A high-elasticity power distribution network optimal load transfer method considering source-load-storage coordination

Abstract

The application discloses a kind of high elasticity power distribution network optimal load transfer method considering source load storage coordination, including, with the network topology structure of power distribution network and source load storage resource calling plan as the position vector of firefly, randomly initialize each firefly;For each firefly individual, based on the network topology structure and source load storage resource calling method corresponding to the individual position of power distribution network, load transfer is carried out, the objective function of model is obtained, and it is converted into firefly brightness;Judge whether optimal firefly brightness converges to set accuracy and meets the constraint requirement, if yes, output result as the final load transfer method of power distribution network, algorithm ends;If not converge but reaches the maximum iteration number, also end program;Otherwise return to the step of converting objective function into firefly brightness;The application solves power distribution network load transfer model using firefly algorithm, reduces solving time, and improves optimization performance.

Description

Technical Field

[0001] This invention relates to the field of load transfer in distribution networks, and in particular to an optimal load transfer method for highly flexible distribution networks that takes into account the coordination of source, load and storage. Background Technology

[0002] In recent years, with the increasing prominence of energy and environmental issues, countries have continuously strengthened the development and utilization of distributed renewable energy sources. Simultaneously, with the ongoing advancement of energy interconnection in power grids, highly resilient distribution networks have become an important form of distribution network for improving system operation economy and power supply reliability. The most important characteristic of highly resilient distribution networks compared to traditional distribution networks lies in their integration of highly resilient resources, including power sources, grids, loads, and storage. Through these resources, they achieve optimal energy allocation over a larger temporal and spatial range, while also providing power support in extreme fault conditions, enhancing their self-healing capabilities. Load shifting refers to restoring power supply and reducing the amount of lost load by adjusting the network topology when the distribution network encounters a large-scale extreme fault. Against this backdrop, how to incorporate power source, load, and storage resources into the optimal load shifting model of the distribution network to improve system power supply reliability has become an important research topic.

[0003] While some literature has addressed the load transfer problem in distribution networks, most current models are designed for traditional distribution networks rather than highly resilient ones. Few studies comprehensively model highly resilient distribution networks, taking into account both source, load, and storage resources. Furthermore, in the area of ​​model solving, current intelligent algorithms for solving load transfer models include chaotic particle swarm optimization (PSO), binary particle swarm optimization (BSO), Empire competition algorithm, and genetic algorithm. However, very few studies have applied the firefly algorithm to solve load transfer models.

[0004] A highly resilient distribution network encompasses a wide variety of source, load, and storage resources. "Source" resources include various controllable distributed generation systems connected to the grid, such as microturbines and fuel cells. For combined cooling, heating, and power (CCHP) distribution networks, CCHP-type microturbines are required. "Load" resources refer to demand-side management agreements signed between distribution network operators and users on the load side, including interruptible loads, shiftable loads, and price-based demand-side management. "Storage" resources include grid-connected energy storage devices that can improve the system load curve by discharging during peak load periods and charging during off-peak periods, thus playing a role in peak shaving and valley filling. In recent years, with the increasing penetration rate of electric vehicles, electric vehicle charging stations and electric vehicle batteries within the distribution network, which enable orderly charging and discharging, can also be considered as energy storage resources. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract and title of the invention. Such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the problems existing in the above and / or prior art, the present invention is proposed.

[0007] Therefore, the technical problem to be solved by the present invention is that the existing power distribution network load transfer model cannot be applied to highly flexible power distribution networks.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a highly flexible distribution network optimal load transfer method considering source-load-storage coordination, comprising,

[0009] A coordinated supply transfer model was constructed based on the firefly algorithm.

[0010] In the coordinated transfer model, the number of sample parameters, the maximum number of algorithm iterations, the maximum attraction force, and the light intensity coefficient are set.

[0011] Using the network topology of the distribution network and the source-load-storage resource mobilization plan as the location vector of the sample parameters, each of the sample parameters is randomly initialized;

[0012] By utilizing the distribution network topology and source-load-storage resource allocation plan corresponding to the individual locations of the sample parameters, load transfer is performed to obtain the objective function of the coordinated transfer model, which is then converted into sample parameter brightness.

[0013] The brightest individual within the brightness perception range of the sample parameters is selected, and its position is updated based on the attraction between the two. A chaotic search is then performed on the current globally optimal sample parameters.

[0014] Based on the search results, determine whether the optimal sample parameter brightness has converged to the set accuracy and meets the constraints. If so, the output result is used as the final load transfer method of the distribution network.

[0015] If the program does not converge but reaches the maximum number of iterations, it should also terminate; otherwise, it should return and recalculate the sample parameter brightness until the constraint requirements are met.

[0016] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in this invention, the objective function is to minimize the comprehensive power outage load loss min. f :

[0017]

[0018] in, This refers to the load loss due to power outages during distribution network faults. Cost of resource allocation for source and load storage.

[0019] As a preferred embodiment of the optimal load transfer method for a highly resilient distribution network considering source-load-storage coordination as described in this invention, wherein: the power loss during the distribution network fault... :

[0020]

[0021] in: This refers to the number of nodes in the distribution network. Does the distribution network in the load transfer scheme affect the first... Each load node is supplied with power. The time indicates that power is being supplied. This indicates that no power is being supplied; For the first The weight of the importance of each load node is determined by the coefficient, which also includes the role of converting the power outage load into economic losses. No. Load level at the moment of failure of each load node.

[0022] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in this invention, wherein: the source-load-storage resource mobilization cost for:

[0023]

[0024] in: The expected duration of the fault can be set with a certain margin based on historical operating experience and the current fault situation; The length of each time period; The fuel cost function of a micro gas turbine is represented. express The power generation capacity of the micro gas turbine during a given time period; Represents the fuel cost function of a fuel cell. express The power generation capacity of the fuel cell during a given time period; for Power purchased during specific time periods; express Time-of-use electricity pricing levels on the external network during specific time periods; This represents the number of pollutant types. ; and The controllable micro-source emission coefficient, Demand-side management resources power dispatched by the distribution network The compensation coefficient for demand-side management resource allocation is agreed upon with users. For energy storage devices in Efforts during a specific time period This represents the energy storage operating cost coefficient.

[0025] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network that considers source-load-storage coordination as described in this invention, the constraints include radial constraints on the distribution network topology, node voltage constraints, branch capacity operation constraints, and source-load-storage resource allocation constraints.

[0026] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in this invention, the radial constraint of the distribution network topology is as follows:

[0027]

[0028] in: for Time period topology It is a set of radial networks.

[0029] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in this invention, the node voltage constraint is:

[0030]

[0031] in: For the first Voltage amplitude at each node; and Within the rated voltage range, the node voltage is calculated using the power balance constraint of the following formula;

[0032]

[0033]

[0034] in: Indicates the number of nodes in the distribution network; and They represent the first The active and reactive power flowing into each node is determined by the power obtained by the highly resilient distribution network from the external grid and the output of source-load-storage resources. , and They respectively represent the connection of the first The node and the first The conductance, susceptance, and voltage phase angle difference of each node line. and They represent the first The node and the first The voltage amplitude of each node.

[0035] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in this invention, the branch capacity operation constraint is as follows:

[0036]

[0037] in: For the first The operating power of each branch circuit For the first The power limit of each branch circuit.

[0038] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network that considers source-load-storage coordination as described in this invention, the source-load-storage resource call constraints include controllable distributed generation operation constraints, demand-side management resource call constraints, and energy storage device operation constraints.

[0039] The constraints for the operation of controllable distributed generation are:

[0040]

[0041] in: and These are the lower limits of MT and FC output, respectively. and These are the rated power of the micro gas turbine and the fuel cell, respectively.

[0042] The operating constraints of energy storage devices are:

[0043]

[0044]

[0045] in: and for and Remaining electricity stored during the time period; and For charge and discharge efficiency; Minimum depth of discharge, This represents the maximum depth of charging. and These are capacity and self-discharge coefficient, respectively;

[0046] The constraints for resource allocation in demand-side management are:

[0047] .

[0048] As a preferred embodiment of the optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in this invention, wherein: [the following is a description of the method] is set... This represents the sensing range of each firefly, when At that time, the position of the j-th weaker firefly is updated to that of the i-th stronger firefly in the form shown in the following formula;

[0049]

[0050]

[0051] in: and for Time period and The position of the j-th firefly in time period; As an attractiveness benchmark; It is the distance between the two; It is the light intensity coefficient, which is a constant; It refers to the number of fireflies, a parameter. The parameter is a random value between [0, 1]. The search quantity is random, and its magnitude is determined by... To adjust.

[0052] The beneficial effects of this invention are as follows: Compared with traditional distribution networks, highly resilient distribution networks with source-load-storage resources can call upon source-load-storage resources to support load transfer schemes during system failures, reducing power loss during failures and improving the power supply reliability of distribution network operation; the use of the firefly algorithm to solve the distribution network load transfer model reduces the solution time and improves the optimization performance. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0054] Figure 1 This is a diagram illustrating the information flow propagation and the relationships between various modules in the highly flexible distribution network load transfer model of this invention.

[0055] Figure 2 A schematic diagram of the high-elasticity distribution network structure based on the IEEE 33-node standard system in the optimal load transfer method for a high-elasticity distribution network that considers source-load-storage coordination, provided by an embodiment of the present invention.

[0056] Figure 3The fuel cost function curves of micro-turbines and fuel cells in the optimal load transfer method for a highly flexible distribution network that considers source-load-storage coordination, as described in an embodiment of the present invention;

[0057] Figure 4 The time-of-use electricity price curve for power exchange between the highly flexible distribution network and the upper-level grid is provided in one embodiment of the optimal load transfer method for a highly flexible distribution network that considers source-load-storage coordination. Detailed Implementation

[0058] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the present invention will be described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure will be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0061] Furthermore, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0062] Example 1

[0063] Reference Figure 1 This embodiment provides a highly flexible distribution network optimal load transfer method that considers source-load-storage coordination, specifically including:

[0064] S1: Construct a coordinated supply transfer model based on the firefly algorithm.

[0065] S2: In the coordinated transfer model, set the number of sample parameters, the maximum number of algorithm iterations, the maximum attraction force, and the light intensity coefficient.

[0066] S3: Using the network topology of the distribution network and the source-load-storage resource allocation plan as the location vector of the sample parameters, randomly initialize each sample parameter;

[0067] S4: Utilize the distribution network topology and source-load-storage resource allocation plan corresponding to the individual locations of the sample parameters to perform load transfer, obtain the objective function of the coordinated transfer model, and transform it into the sample parameter brightness.

[0068] S5: Select the brightest individual within the brightness perception range of the sample parameters, update its position based on the attraction between the two, and perform a chaotic search for the current globally optimal sample parameters.

[0069] S6: Determine whether the optimal sample parameter brightness has converged to the set accuracy and meets the constraint requirements based on the search results. If so, the output result is used as the final load transfer method of the distribution network.

[0070] S7: If the program does not converge but reaches the maximum number of iterations, the program will terminate; otherwise, it will return and recalculate the sample parameter brightness until the constraint requirements are met.

[0071] Furthermore, to facilitate understanding of the method of the present invention by those not skilled in the art, this embodiment also needs to provide a detailed description of the following:

[0072] The objective function for load transfer in the distribution network is to minimize the overall power outage load loss. This overall power outage load loss consists of two parts: the power outage load loss during a distribution network fault and the cost of mobilizing source, load, and storage resources. The objective function is to minimize the overall power outage load loss minf.

[0073]

[0074] in, This refers to the load loss due to power outages during distribution network faults. Cost of resource allocation for source and load storage.

[0075] Power loss during distribution network faults :

[0076]

[0077] in: This refers to the number of nodes in the distribution network. Does the distribution network in the load transfer scheme affect the first... Each load node is supplied with power. The time indicates that power is being supplied. This indicates that no power is being supplied; For the first The weight of the importance of each load node is determined by the coefficient, which also includes the role of converting the power outage load into economic losses. No. Load level at the moment of failure of each load node.

[0078] The brightest individual within the perception range is selected by calculating the brightness of each sample parameter (represented by the firefly in the algorithm). Brightness is determined by substituting the position vector into the objective function, sorting the sample parameters by brightness in descending order, and taking the maximum value. This represents the sensing range of each firefly, when At that time, the position of the j-th weaker firefly is updated to that of the i-th stronger firefly in the form shown in the following formula;

[0079]

[0080]

[0081] in: and for Time period and The position of the j-th firefly in time period; As an attractiveness benchmark; It is the distance between the two; It is the light intensity coefficient, which is a constant; It represents the number of fireflies, and the parameter in the formula is... The parameter is a random value between [0, 1]. The search quantity is random, and its magnitude is determined by... To adjust.

[0082] Preferably, location updates include:

[0083] (1) Update the attraction of the two fireflies (i.e., the two sample parameters) based on the maximum attraction and light intensity coefficient;

[0084] (2) Determine whether the attraction value in (1) is less than the perception range. If it is less, update the position of the firefly with lower brightness.

[0085] (3) Update the firefly position based on the attraction benchmark, the distance between them, the light intensity coefficient and the random number.

[0086] Furthermore, the cost of resource allocation from source, load, and storage. for:

[0087]

[0088] in: The expected duration of the fault can be set with a certain margin based on historical operating experience and the current fault situation; The length of each time period; The fuel cost function of a micro gas turbine is represented. express The power generation capacity of the micro gas turbine during a given time period; Represents the fuel cost function of a fuel cell. express The power generation capacity of the fuel cell during a given time period; for Power purchased during specific time periods; express Time-of-use electricity pricing levels on the external network during specific time periods; This represents the number of pollutant types. ; and The controllable micro-source emission coefficient, Demand-side management resources power dispatched by the distribution network The compensation coefficient for demand-side management resource allocation is agreed upon with users. For energy storage devices in Efforts during a specific time period This represents the energy storage operating cost coefficient.

[0089] The constraints include the radial topology constraints of the distribution network structure, node voltage constraints, branch capacity operation constraints, and source-load-storage resource allocation constraints.

[0090] In layman's terms, power distribution networks generally adopt a closed-loop design but open-loop operation principle. This is primarily to prevent large circulating currents between different voltage levels, which would waste energy and increase equipment operating losses. Therefore, when implementing load transfer plans, power distribution networks also need to meet the radial network topology constraints. The radial network topology constraints of the power distribution network structure are as follows:

[0091]

[0092] in: for Time period topology It is a set of radial networks.

[0093] Ideally, the resource allocation plan for the distribution network should meet both the constraints of the network topology and the impact of resource allocation on the node voltage distribution. Under any circumstances, the acceptable node voltage range of the distribution network must be met; therefore, the system needs to satisfy node voltage constraints, which are as follows:

[0094]

[0095] in: For the first Voltage amplitude at each node; and Within the rated voltage range, the node voltage is calculated using the power balance constraint of the following formula;

[0096]

[0097]

[0098] in: Indicates the number of nodes in the distribution network; and They represent the first The active and reactive power flowing into each node is determined by the power obtained by the highly resilient distribution network from the external grid and the output of source-load-storage resources. , and They respectively represent the connection of the first The node and the first The conductance, susceptance, and voltage phase angle difference of each node line. and They represent the first The node and the first The voltage amplitude of each node.

[0099] Transmission lines or cables are equipment in a power system that are not allowed to be overloaded. Therefore, in order to prevent overload of individual feeders, the branch capacity operation constraints must be met as follows:

[0100]

[0101] in: For the first The operating power of each branch circuit For the first The power limit of each branch circuit.

[0102] The constraints on the allocation of energy resources include the operational constraints of controllable distributed generation, the resource allocation constraints of demand-side management, and the operational constraints of energy storage devices.

[0103] The constraints for the operation of controllable distributed generation are:

[0104]

[0105] in: and These are the lower limits of MT and FC output, respectively. and These are the rated power of the micro gas turbine and the fuel cell, respectively.

[0106] The operating constraints of energy storage devices are:

[0107]

[0108]

[0109] in: and for and Remaining electricity stored during the time period; and For charge and discharge efficiency; Minimum depth of discharge, This represents the maximum depth of charging. and These are capacity and self-discharge coefficient, respectively;

[0110] The constraints for resource allocation in demand-side management are:

[0111]

[0112] In summary, the information flow propagation and the relationships between various modules involved in the high-elasticity distribution network load transfer model are as follows: Figure 1 As shown, from Figure 1 As can be seen from this, when the distribution network takes into account the coordination between source-load-storage resources and traditional load transfer schemes, i.e., network topology improvement, the two need to work closely together.

[0113] Preferably, the network topology adjustment module needs to adjust the network topology in a timely manner based on fault conditions, system network structure, and load information, and feed back the current network structure to the source-load-storage resource mobilization module. This adjustment also needs to take into account the impact of the source-load-storage resource mobilization module on the power injected into the nodes. On the other hand, the source-load-storage resource mobilization scheme formulated by the source-load-storage resource mobilization module also needs to be based on the current network structure. The two form a complementary and coordinated situation.

[0114] Example 2

[0115] Reference Figure 2 , Figure 3 and Figure 4 This is the second embodiment of the present invention. Unlike the first embodiment, this embodiment provides a verification of a highly resilient distribution network optimal load transfer method that considers source-load-storage coordination. Specifically, it includes:

[0116] This embodiment uses, as follows Figure 2 The effectiveness of this invention is verified by simulating a highly resilient power distribution network constructed based on the IEEE 33-node standard system shown.

[0117] The system integrates controllable distributed generation, demand-side management resources, and energy storage devices to form a highly resilient distribution network. The grid connection status of source, load, and storage resources in the system is shown in Table 1. The energy storage self-discharge coefficient in the model is 0.032. The initial remaining energy storage capacity at the beginning of the fault is 1.5MW. Under normal operating conditions without power shortages, the distribution network generally does not call upon controllable distributed generation and demand-side management resources. Therefore, at the beginning of the fault, the output of the micro-turbine and fuel cell is zero, and the power called upon by demand-side management resources is zero.

[0118] Specifically, the coordinated transfer model constructed using the firefly algorithm as described in Example 1 is used for computation. The maximum number of iterations of the algorithm is set to 350 generations, the firefly population size is 50, and the two learning coefficients in the position update formula are set to 0.75 and 0.85 respectively. The chaotic search generations for the brightest firefly (i.e., the sample parameter) are 25 generations. In the random distribution of light intensity, the parameters... and The values ​​are set to 0.55 and 9.00 respectively, with a total distribution network load of 3715kW. It contains four connecting branch lines: branch 20-7, branch 21-11, branch 8-14, branch 32-17, and branch 24-28.

[0119] Table 1: Distribution of source, load and storage resources in a highly resilient distribution network.

[0120]

[0121] The fuel cost curves for micro-turbines and fuel cells in the source-load-storage resource utilization cost model are as follows: Figure 3 As shown, the time-of-use pricing curve implemented in the distribution network is as follows: Figure 4 As shown.

[0122] Define a typical operating day at 21:00 during the peak load period. Due to extreme weather conditions, the distribution network experiences line faults and large-scale power outages. Based on the model established in this embodiment, a load transfer scheme and a source-load-storage resource mobilization scheme for the distribution network are formulated. To verify the effectiveness of the model, two types of faults are set up in the form of multiple faults, as shown in Table 2.

[0123] Table 2: Typical Multiple Fault Settings for Highly Resilient Distribution Networks

[0124]

[0125] In addition, the importance weighting coefficients of each load node in a highly resilient distribution network are shown in Table 3.

[0126] Table 3: Weighting coefficients for the importance of each load node in a highly resilient distribution network.

[0127]

[0128] The following is a simulation and analysis of load transfer in the distribution network:

[0129] By running the highly flexible distribution network load transfer model established in this embodiment, which considers source-load-storage coordination, the distribution network load transfer schemes under fault mode are shown in Table 4.

[0130] Table 4: Distribution network load transfer scheme under fault mode.

[0131]

[0132] As can be seen from Table 4, when branch 8-9 and branch 12-13 fail, the distribution network immediately disconnects these two faulty branches, and then connects the three tie-line branches 21-11, 8-14 and 32-17 to supply power to subsequent nodes.

[0133] To avoid the formation of a ring network due to the operation of branches 32-17 and 8-14, and also because the power supply capacity of the tie line branches is limited, the distribution network disconnected branches 15-16 and 16-17, making node 16, which has a lower importance coefficient, a power-off load. In addition, the system also disconnected branches 9-10, as well as branches 12-13 and 13-14, making nodes 9 and 13 power-off to maintain power balance.

[0134] Regarding the allocation of energy resources, the energy storage device at node 11 was fully discharged, alleviating the power shortage at the end load nodes of the connecting line branch 21-11. At the same time, node 15 initiated demand-side management, reducing power supply demand and preventing node 14 or node 15 from becoming power-out load nodes. The output of the fuel cell increased, improving the power supply capacity to subsequent nodes, but the output of the micro-turbine did not change much, mainly because the power supply capacity to the end load nodes is mainly limited by the branch capacity. In fact, the power loss of the system under fault mode 1 reached 436.23 kW, with a power loss of RMB 1654.57 and an energy resource allocation cost of RMB 365.41.

[0135] Similarly, the distribution network load transfer scheme under fault mode 2 can also be obtained as shown in Table 5.

[0136] Table 5: Distribution Network Load Transfer Scheme under Fault Mode 2.

[0137]

[0138] Since branches 2-3 and 7-8 are close to the power supply end, a failure in these branches would lead to a large-scale power outage if no measures are taken. The distribution network first disconnected the faulty branches 2-3 and 7-8 for isolation, and then quickly restored power to the tie line branches 20-7, 21-11, and 24-28. Due to limited power supply capacity, the system disconnected necessary branches to make nodes 13, 16, 17, 25, 26, 31, and 32 become power-out load nodes.

[0139] It should also be noted that nodes 3, 4, 5, and 6, as well as the micro-turbines and fuel cells operating internally in parallel with the grid, form an islanded operation. The purpose of this islanding is to improve power supply capacity while avoiding the formation of a ring network. In terms of source-load-storage resource allocation, the energy storage at node 9 is in a discharged state, and the demand-side resources at node 15 are also activated to prevent nodes 15 and 14 from becoming power-loss load nodes. In fact, under fault mode two, the system's power-loss load loss reached 963.84 kW, the power-loss load loss was 4216.30 yuan, and the source-load-storage resource allocation cost was 685.62 yuan.

[0140] To compare the power supply reliability of a highly resilient distribution network with that of a traditional distribution network without source, load, and storage resources in the face of extreme fault conditions, taking fault mode 2 as an example, the load transfer indicators of the two networks were compared under the same fault mode by running the model in this paper on the IEEE 33-node system without source, load, and storage resources. Table 6 shows the comparison.

[0141] Table 6: Comparison of load transfer indicators between high-elasticity distribution networks and traditional distribution networks.

[0142]

[0143] As shown in Table 6, compared with the traditional distribution network, the highly resilient distribution network reduced two power outage load nodes under fault mode 2, reducing the power outage load loss from 5369.41 yuan to 4216.30 yuan. Although there are certain costs associated with the use of source, load, and storage resources, the overall power outage load loss decreased from 5369.41 yuan to 4901.92 yuan, a reduction of 8.72%, achieving significant results.

[0144] To verify the advantages of the Firefly Algorithm over traditional intelligent algorithms in solving load transfer models, taking fault mode 2 as an example, the solution was obtained by using chaotic particle swarm optimization, genetic algorithm and the Firefly Algorithm provided in this invention for 20 times. The results are shown in Table 7.

[0145] Table 7: Comparison of the solution metrics of the three algorithms.

[0146]

[0147] As can be seen from Table 7, all three algorithms meet the requirements for optimizing the load transfer model, and the optimal solution is consistent. However, the Firefly algorithm has a shorter optimization time and higher optimization efficiency compared to the other two.

[0148] Furthermore, in order to provide a concise description of exemplary embodiments, not all features of actual embodiments (i.e., those features that are not relevant to the currently considered best mode for carrying out the invention, or those features that are not relevant to implementing the invention) may be omitted.

[0149] It should be understood that numerous specific implementation decisions can be made during the development of any practical implementation, such as in any engineering or design project. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, the development effort will be a routine task in design, manufacturing, and production without requiring extensive experimentation.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A highly flexible distribution network optimal load transfer method considering source-load-storage coordination, characterized in that: include, A coordinated supply transfer model was constructed based on the firefly algorithm. In the coordinated transfer model, the number of sample parameters, the maximum number of algorithm iterations, the maximum attraction force, and the light intensity coefficient are set. Using the network topology of the distribution network and the source-load-storage resource mobilization plan as the location vector of the sample parameters, each of the sample parameters is randomly initialized; By utilizing the distribution network topology and source-load-storage resource allocation plan corresponding to the individual locations of the sample parameters, load transfer is performed to obtain the objective function of the coordinated transfer model, which is then converted into sample parameter brightness. The brightest individual within the brightness perception range of the sample parameters is selected, and its position is updated based on the attraction between the two. A chaotic search is then performed on the current globally optimal sample parameters. Based on the search results, determine whether the optimal sample parameter brightness has converged to the set accuracy and meets the constraints. If so, the output result is used as the final load transfer method of the distribution network. If the program does not converge but reaches the maximum number of iterations, it should also terminate; otherwise, it should return and recalculate the sample parameter brightness until the constraint requirements are met.

2. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 1, characterized in that: The objective function is to minimize the total power outage load loss min. f : ； in, This refers to the load loss due to power outages during distribution network faults. Cost of resource allocation for source and load storage.

3. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 1 or 2, characterized in that: The power loss during the distribution network fault : ； in: This refers to the number of nodes in the distribution network. Does the distribution network in the load transfer scheme affect the first... Each load node is supplied with power. The time indicates that power is being supplied. This indicates that no power is being supplied; For the first The weight of the importance of each load node is determined by the coefficient, which also includes the role of converting the power outage load into economic losses. No. Load level at the moment of failure of each load node.

4. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 3, characterized in that: The cost of resource mobilization of source and load storage for: ； in: This represents the expected duration of the fault. The length of each time period; The fuel cost function of a micro gas turbine is represented. express The power generation capacity of the micro gas turbine during a given time period; Represents the fuel cost function of a fuel cell. express The power generation capacity of the fuel cell during a given time period; for Power purchased during specific time periods; express Time-of-use electricity pricing levels on the external network during specific time periods; This represents the number of pollutant types. ; and The controllable micro-source emission coefficient; Demand-side management resources power dispatched by the distribution network The compensation coefficient for demand-side management resource allocation is agreed upon with users. For energy storage devices in Efforts during a specific time period This represents the energy storage operating cost coefficient.

5. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 4, characterized in that: The constraints include the radial topology constraints of the distribution network structure, node voltage constraints, branch capacity operation constraints, and source-load-storage resource allocation constraints.

6. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 5, characterized in that: The radial constraint of the distribution network structure topology is as follows: ； in: for Time period topology It is a set of radial networks.

7. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in any one of claims 4 to 6, characterized in that: The node voltage constraint is: ； in: For the first Voltage amplitude at each node; and Within the rated voltage range, the node voltage is calculated using the power balance constraint of the following formula; ； ； in: Indicates the number of nodes in the distribution network; and They represent the first The active and reactive power flowing into each node is determined by the power obtained by the highly resilient distribution network from the external grid and the output of source-load-storage resources. , and They respectively represent the connection of the first The node and the first The conductance, susceptance, and voltage phase angle difference of each node line; and They represent the first The node and the first The voltage amplitude of each node.

8. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 7, characterized in that: Branch capacity operation constraints are: ； in: For the first The operating power of each branch circuit For the first The power limit of each branch circuit.

9. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 8, characterized in that: The constraints on the allocation of source, load and storage resources include constraints on the operation of controllable distributed generation, constraints on the allocation of demand-side management resources, and constraints on the operation of energy storage devices. The constraints for the operation of controllable distributed generation are: ； in: and These are the lower limits of MT and FC output, respectively. and These are the rated power of the micro gas turbine and the fuel cell, respectively. The operating constraints of energy storage devices are: ； ； in: and for and Remaining electricity stored during the time period; and For charge and discharge efficiency; Minimum depth of discharge, This represents the maximum depth of charging. The constraints for resource allocation in demand-side management are: 。 10. The optimal load transfer method for a highly flexible distribution network considering source-load-storage coordination as described in claim 8 or 9, characterized in that: set up This represents the sensing range of each firefly, when At that time, the position of the j-th weaker firefly is updated to that of the i-th stronger firefly in the form shown in the following formula; ； ； in: and for Time period and The position of the j-th firefly in time period; As an attractiveness benchmark; It is the distance between the two; It is the light intensity coefficient, which is a constant; It refers to the number of fireflies, a parameter. The parameter is a random value between [0, 1]. The search quantity is random, and its magnitude is determined by... To adjust.

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