Multi-objective energy optimization method and system for energy internet based on power router

Abstract

The application belongs to the technical field of energy internet, and provides an energy internet multi-target energy optimization method and system based on an electric energy router, which aims to minimize the total operation cost of an electric energy local area network, determines the output power of a distributed power supply and other power grids, determines the transmission power on each path according to a target function of calculating the power supply load on the transmission power of the preset path and the preset number of power supply electric energy routers, adopts multiple path transmission powers, effectively reduces the power transmission loss between electric energy routers, improves the utilization rate of transmission lines, avoids excessive concentration of transmission power on a certain transmission line, reduces the power transmission pressure of transmission lines, prevents power transmission congestion of transmission lines, adaptively allocates transmission power of different paths, preferentially selects transmission lines with low failure rates and electric energy routers with high renewable energy proportions, thereby reducing the damage of transmission line failures and improving the utilization rate of renewable energy.

Description

Technical Field

[0001] This invention belongs to the field of energy internet technology, and in particular relates to a multi-objective energy optimization method and system for energy internet based on power routers. Background Technology

[0002] Power transmission between power routers is considered similar to data transmission in the information internet, enabling directional flow and active control. Therefore, existing research has proposed energy optimization strategies for the energy internet based on power routers, such as distributing computational load across individual power routers and resolving congestion and conflict issues through cooperative routing.

[0003] When transmitting electrical energy between power routers, problems such as power transmission loss between power routers and power transmission line faults caused by the unbalanced and insufficient development of renewable energy, as well as power transmission pressure on transmission lines, exist, resulting in large power transmission loss, power transmission blockage on transmission lines, and a high failure rate of transmission lines. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a multi-objective energy optimization method and system for the energy internet based on power routers. This invention employs multiple path power transmission, effectively reducing power transmission losses between power routers, improving the utilization rate of transmission lines, avoiding excessive concentration of transmission power on a single transmission line, reducing power transmission pressure on transmission lines, and preventing power transmission congestion. By adaptively allocating transmission power to different paths based on the renewable energy ratio of power routers and the failure rate of transmission lines, prioritizing transmission lines with low failure rates and power routers with high renewable energy ratios, the invention reduces the harm caused by transmission line failures and improves the utilization rate of renewable energy.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the present invention provides a multi-objective energy optimization method for the energy internet based on an energy router, comprising: With the goal of minimizing the total operating cost of the power local area network (PUN), the output power of distributed generation sources and other power grids is determined based on the power balance constraints of the PUN and the operating constraints of distributed generation sources and other power grids. Based on the determined output power and the objective function for calculating the power transmission power of the power load through preset paths and preset number of power routers, the transmission power on each path is determined; wherein, the optimization objectives in the objective function include the failure rate of the transmission line and the proportion of renewable energy in the power router, and priority is given to selecting transmission lines with low failure rates and power routers with high proportions of renewable energy.

[0006] Furthermore, the total operating cost of the power local area network is the sum of the levelized electricity cost of the photovoltaic power generation module, the levelized electricity cost of the wind turbine generator set, the fuel cost of the fuel unit, the operation and maintenance cost of the fuel unit, the operation and maintenance cost of the energy storage equipment, and the cost of the power local area network purchasing electricity from other power grids.

[0007] Furthermore, the levelized cost of electricity (LCOE) of the photovoltaic (PV) power generation module is equal to the product of its LCOE coefficient, its output power, and the time it takes for the PV module to operate at that output power; the LCOE of the wind turbine generator set is equal to the product of its LCOE coefficient, its output power, and the time it takes for the wind turbine generator set to operate at that output power; the operation and maintenance cost of the fuel cell generator set is equal to the product of its operation and maintenance cost coefficient, its output power, and the time it takes for the fuel cell generator set to operate at that output power; the operation and maintenance cost of the energy storage device is equal to the product of its operation and maintenance cost coefficient, its absolute output power, and the time it takes for the energy storage device to operate at that output power; the cost for the local area network (LAN) to purchase electricity from other grids is equal to the product of the price of electricity, the power output from other grids to the LAN, and the time it takes for other grids to exchange electricity at that output power; the fuel cost of the fuel cell generator set is: ; in, Fuel cost for fuel-powered units; k 2. k 1 and k 0 represents the fuel cost coefficient for fuel-powered units; P fu This refers to the output power of the fuel unit; t fu This refers to the time the fuel unit operates at this output power.

[0008] Furthermore, the power balance constraints of the power local area network and the operational constraints of the distributed generation and other power grids are as follows: Distributed generation and other power grids jointly supply power to the load: ; Distributed power sources and other grid components must meet the upper and lower limits of their power output: ; in, This refers to the output power of the photovoltaic power generation module; This refers to the output power of the wind turbine generator set; This refers to the output power of the fuel unit; The output power of the energy storage device; For the output power of other power grids; P lo The power required by the load; P pvmin and P pvmax These are the minimum and maximum output power of the photovoltaic power generation module, respectively. P wtmin and P wtmax These are the minimum and maximum output power of the wind turbine generator set, respectively. P fumin and P fumax These are the minimum and maximum output power of the fuel unit, respectively; P stmin and P stmax These are the minimum and maximum output power of the energy storage device, respectively. P grmin and P grmax These represent the minimum and maximum output power of other power grids, respectively.

[0009] Furthermore, the objective function for calculating the power load transmitted through a preset path and a preset number of power routers is: ; in, This represents the number of paths. l This refers to the number of power-powered routers. For transmission power; Transmission power Δ P p Total loss; The power factor for renewable energy; Power router e Additional transmission power during this transmission process The renewable energy power provided at that time.

[0010] Furthermore, the total loss is: ; ; in, The power router conversion loss coefficient; This refers to the number of interfaces on the power router. Interface converter for power router r Transformation loss; This refers to the transmission loss coefficient of the power transmission line. For power transmission lines t Transmission loss; This refers to the number of transmission lines. The power factor affected by transmission line faults; Power affected by transmission line faults; For power transmission lines f The failure rate; For power transmission lines f The additional transmission power during transmission.

[0011] Furthermore, the renewable energy power provided by the power router is equal to the product of the renewable energy ratio of the power router and the additional transmission power added during the transmission process of the power router.

[0012] Secondly, the present invention also provides a multi-objective energy optimization system for the energy internet based on an electric power router, comprising: The power local area network optimization module is configured to: determine the output power of distributed power sources and other power grids based on the power balance constraints of the power local area network and the operating constraints of distributed power sources and other power grids, with the goal of minimizing the total operating cost of the power local area network; The power wide area network optimization module is configured to: determine the transmission power on each path based on the determined output power and the objective function that calculates the power transmission power of the power load through preset paths and preset number of power routers; wherein, the optimization objectives in the objective function include the failure rate of the transmission line and the proportion of renewable energy in the power router, and prioritize the selection of transmission lines with low failure rates and power routers with high proportions of renewable energy.

[0013] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in the first aspect.

[0014] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in the first aspect.

[0015] Fifthly, the present invention also provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention first aims to minimize the total operating cost of a local area network (LAN). Based on the LAN's power balance constraints and the operational constraints of distributed generation (DG) and other power grids, the output power of DG and other power grids is determined. Then, based on the determined output power and an objective function calculating the power transmission through preset paths and a preset number of power routers, the transmission power on each path is determined. The optimization objectives in the objective function include the transmission line failure rate and the proportion of renewable energy in the power routers. Employing multiple paths for power transmission effectively reduces power transmission losses between power routers, improves transmission line utilization, avoids excessive power concentration on a single transmission line, reduces power transmission pressure on transmission lines, and prevents power transmission congestion. By adaptively allocating transmission power to different paths based on the proportion of renewable energy in the power routers and the transmission line failure rate, prioritizing transmission lines with low failure rates and power routers with high renewable energy proportions, the harm caused by transmission line failures is reduced, and the utilization rate of renewable energy is improved. Attached Figure Description

[0017] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0018] Figure 1 This is a schematic diagram of an energy internet based on an electric power router according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the optimization results of the power local area network 1 in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram comparing the target values ​​of failure rate in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram comparing the target values ​​for the proportion of renewable energy in Embodiment 1 of the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0021] Example 1: The Energy Internet is a new generation of integrated energy system proposed in recent years, with electricity as its hub and platform. It is a product of the deep integration of energy systems with emerging internet information technologies such as big data and artificial intelligence. Distributed power sources, such as photovoltaic and wind power, are important drivers of the Energy Internet's development, providing clean and efficient electricity suitable for distributed environments. Under distributed conditions, the Energy Internet needs to ensure plug-and-play energy availability and real-time energy balance. However, distributed power output is characterized by intermittency, randomness, and time-varying nature, posing significant challenges to the distributed optimization of the Energy Internet.

[0022] An energy router is an energy management and regulation device for the energy internet, providing plug-and-play AC and DC interfaces for distributed power sources. As a core device of the energy internet, the energy router leverages new information technology to imbue electricity with intelligent electronic tags during its flow, facilitating intelligent scheduling and demand-side response. The energy router can achieve efficient energy transmission and precise routing through the mutual constraints of information and energy flows, enabling optimized sharing of energy generated by distributed power sources. Traditional centralized grid scheduling methods are no longer adequate for the bidirectional energy flow and complex scheduling management issues arising from the large-scale integration of distributed power sources into the energy internet. Energy transmission between energy routers is considered similar to data transmission in the information internet, allowing for directional flow and active control. Therefore, existing research proposes energy optimization strategies for the energy internet based on energy routers, such as distributing computational load across various energy routers and resolving congestion and conflict issues through cooperative routing.

[0023] Renewable energy is a crucial driving force for the development of the energy internet and a vital force in energy transformation. However, the imbalance and inadequacy in the development of renewable energy are becoming increasingly prominent, particularly the issue of renewable energy consumption, which has severely constrained the healthy and sustainable development of the power industry. Furthermore, because the various parts of the energy internet are interconnected and inseparable, a failure in one part can trigger a chain reaction, affecting the entire power system network, leading to large-scale blackouts, causing enormous economic losses and serious social impacts. With the increasing penetration rate of distributed power sources, transmission lines are facing increasingly severe challenges.

[0024] To address the aforementioned issues, this embodiment provides a multi-objective energy optimization method for the energy internet based on power routers. To meet the evolving needs of the energy internet and improve the utilization rate of renewable energy and the reliability of power supply, the proportion of renewable energy and the failure rate of transmission lines are incorporated into the energy optimization strategy. The lower-level local area network (LAN) adopts an economical operation energy optimization strategy, aiming to minimize the total daily operating cost of the LAN. The upper-level wide area network (WAN) employs a multi-objective energy optimization strategy, considering the failure rate of transmission lines and the proportion of renewable energy in the power routers, and utilizing multiple path power transmission. The method in this embodiment includes: S1. Calculate the levelized cost of electricity (LCOE) for photovoltaic power generation modules and wind turbine generators: Levelized cost of electricity (LCOE) refers to the operating cost per unit of electricity generated during the operating cycle. It is an indicator that directly reflects the economic efficiency of a power generation system and can be expressed as: (1) in, The levelized cost of electricity for photovoltaic power generation modules; k pv This is the levelized cost of electricity (LCOE) coefficient for photovoltaic power generation modules; P pv This refers to the output power of the photovoltaic power generation module; t pv This refers to the time the photovoltaic power generation module operates at this output power.

[0025] (2) in, The levelized cost of electricity for wind turbine generators; k wt The levelized cost of electricity for wind turbine generators; P wt This refers to the output power of the wind turbine generator set; t wt This refers to the operating time of the wind turbine generator at this output power.

[0026] S2. Calculate the fuel cost of the fuel unit: Fuel costs are affected by fuel prices and have a non-linear relationship with the output power of fuel units, which can be expressed as: (3) in, Fuel cost for fuel-powered units; k 2. k 1 and k 0 represents the fuel cost coefficient for fuel-powered units; P fu This refers to the output power of the fuel unit;t fu This refers to the time the fuel unit operates at this output power.

[0027] S3. Calculate the operation and maintenance costs of fuel units and energy storage equipment: Operation and maintenance costs refer to the costs incurred during operation and maintenance, including labor costs, spare parts costs, etc., and can be expressed as: (4) in, For the operation and maintenance costs of fuel units; k fm This is the operating and maintenance cost coefficient for fuel unit; P fu This refers to the output power of the fuel unit; t fu This refers to the time the fuel unit operates at this output power.

[0028] (5) in, The operating and maintenance costs of energy storage equipment; k sm This is the coefficient for the operation and maintenance costs of energy storage equipment; P st This represents the output power of the energy storage device; a positive output power indicates that the energy storage device is discharging, while a negative output power indicates that the energy storage device is charging. t st This refers to the time the energy storage device operates at this output power.

[0029] S4. Calculate the cost for the local area network to purchase electricity from other power grids: To achieve economical operation, a local area network (LAN) needs to purchase electricity from other power grids when electricity prices are low and sell electricity to other power grids when electricity prices are high. Its costs can be expressed as follows: (6) in, k gr The price of electricity; P gr This represents the power output from other power grids to this local area network (LAN). A positive output power indicates that the LAN purchases electricity from other power grids, while a negative output power indicates that the LAN sells electricity to other power grids. t gr This refers to the time it takes for other power grids to exchange electrical energy at this output power.

[0030] S5. The objective function for calculating the economic operation energy optimization strategy of the lower-level power local area network can be expressed as: (7) S6. The constraints are satisfied: S6.1 Power balance constraints of local area networks: Distributed power sources work together with the power grid to deliver power to the load: (8) in, P lo This refers to the power required by the load.

[0031] S6.2 Operational constraints of distributed generation and other power grids: Distributed power sources and other grid components must meet the upper and lower limits of their power output: (9) in, P pvmin and P pvmax These are the minimum and maximum output power of the photovoltaic power generation module, respectively. P wtmin and P wtmax These are the minimum and maximum output power of the wind turbine generator set, respectively. P fumin and P fumax These are the minimum and maximum output power of the fuel unit, respectively; P stmin and P stmax These are the minimum and maximum output power of the energy storage device, respectively. P grmin and P grmax These represent the minimum and maximum output power of other power grids, respectively.

[0032] S6.3, State of Charge Constraints for Energy Storage Devices: Maintaining the state of charge of energy storage devices within a reasonable range can extend their lifespan and reduce maintenance costs.

[0033] (10) in, E The amount of electricity stored in the energy storage device; E min and E max These represent the energy storage device's capacity under minimum and maximum charge conditions, respectively.

[0034] S7. The lower-level power local area network obtains the optimization result based on the objective function in step S5 and the constraints in step S6.

[0035] S8. Calculate the conversion loss of the power router: In a power local area network (LAN), electrical energy is transmitted from the power source router to the transmission router or directly to the load router. When electrical energy passes through a power source router, it first enters the internal DC bus through the router's interface converter, and then is output to the target transmission line through the corresponding interface converter. During this process, the router's interface converter... r Transformation loss It can be represented as: (11) in, E r It is the conversion efficiency of the interface converter of the power router; Δ P r It is an interface converter for power routers. r Transmitted power.

[0036] S9. Calculate the transmission loss of the transmission line: In a local area network (LAN), the transmission loss of a transmission line is the transmission loss caused by the line impedance. This loss is related to both the line impedance and the magnitude of the transmitted power. t Transmission loss It can be represented as: (12) in, R It is the resistance of the power transmission line; P t It is a power transmission line t Existing transmission power; Δ P t It is a power transmission line t The additional transmission power during this transmission process.

[0037] S10. Calculate the power that may be affected by transmission line faults: In a local area network (LAN), transmission lines may experience faults due to internal power system factors such as equipment reliability and external factors such as icing, potentially preventing power transmission. To ensure the effectiveness of energy optimization strategies, it is necessary to include transmission line faults within the scope of these strategies. This involves considering the potential impact of transmission line faults on power transmission. It can be represented as: (13) Among them, transmission lines fThis is the transmission line with the highest failure rate in the path. The highest failure rate is chosen because if any transmission line in the path fails, the path cannot deliver power to the load power router. F f It is a power transmission line f Failure rate; Δ P f It is a power transmission line f The additional transmission power during this transmission process.

[0038] The failure rate of a transmission line represents the number of times a failure has occurred on that transmission line over a period of time. In this embodiment, the unit is discarded, and only the numerical value is retained and expressed as a percentage. This is done to represent the relative magnitude of the probability of failure for different transmission lines during power transmission. That is, if the failure rate of transmission line 1 is twice that of transmission line 2, then the probability of transmission line 1 failing is twice that of transmission line 2. In this way, the failure rate guides the selection of different paths and the allocation of power across different paths during power transmission, allowing energy optimization strategies to prioritize transmission lines with lower failure probabilities to transmit power, thereby improving the reliability of power supply.

[0039] S11. Calculate the transmitted renewable energy power: Renewable energy is a crucial force in energy transition and development. However, the imbalance and inadequacy in renewable energy development are becoming increasingly prominent, particularly the issue of renewable energy consumption, which has severely hampered the healthy and sustainable development of the power industry. To improve the utilization rate of clean power sources such as wind and solar power, it is necessary to incorporate renewable energy utilization into energy optimization strategies. (Power supply router) e Provided renewable energy power It can be represented as (14) in, N e It is a power supply router. e The proportion of renewable energy, that is, the proportion of new energy power capacity to all power capacity; Δ P e It is a power supply router. e The additional transmission power during this transmission process.

[0040] In summary, a passage m One power router interface and n The path of the transmission line p Power Δ is transmitted through this path P p Total loss It can be represented as: (15) in, The power router conversion loss coefficient; This refers to the transmission loss coefficient of the power transmission line. The power factor affected by transmission line faults.

[0041] S12, Calculate the power load through k Path, l The power router transmits a total power Δ P The objective function can be expressed as: (16) in, The power factor for renewable energy; Power router e Additional transmission power during this transmission process The renewable energy power provided at that time.

[0042] The constraints are satisfied: (17) in, P pt It is a path p medium-sized transmission lines t Existing transmission power; Δ P pt It is a power transmission line t The additional transmission power added during this transmission process; P tc It is a power transmission line t The capacity.

[0043] S13. Using Matlab, solve the objective function and constraints from step S12 to obtain the transmission power of each power load pair on each path.

[0044] To verify the method in this implementation, such as Figure 1 As shown, a simulation model was built using MATLAB to analyze the proposed lower-level energy optimization strategy, taking the power local area network 1 managed by power router 1 as an example. Power local area network 1 includes photovoltaic power generation modules, wind turbine generators, energy storage devices, and fuel cell generators. Based on the economical operation energy optimization strategy of the lower-level power local area network proposed in this paper, the output power of each distributed power source in power local area network 1 and other grids from 0-24 hours a day, as well as the load power, are shown below. Figure 2 As shown, the output power and load power of the photovoltaic power generation module and the wind turbine generator are predicted values.

[0045] The proposed upper-level energy optimization strategy was simulated in MATLAB, simulating power transmission scenarios from power router 7 to power router 3 at two power levels: 14kW and 20kW.

[0046] Based on the initial screening, there are two alternative paths from Power Router 7 to Power Router 3: Path 1: Power Router 7 — Power Router 2 — Power Router 3; Path 2: Power Router 7 — Power Router 2 — Power Router 4 — Power Router 3.

[0047] Three different strategies are employed for energy optimization: Dijkstra's algorithm, a multi-path energy optimization strategy, and the multi-path energy optimization strategy proposed in this embodiment, which comprehensively considers factors such as the transformation loss of the power router, the transmission loss of the transmission line, and the transmission line failure rate. To more clearly illustrate the impact of the transmission line failure rate, coefficients are used... K e =0. The strategy proposed in this embodiment compares the target value with conventional strategies and Dijkstra's algorithm for transmitting different power levels, for example... Figure 3 As shown.

[0048] The proposed upper-level energy optimization strategy was simulated in MATLAB, simulating power transmission scenarios from power router 5 and power router 2 to power router 4 at two power levels of 100kW and 171kW. Preliminary screening revealed the following two transmission paths: Path 1: Power router 5 - Power router 4; Path 2: Power router 2 - Power router 4.

[0049] Three different strategies are employed for energy optimization: Dijkstra's algorithm, a multi-path energy optimization strategy, and the multi-path energy optimization strategy proposed in this embodiment, which comprehensively considers factors such as the transformation loss of the power router, the transmission loss of the transmission line, and the transmission line failure rate. To more clearly demonstrate the impact of the renewable energy ratio of the power router, a coefficient is used in this embodiment. K f =0. The strategy proposed in this embodiment compares the target value with conventional strategies and Dijkstra's algorithm for transmitting different power levels, for example... Figure 4 As shown.

[0050] Example 2: This embodiment provides a multi-objective energy optimization system for the energy internet based on a power router, including: The power local area network optimization module is configured to: determine the output power of distributed power sources and other power grids based on the power balance constraints of the power local area network and the operating constraints of distributed power sources and other power grids, with the goal of minimizing the total operating cost of the power local area network; The power wide area network optimization module is configured to: determine the transmission power on each path based on the determined output power and the objective function that calculates the power transmission power of the power load through preset paths and preset number of power routers; wherein, the optimization objectives in the objective function include the failure rate of the transmission line and the proportion of renewable energy in the power router, and prioritize the selection of transmission lines with low failure rates and power routers with high proportions of renewable energy.

[0051] The working method of the system is the same as that of the multi-objective energy optimization method for the energy internet based on power router in Embodiment 1, and will not be repeated here.

[0052] Example 3: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in Embodiment 1.

[0053] Example 4: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in Embodiment 1.

[0054] Example 5: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in Embodiment 1.

[0055] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A multi-objective energy optimization method for the energy internet based on power routers, characterized in that, include: With the goal of minimizing the total operating cost of the power local area network (PUN), the output power of distributed generation sources and other power grids is determined based on the power balance constraints of the PUN and the operating constraints of distributed generation sources and other power grids. Based on the determined output power and the objective function for calculating the power transmission power of the power load through preset paths and preset number of power routers, the transmission power on each path is determined; wherein, the optimization objectives in the objective function include the failure rate of the transmission line and the proportion of renewable energy in the power router, and priority is given to selecting transmission lines with low failure rates and power routers with high proportions of renewable energy.

2. The multi-objective energy optimization method for the energy internet based on power routers as described in claim 1, characterized in that, The total operating cost of the power local area network is the sum of the levelized cost of electricity (LCOE) of the photovoltaic power generation modules, the LCOE of the wind turbine generators, the fuel cost of the fuel generators, the operation and maintenance cost of the fuel generators, the operation and maintenance cost of the energy storage equipment, and the cost of the power local area network purchasing electricity from other power grids.

3. The multi-objective energy optimization method for the energy internet based on power routers as described in claim 2, characterized in that, The levelized cost of electricity (LCOE) of the photovoltaic (PV) power generation module is equal to the product of its LCOE coefficient, its output power, and the time it takes for the module to operate at that output power. The LCOE of the wind turbine generator set is equal to the product of its LCOE coefficient, its output power, and the time it takes for the turbine to operate at that output power. The operation and maintenance cost of the fuel cell generator set is equal to the product of its operation and maintenance cost coefficient, its output power, and the time it takes for the fuel cell generator set to operate at that output power. The operation and maintenance cost of the energy storage device is equal to the product of its operation and maintenance cost coefficient, its absolute output power, and the time it takes for the device to operate at that output power. The cost for the local area network (LAN) to purchase electricity from other grids is equal to the product of the price of electricity, the power supplied to the LAN by other grids, and the time taken for other grids to exchange electricity at that output power. The fuel cost of the fuel cell generator set is: ； in, Fuel cost for fuel-powered units; k 2. k 1 and k 0 represents the fuel cost coefficient for fuel-powered units; P fu This refers to the output power of the fuel unit; t fu This refers to the time the fuel unit operates at this output power.

4. The multi-objective energy optimization method for the energy internet based on power routers as described in claim 1, characterized in that, The power balance constraints of the local area network and the operational constraints of the distributed generation and other power grids are as follows: Distributed generation and other power grids jointly supply power to the load: ； Distributed power sources and other grid components must meet the upper and lower limits of their power output: ； in, This refers to the output power of the photovoltaic power generation module; This refers to the output power of the wind turbine generator set; This refers to the output power of the fuel unit; This refers to the output power of the energy storage device. For the output power of other power grids; P lo The power required by the load; P pvmin and P pvmax These are the minimum and maximum output power of the photovoltaic power generation module, respectively. P wtmin and P wtmax These are the minimum and maximum output power of the wind turbine generator set, respectively. P fumin and P fumax These are the minimum and maximum output power of the fuel unit, respectively; P stmin and P stmax These are the minimum and maximum output power of the energy storage device, respectively. P grmin and P grmax These represent the minimum and maximum output power of other power grids, respectively.

5. The multi-objective energy optimization method for the energy internet based on power routers as described in claim 1, characterized in that, The objective function for calculating the power load transmitted through a preset number of power routers and paths is: ； in, This represents the number of paths. l This refers to the number of power-powered routers. For transmission power; Transmission power Δ P p Total loss; The power factor for renewable energy; Power router e Additional transmission power during this transmission process The renewable energy power provided at that time.

6. The multi-objective energy optimization method for the energy internet based on power routers as described in claim 5, characterized in that, The total loss is: ； ； in, The power router conversion loss coefficient; This refers to the number of interfaces on the power router. Interface converter for power router r Transformation loss; This refers to the transmission loss coefficient of the power transmission line. For power transmission lines t Transmission loss; This refers to the number of transmission lines. The power factor affected by transmission line faults; Power affected by transmission line faults; For power transmission lines f The failure rate; For power transmission lines f The additional transmission power during transmission.

7. The multi-objective energy optimization method for the energy internet based on power routers as described in claim 5, characterized in that, The renewable energy power provided by the power router is equal to the product of the proportion of renewable energy in the power router and the additional transmission power added during the transmission process of the power router.

8. A multi-objective energy optimization system for the energy internet based on power routers, characterized in that, include: The power local area network optimization module is configured to: determine the output power of distributed power sources and other power grids based on the power balance constraints of the power local area network and the operating constraints of distributed power sources and other power grids, with the goal of minimizing the total operating cost of the power local area network; The power wide area network optimization module is configured to: determine the transmission power on each path based on the determined output power and the objective function that calculates the power transmission power of the power load through preset paths and preset number of power routers; wherein, the optimization objectives in the objective function include the failure rate of the transmission line and the proportion of renewable energy in the power router, and prioritize the selection of transmission lines with low failure rates and power routers with high proportions of renewable energy.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the program, it implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the multi-objective energy optimization method for the energy internet based on an electric power router as described in any one of claims 1-6.

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