Dispatching method and system for distributed energy equipment

Abstract

The invention relates to the technical field of distributed energy, and provides a scheduling method and system for distributed energy equipment in order to solve the problems of high energy consumption and low productivity efficiency of the distributed energy equipment, and the method comprises the steps: obtaining an energy equipment irrelevant factor, an energy equipment irrelevant constant and an action constant of a power system; collecting a capacity strategy of the power system, determining a condition, an attitude, an excitation target and an accumulated excitation mean value of the intelligent control system, designing a distributed energy loss estimation algorithm, and estimating an optimal coordination scheme; predicting an influence rate and an excitation value of each distributed energy device relative to a consumption value, marking the lowest consumption value as an improvement target, constructing a boundary factor, and obtaining an optimal energy scheduling command to operate an energy device irrelevant factor, productivity efficiency and productivity condition of each distributed energy device; and calculating a comprehensive energy value and an energy reduction loss amount, and improving a distributed energy loss estimation algorithm according to the comprehensive energy value and the energy reduction loss amount.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy, and in particular to a dispatching method for distributed energy equipment and a dispatching system for distributed energy equipment. Background Art

[0002] As more and more distributed energy sources are added to the power grid, the availability and transmission of electricity are becoming more and more complex. Distributed power supply equipment has become an important step in the application of power systems, and the coordinated control between distributed power supply equipment also directly affects the operation of the power system. The scheduling between traditional distributed energy devices is mainly carried out through remote terminals and distributed network protocols, and the scheduling between distributed energy generation equipment and energy storage equipment also mainly relies on simple automatic control, which leads to the problem of high energy consumption and low production efficiency of distributed energy equipment. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a scheduling method and system for distributed energy equipment, which can intelligently schedule multiple distributed energy equipment, improve the production capacity and energy storage efficiency of distributed energy equipment and reduce energy loss, making the operation of the power system more stable.

[0004] The technical solution adopted by the present invention is as follows: A method for dispatching distributed energy equipment comprises the following steps: obtaining energy equipment-independent factors, energy equipment-independent constants and action constants of an electric power system; collecting the power system's capacity strategy, determining the status, posture, incentive target and cumulative incentive mean of an intelligent control system through the energy equipment-independent factors, energy equipment-independent constants and action constants, designing and optimizing a distributed energy loss prediction algorithm, estimating an optimal coordination scheme through the optimized distributed energy loss prediction algorithm, and obtaining energy loss prediction data; marking each distributed energy device as a communication endpoint, and predicting the impact rate of each distributed energy device relative to the consumption value through the energy loss prediction data and the instantaneous energy value. and incentive values, and marks the lowest consumption value as an improvement target, constructs boundary factors to calculate the improvement target, so as to obtain the best energy dispatching command; according to the best energy dispatching command, operates the energy device-independent factors, production efficiency and production capacity of each distributed energy device through the power operating system; obtains the energy usage information of each distributed energy device through the power operating system, calculates the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination, and uses the energy usage information, energy device-independent constants and action constants of the distributed energy devices whose energy loss reduction is higher than the critical point of the loss reduction as sampling data to improve the distributed energy loss prediction algorithm.

[0005] In one embodiment of the present invention, the power generation strategy of the power system is collected, the state, attitude, incentive target and cumulative incentive mean of the intelligent control system are determined by the energy device-independent factors, energy device-independent constants and action constants, and a distributed energy loss prediction algorithm is designed and optimized. The optimal coordination scheme is estimated by the optimized distributed energy loss prediction algorithm to obtain energy loss prediction data, which specifically includes: collecting the power system's capacity strategy, constructing a distributed energy loss prediction algorithm based on a neural network, determining the state of the intelligent control system , wherein the status of the intelligent control system Contains collection capacity strategy , Equipment production capacity , Load Condition , operating power , Lighting conditions , Water source situation and temperature conditions , marked as ={ , , , , , , },in, , and For the power system The equipment capacity, load and operating power of each distributed energy device; determine the posture of the intelligent control system , wherein the posture of the intelligent control system Contains energy equipment independent factors and capacity constant , respectively marked as ={ , },in, For the power system Energy device-independent factors of distributed energy devices, For the power system The capacity constant of a distributed energy device includes the capacity efficiency and the capacity situation; determine the incentive target , motivational goals The function is expressed as: ;

[0006] in, is the number of distributed energy devices, is the proportion of operating power; determine the cumulative excitation mean , the cumulative incentive mean The function is expressed as: ;

[0007] in, To find the mean function, is the attitude factor, For the situation Take the best attitude The highest cumulative incentive mean value is obtained; a recycling box is established, and any part of the recycling box is selected The sampling data of the change is Change sampling data ( ), calculate the The target value of the variable sampling data , through the current Network Status The posture below Make an estimate to get the estimated data , for the current Network constants; calculate the The target data of the changing sampling data And the target data With estimated data The difference , among which, The target data of the changing sampling data The function is expressed as: ;

[0008] in, For the target The constants of the network, For intelligent control systems in the situation Select posture The incentive target data, For the target Network status Downward posture Estimated data; target data With estimated data The difference The function is expressed as: ;

[0009] Calculate the target data and estimated data The cost function C of , the expression of cost function C is: ;

[0010] The lowest cost function value is taken as the optimization target, and the current The network is iterated constantly. When the number of iterations is equal to the preset number of optimizations, the optimization of the distributed energy loss estimation algorithm is completed. According to the optimized distributed energy loss estimation algorithm, the next posture is estimated through the current situation and used as the best energy coordination solution. The energy device-independent factors in the next posture are the energy loss estimation data of each distributed energy device, and the sum of the energy loss estimation data of all distributed energy devices is calculated to obtain the total energy loss estimation data. The function of the total energy loss estimation data is expressed as: ;

[0011] in, For time Total estimated energy losses For time No. Energy loss estimates for distributed energy devices.

[0012] In one embodiment of the present invention, the method of marking each distributed energy device as a communication endpoint, predicting the impact rate and incentive value of each distributed energy device relative to the comprehensive energy value through the energy loss estimation data and the instant energy value, marking the lowest comprehensive energy value as the improvement target, constructing boundary factors to calculate the improvement target, and obtaining the best energy dispatch command specifically includes: marking each distributed energy device in the power system as a communication endpoint, calculating the impact rate of each distributed energy device relative to the comprehensive energy value, and the expression of the impact rate of each distributed energy device relative to the consumption value is: ;

[0013] in, For the The impact rate of distributed energy devices on the comprehensive energy value, is the value group of the communication endpoints of all distributed energy devices, Communication endpoint value group Exclude the first A grouping of distributed energy devices, Communication endpoint value group Exclude the A value group of distributed energy devices, is the energy value of the group, For grouping Merge to Energy value of distributed energy equipment; preset incentive relationship value , calculate the The incentive value of distributed energy devices , incentive value The expression is: ;

[0014] Compare and analyze the estimated energy loss, instant energy unit price and incentive value, take the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command, where the expression of the improvement target is: ;

[0015] in, is the combined time during the coordination period, For time The instant energy price.

[0016] In one embodiment of the present invention, the boundary factors include the estimated loss data of the distributed energy equipment, the energy demand of the distributed energy equipment and the production efficiency range of the distributed energy equipment, wherein the estimated loss data of the distributed energy equipment is greater than or equal to 0, the energy demand of the distributed energy equipment is greater than or equal to the time The comprehensive energy needs of the distributed energy equipment, the production capacity efficiency range of the distributed energy equipment is greater than or equal to the minimum production capacity efficiency and less than or equal to the maximum production capacity efficiency.

[0017] In one embodiment of the present invention, the energy usage information of each distributed energy device is obtained through the power operation system, the consumption value during the actual coordination period and the energy loss reduction after coordination are calculated, and the energy usage information of the distributed energy device whose energy loss reduction is higher than the critical point of the loss reduction, the energy device-independent constant and the action constant are used as sampling data to improve the distributed energy loss prediction method, specifically including: obtaining time of Actual energy loss of distributed energy devices , and the actual energy loss To calculate, the expression is: ;

[0018] in, , and The time hour The actual wind energy limit, photovoltaic energy limit and fuel energy limit of each distributed energy device, , and are the energy utilization relationship values ​​of wind power, photovoltaic power and fuel respectively; get the The comprehensive energy value of distributed energy devices during the actual coordination period, The expression of the comprehensive energy value of a distributed energy device during the actual coordination period is: ;

[0019] in, For the The comprehensive energy value of distributed energy devices during the actual coordination period, For time The actual energy value at that time, is the comprehensive time during the actual coordination period; calculate the Energy loss reduction of distributed energy devices during real coordination , the energy loss reduction amount The expression is: ;

[0020] in, For the The energy value of the distributed energy equipment before improvement; set the critical point for reducing the loss amount. The energy loss reduction of the distributed energy equipment is higher than the critical point of loss reduction, then The energy loss of each distributed energy device and the corresponding energy device-independent constants and action constants during the actual coordination period are used as sampling data to improve the distributed energy loss prediction method.

[0021] A dispatching system for distributed energy equipment comprises: a first acquisition module, the first acquisition module is used to acquire energy equipment-independent factors, energy equipment-independent constants and action constants of an electric power system; a second acquisition module, the second acquisition module is used to collect the power system's production capacity strategy, determine the status, posture, incentive target and cumulative incentive mean of an intelligent control system through the energy equipment-independent factors, energy equipment-independent constants and action constants, design a distributed energy loss prediction algorithm and optimize it, estimate the best coordination plan through the optimized distributed energy loss prediction algorithm, and acquire energy loss prediction data; a third acquisition module, the third acquisition module is used to mark each distributed energy device as a communication endpoint, predict each distributed energy device through the energy loss prediction data and the real-time energy value The impact rate and incentive value relative to the consumption value are marked, and the lowest consumption value is marked as the improvement target, and the boundary factors are constructed to calculate the improvement target to obtain the best energy scheduling command; an operation module, which is used to operate the energy device-independent factors, production efficiency and production capacity of each distributed energy device through the power operating system according to the best energy scheduling command; an improvement module, which is used to obtain the energy usage information of each distributed energy device through the power operating system, calculate the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination, and use the energy usage information of the distributed energy devices whose energy loss reduction is higher than the critical point of the loss reduction, the energy device-independent constants and the action constants as sampling data to improve the distributed energy loss prediction algorithm.

[0022] In one embodiment of the present invention, the second acquisition module is specifically used to: collect the power system's production capacity strategy, construct a distributed energy loss prediction algorithm based on a neural network, and determine the status of the intelligent control system. , wherein the status of the intelligent control system Contains collection capacity strategy , Equipment production capacity , Load Condition , operating power , Lighting conditions , Water source situation and temperature conditions , respectively marked as ={ , , , , , , },in, , and For the power system The equipment capacity, load and operating power of each distributed energy device; determine the posture of the intelligent control system , wherein the posture of the intelligent control system Contains energy equipment independent factors and capacity constant , respectively marked as ={ , },in, For the power system Energy device-independent factors of distributed energy devices, For the power system The capacity constant of a distributed energy device includes the capacity efficiency and the capacity situation; determine the incentive target , motivational goals The function is expressed as: ;

[0023] in, is the number of distributed energy devices, is the proportion of operating power; determine the cumulative excitation mean , the cumulative incentive mean The function is expressed as: ;

[0024] in, To find the mean function, is the attitude factor, For the situation Take the best attitude The highest cumulative incentive mean value is obtained; a recycling box is established, and any part of the recycling box is selected The sampling data of the change is Change sampling data ( ), calculate the The target value of the variable sampling data , through the current Network Status The posture below Make an estimate to get the estimated data , for the current Network constants; calculate the The target data of the changing sampling data And the target data With estimated data The difference , among which, The target data of the changing sampling data The function is expressed as: ;

[0025] in, For the target The constants of the network, For intelligent control systems in the situation Select posture The incentive target data, For the target Network status Downward posture Estimated data; target data With estimated data The difference The function is expressed as: ;

[0026] Calculate the target data and estimated data The cost function C of , the expression of cost function C is: ;

[0027] The lowest cost function value is taken as the optimization target, and the current The network is iterated constantly. When the number of iterations is equal to the preset number of optimizations, the optimization of the distributed energy loss estimation algorithm is completed. According to the optimized distributed energy loss estimation algorithm, the next posture is estimated through the current situation and used as the best energy coordination solution. The energy device-independent factors in the next posture are the energy loss estimation data of each distributed energy device, and the sum of the energy loss estimation data of all distributed energy devices is calculated to obtain the total energy loss estimation data. The function of the total energy loss estimation data is expressed as: ;

[0028] in, For time Total estimated energy losses For time No. Energy loss estimates for distributed energy devices.

[0029] In one embodiment of the present invention, the third acquisition module is specifically used to mark each distributed energy device in the power system as a communication endpoint, calculate the impact rate of each distributed energy device relative to the comprehensive energy value, and the expression of the impact rate of each distributed energy device relative to the consumption value is: ;

[0030] in, For the The impact rate of distributed energy devices on the comprehensive energy value, is the value group of the communication endpoints of all distributed energy devices, Communication endpoint value group Exclude the first A grouping of distributed energy devices, Communication endpoint value group Exclude the A value group of distributed energy devices, is the energy value of the group, For grouping Merge to Energy value of distributed energy equipment; preset incentive relationship value , calculate the The incentive value of distributed energy devices , incentive value The expression is: ;

[0031] Compare and analyze the estimated energy loss, instant energy unit price and incentive value, take the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command, where the expression of the improvement target is: ;

[0032] in, is the combined time during the coordination period, For time The instant energy price.

[0033] In one embodiment of the present invention, the boundary factors include the estimated loss data of the distributed energy equipment, the energy demand of the distributed energy equipment and the production efficiency range of the distributed energy equipment, wherein the estimated loss data of the distributed energy equipment is greater than or equal to 0, the energy demand of the distributed energy equipment is greater than or equal to the time The comprehensive energy needs of the distributed energy equipment, the production capacity efficiency range of the distributed energy equipment is greater than or equal to the minimum production capacity efficiency and less than or equal to the maximum production capacity efficiency.

[0034] In one embodiment of the present invention, the improvement module is specifically used to: obtain the time of Actual energy loss of distributed energy devices , and the actual energy loss To calculate, the expression is: ;

[0035] in, , and The time hour The actual wind energy limit, photovoltaic energy limit and fuel energy limit of each distributed energy device, , and are the energy utilization relationship values ​​of wind power, photovoltaic power and fuel respectively; get the The comprehensive energy value of distributed energy devices during the actual coordination period, The expression of the comprehensive energy value of a distributed energy device during the actual coordination period is: ;

[0036] in, For the The comprehensive energy value of distributed energy devices during the actual coordination period, For time The actual energy value at that time, is the comprehensive time during the actual coordination period; calculate the Energy loss reduction of distributed energy devices during real coordination , the energy loss reduction amount The expression is: ;

[0037] in, For the The energy value of the distributed energy equipment before improvement; set the critical point for reducing the loss amount. The energy loss reduction of the distributed energy equipment is higher than the critical point of loss reduction, then The energy loss of each distributed energy device and the corresponding energy device-independent constants and action constants during the actual coordination period are used as sampling data to improve the distributed energy loss prediction method.

[0038] Beneficial effects of the present invention: The present invention obtains energy device-independent factors, energy device-independent constants and action constants of the power system, determines the status, posture, incentive target and cumulative incentive mean of the intelligent control system, designs a distributed energy loss prediction algorithm, estimates the best coordination plan, and then predicts the impact rate and incentive value of each distributed energy device relative to the consumption value, and marks the lowest consumption value as the improvement target, constructs boundary factors, obtains the best energy scheduling command to operate the energy device-independent factors, production efficiency and production capacity of each distributed energy device, and finally calculates the comprehensive energy value and energy loss reduction amount, and improves the distributed energy loss prediction algorithm through the energy use information, energy device-independent constants and action constants of the distributed energy devices whose energy loss reduction amount is higher than the critical point of the loss reduction amount, thereby being able to intelligently schedule multiple distributed energy devices, improve the production capacity and energy storage efficiency of the distributed energy devices, reduce energy loss, and make the operation of the power system more stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of a method for dispatching distributed energy equipment according to an embodiment of the present invention; Figure 2 A block diagram of a dispatching system for distributed energy equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Figure 1 The present invention is a flowchart of a method for dispatching distributed energy equipment according to an embodiment of the present invention.

[0042] like Figure 1 As shown, a scheduling method for distributed energy equipment in an embodiment of the present invention includes the following steps: S1, obtain the energy equipment independent factors, energy equipment independent constants and action constants of the power system.

[0043] In one embodiment of the present invention, energy equipment-independent factors may include wind energy limitation, fuel energy limitation and photovoltaic energy limitation, energy equipment-independent constants may include the equipment's production capacity status, load conditions, operating power and production capacity constants, and action constants may include the lighting conditions, water source conditions and temperature conditions at the equipment's location.

[0044] S2, collects the production capacity strategy of the power system, determines the status, posture, incentive target and cumulative incentive mean of the intelligent control system through energy equipment-independent factors, energy equipment-independent constants and action constants, designs and optimizes the distributed energy loss prediction algorithm, and estimates the best coordination plan through the optimized distributed energy loss prediction algorithm to obtain energy loss estimation data.

[0045] Specifically, first, the power system capacity strategy can be collected, a distributed energy loss prediction algorithm based on neural network can be constructed, and the status of the intelligent control system can be determined. , among which, the status of intelligent control system Contains collection capacity strategy , Equipment production capacity , Load Condition , operating power , Lighting conditions , Water source situation and temperature conditions , respectively marked as ={ , , , , , , },in, , and For the power system The equipment capacity, load and operating power of each distributed energy device can then be determined. , where the attitude of the intelligent control system Contains energy equipment independent factors and capacity constant , respectively marked as ={ , },in, For the power system Energy device-independent factors of distributed energy devices, For the power system The capacity constant of a distributed energy device, the capacity constant includes capacity efficiency and capacity status.

[0046] Furthermore, the incentive goal can be determined and the cumulative incentive mean , where the motivational goal is The function is expressed as: ;

[0047] in, is the number of distributed energy devices, is the proportion of operating power; the cumulative excitation mean The function is expressed as: ;

[0048] in, To find the mean function, is the attitude factor, For the situation Take the best attitude The amount obtained is the maximum cumulative incentive mean.

[0049] Go a step further and create a recycling bin where you can select any part of the The sampling data of the change is Change sampling data ( ), calculate the The target value of the variable sampling data , through the current Network Status The posture below Make an estimate to get the estimated data , for the current Constants for the network.

[0050] Calculate the The target data of the changing sampling data And the target data With estimated data The difference , among which, The target data of the changing sampling data The function is expressed as: ;

[0051] in, For the target The constants of the network, For intelligent control systems in the situation Select posture The incentive target data, For the target Network status Downward posture estimated data.

[0052] Target data With estimated data The difference The function is expressed as: ;

[0053] Calculate the target data and estimated data The cost function C of , the expression of cost function C is: ;

[0054] The lowest cost function value is taken as the optimization target, and the current The network is iterated constantly. When the number of iterations is equal to the preset number of optimizations, the optimization of the distributed energy loss estimation algorithm is completed.

[0055] Furthermore, according to the optimized distributed energy loss prediction algorithm, the next posture can be estimated through the current situation and used as the best energy coordination solution, wherein the energy device-independent factor in the next posture is the energy loss estimation data of each distributed energy device, and the sum of the energy loss estimation data of all distributed energy devices is calculated to obtain the total energy loss estimation data. The function of the total energy loss estimation data is expressed as: ;

[0056] in, For time Total estimated energy losses For time No. Energy loss estimates for distributed energy devices.

[0057] S3, marks each distributed energy device as a communication endpoint, predicts the impact rate and incentive value of each distributed energy device relative to the consumption value through energy loss estimation data and real-time energy value, marks the lowest consumption value as the improvement target, constructs boundary factors to calculate the improvement target, and obtains the best energy scheduling command.

[0058] Specifically, each distributed energy device in the power system can be marked as a communication endpoint, and the impact rate of each distributed energy device relative to the comprehensive energy value can be calculated. The expression of the impact rate of each distributed energy device relative to the consumption value is: ;

[0059] in, For the The impact rate of distributed energy devices on the comprehensive energy value, is the value group of the communication endpoints of all distributed energy devices, Communication endpoint value group Exclude the first A grouping of distributed energy devices, Communication endpoint value group Exclude the A value group of distributed energy devices, is the energy value of the group, For grouping Merge to The energy value of a distributed energy device.

[0060] Preset incentive relationship value , calculate the The incentive value of distributed energy devices , incentive value The expression is: ;

[0061] Compare and analyze the estimated energy loss, instant energy unit price and incentive value, take the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command. The expression of the improvement target is: ;

[0062] in, is the combined time during the coordination period, For time The instant energy price.

[0063] In one embodiment of the present invention, the boundary factors may include the estimated loss data of the distributed energy device, the energy demand of the distributed energy device, and the production efficiency range of the distributed energy device, wherein the estimated loss data of the distributed energy device is greater than or equal to 0, the energy demand of the distributed energy device is greater than or equal to the time The comprehensive energy needs and the production capacity efficiency range of distributed energy equipment are greater than or equal to the minimum production capacity efficiency and less than or equal to the maximum production capacity efficiency.

[0064] S4, according to the optimal energy dispatching command, operates the energy equipment-independent factors, production efficiency and production capacity of each distributed energy device through the power operating system.

[0065] In one embodiment of the present invention, the production capacity efficiency can be specified as an efficiency scheduling command for each distributed energy device, and the production capacity situation can be specified as a scheduling start and stop command, and each distributed energy can be scheduled through the power operating system through the efficiency scheduling command and the scheduling start and stop command.

[0066] S5, obtains the energy usage information of each distributed energy device through the power operating system, calculates the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination, and uses the energy usage information of distributed energy devices whose energy loss reduction is higher than the critical point of energy loss reduction, energy device-independent constants and action constants as sampling data to improve the distributed energy loss prediction algorithm.

[0067] Specifically, the available time of Actual energy loss of distributed energy devices , and the actual energy loss To calculate, the expression is: ;

[0068] in, , and The time hour The actual wind energy limit, photovoltaic energy limit and fuel energy limit of each distributed energy device, , and They are the energy utilization relationship values ​​of wind power, photovoltaic power and fuel respectively.

[0069] Get the The comprehensive energy value of distributed energy devices during the actual coordination period, The expression of the comprehensive energy value of a distributed energy device during the actual coordination period is: ;

[0070] in, For the The comprehensive energy value of distributed energy devices during the actual coordination period, For time The actual energy value at that time, It is the comprehensive time taken during the actual coordination period.

[0071] Calculate the number Energy loss reduction of distributed energy devices during real coordination , energy loss reduction The expression is: ;

[0072] in, For the The energy value of a distributed energy device before improvement.

[0073] Set the critical point for reducing losses. The energy loss reduction of the distributed energy equipment is higher than the critical point of loss reduction, then The energy loss of each distributed energy device and the corresponding energy device-independent constants and action constants during the actual coordination period are used as sampling data to improve the distributed energy loss prediction method.

[0074] According to a method for dispatching distributed energy equipment of an embodiment of the present invention, by obtaining energy equipment-independent factors, energy equipment-independent constants and action constants of the power system, the status, posture, incentive target and cumulative incentive mean of the intelligent control system are determined, a distributed energy loss prediction algorithm is designed, the optimal coordination scheme is estimated, and then the impact rate and incentive value of each distributed energy device relative to the consumption value are predicted, and the lowest consumption value is marked as the improvement target, and boundary factors are constructed to obtain the optimal energy dispatching command to operate the energy equipment-independent factors, production efficiency and production capacity of each distributed energy device. Finally, the comprehensive energy value and energy reduction loss amount are calculated, and the distributed energy loss prediction algorithm is improved through the energy usage information, energy equipment-independent constants and action constants of the distributed energy devices whose energy reduction loss amount is higher than the critical point of the reduction loss amount. As a result, multiple distributed energy devices can be intelligently dispatched, the production capacity and energy storage efficiency of the distributed energy devices are improved, and the energy loss is reduced, so that the operation of the power system is more stable.

[0075] In order to implement a distributed energy equipment scheduling method of the above embodiment, the present invention also proposes a distributed energy equipment scheduling system.

[0076] like Figure 2As shown, a dispatching system for distributed energy equipment in an embodiment of the present invention includes: a first acquisition module 100, a second acquisition module 200, a third acquisition module 300, an operation module 400 and an improvement module 500, wherein the first acquisition module 100 is used to obtain energy equipment-independent factors, energy equipment-independent constants and action constants of the power system; the second acquisition module 200 is used to collect the power system's production capacity strategy, determine the status, posture, incentive target and cumulative incentive mean of the intelligent control system through the energy equipment-independent factors, energy equipment-independent constants and action constants, design a distributed energy loss prediction algorithm and optimize it, estimate the best coordination plan through the optimized distributed energy loss prediction algorithm, and obtain energy loss prediction data; the third acquisition module 300 is used to mark each distributed energy device as a communication endpoint, through The energy loss estimation data and the real-time energy value predict the impact rate and incentive value of each distributed energy device relative to the consumption value, and mark the lowest consumption value as the improvement target, construct boundary factors to calculate the improvement target, so as to obtain the best energy scheduling command; the operation module 400 is used to operate the energy device-independent factors, production efficiency and production capacity of each distributed energy device through the power operating system according to the best energy scheduling command; the improvement module 500 is used to obtain the energy usage information of each distributed energy device through the power operating system, calculate the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination, and use the energy usage information, energy device-independent constants and action constants of distributed energy devices whose energy loss reduction is higher than the critical point of the loss reduction as sampling data to improve the distributed energy loss estimation algorithm.

[0077] In one embodiment of the present invention, energy equipment-independent factors may include wind energy limitation, fuel energy limitation and photovoltaic energy limitation, energy equipment-independent constants may include the equipment's production capacity status, load conditions, operating power and production capacity constants, and action constants may include the lighting conditions, water source conditions and temperature conditions at the equipment's location.

[0078] In one embodiment of the present invention, the second acquisition module 200 can be specifically used to: first, collect the power system's production capacity strategy, construct a distributed energy loss prediction algorithm based on a neural network, and determine the status of the intelligent control system. , among which, the status of intelligent control system Contains collection capacity strategy , Equipment production capacity , Load Condition , operating power , Lighting conditions , Water source situation and temperature conditions , respectively marked as ={ , , , , , , },in, , and For the power system The equipment capacity, load conditions and operating power of each distributed energy device.

[0079] Then, the attitude of the intelligent control system can be determined , where the attitude of the intelligent control system Contains energy equipment independent factors and capacity constant , respectively marked as ={ , },in, For the power system Energy device-independent factors of distributed energy devices, For the power system The capacity constant of a distributed energy device, the capacity constant includes capacity efficiency and capacity status.

[0080] Furthermore, determine the incentive goals and the cumulative incentive mean , where the motivational goal is The function is expressed as: ;

[0081] in, is the number of distributed energy devices, is the proportion of operating power; the cumulative excitation mean , the cumulative incentive mean The function is expressed as: ;

[0082] in, To find the mean function, is the attitude factor, For the situation Take the best attitude The amount obtained is the maximum cumulative incentive mean.

[0083] Go a step further and create a recycling bin where you can select any part of the The sampling data of the change is Change sampling data ( ), calculate the The target value of the variable sampling data , through the current Network Status The posture below Make an estimate to get the estimated data , for the current Constants for the network.

[0084] Calculate the The target data of the changing sampling data And the target data With estimated data The difference , among which, The target data of the changing sampling data The function is expressed as: ;

[0085] in, For the target The constants of the network, For intelligent control systems in the situation Select posture The incentive target data, For the target Network status Downward posture estimated data.

[0086] Target data With estimated data The difference The function is expressed as: ;

[0087] Calculate the target data and estimated data The cost function C of , the expression of cost function C is: ;

[0088] The lowest cost function value is taken as the optimization target, and the current The network is iterated constantly. When the number of iterations is equal to the preset number of optimizations, the optimization of the distributed energy loss estimation algorithm is completed.

[0089] Furthermore, according to the optimized distributed energy loss estimation algorithm, the next posture is estimated through the current situation and used as the best energy coordination solution, wherein the energy device-independent factor in the next posture is the energy loss estimation data of each distributed energy device, and the sum of the energy loss estimation data of all distributed energy devices is calculated to obtain the total energy loss estimation data. The function of the total energy loss estimation data is expressed as: ;

[0090] in, For time Total estimated energy losses For time No. Energy loss estimates for distributed energy devices.

[0091] In one embodiment of the present invention, the third acquisition module 300 can be specifically used to mark each distributed energy device in the power system as a communication endpoint, calculate the impact rate of each distributed energy device relative to the comprehensive energy value, and the expression of the impact rate of each distributed energy device relative to the consumption value is: ;

[0092] in, For the The impact rate of distributed energy devices on the comprehensive energy value, is the value group of the communication endpoints of all distributed energy devices, Communication endpoint value group Exclude the first A grouping of distributed energy devices, Communication endpoint value group Exclude the A value group of distributed energy devices, is the energy value of the group, For grouping Merge to The energy value of a distributed energy device.

[0093] Preset incentive relationship value , calculate the The incentive value of distributed energy devices , incentive value The expression is: ;

[0094] Compare and analyze the estimated energy loss, instant energy unit price and incentive value, take the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command. The expression of the improvement target is: ;

[0095] in, is the combined time during the coordination period, For time The instant energy price.

[0096] In one embodiment of the present invention, the boundary factors may include the estimated loss data of the distributed energy device, the energy demand of the distributed energy device, and the production efficiency range of the distributed energy device, wherein the estimated loss data of the distributed energy device is greater than or equal to 0, the energy demand of the distributed energy device is greater than or equal to the time The comprehensive energy needs and the production capacity efficiency range of distributed energy equipment are greater than or equal to the minimum production capacity efficiency and less than or equal to the maximum production capacity efficiency.

[0097] In one embodiment of the present invention, the operation module 400 can specify the production capacity efficiency as the efficiency scheduling command of each distributed energy device, and specify the production capacity situation as the scheduling start and stop command, and schedule each distributed energy through the efficiency scheduling command and the scheduling start and stop command through the power operating system.

[0098] In one embodiment of the present invention, the improvement module 500 can be specifically used to: obtain the time of Actual energy loss of distributed energy devices , and the actual energy loss To calculate, the expression is: ;

[0099] in, , and The time hour The actual wind energy limit, photovoltaic energy limit and fuel energy limit of each distributed energy device, , and They are the energy utilization relationship values ​​of wind power, photovoltaic power and fuel respectively.

[0100] Get the The comprehensive energy value of distributed energy devices during the actual coordination period, The expression of the comprehensive energy value of a distributed energy device during the actual coordination period is: ;

[0101] in, For the The comprehensive energy value of distributed energy devices during the actual coordination period, For time The actual energy value at that time, It is the comprehensive time taken during the actual coordination period.

[0102] Calculate the number Energy loss reduction of distributed energy devices during real coordination , energy loss reduction The expression is: ;

[0103] in, For the The energy value of a distributed energy device before improvement.

[0104] Set the critical point for reducing losses. The energy loss reduction of the distributed energy equipment is higher than the critical point of loss reduction, then The energy loss of each distributed energy device and the corresponding energy device-independent constants and action constants during the actual coordination period are used as sampling data to improve the distributed energy loss prediction method.

[0105] In summary, the present invention obtains the energy device-independent factors, energy device-independent constants and action constants of the power system through the first acquisition module, determines the status, posture, incentive target and cumulative incentive mean of the intelligent control system through the second acquisition module, designs a distributed energy loss prediction algorithm, and estimates the optimal coordination plan. The third acquisition module predicts the impact rate and incentive value of each distributed energy device relative to the consumption value, and marks the lowest consumption value as the improvement target, constructs boundary factors, obtains the best energy scheduling command, and operates the energy device-independent factors, production efficiency and production capacity of each distributed energy device through the operation module. The comprehensive energy value and energy reduction loss amount are calculated through the improvement module, and the distributed energy loss prediction algorithm is improved through the energy usage information, energy device-independent constants and action constants of the distributed energy devices whose energy reduction loss amount is higher than the critical point of the reduction loss amount. As a result, multiple distributed energy devices can be intelligently scheduled, the production capacity and energy storage efficiency of the distributed energy devices are improved, and the energy loss is reduced, making the operation of the power system more stable.

[0106] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0107] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0108] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0109] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0110] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0112] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0113] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0114] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0115] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for dispatching distributed energy equipment, characterized in that: The following steps are involved: Obtain energy equipment-independent factors, energy equipment-independent constants and action constants of the power system; Collect the power system's capacity strategy, determine the status, attitude, incentive target and cumulative incentive mean of the intelligent control system through the energy equipment-independent factors, energy equipment-independent constants and action constants, design and optimize the distributed energy loss prediction algorithm, estimate the best coordination plan through the optimized distributed energy loss prediction algorithm, and obtain energy loss prediction data; Mark each distributed energy device as a communication endpoint, predict the impact rate and incentive value of each distributed energy device relative to the comprehensive energy value through the energy loss estimation data and the instant energy value, mark the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command; According to the optimal energy dispatch command, the energy equipment-independent factors, production efficiency and production capacity of each of the distributed energy equipment are operated through the power operating system; The energy usage information of each distributed energy device is obtained through the power operating system, the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination are calculated, and the energy usage information of distributed energy devices whose energy loss reduction is higher than the critical point of loss reduction, energy device-independent constants and action constants are used as sampling data to improve the distributed energy loss prediction algorithm.

2. A method for dispatching distributed energy equipment according to claim 1, characterized in that: The power generation strategy of the power collection system described above determines the status, attitude, incentive target and cumulative incentive mean of the intelligent control system through the energy equipment-independent factors, energy equipment-independent constants and action constants, designs and optimizes the distributed energy loss prediction algorithm, and estimates the best coordination plan through the optimized distributed energy loss prediction algorithm to obtain energy loss prediction data, specifically including: Collect the power system's capacity strategy, construct a distributed energy loss prediction algorithm based on neural networks, and determine the status of the intelligent control system , wherein the status of the intelligent control system Contains collection capacity strategy , Equipment production capacity , Load Condition , operating power , Lighting conditions , Water source situation and temperature conditions , respectively marked as ={ , , , , , , },in, , and For the power system Equipment capacity, load and operating power of each distributed energy device; Determine the attitude of the intelligent control system , wherein the posture of the intelligent control system Contains energy equipment independent factors and capacity constant , respectively marked as ={ , },in, For the power system Energy device-independent factors of distributed energy devices, For the power system A capacity constant of a distributed energy device, wherein the capacity constant includes capacity efficiency and capacity status; Determine motivational goals , motivational goals The function is expressed as: ; in, is the number of distributed energy devices, is the proportion of operating power; Determine the cumulative incentive mean , the cumulative incentive mean The function is expressed as: ; in, To find the mean function, is the attitude factor, For the situation Take the best attitude The amount obtained is the highest cumulative incentive mean; Create a recycling bin and select any part in the recycling bin The sampling data of the change is Change sampling data ( ), calculate the The target value of the variable sampling data , through the current Network Status The posture below Make an estimate to get the estimated data , for the current Network constants; Calculate the The target data of the changing sampling data And the target data With estimated data The difference , among which, The target data of the changing sampling data The function is expressed as: ; in, For the target The constants of the network, For intelligent control systems in the situation Select posture The incentive target data, For the target Network status Downward posture Estimated data of Target data With estimated data The difference The function is expressed as: ; Calculate the target data and estimated data The cost function C of , the expression of the cost function C is: ; The minimum cost function value is taken as the optimization target, and the constant of the current U network is iterated by using the hierarchical dimension reduction method. When the number of iterations is equal to the preset optimization number, the optimization of the distributed energy loss estimation algorithm is completed; According to the optimized distributed energy loss prediction algorithm, the next posture is estimated by the current situation and used as the best energy coordination solution, wherein the energy device-independent factor in the next posture is the energy loss estimation data of each distributed energy device, and the sum of the energy loss estimation data of all distributed energy devices is calculated to obtain the total energy loss estimation data, and the function of the total energy loss estimation data is expressed as: ; in, For time Total estimated energy losses For time No. Estimated energy loss data for distributed energy devices.

3. A method for dispatching distributed energy equipment according to claim 2, characterized in that: The method of marking each distributed energy device as a communication endpoint, predicting the impact rate and incentive value of each distributed energy device relative to the comprehensive energy value through the energy loss estimation data and the instant energy value, marking the lowest comprehensive energy value as the improvement target, constructing boundary factors to calculate the improvement target, and obtaining the best energy scheduling command specifically includes: Mark each distributed energy device in the power system as a communication endpoint, and calculate the impact rate of each distributed energy device relative to the comprehensive energy value. The expression of the impact rate of each distributed energy device relative to the comprehensive energy value is: ; in, For the The impact rate of distributed energy devices on the comprehensive energy value, is the value group of the communication endpoints of all distributed energy devices, Communication endpoint value group Exclude the first A grouping of distributed energy devices, Communication endpoint value group Exclude the A value group of distributed energy devices, is the energy value of the group, For grouping Merge to The energy value of each distributed energy device; Preset incentive relationship value , calculate the The incentive value of distributed energy devices , incentive value The expression is: ; Compare and analyze the estimated energy loss, instant energy unit price and incentive value, take the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command. The expression of the improvement target is: ; in, is the combined time during the coordination period, For time The instant energy price.

4. A method for dispatching distributed energy equipment according to claim 3, characterized in that: The boundary factors include the estimated loss data of the distributed energy equipment, the energy demand of the distributed energy equipment and the production efficiency range of the distributed energy equipment, wherein the estimated loss data of the distributed energy equipment is greater than or equal to 0, the energy demand of the distributed energy equipment is greater than or equal to the time The comprehensive energy needs of the distributed energy equipment, the production capacity efficiency range of the distributed energy equipment is greater than or equal to the minimum production capacity efficiency and less than or equal to the maximum production capacity efficiency.

5. A method for dispatching distributed energy equipment according to claim 4, characterized in that: The energy usage information of each distributed energy device is obtained through the power operation system, the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination are calculated, and the energy usage information of the distributed energy devices whose energy loss reduction is higher than the critical point of the loss reduction, the energy device-independent constants and the action constants are used as sampling data to improve the distributed energy loss prediction method, specifically including: Get time of Actual energy loss of distributed energy devices , and the actual energy loss To calculate, the expression is: ; in, , and The time hour The actual wind energy limit, photovoltaic energy limit and fuel energy limit of each distributed energy device, , and are the energy utilization relationship values ​​of wind power, photovoltaic power and fuel respectively; Get the The comprehensive energy value of distributed energy devices during the actual coordination period, The expression of the comprehensive energy value of a distributed energy device during the actual coordination period is: ; in, For the The comprehensive energy value of distributed energy devices during the actual coordination period, For time The actual energy value at that time, The comprehensive time taken during the actual coordination period; Calculate the number Energy loss reduction of distributed energy devices during real coordination , the energy loss reduction amount The expression is: ; in, For the The energy value of each distributed energy device before improvement; Set the critical point for reducing losses. The energy loss reduction of the distributed energy equipment is higher than the critical point of loss reduction, then The energy loss of each distributed energy device and the corresponding energy device-independent constants and action constants during the actual coordination period are used as sampling data to improve the distributed energy loss prediction method.

6. A dispatching system for distributed energy equipment, characterized in that: include: A first acquisition module, the first acquisition module is used to obtain energy equipment-independent factors, energy equipment-independent constants and action constants of the power system; A second acquisition module, the second acquisition module is used to collect the power system's capacity strategy, determine the status, attitude, incentive target and cumulative incentive mean of the intelligent control system through the energy equipment-independent factors, energy equipment-independent constants and action constants, design and optimize the distributed energy loss estimation algorithm, estimate the best coordination plan through the optimized distributed energy loss estimation algorithm, and obtain energy loss estimation data; A third acquisition module, the third acquisition module is used to mark each distributed energy device as a communication endpoint, predict the impact rate and incentive value of each distributed energy device relative to the comprehensive energy value through the energy loss estimation data and the instant energy value, mark the lowest comprehensive energy value as an improvement target, construct boundary factors to calculate the improvement target, so as to obtain the best energy scheduling command; An operation module, the operation module is used to operate the energy device-independent factor, production efficiency and production capacity of each of the distributed energy devices through a power operation system according to the optimal energy dispatch command; An improvement module is used to obtain energy usage information of each distributed energy device through the power operating system, calculate the comprehensive energy value during the actual coordination period and the energy loss reduction after coordination, and use the energy usage information of distributed energy devices whose energy loss reduction is higher than the critical point of the loss reduction, energy device-independent constants and action constants as sampling data to improve the distributed energy loss prediction algorithm.

7. A dispatching system for distributed energy equipment according to claim 6, characterized in that: The second acquisition module is specifically used for: Collect the power system's capacity strategy, construct a distributed energy loss prediction algorithm based on neural networks, and determine the status of the intelligent control system , wherein the status of the intelligent control system Contains collection capacity strategy , Equipment production capacity , Load Condition , operating power , Lighting conditions , Water source situation and temperature conditions , respectively marked as ={ , , , , , , },in, , and For the power system Equipment capacity, load and operating power of each distributed energy device; Determine the attitude of the intelligent control system , wherein the posture of the intelligent control system Contains energy equipment independent factors and capacity constant , respectively marked as ={ , },in, For the power system Energy device-independent factors of distributed energy devices, For the power system A capacity constant of a distributed energy device, wherein the capacity constant includes capacity efficiency and capacity status; Determine motivational goals , motivational goals The function is expressed as: ; in, is the number of distributed energy devices, is the proportion of operating power; Determine the cumulative incentive mean , the cumulative incentive mean The function is expressed as: ; in, To find the mean function, is the attitude factor, For the situation Take the best attitude The amount obtained is the highest cumulative incentive mean; Create a recycling bin and select any part in the recycling bin The sampling data of the change is Change sampling data ( ), calculate the The target value of the variable sampling data , through the current Network Status The posture below Make an estimate to get the estimated data , for the current Network constants; Calculate the The target data of the changing sampling data And the target data With estimated data The difference , among which, The target data of the changing sampling data The function is expressed as: ; in, For the target The constants of the network, For intelligent control systems in the situation Select posture The incentive target data, For the target Network status Downward posture Estimated data of Target data With estimated data The difference The function is expressed as: ; Calculate the target data and estimated data The cost function C of , the expression of the cost function C is: ; The lowest cost function value is taken as the optimization target, and the current Constant iteration of the network, when the number of iterations is equal to the preset optimization number, the optimization of the distributed energy loss estimation algorithm is completed; According to the optimized distributed energy loss prediction algorithm, the next posture is estimated by the current situation and used as the best energy coordination solution, wherein the energy device-independent factor in the next posture is the energy loss estimation data of each distributed energy device, and the sum of the energy loss estimation data of all distributed energy devices is calculated to obtain the total energy loss estimation data, and the function of the total energy loss estimation data is expressed as: ; in, For time Total estimated energy losses For time No. Estimated energy loss data for distributed energy devices.

8. A dispatching system for distributed energy equipment according to claim 7, characterized in that: The third acquisition module is specifically used for: Mark each distributed energy device in the power system as a communication endpoint, and calculate the impact rate of each distributed energy device relative to the comprehensive energy value. The expression of the impact rate of each distributed energy device relative to the comprehensive energy value is: ; in, For the The impact rate of distributed energy devices on the comprehensive energy value, is the value group of the communication endpoints of all distributed energy devices, Communication endpoint value group Exclude the first A grouping of distributed energy devices, Communication endpoint value group Exclude the A value group of distributed energy devices, is the energy value of the group, For grouping Merge to The energy value of each distributed energy device; Preset incentive relationship value , calculate the The incentive value of distributed energy devices , incentive value The expression is: ; Compare and analyze the estimated energy loss, instant energy unit price and incentive value, take the lowest comprehensive energy value as the improvement target, construct boundary factors to calculate the improvement target, and obtain the best energy scheduling command. The expression of the improvement target is: ; in, is the combined time during the coordination period, For time The instant energy price.

9. A dispatching system for distributed energy equipment according to claim 8, characterized in that: The boundary factors include the estimated loss data of the distributed energy equipment, the energy demand of the distributed energy equipment and the production efficiency range of the distributed energy equipment, wherein the estimated loss data of the distributed energy equipment is greater than or equal to 0, the energy demand of the distributed energy equipment is greater than or equal to the time The comprehensive energy needs of the distributed energy equipment, the production capacity efficiency range of the distributed energy equipment is greater than or equal to the minimum production capacity efficiency and less than or equal to the maximum production capacity efficiency.

10. A dispatching system for distributed energy equipment according to claim 9, characterized in that: The improvement module is specifically used for: Get time of Actual energy loss of distributed energy devices , and the actual energy loss To calculate, the expression is: ; in, , and The time hour The actual wind energy limit, photovoltaic energy limit and fuel energy limit of each distributed energy device, , and are the energy utilization relationship values ​​of wind power, photovoltaic power and fuel respectively; Get the The comprehensive energy value of distributed energy devices during the actual coordination period, The expression of the comprehensive energy value of a distributed energy device during the actual coordination period is: ; in, For the The comprehensive energy value of distributed energy devices during the actual coordination period, For time The actual energy value at that time, The comprehensive time taken during the actual coordination period; Calculate the number Energy loss reduction of distributed energy devices during real coordination , the energy loss reduction amount The expression is: ; in, For the The energy value of each distributed energy device before improvement; Set the critical point for reducing losses. The energy loss reduction of the distributed energy equipment is higher than the critical point of loss reduction, then The energy loss of each distributed energy device and the corresponding energy device-independent constants and action constants during the actual coordination period are used as sampling data to improve the distributed energy loss prediction method.

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