar power output and load power demand during energy optimization, avoiding power backfeeding or over-distribution; and thus achieving a reasonable allocation of energy from distributed resources in a reconfigurable distribution network with high penetration of new energy. Attached Figure Description
[0048] Figure 1 The diagram shown is a flowchart of the reconfigurable distribution network energy optimization method in Embodiment 1 of the present invention;
[0049] Figure 2 The diagram shown is a flowchart of the reconfigurable distribution network energy optimization method in Embodiment 2 of the present invention;
[0050] Figure 3 The diagram shown illustrates the generation of individual power values based on population complementarity rules in one embodiment of the present invention. Detailed Implementation
[0051] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.
[0052] Example 1
[0053] like Figure 1 As shown, this embodiment provides a reconfigurable distribution network energy optimization method, including:
[0054] Construct a reconfigurable distribution network energy optimization model based on the distributed resources accessed by the reconfigurable distribution network;
[0055] Based on the power constraints of distributed resources, a population complementarity rule and a random population generation method are constructed.
[0056] Based on the population complementarity rule and the random population generation method, the energy optimization model of the distribution network is solved to obtain a reconfigurable energy optimization scheme for the distribution network.
[0057] By comprehensively considering the distributed resources of reconfigurable distribution network access, a distribution network energy optimization model can be constructed, which can more accurately reflect the actual operating status of the distribution network. Furthermore, by combining the population complementarity rule and random population generation method designed for the power constraints of distributed resources with the vulture algorithm, a solution space can be searched more efficiently while adapting to resource characteristics, avoiding getting trapped in local optima, thereby improving the accuracy and efficiency of the solution.
[0058] Example 2
[0059] Based on Example 1, this example also incorporates the following design.
[0060] like Figure 2 As shown, the reconfigurable distribution network energy optimization method specifically includes the following steps:
[0061] S1. Construct a reconfigurable distribution network distributed resource operation cost optimization model.
[0062] In reconfigurable distribution networks with high renewable energy penetration, distributed resources mainly take the form of wind power systems and photovoltaic power systems. Distributed resources connected to the distribution network are defined as several subsystems with identical unit operating costs. Based on power balance constraints, the output of distributed resources can be expressed as follows:
[0063] (1)
[0064] in, This represents the total load power required by the distributed resources, where m represents the number of wind power generation systems. This represents the output active power of the i-th subsystem, and n represents the number of photovoltaic power generation systems. This represents the output active power of the i-th subsystem, which is subject to the following constraints:
[0065] (2)
[0066] in, and These represent the minimum output active power of wind power generation and photovoltaic power generation systems, respectively. and These represent the minimum active power output of wind power generation and photovoltaic power generation systems, respectively.
[0067] Total load power requirement based on the maximum output power of distributed resources Distribute according to and Configure the respective constraints for wind power and solar power:
[0068] (3)
[0069] in, This indicates the total load power required by the wind power generation system. This indicates the total load power required by the photovoltaic power generation system.
[0070] Establish the objective function for the operating cost of distributed resources:
[0071] (4)
[0072] Where C represents the total operating cost, expressed in ten thousand yuan. and Let be the weighting coefficient, and let its value satisfy . , The weighting coefficients include terms that describe the operating costs of distributed generation in reconfigurable distribution networks with high renewable energy penetration. The weighting coefficients include terms describing power imbalances in reconfigurable distribution networks, such as power backflow caused by over-generation during live-line work or power shortages caused by insufficient power generation. and These represent the operating cost coefficients for wind power generation and photovoltaic power generation, respectively, in units of 10,000 yuan / kW.
[0073] S2. Design population complementarity rules and random population generation methods based on the inherent limitations of distributed resources.
[0074] The population complementarity rule is specifically expressed as follows:
[0075] (5)
[0076] Where k is the number of individuals in the population. The sum of the expected values of all individuals in the population. The mean value of individuals in the population. Let represent the value of the i-th individual. The above rule establishes pairwise relationships between individuals; by determining only half of the individual values, the other half can be derived, and the sum of the generated individual values satisfies the expected sum. In reconfigurable distribution network energy optimization, individual values It can be the output power of a distributed renewable energy subsystem, and the expected and It can be the load demand power. When all individuals satisfy the above population complementarity rule, the power balance constraint condition is met.
[0077] Random population generation methods, such as Figure 3 As shown, according to the population complementarity rule, only half of the individual values need to be generated to supplement all the individual values. This is done using the following method:
[0078] (6)
[0079] in, and These are the upper and lower limits of the individual value, respectively. To generate the variation amplitude of random individuals, It is a range The random variation of .
[0080] S3. Introduce population complementarity rules and random population generation methods into the traditional vulture algorithm to optimize distributed resource energy.
[0081] The initial population is generated according to equations (5) and (6), where each vulture represents an energy allocation scheme, represented as an m+n dimensional vector. This represents the information about the j-th vulture, i.e., the j-th candidate solution:
[0082] (7)
[0083] During the region search phase, the vulture determines the optimal search region based on the best result from the previous iteration and updates it according to the following formula:
[0084] (8)
[0085] in, This is the optimal result from the previous iteration. As a control parameter, it is generally taken as 1.5~2.0. for The random number is generated and re-randomized each time it is used. The mean of the elements of the vector represented by each bald eagle. This is to update the information of the j-th bald eagle. After the update, the population complementarity rule will be applied to... Make corrections.
[0086] During the prey search phase, the prey approaches the optimal position via a spiral acceleration mechanism, and the position is updated using the following formula:
[0087] (9)
[0088] (10)
[0089] in, and Let x and y represent the position information of the j-th bald eagle on the horizontal and vertical axes, respectively. and Let x and y represent the unit-level horizontal and vertical positions of the j-th bald eagle, respectively. and Describe the polar radius and polar angle in the spiral path respectively. and To determine the parameters of the spiral path, the updated parameters are determined based on the population complementarity rule. Make corrections.
[0090] During the hunting sprint phase, accelerate towards the prey based on the optimal position from the previous phase, with the following update formula:
[0091] (11)
[0092] (12)
[0093] in, and These represent the horizontal and vertical position information of the prey after the unit mark, respectively. and This represents the motion parameters, typically taken as 2. After the update, the population complementarity rule is applied... The data is updated in three stages with the goal of minimizing equation (4) until the iteration result meets the requirements.
[0094] Example 3
[0095] This embodiment provides a reconfigurable distribution network energy optimization device, including:
[0096] Reconfigurable distribution network energy optimization model construction module: used to construct a reconfigurable distribution network energy optimization model based on the distributed resources accessed by the reconfigurable distribution network;
[0097] Population generation rule construction module: used to construct population complementarity rules and random population generation methods based on the power constraints of distributed resources;
[0098] The module for obtaining reconfigurable distribution network energy optimization schemes is used to solve the distribution network energy optimization model based on the population complementarity rule and the random population generation method, so as to obtain reconfigurable distribution network energy optimization schemes that meet the maximum benefit objective.
[0099] The device provided in this embodiment can execute the reconfigurable distribution network energy optimization method provided in either embodiment 1 or embodiment 2, and has the corresponding functional modules and beneficial effects of executing the method.
[0100] Example 4
[0101] This embodiment provides a computer storage medium storing a computer program. When the computer program is executed by a processor, it implements the reconfigurable distribution network energy optimization method provided in either embodiment 1 or embodiment 2.
[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0103] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0106] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. A method for optimizing the energy of a reconfigurable distribution network, characterized in that, include: Construct a reconfigurable distribution network energy optimization model based on the distributed resources accessed by the reconfigurable distribution network; Based on the power constraints of distributed resources, a population complementarity rule and a random population generation method are constructed, wherein each individual in the population represents a candidate reconfigurable distribution network energy optimization scheme. The reconfigurable distribution network energy optimization model is solved using the population complementarity rule and the random population generation method to obtain a reconfigurable distribution network energy optimization scheme that satisfies the maximum benefit objective.
2. The reconfigurable distribution network energy optimization method according to claim 1, characterized in that, Based on the distributed resources accessed by the reconfigurable distribution network, a reconfigurable distribution network energy optimization model is constructed, including: Distributed resources connected to a reconfigurable distribution network are defined as including multiple wind power generation systems and photovoltaic power generation systems with the same unit operating cost. Based on power balance constraints, the total load power required by these distributed resources is expressed as follows: ， in, This represents the total load power required by the distributed resources, where m represents the number of wind power generation systems. This represents the output active power of the i-th wind power generation system, and n represents the number of photovoltaic power generation systems. This represents the output active power of the i-th photovoltaic power generation system. and Subject to the following constraints: , and These represent the minimum output active power of wind power generation and photovoltaic power generation systems, respectively. and These represent the maximum active power output of the wind power generation and photovoltaic power generation systems, respectively; Based on the total load power required by distributed resources, a distribution network energy optimization model is obtained, with the output active power of wind power generation systems and photovoltaic power generation systems as planning variables and minimizing the operating cost of distributed resources as the maximum benefit objective. The operating cost of distributed resources is expressed as: ， Where C represents the operating cost of the distributed resource. and All are weighting coefficients, and satisfy the following conditions: , and These represent the operating cost coefficients for wind power generation and photovoltaic power generation, respectively. This indicates the total load power required by the wind power generation system. This indicates the total load power required by the photovoltaic power generation system.
3. The reconfigurable distribution network energy optimization method according to claim 2, characterized in that, The constraints of the reconfigurable distribution network energy optimization model include: ， ， ， 。 4. The reconfigurable distribution network energy optimization method according to claim 1, characterized in that, The population complementarity rule includes: obtaining the other half of the individual value based on a determined half of the individual value, as expressed below:
5. Among them, k is the number of individuals in the population. The expected sum of the values of all individuals in the population. The mean value of individuals in the population. Let represent the i-th individual value, which includes the output active power of distributed resources in the corresponding reconfigurable distribution network energy optimization scheme, the expected value of all individual values, and the total load power required by distributed resources in all candidate reconfigurable distribution network energy optimization schemes.
6. The reconfigurable distribution network energy optimization method according to claim 4, characterized in that, The random population generation method includes: According to the population complementarity rule, half of the individual values are generated, and the remaining individual values are supplemented based on the generated half of the individual values. The expression is as follows: ， in, and These are the upper and lower limits of the individual value, respectively. To generate the variation amplitude of random individuals, It is a range Random variations within.
7. The reconfigurable distribution network energy optimization method according to claim 2, characterized in that, The process of solving the reconfigurable distribution network energy optimization model based on population complementarity rules and random population generation methods to obtain a reconfigurable distribution network energy optimization scheme that satisfies the maximum profit objective includes: An initial population is generated based on the population complementarity rule and the random population generation method. Using the vulture algorithm, the search area of the initial population is narrowed during the area search phase. During the prey search phase, the optimal search position is determined within the narrowed search area. During the hunting sprint phase, candidate individuals are iterated at the optimal search position until the reconfigurable distribution network energy optimization scheme converges to meet the objective of minimizing the operating cost of distributed resources, thus obtaining the optimal solution of the reconfigurable distribution network energy optimization model. In the area search phase, prey search phase, and hunting sprint phase, the updated population is modified according to the population complementarity rule.
8. The reconfigurable distribution network energy optimization method according to claim 6, characterized in that, The expression for narrowing the search area of the initial population during the region search phase is shown below: ， in, This is the optimal result from the previous iteration. For control parameters, For range Random numbers within, The mean of the elements of the vector represented by each bald eagle. To update the information of the j-th vulture, This represents the information of the j-th vulture before the update. Each bald eagle represents an individual.
9. The energy optimization method for reconfigurable distribution networks according to claim 6, characterized in that, The expression for determining the optimal search location within the narrowed search area during the prey search phase is as follows: ， ， in, and Let x and y represent the position information of the j-th bald eagle on the horizontal and vertical axes, respectively. and Let x and y represent the unit-level horizontal and vertical positions of the j-th bald eagle, respectively. and Describe the polar radius and polar angle in the spiral path respectively. and These are the parameters that determine the spiral path.
10. The reconfigurable distribution network energy optimization method according to claim 6, characterized in that, The expression for iterating over candidate individuals at the optimal search location during the hunting sprint phase is as follows: ， ， in, and These represent the horizontal and vertical position information of the prey after the unit mark, respectively. and These represent motion parameters.
11. A reconfigurable distribution network energy optimization device, characterized in that, include: Reconfigurable distribution network energy optimization model construction module: used to construct a reconfigurable distribution network energy optimization model based on the distributed resources accessed by the reconfigurable distribution network; Population generation rule construction module: used to construct population complementarity rules and random population generation methods based on the power constraints of distributed resources, wherein each individual in the population represents a candidate reconfigurable distribution network energy optimization scheme; The module for obtaining reconfigurable distribution network energy optimization schemes is used to solve the reconfigurable distribution network energy optimization model based on the population complementarity rule and the random population generation method, so as to obtain the reconfigurable distribution network energy optimization scheme that satisfies the maximum benefit objective.
12. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the reconfigurable distribution network energy optimization method as described in any one of claims 1-9.