A multi-energy-based intelligent power distribution method and device

Abstract

This application discloses a smart power distribution method and device based on multiple energy sources, belonging to the field of power distribution technology. By constructing a capacity configuration model for a multi-energy power distribution system, wherein the multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources, the capacity configuration model includes an objective function aimed at minimizing the overall operating cost of the multi-energy power distribution system, and sets multiple power supply constraints. Based on the multiple power supply constraints, a pre-set particle swarm optimization algorithm is used to solve the objective function to obtain a target capacity configuration scheme for the multi-energy power distribution system. Based on the target capacity configuration scheme, the multiple power supply devices to be configured are configured, realizing the coordinated optimization configuration of the capacity of each power supply device in the multi-energy power distribution system, effectively reducing the overall operating cost of the multi-energy power distribution system, and improving the operating economy of the power distribution system in multi-energy complementary scenarios.

Description

Technical Field

[0001] This application belongs to the field of power distribution technology, specifically relating to a smart power distribution method, device, electronic equipment, and storage medium based on multiple energy sources. Background Technology

[0002] Traditional distribution network capacity configuration methods often only set objective functions from the perspective of investment and operation and maintenance costs, and the constraints are relatively simple. They fail to fully consider the complexity of grid operation after the access of multi-energy complementary distributed power sources, resulting in limited optimization effects and high operating costs. Summary of the Invention

[0003] The purpose of this application is to provide a smart power distribution method and device based on multiple energy sources, which can solve the problem that the complexity of power grid operation after the access of multi-energy complementary distributed power sources is not fully considered, resulting in limited optimization effect and high operating cost.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a smart power distribution method based on multiple energy sources, the method comprising: A capacity configuration model for a multi-energy power distribution system is constructed. The multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources. The capacity configuration model includes an objective function that aims to minimize the overall operating cost of the multi-energy power distribution system and sets multiple power supply constraints. Based on the multiple power supply constraints, a preset particle swarm optimization algorithm is used to solve the objective function to obtain the target capacity configuration scheme of the multi-energy power distribution system. Based on the target capacity configuration scheme, the plurality of power supply devices to be configured are configured.

[0005] Optionally, it also includes: In the event of a fault in the multi-energy power distribution system, a fault recovery model is constructed based on master-slave game theory. The fault recovery model uses the set of switch states corresponding to the multi-energy power distribution system as the strategy of the master player and the operation plans of the multiple energy sub-networks corresponding to the multi-energy power distribution system as the strategy of the slave players. The fault recovery model is solved using a pre-set chaotic particle swarm optimization algorithm to obtain the target switch state combination and the target operation plan; Based on the target switch state combination and target operation plan, the multi-energy power distribution system is restored and controlled.

[0006] Optionally, based on the multiple power supply constraints, a preset particle swarm optimization algorithm is used to solve the objective function to obtain the target capacity configuration scheme of the multi-energy power distribution system, including: Initialize the first particle swarm; wherein, the position vector of each particle in the first particle swarm represents the capacity value of each power supply device to be configured in the power distribution system; Based on the multiple power supply constraints, the objective function of each particle in the first particle swarm is calculated to obtain the corresponding fitness value. Based on the constructed adaptive penalty function, the adaptive penalty function value of each particle in the first particle swarm is determined; The global target position of the first particle swarm is updated based on the fitness and the adaptive penalty function value. Based on the global target position, a first particle is determined from the first particle swarm, and a second particle swarm is generated based on the mutation operation; Based on the second particle swarm, an iterative update operation is performed, and when the number of iterations reaches the first preset maximum number of iterations or the first target particle is determined, the target capacity configuration scheme corresponding to the first target particle is output.

[0007] Optionally, the multi-energy power distribution system includes sectionalizing switches and tie switches, the multiple energy subgrids include an electrical subgrid, a thermal subgrid, a gas subgrid, and a transportation subgrid, and the fault recovery model constructed based on master-slave game theory includes: The switch state set of the segmented switch and the interconnecting switch is used as the strategy of the main player, and the minimum power loss load is used as the payoff function of the main player. The operation plans of the electrical energy subnetwork, the thermal energy subnetwork, the gas energy subnetwork, and the transportation subnetwork are used as the strategies of the slave players, and the minimization of the overall operating cost is used as the payoff function of the slave players. A fault recovery model is constructed based on the strategies and payoff functions of the main players and the subordinate players.

[0008] Optionally, a pre-set chaotic particle swarm optimization algorithm is used to solve the fault recovery model to obtain the target switching state combination and the target operation plan, including: Obtain the network topology, fault location information, and the switching state set of the main game players in the multi-energy power distribution system; Based on the network topology, the fault location information, and the set of switching states, the third particle swarm of the chaotic particle swarm algorithm is initialized; wherein, each particle in the third particle swarm corresponds to a set of switching state combinations; For each particle in the third particle swarm, the switch state combination corresponding to the current particle is sent to the slave player, so that the main player can obtain the power loss load fed back by the slave player, calculate the fitness value of the current particle based on the power loss load, and update the global objective solution of the third particle swarm based on the fitness value. Based on the global target solution, perform iterative operations, and when the number of iterations reaches the second preset maximum number of iterations or the second target particle is determined, output the target switch state combination and target operation plan corresponding to the second target particle.

[0009] Optionally, the operational plan of the subordinate players includes: the charging and discharging power of energy storage devices in the power subgrid, the power purchased and sold from the external grid, the output of the combined cooling, heating and power micro-gas turbine, the output of the fuel cell, and the adjustment strategy for the charging load of electric vehicles; the charging and discharging power of energy storage devices in the thermal energy subgrid and the power purchased from the external grid; the charging and discharging volume of gas storage tanks in the gas energy subgrid and the amount of gas purchased from the external grid; and the shifting strategy for the charging time of electric vehicles in the transportation subgrid.

[0010] Optionally, the overall operating cost includes the investment cost, operation and maintenance cost, fuel cost, and penalty cost of the multi-energy power distribution system; The multiple power supply constraints include power balance constraints, power supply equipment output constraints, system capacity constraints, charging and discharging power constraints, standby capacity operation constraints, power supply reliability constraints, and renewable energy utilization constraints.

[0011] Secondly, embodiments of this application provide a multi-energy-based intelligent power distribution device, which includes: The first model construction module is used to construct a capacity configuration model for a multi-energy power distribution system. The multi-energy power distribution system includes multiple power supply devices to be configured for each energy source. The capacity configuration model includes an objective function that aims to minimize the overall operating cost of the multi-energy power distribution system and sets multiple power supply constraints. The scheme determination module is used to solve the objective function based on the multiple power supply constraints using a preset particle swarm optimization algorithm to obtain the target capacity configuration scheme of the multi-energy power distribution system. The scheme configuration module is used to configure the plurality of power supply devices to be configured based on the target capacity configuration scheme.

[0012] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0013] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0014] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0015] In this embodiment, a capacity configuration model for a multi-energy power distribution system is constructed. The multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources. The capacity configuration model includes an objective function aimed at minimizing the overall operating cost of the multi-energy power distribution system, and sets multiple power supply constraints. Based on the power supply constraints, a pre-set particle swarm optimization algorithm is used to solve the objective function to obtain a target capacity configuration scheme for the multi-energy power distribution system. Based on the target capacity configuration scheme, the multiple power supply devices are configured, achieving coordinated optimization of the capacity of each power supply device in the multi-energy power distribution system. This effectively reduces the overall operating cost of the multi-energy power distribution system and improves the economic efficiency of the power distribution system operation in multi-energy complementary scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the present invention will be briefly introduced below. Obviously, the accompanying 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.

[0017] Figure 1 This is a flowchart of a multi-energy-based intelligent power distribution method provided in some embodiments of the present invention; Figure 2 This is a structural block diagram of a multi-energy-based intelligent power distribution device provided in some embodiments of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in some embodiments of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0020] The following description, in conjunction with the accompanying drawings, details a smart power distribution method based on multiple energy sources provided in this application through specific embodiments and application scenarios.

[0021] Reference Figure 1 The diagram illustrates a flowchart of a multi-energy-based intelligent power distribution method according to some embodiments of the present invention, which may specifically include the following steps: Step 101: Construct a capacity configuration model for a multi-energy power distribution system; wherein the multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources, and the capacity configuration model includes an objective function aimed at minimizing the overall operating cost of the multi-energy power distribution system, and sets multiple power supply constraints.

[0022] In step 101, the multi-energy power distribution system refers to a distributed power distribution system in which multiple energy sources complement each other. In order to determine the power supply capacity configuration corresponding to multiple energy sources, a capacity configuration model for the multi-energy power distribution system can be constructed. The capacity configuration model can include an objective function with the goal of minimizing the overall operating cost of the multi-energy power distribution system, and set multiple power supply constraints.

[0023] In some embodiments of this application, the comprehensive operating cost includes the investment cost, operation and maintenance cost, fuel cost, and penalty cost of the multi-energy power distribution system; The power supply constraints include power balance constraints, power supply equipment output constraints, system capacity constraints, charging and discharging power constraints, standby capacity operation constraints, power supply reliability constraints, and renewable energy utilization rate constraints.

[0024] Specifically, the capacity configuration model takes the minimum comprehensive operating cost, which includes investment cost, operation and maintenance cost, fuel cost and penalty cost, as its objective function, and sets multiple power supply constraints, including power balance constraints, power supply equipment output constraints, system capacity constraints, charging and discharging power constraints, standby capacity operation constraints, power supply reliability constraints and renewable energy utilization rate constraints.

[0025] In practical applications, investment costs can be calculated based on the unit capacity investment cost of each power supply equipment, equipment capacity, equipment service life, and cost changes; operation and maintenance costs can be calculated based on the fixed cost of the equipment and the variable cost corresponding to the operating power; fuel costs can be calculated based on the purchase cost of fuel equipment, power consumption, equipment utilization rate, and minimum calorific value of fuel; and penalty costs can be determined based on the weighted sum of renewable energy curtailment, load reduction, and voltage over-limit penalties.

[0026] The power balance constraint is that the sum of the output power of distributed power sources and the output power of power supply equipment in a multi-energy distribution system should equal the power demand of the multi-energy distribution system; the power supply equipment output constraint is that the output power of each distributed power source is between its minimum and maximum output power; the system capacity constraint is that the total capacity of the multi-energy distribution system is between its minimum and maximum capacity; the charging and discharging power constraints are that the output power in the charging and discharging states is between its corresponding minimum and maximum power; the reserve capacity operation constraint is that the reserve capacity of the power supply equipment is not less than the weighted sum of peak load and valley load according to their respective reserve coefficients; the power supply reliability constraint is that the ratio of the actual total capacity of the multi-energy distribution system to the annual total power demand does not exceed the rated access capacity; and the renewable energy utilization rate constraint is that the proportion of renewable energy power generation to total power generation is not less than the minimum utilization rate.

[0027] In one example, taking a multi-energy power distribution system in an industrial park as an example, the park is equipped with multi-energy complementary distributed power sources, including a photovoltaic power generation system with a rated capacity of 500 kW, a combined cooling, heating and power (CCHP) micro-turbine with a rated power of 200 kW, and a 1000 kWh energy storage system. The park's power distribution system has a total of 12 power supply devices, with a total maximum capacity of 10 MVA and a design service life of 15 years.

[0028] When constructing the capacity configuration model, the objective function is first set as minimizing the overall operating cost. The investment cost is calculated by multiplying the unit capacity investment cost of each power supply device to be configured by the device capacity, and then converting it to an equivalent annual value considering the device's service life and cost changes. The operation and maintenance cost consists of fixed operation and maintenance costs and variable operation and maintenance costs corresponding to the operating power. The fuel cost is calculated based on the amount of natural gas consumed by the micro-turbine, the unit price of natural gas, and the equipment's operating efficiency. The penalty cost is calculated by multiplying each of the potential renewable energy curtailment, load shedding, and voltage exceeding limits that may occur during the operation of the multi-energy distribution system by the corresponding penalty coefficients and then summing them. Finally, the investment cost, operation and maintenance cost, fuel cost, and penalty cost are multiplied by preset weighting coefficients and then weighted and summed to form the objective function for the overall operating cost.

[0029] Furthermore, several constraints are set for the capacity configuration model. Among these, the power balance constraint requires that the sum of photovoltaic output, micro-turbine output, and energy storage charging / discharging power, plus the power purchased from the external grid, equal the real-time electricity load demand of the park. The power supply equipment output constraint stipulates that photovoltaic output cannot exceed its rated capacity, and micro-turbine output must be between its minimum stable output and rated power. The system capacity constraint requires that the remaining capacity of the energy storage system be between its minimum and maximum allowable capacity. The charging / discharging power constraint stipulates that the charging and discharging power of the energy storage system can not exceed its maximum charging and discharging power, respectively. The reserve capacity operation constraint requires that the system reserve capacity be no less than the sum of 5% of peak load and 3% of valley load. The power supply reliability constraint requires that the ratio of the system's annual actual power supply to its annual total electricity demand not exceed the rated access capacity ratio. The renewable energy utilization rate constraint requires that the proportion of photovoltaic power generation to the total system power generation be no less than 10%.

[0030] By setting the above objective function and multiple power supply constraints, a capacity configuration model for a multi-energy complementary power distribution system suitable for this industrial park was constructed. This model can then be used as the solution object for particle swarm optimization algorithms to determine the optimal capacity configuration scheme for photovoltaic, micro-turbine, and energy storage systems.

[0031] Step 102: Based on the multiple power supply constraints, the objective function is solved using a preset particle swarm optimization algorithm to obtain the target capacity configuration scheme of the multi-energy power distribution system.

[0032] In step 102, based on multiple power supply constraints, the objective function of the capacity configuration model can be solved using a pre-set particle swarm optimization algorithm. By calculating the adaptive penalty function and introducing a mutation operation to update the parameters of the capacity configuration model, the target capacity configuration scheme of the multi-energy power distribution system can be obtained.

[0033] In some embodiments of this application, based on the multiple power supply constraints, a preset particle swarm optimization algorithm is used to solve the objective function to obtain the target capacity configuration scheme of the multi-energy power distribution system, including: Sub-step 11: Initialize the first particle swarm; wherein, the position vector of each particle in the first particle swarm represents the capacity value of each power supply device to be configured in the power distribution system.

[0034] In sub-step 11, a first particle swarm is initialized. This swarm contains multiple particles, each with a position vector representing the capacity value of each power supply device to be configured in the multi-energy power distribution system. For example, if the multi-energy power distribution system includes photovoltaic power supply equipment, wind power supply equipment, energy storage equipment, and a gas turbine, then the position vector of each particle is a four-dimensional vector, with each dimension corresponding to the capacity value to be configured for each of the aforementioned power supply devices. During initialization, the initial positions of the particles are randomly generated within the allowable capacity range of each power supply device to be configured.

[0035] Sub-step 12: Based on the multiple power supply constraints, calculate the objective function for each particle in the first particle swarm to obtain the corresponding fitness value.

[0036] In sub-step 12, the objective function of each particle in the first particle swarm can be calculated based on multiple pre-set power supply constraints to obtain the fitness value corresponding to each particle. Specifically, the position vector of each particle (i.e., the capacity configuration scheme of each power supply device to be configured) is substituted into the objective function, and it is verified whether the scheme meets the power supply constraints such as power balance constraints, power supply output constraints, system capacity constraints, charging and discharging power constraints, and standby capacity operation constraints. The comprehensive operating cost under the scheme represented by the particle is calculated, and this comprehensive operating cost is the fitness value. The smaller the fitness value, the better the economic efficiency of the capacity configuration scheme.

[0037] Sub-step 13: Based on the constructed adaptive penalty function, determine the adaptive penalty function value for each particle in the first particle swarm.

[0038] In sub-step 13, for particles that do not meet the power supply constraints, an adaptive penalty function is used to correct their fitness values. Specifically, an adaptive penalty function is constructed based on the comprehensive deviation of each particle's corresponding capacity configuration scheme from power balance constraints, power supply equipment output constraints, system capacity constraints, charging and discharging power constraints, and standby capacity operation constraints, and is used to evaluate the comprehensive feasibility of the scheme. Furthermore, a penalty term can be dynamically calculated according to the degree to which each particle violates the power supply constraints, and the penalty term is added to the original fitness value to obtain the adaptive penalty function value for each particle. This penalty function value is used for subsequent particle comparison, so that particles that violate the power supply constraints more severely have larger penalty function values, and are thus gradually eliminated during the optimization process.

[0039] Sub-step 14: Update the global target position of the first particle swarm based on the fitness and the adaptive penalty function value.

[0040] In sub-step 14, the global target position of the first particle swarm can be updated based on the fitness value and adaptive penalty function value of each particle. The global target position refers to the globally optimal position found by all particles in the entire first particle swarm in history, that is, the optimal capacity configuration scheme with the minimum overall operating cost and satisfying the constraints.

[0041] The specific update method can be as follows: compare the adaptive penalty function value of each particle with the adaptive penalty function value corresponding to the current global target position. If there is a particle with a smaller penalty function value, then update the position of that particle to the new global target position.

[0042] Sub-step 15: Based on the global target position, determine the first particle from the first particle swarm, and generate the second particle swarm based on the mutation operation.

[0043] In sub-step 15, based on the updated global target position, the first particle is determined from the first particle swarm, which is the current global optimal particle, and a mutation operation is performed on the first particle.

[0044] The mutation operation works as follows: In the position vector of the first particle, one or more dimensions are randomly selected, and the capacity value of that dimension is changed within a preset mutation range, thereby generating a new particle. This mutation operation is repeated several times to generate multiple new particles. These new particles, along with some particles from the first particle swarm, constitute the second particle swarm. The introduction of the mutation operation increases the diversity of the particle swarm, helping to avoid the algorithm getting trapped in local optima.

[0045] Sub-step 16: Based on the second particle swarm, perform an iterative update operation, and when the number of iterative updates reaches the first preset maximum number of iterations or the first target particle is determined, output the target capacity configuration scheme corresponding to the first target particle.

[0046] In sub-step 16, the second particle swarm is used as the next generation particle swarm, and the iterative update operations of sub-steps 12 to 15 are repeated.

[0047] After each iteration, it is determined whether the iteration termination condition is met: whether the number of iterations has reached the first preset maximum number of iterations, or whether the first target particle has been determined, meaning the global target position no longer changes after multiple consecutive iterations. When either termination condition is met, the iteration stops, and the position vector corresponding to the current first target particle is output. This position vector is the target capacity configuration scheme of the multi-energy power distribution system.

[0048] Step 103: Configure the plurality of power supply devices to be configured based on the target capacity configuration scheme.

[0049] In step 103, after obtaining the target capacity configuration scheme, the capacity of each power supply device to be configured can be configured based on the target capacity configuration scheme, thereby minimizing the overall operating cost. In this embodiment, a capacity configuration model for a multi-energy power distribution system is constructed. The multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources. The capacity configuration model includes an objective function aimed at minimizing the overall operating cost of the multi-energy power distribution system, and sets multiple power supply constraints. Based on these constraints, a pre-set particle swarm optimization algorithm is used to solve the objective function to obtain a target capacity configuration scheme for the multi-energy power distribution system. Based on this target capacity configuration scheme, the multiple power supply devices to be configured are configured, achieving coordinated optimization of the capacity of each power supply device in the multi-energy power distribution system. This effectively reduces the overall operating cost of the multi-energy power distribution system and improves the operational economy of the power distribution system in multi-energy complementary scenarios.

[0050] In some embodiments of this application, it also includes: Step 201: In the event of a fault in the multi-energy power distribution system, a fault recovery model is constructed based on master-slave game theory; wherein, the fault recovery model uses the set of switch states corresponding to the multi-energy power distribution system as the strategy of the master player, and the operation plans of the multiple energy sub-networks corresponding to the multi-energy power distribution system as the strategy of the slave players.

[0051] In step 201, in the event of a fault in the multi-energy power distribution system, a fault recovery model can be constructed based on master-slave game theory. Specifically, the fault recovery model uses the set of switch states corresponding to the multi-energy power distribution system as the strategy of the master player, and the operation plans of the multiple energy subgrids corresponding to the multi-energy power distribution system as the strategies of the slave players.

[0052] In some embodiments of this application, the operating plan of the slave player includes: the charging and discharging power of the energy storage device in the power subgrid, the power purchased and sold from the external grid, the output of the combined cooling, heating and power micro-gas turbine, the output of the fuel cell, and the adjustment strategy of the electric vehicle charging load; the charging and discharging heat power of the energy storage device in the thermal energy subgrid and the heat purchased from the external grid; the charging and discharging volume of the gas storage tank in the gas energy subgrid and the gas purchased from the external grid; and the shifting strategy of the electric vehicle charging period in the transportation subgrid.

[0053] In some embodiments of this application, the multi-energy power distribution system includes sectionalizing switches and tie switches, the multiple energy subgrids include an electrical subgrid, a thermal subgrid, a gas subgrid, and a transportation subgrid, and the fault recovery model constructed based on master-slave game theory includes: Sub-step 21: The switch state set of the segmented switch and the interconnecting switch is used as the strategy of the main player, and the minimum power loss load is used as the payoff function of the main player.

[0054] A multi-energy power distribution system may include sectionalizing switches and tie switches, and multiple energy subgrids may include an electrical subgrid, a thermal subgrid, a gas subgrid, and a transportation subgrid.

[0055] In sub-step 21, the switch state set of the segmented switch and the interconnecting switch can be used as the strategy of the main player, and the minimum power loss load can be used as the payoff function of the main player.

[0056] Sub-step 22 involves using the operation plans of the electrical energy subnetwork, the thermal energy subnetwork, the gas energy subnetwork, and the transportation subnetwork as the strategies of the slave players, and using the minimization of the overall operating cost as the payoff function of the slave players.

[0057] In sub-step 22, the operation plans of each energy sub-network, including the electricity sub-network, the thermal energy sub-network, the gas energy sub-network, and the transportation sub-network, can be used as the strategies of the slave players, and the minimization of the overall operating cost can be used as the payoff function of the slave players.

[0058] Sub-step 23: Construct a fault recovery model based on the strategies and payoff functions of the main player and the subordinate player.

[0059] In sub-step 23, a fault recovery model can be constructed based on the strategies and payoff functions of the main players and subordinate players.

[0060] Specifically, the switch state set of the main players includes the open and closed states of sectional switches and tie switches in the power distribution system, and the switch state set satisfies the radial topology constraints of the power distribution network, node voltage constraints, branch capacity constraints, and switch operation number constraints.

[0061] The operational plans of the players in the subgrid can include the charging and discharging power of energy storage devices in the power grid, the power purchased and sold from the external grid, the output of combined cooling, heating and power micro-turbines, the output of fuel cells, and the adjustment strategy for electric vehicle charging load; the charging and discharging power of energy storage devices in the thermal energy subgrid and the power purchased from the external grid; the charging and discharging volume of gas storage tanks in the gas energy subgrid and the volume of gas purchased from the external grid; and the shifting strategy for electric vehicle charging time in the transportation subgrid.

[0062] Based on satisfying the power balance constraints of the power subgrid, the cold and hot power balance constraints of the thermal subgrid, the natural gas balance constraints of the gas subgrid, the upper and lower limits of the output of each device, the remaining power and charging and discharging rate constraints of the energy storage device, the capacity constraints of the gas storage tank, and the time constraints of the electric vehicle charging completion period of the transportation subgrid, the operation plan of the system game can achieve energy complementarity and coordinated support between electricity, heat, gas, and transportation through multi-energy coupling equipment.

[0063] The fault recovery model satisfies the Nash equilibrium condition, which means that the main player, knowing the optimal response strategy of the slave player, chooses the set of switch states that minimizes its own payoff, and the slave player, given the set of switch states provided by the main player, chooses the running plan that minimizes its own payoff. The strategies of both sides are the optimal responses of each other.

[0064] Step 202: The fault recovery model is solved using a pre-set chaotic particle swarm optimization algorithm to obtain the target switch state combination and the target operation plan.

[0065] In step 202, when solving the fault recovery model, a pre-set chaotic particle swarm optimization algorithm can be used to solve the fault recovery model. That is, both the main player and the slave player can use the chaotic particle swarm optimization algorithm to iteratively find the optimal solution. The main player inputs the current set of switching states into the slave player. The slave player returns the optimal operating plan for that state, as well as the corresponding power loss load and comprehensive operating cost. The main player can update its own strategy based on the optimal operating plan returned by the slave player and the corresponding power loss load and comprehensive operating cost until it converges to the master-slave game equilibrium state, thereby obtaining the target switching state combination and the target operating plan.

[0066] In some embodiments of this application, a preset chaotic particle swarm optimization algorithm is used to solve the fault recovery model to obtain the target switch state combination and the target operation plan, including: Sub-step 31: Obtain the network topology, fault location information, and switch state set of the main game players of the multi-energy power distribution system.

[0067] In sub-step 31, the network topology, fault location information, and switch state set of the main players in the multi-energy power distribution system can be obtained.

[0068] The network topology describes the connection relationships of nodes, branches, sectionalizing switches and tie switches in the power distribution system. The fault location information indicates the specific node or branch where the fault occurred. The switch state set of the main players contains the current open / closed state of all sectionalizing switches and tie switches.

[0069] Sub-step 32: Based on the network topology, the fault location information, and the set of switch states, initialize the third particle swarm of the chaotic particle swarm algorithm; wherein, each particle in the third particle swarm corresponds to a set of switch state combinations.

[0070] In sub-step 32, a third particle swarm can be initialized using a chaotic particle swarm optimization algorithm based on the acquired network topology, fault location information, and switch state set. The third particle swarm contains multiple particles, and the position vector of each particle corresponds to a set of switch state combinations. That is, each dimension represents the open / closed state of a segmented switch or tie switch (e.g., 1 represents closed and 0 represents open).

[0071] The chaotic particle swarm optimization algorithm utilizes the ergodicity and randomness of chaotic sequences for population initialization, making the third particle swarm more evenly distributed in the solution space, thereby improving the algorithm's global search capability.

[0072] Sub-step 33: For each particle in the third particle swarm, the switch state combination corresponding to the current particle is sent to the slave player, so that the master player can obtain the power loss load fed back by the slave player, calculate the fitness value of the current particle based on the power loss load, and update the global objective solution of the third particle swarm based on the fitness value.

[0073] In sub-step 33, for each particle in the third particle swarm, the following operations can be performed: the switching state combination corresponding to the current particle is sent to the slave players. The slave players optimize the operation plan of each energy sub-network, including the power sub-network, thermal sub-network, gas sub-network, and transportation sub-network, with the goal of minimizing the overall operating cost, based on the received switching state combination. The optimized power loss load is then fed back to the master player. After obtaining the feedback power loss load, the master player can calculate the fitness value of the current particle based on the feedback power loss load, combined with the power flow calculation results, using the penalty function to handle the node voltage constraints and branch capacity constraints. Then, based on the calculated fitness value, the global objective solution of the third particle swarm is updated, which is the optimal switching state combination and its corresponding optimal operation plan found by the entire third particle swarm in history.

[0074] Sub-step 34: Based on the global target solution, perform an iterative operation, and if the number of iterations reaches the second preset maximum number of iterations or the second target particle is determined, output the target switch state combination and target operation plan corresponding to the second target particle.

[0075] In sub-step 34, an iterative operation can be performed based on the updated global target solution. In each iteration, the particle fitness evaluation and global target solution update process of sub-step 33 above is repeated, while updating the position and velocity of each particle.

[0076] During the iteration process, it is determined whether the number of iterations has reached the second preset maximum number of iterations, or whether the second target particle has been determined, i.e., the global target solution no longer changes after multiple consecutive iterations. When either termination condition is met, the iteration operation stops, and the target switch state combination and target operation plan corresponding to the current second target particle are output. The target switch state combination indicates the segmented switch and tie switch operation schemes to be executed during fault recovery, while the target operation plan provides the optimal operation strategy for each energy subgrid under this switch state. Both are used together to perform fault recovery control of the multi-energy distribution system. That is, the output target switch state combination and target operation plan are the optimal fault recovery scheme for the multi-energy distribution system under the master-slave game equilibrium state.

[0077] Step 203: Based on the target switch state combination and target operation plan, perform recovery control on the multi-energy power distribution system.

[0078] In step 203, based on the target switch state combination and target operation plan obtained by the solution, the multi-energy power distribution system is restored and controlled.

[0079] In practical applications, based on the target switch state combination, closing or opening commands can be issued to the corresponding sectionalizing switches and tie switches in the multi-energy power distribution system. This alters the network topology, isolates faulty areas, and transfers loads from non-faulty areas to backup or distributed power sources. Simultaneously, based on the target operating plan, charging and discharging power and output adjustment commands can be issued to energy storage devices, combined cooling, heating and power micro-turbines, fuel cells, and other equipment in the electrical subgrid; charging and discharging power adjustment commands can be issued to energy storage devices in the thermal subgrid; charging and discharging volume adjustment commands can be issued to gas storage tanks in the gas subgrid; and charging time shifting strategies can be issued to electric vehicles in the transportation subgrid. This minimizes the amount of power loss load and reduces the impact of faults on users.

[0080] As can be seen, this application introduces master-slave game theory into the fault recovery phase of a multi-energy power distribution system, and optimizes the switching state adjustment and multi-energy subgrid operation plan in a hierarchical manner. It makes full use of the complementary and coordinated capabilities of various energy sources such as electricity, heat, gas, and transportation, effectively reducing the amount of power loss load and the overall operating cost, and improving the overall economy and power supply reliability of the power distribution system under normal and fault conditions.

[0081] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0082] Reference Figure 2 The diagram illustrates a structural schematic of a multi-energy-based intelligent power distribution device according to some embodiments of the present invention, which may specifically include the following modules: The first model construction module 201 is used to construct a capacity configuration model for a multi-energy power distribution system; wherein, the multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources, and the capacity configuration model includes an objective function aimed at minimizing the overall operating cost of the multi-energy power distribution system, and sets multiple power supply constraints. The scheme determination module 202 is used to solve the objective function based on the multiple power supply constraints using a preset particle swarm optimization algorithm to obtain the target capacity configuration scheme of the multi-energy power distribution system. The scheme configuration module 203 is used to configure the plurality of power supply devices to be configured based on the target capacity configuration scheme.

[0083] In one embodiment of this application, it further includes: The second model construction module is used to construct a fault recovery model based on master-slave game theory in the event of a fault in the multi-energy power distribution system. The fault recovery model uses the set of switch states corresponding to the multi-energy power distribution system as the strategy of the master player and the operation plans of the multiple energy sub-networks corresponding to the multi-energy power distribution system as the strategy of the slave players. The model solving module is used to solve the fault recovery model using a preset chaotic particle swarm optimization algorithm to obtain the target switch state combination and the target operation plan; The recovery control module is used to perform recovery control on the multi-energy power distribution system based on the target switch state combination and the target operation plan.

[0084] In one embodiment of this application, the scheme determination module 202 includes: An initialization submodule is used to initialize the first particle swarm; wherein, the position vector of each particle in the first particle swarm represents the capacity value of each power supply device to be configured in the power distribution system; The fitness calculation submodule is used to calculate the objective function of each particle in the first particle swarm based on the multiple power supply constraints, and obtain the corresponding fitness value. The penalty determination submodule is used to determine the adaptive penalty function value for each particle in the first particle swarm based on the constructed adaptive penalty function. The global optimal position update submodule is used to update the global target position of the first particle swarm based on the fitness and the adaptive penalty function value. The mutation operation submodule is used to determine the first particle from the first particle swarm based on the global target position, and generate the second particle swarm based on the mutation operation; The iterative update module is used to perform iterative update operations based on the second particle swarm, and output the target capacity configuration scheme corresponding to the first target particle when the number of iterative updates reaches the first preset maximum number of iterations or when the first target particle is determined.

[0085] In one embodiment of this application, the multi-energy power distribution system includes sectionalizing switches and tie switches, the multiple energy subgrids include an electrical subgrid, a thermal subgrid, a gas subgrid, and a transportation subgrid, and the second model construction module includes: The main player determination submodule is used to take the switch state set of the segmented switch and the tie switch as the main player's strategy, and to take minimizing the power loss load as the main player's payoff function. The player determination submodule is used to take the operation plans of the power subnetwork, the thermal subnetwork, the gas subnetwork and the transportation subnetwork as the player's strategy, and to take minimizing the overall operating cost as the player's payoff function. The second model construction submodule is used to construct a fault recovery model based on the strategies and payoff functions of the main player and the subordinate player.

[0086] In one embodiment of this application, the model solving module includes: The data acquisition submodule is used to acquire the network topology, fault location information, and switch state set of the main game players of the multi-energy power distribution system. The chaotic particle initialization submodule is used to initialize the third particle swarm of the chaotic particle swarm algorithm based on the network topology, the fault location information and the switch state set; wherein, each particle in the third particle swarm corresponds to a set of switch state combinations; The target solution update submodule is used to send the switch state combination corresponding to the current particle to the slave player for each particle in the third particle swarm, so that the main player can obtain the power loss load fed back by the slave player, calculate the fitness value of the current particle based on the power loss load, and update the global target solution of the third particle swarm based on the fitness value. The iterative operation submodule is used to perform iterative operations based on the global target solution, and output the target switch state combination and target operation plan corresponding to the second target particle when the number of iterations reaches the second preset maximum number of iterations or when the second target particle is determined.

[0087] In one embodiment of this application, the operating plan of the slave player includes: the charging and discharging power of the energy storage device in the power subgrid, the power purchased and sold from the external grid, the output of the combined cooling, heating and power micro-gas turbine, the output of the fuel cell, and the adjustment strategy of the electric vehicle charging load; the charging and discharging heat power of the energy storage device in the thermal energy subgrid and the heat purchased from the external grid; the charging and discharging volume of the gas storage tank in the gas energy subgrid and the gas purchased from the external grid; and the shifting strategy of the electric vehicle charging period in the transportation subgrid.

[0088] In one embodiment of this application, the comprehensive operating cost includes the investment cost, operation and maintenance cost, fuel cost, and penalty cost of the multi-energy power distribution system; The power supply constraints include power balance constraints, power supply equipment output constraints, system capacity constraints, charging and discharging power constraints, standby capacity operation constraints, power supply reliability constraints, and renewable energy utilization rate constraints.

[0089] The multi-energy-based intelligent power distribution device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0090] The multi-energy-based intelligent power distribution device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit its application.

[0091] The intelligent power distribution device based on multiple energy sources provided in this application embodiment can achieve... Figure 1 The various processes implemented by a multi-energy intelligent power distribution device in the method embodiment are not described in detail here to avoid repetition.

[0092] In this embodiment, a capacity configuration model for a multi-energy power distribution system is constructed. The multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources. The capacity configuration model includes an objective function aimed at minimizing the overall operating cost of the multi-energy power distribution system, and sets multiple power supply constraints. Based on these constraints, a pre-set particle swarm optimization algorithm is used to solve the objective function to obtain a target capacity configuration scheme for the multi-energy power distribution system. Based on this target capacity configuration scheme, the multiple power supply devices to be configured are configured, achieving coordinated optimization of the capacity of each power supply device in the multi-energy power distribution system. This effectively reduces the overall operating cost of the multi-energy power distribution system and improves the operational economy of the power distribution system in multi-energy complementary scenarios.

[0093] Optionally, this application embodiment also provides an electronic device, including a processor 310, a memory 309, and a program or instructions stored in the memory 309 and executable on the processor 310. When the program or instructions are executed by the processor 310, they implement the various processes of the above-described intelligent power distribution method embodiment based on multiple energy sources and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0094] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0095] Figure 3 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application. The electronic device 300 includes, but is not limited to, components such as: a radio frequency unit 301, a network module 302, an audio output unit 303, an input unit 304, a sensor 305, a display unit 306, a user input unit 307, an interface unit 308, a memory 309, and a processor 310. The user input unit 307 includes a touch panel 3071 and other input devices 3072; the display unit 306 includes a display panel 3061; and the input unit includes a graphics processor 3041 and a microphone 3042.

[0096] Those skilled in the art will understand that the electronic device 300 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 310 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 3 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here. This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiments of the intelligent power distribution method based on multiple energy sources and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0097] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0098] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the intelligent power distribution method based on multiple energy sources, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0099] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0100] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0102] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application 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 this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A multi-energy based intelligent power distribution method, characterized in that, The method includes: A capacity configuration model for a multi-energy power distribution system is constructed. The multi-energy power distribution system includes multiple power supply devices to be configured corresponding to multiple energy sources. The capacity configuration model includes an objective function that aims to minimize the overall operating cost of the multi-energy power distribution system and sets multiple power supply constraints. Based on the multiple power supply constraints, a preset particle swarm optimization algorithm is used to solve the objective function to obtain the target capacity configuration scheme of the multi-energy power distribution system. Based on the target capacity configuration scheme, the plurality of power supply devices to be configured are configured.

2. The method according to claim 1, characterized in that, Also includes: In the event of a fault in the multi-energy power distribution system, a fault recovery model is constructed based on master-slave game theory. The fault recovery model uses the set of switch states corresponding to the multi-energy power distribution system as the strategy of the master player and the operation plans of the multiple energy sub-networks corresponding to the multi-energy power distribution system as the strategy of the slave players. The fault recovery model is solved using a pre-set chaotic particle swarm optimization algorithm to obtain the target switch state combination and the target operation plan; Based on the target switch state combination and target operation plan, the multi-energy power distribution system is restored and controlled.

3. The method according to claim 1, characterized in that, Based on the aforementioned multiple power supply constraints, a pre-set particle swarm optimization algorithm is used to solve the objective function, resulting in a target capacity configuration scheme for the multi-energy power distribution system, including: Initialize the first particle swarm; wherein, the position vector of each particle in the first particle swarm represents the capacity value of each power supply device to be configured in the power distribution system; Based on the multiple power supply constraints, the objective function of each particle in the first particle swarm is calculated to obtain the corresponding fitness value. Based on the constructed adaptive penalty function, the adaptive penalty function value of each particle in the first particle swarm is determined; The global target position of the first particle swarm is updated based on the fitness and the adaptive penalty function value. Based on the global target position, a first particle is determined from the first particle swarm, and a second particle swarm is generated based on the mutation operation; Based on the second particle swarm, an iterative update operation is performed, and when the number of iterations reaches the first preset maximum number of iterations or the first target particle is determined, the target capacity configuration scheme corresponding to the first target particle is output.

4. The method according to claim 2, characterized in that, The multi-energy power distribution system includes sectionalizing switches and interconnecting switches; the multiple energy subgrids include an electrical subgrid, a thermal subgrid, a gas subgrid, and a transportation subgrid; and the fault recovery model constructed based on master-slave game theory includes: The switch state set of the segmented switch and the interconnecting switch is used as the strategy of the main player, and the minimum power loss load is used as the payoff function of the main player. The operation plans of the electrical energy subnetwork, the thermal energy subnetwork, the gas energy subnetwork, and the transportation subnetwork are used as the strategies of the slave players, and the minimization of the overall operating cost is used as the payoff function of the slave players. A fault recovery model is constructed based on the strategies and payoff functions of the main players and the subordinate players.

5. The method according to claim 2, characterized in that, The fault recovery model is solved using a pre-defined chaotic particle swarm optimization algorithm to obtain the target switching state combination and the target operation plan, including: Obtain the network topology, fault location information, and the switching state set of the main game players in the multi-energy power distribution system; Based on the network topology, the fault location information, and the set of switching states, the third particle swarm of the chaotic particle swarm algorithm is initialized; wherein, each particle in the third particle swarm corresponds to a set of switching state combinations; For each particle in the third particle swarm, the switch state combination corresponding to the current particle is sent to the slave player, so that the main player can obtain the power loss load fed back by the slave player, calculate the fitness value of the current particle based on the power loss load, and update the global objective solution of the third particle swarm based on the fitness value. Based on the global target solution, perform iterative operations, and when the number of iterations reaches the second preset maximum number of iterations or the second target particle is determined, output the target switch state combination and target operation plan corresponding to the second target particle.

6. The method according to claim 4, characterized in that, The operational plans of the players in the sub-network include: the charging and discharging power of energy storage devices in the electrical sub-network, the power purchased and sold from the external grid, the output of combined cooling, heating and power micro-gas turbines, the output of fuel cells, and the adjustment strategy for electric vehicle charging load; the charging and discharging power of energy storage devices in the thermal sub-network and the power purchased from the external grid; the charging and discharging volume of gas storage tanks in the gas sub-network and the amount of gas purchased from the external grid; and the shifting strategy for electric vehicle charging time periods in the transportation sub-network.

7. The method according to claims 1-5, characterized in that, The comprehensive operating cost includes the investment cost, operation and maintenance cost, fuel cost, and penalty cost of the multi-energy power distribution system; The multiple power supply constraints include power balance constraints, power supply equipment output constraints, system capacity constraints, charging and discharging power constraints, standby capacity operation constraints, power supply reliability constraints, and renewable energy utilization constraints.

8. A multi-energy-based intelligent power distribution device, characterized in that, The device includes: The first model construction module is used to construct a capacity configuration model for a multi-energy power distribution system. The multi-energy power distribution system includes multiple power supply devices to be configured for each energy source. The capacity configuration model includes an objective function that aims to minimize the overall operating cost of the multi-energy power distribution system and sets multiple power supply constraints. The scheme determination module is used to solve the objective function based on the multiple power supply constraints using a preset particle swarm optimization algorithm to obtain the target capacity configuration scheme of the multi-energy power distribution system. The scheme configuration module is used to configure the plurality of power supply devices to be configured based on the target capacity configuration scheme.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, A computer program is stored on the readable storage medium, which, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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